Portail des publications scientifiques IMT Mines Alès
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Living Ecohydrological Systems: How Biodiversity Enhance Constructed Wetlands for Sustainable Water Reuse
No full textInternational audienceConstructed Wetlands with Macrophytes (CWMs) are increasingly used for treating domestic wastewater. While they are often considered passive filters made of sand and gravel, our research highlights that they rapidly evolve into living soil systems, colonized by soil macrofauna such as earthworms and other invertebrates such as bacterial communities. Like river banks, CWMs are artificial soil systems that are intermittently saturated and unsaturated with water, likely accelerating the mineralization kinetics of degradable materials and the life trajectories of microbial communities (Gérino et al., 2022)
Product Line Systems Engineering for Critical Nuclear Infrastructures: Research Questions and Opportunities
No full textInternational audienc
Contribution to Model-Based Interoperable Simulations : the MBS Approach
No full textInternational audienceThe Model-Based Simulation (MBS) approach introduced by authors is intended to be a general distributed simulation methodology to develop complex systems, particularly Critical Infrastructure Systems (CIS). Our approach aligns with the guidelines and processes of the IEEE1730 standard and is a key component of the Unified Engineering Framework (UEF). The UEF framework facilitates the integration of systems engineering, dependability engineering and simulation engineering by ensuring interoperability and effective information exchange through model reuse. We intend to present our contribution to the MBS approach, highlighting its theoretical framework, engineering process, and interest in distributed simulation (High Level Architecture (HLA)) to implement interoperability methodology. A nuclear infrastructure systems modeling and simulation use case is currently under development to demonstrate the practical application and benefits of the MBS approach. We believe that the MBS approach can significantly enhance the development and simulation of complex systems by promoting model reuse and interoperability across different engineering domains. Our work will also attempt to contribute to the UEF framework by underscoring the importance of model-based simulation in the engineering of complex systems
Flammability of animal protein-based materials
No full textInternational audienceThe fire behavior of 6 animal protein-based materials are comprehensively characterized at micro and bench-scale by pyrolysis-combustion flow calorimetry, cone calorimetry, and limiting oxygen index (LOI) and compared to bagasse as lignocellulosic reference. All samples were tested as loose-fill materials but also as dense sheets (using an original process). At microscale, leather and silk exhibit higher char content (28 % versus 14–17 % for horn, hair, wool and feather). At bench-scale, an expanded char enables limiting the heat transfer and the heat release rate. The char is expanded only for protein-based materials, not for bagasse which exhibits the worst behavior in cone calorimeter (as dense sheet) and in LOI. Moreover, the char appears much more cohesive for silk and leather accounting for their better performances, especially as dense sheets. Other huge differences are detected in terms of char thermo-stability and combustion efficiency profile and needs further investigations to be closely related to the composition
Microclimatic analysis of the impact of irrigated vegetation in an experimental street canyon
No full textInternational audienceUnderstanding microclimatic processes at the street-canyon scale is essential for advancing physical knowledge of urban heat and supporting effective adaptation strategies. This research investigates the influence of irrigated planters, containing climbing and shrub plants, on the microclimate of an experimental street canyon in Montpellier, France, monitored with a dense sensor network. The analysis focused on two heatwave days in August 2023, representative of the Mediterranean climate. Results show that under low wind speeds (<1 m s−1), air temperature variability was mainly controlled by the distribution and intensity of solar radiation. Vegetation had a buffer and retardation effect on the warming of the nearby air regions, with mean air temperature difference between the vegetated and non-vegetated reference zones ranging from −0.17 to 0.44 °C. Comparisons between plant types indicated slightly higher air temperatures (−0.15 to 0.62 °C) and relative humidity increases (1.73 to 1.99 %) near climbing plants compared to shrubs. Irrigation produced no detectable short-term effect on air temperature, while only marginally increasing relative humidity. Thermal comfort assessment using the Universal Thermal Climate Index (UTCI) confirmed that vegetation and irrigation altered microclimatic dynamics but did not substantially reduce heat stress levels for pedestrians. Instead, radiative effects of vegetation and the morphology of the canyon—through shading and surface temperature reduction—were the dominant drivers of thermal comfort modulation in the canyon
Deep Learning at Two Timescales: Dual Neural Networks for Predicting Fast Urban and Slow Karst Floods
No full textInternational audienceFlash floods in urban and karst environments present major modeling challenges due to their complex hydrodynamics, characterized by a rapid urban runoff response and a delayed slower karst groundwater response. This study explores the use of artificial neural networks ANN (multilayer perceptron in particular) to predict flash floods at the downstream of the Las River in Toulon (France). The Las River is fed in a larger proportion by the nearby karst springs and in a smaller proportion by the urban drainage network. In this study, we propose an ensemble modeling strategy to address the system’s double hydrological regime. The initial step was to identify rainfall events in the six-year hydrometeorological database and classify them according to their karst contributions. Two specialized models: urban runoff model (UM) and karst model (KM) were trained solely on each event type (urban runoff and karst events). These models were combined by two methods in an attempt model all events, disregarding of their event type: the first approach was to combine the outputs of the specialized models in an ANN called output combination model (OM), the second approach was to combine the structures of the specialized models and retraining the model parameters called structure combination model (SM). A third more “basic” approach, called bulk model (BM), was to optimize the ANN by selecting the inputs with the best performance improvements. As expected, the specialized models (UM and KM) performed the best on the cross-validation sets and on the test sets on their respective event types but failed to generalize across regimes. The OM was the most robust ensemble strategy across all event types with consistent accuracy on predicting both urban runoff and karst flood events. The BM was better on the karst events while having worst performance on karst events and the SM was the least accurate model. These findings confirm the added value of combining specialized ANNs to model complex hydrological systems. In addition, selecting the right inputs to the models has a bigger impact on the model’s performance than choosing its structure by changing its hyperparameters
Advanced Removal and Quantification of Trace Micropollutants in Complex Effluents by Boron-Doped Diamond Electrooxidation
No full textInternational audienceThe growing prevalence of micropollutants in water resources has emerged as a critical environmental and public health challenge. Micropollutants, often originating from pharmaceuticals, personal care products, pesticides, and industrial discharges, are typically present at trace concentrations (parts per billion or million) but pose disproportionate ecological and toxicological risks. Among emerging water treatment strategies, electrooxidation with boron-doped diamond (BDD) electrodes has gained attention as a promising approach to remove these persistent contaminants and meet stricter water quality standards, such as those established by the European Water Framework Directive.This study investigates the electrooxidative removal of carbamazepine, diuron, and perfluorooctane sulfonate (PFOS) from synthetic wastewater effluents containing organic matter concentrations representative of membrane filtration outputs, simulating advanced-stage treatment conditions relevant to municipal and industrial polishing processes. Emphasis was placed on the quantification of trace-level micropollutants and some of their degradation products using a validated solid-phase extraction method coupled with liquid chromatography-mass spectrometry, enabling reliable analysis in complex water matrices.Electrooxidation experiments were conducted using a laboratory-scale, single-compartment electrochemical cell fitted with a BDD anode and a stainless-steel cathode. An optimized experimental design was employed to investigate the influence of four main factors: applied current intensity (1.06 - 3.33 A), electrolysis duration (13 - 56 min), micropollutant concentration (0.26 - 8.5 µg/L), and organic matter concentration (0.86 - 29 mg/L COD). The selected matrix concentrations reflect membrane-treated municipal and low-strength industrial effluents, while current and time ranges were determined from preliminary trials to ensure effective removal within operational limits reported in the literature.Carbamazepine exhibited the most consistent removal, reaching up to 99.8% across all tested conditions. Diuron showed moderate variability, with removal ranging from 52.7% to 98.5%, influenced mainly by electrolysis time. PFOS, known for its persistence, was the most challenging compound to eliminate; nonetheless, up to 93.2% removal was achieved under optimized conditions. To further assess treatment performance, additional parameters including dissolved organic carbon (DOC), specific UV absorbance, and acute toxicity were measured. While DOC mineralization remained moderate (<20%), a decrease in UV absorbance was observed, with statistical analysis highlighting a strong correlation with organic matter concentration. Acute toxicity was assessed using Microtox® assays based on Vibrio fischeri bioluminescence inhibition. An initial increase in toxicity was observed, indicating the formation of oxidation intermediates, followed by a substantial reduction after prolonged treatment. Organic matter slightly reduced the removal of carbamazepine and diuron, whereas PFOS degradation was less affected, suggesting compound-specific degradation mechanismsOperational conditions were identified under which carbamazepine, diuron, and PFOS removals exceeded 90%, with corresponding energy consumption ranging from 0.52 to 1.46 kWh per mg of micropollutant removed over a treatment time of 50 minutes. These findings highlight the potential of BDD electrooxidation, combined with robust trace-level quantification techniques, as an energy-efficient polishing step for the removal of trace micropollutants in advanced wastewater treatment, in line with evolving regulatory standards
Robust health indicator extraction and RUL prediction for PEMFCs under highly dynamic industrial conditions
No full textInternational audienceProton Exchange Membrane Fuel Cells (PEMFCs) are increasingly deployed in clean energy systems, such as GEH2 hydrogen generators, where they operate under highly dynamic and unpredictable load conditions. Accurate prediction of their Remaining Useful Life (RUL) is essential for ensuring reliable, cost-effective, and proactive maintenance strategies. However, conventional voltage-based Health Indicators (HIs) are highly sensitive to power fluctuations and fail to provide consistent degradation trends in real-world industrial scenarios, particularly when system usage varies significantly across different clients, as in the GEH2 case. In this paper, we propose a scalable two-stage framework for RUL prediction of PEMFCs operating under such conditions. First, we introduce a machine learning-based method to extract a degradation-specific Health Indicator directly from voltage measurements, effectively filtering out transient operational effects. Second, we develop a hybrid deep learning architecture that combines Transformer networks and Gated Recurrent Units (GRUs) to model temporal dependencies and provide accurate RUL predictions under dynamic conditions. The proposed approach is validated on a real-world industrial dataset collected from three PEMFC stacks deployed in GEH2 systems operating under highly variable conditions. Comparative results show that our method consistently outperforms baseline machine learning and deep learning models, achieving superior accuracy, robustness, and generalization across diverse mission profiles
“The second I laid eyes on him I knew”: First impressions predict willingness to interact with individuals with schizophrenia
No full textInternational audienceIndividuals diagnosed with schizophrenia encounter significant challenges in their daily social interactions. These deficits emphasize neurocognitive disabilities, impaired social cognition, and stigma. However, social presentation especially public perception of patients’ social behavior has been poorly studied to date in this mental disorder despite the fundamental importance of first impression in human interaction. This study aims to investigate whether a schizophrenia patient leads to a lower first impression than a depressive individual and a healthy control when there is no diagnostic label, and on which features these first impressions are created. We extracted nonverbal behavioral measures from thin slice of social behaviour of the stimulus participants and presented audio and/or video clips to naive observers. We found that the general population had significantly more negative impressions and behavioral intentions to interact with the schizophrenia patient than the depressive and the control participant, regardless of the modality presented. As patients displayed fewer nonverbal behaviors, it suggests that social behavior drives first impression in schizophrenia. Such findings may lead to new ways of developing intervention programs targeting motor nonverbal behavior, leading to reduce social rejection in these populations
ALGIFOAM: AN INNOVATIVE BIOBASED FOAM WITH OUTSTANDING PROPERTIES
No full textInternational audienceAn innovative biobased foam has been developed as expanded alginate beads and patented (registered trademark AlgiFoam). Beads can be easily assembled through an ecofriendly process to shape lightweight structured objects with desirable properties, including low density, biodegradability, low thermal conductivity, tunable mechanical properties and above all outstanding fire properties. This article details the latter aspec