Portail des publications scientifiques IMT Mines Alès
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Eco-friendly Conductive biopolymer nanocomposites and Life Cycle Assessment: a review
No full textInternational audienceConductive bionanocomposites are attracting growing interest as multifunctional materials. They can meet the requirements of electrical applications while supporting sustainable development. This review summarizes recent research on bionanocomposites made from biopolymer matrices and carbon conductive fillers that can be processed by additive manufacturing. These materials offer several advantages, including reduced dependence on fossil resources, possibility of low-impact processing, minimized risks in case of dissemination, and satisfactory electrical properties with low amounts of conductive fillers. However, despite their “green' label, their actual environmental performance has not been fully demonstrated. Only a limited number of comprehensive Life Cycle Assessments (LCA) are available. This review discusses the potential of these materials, while underscoring the necessity for rigorous environmental analysis. Such assessments are essential to validate their sustainability from a circular economy perspective using LCA
Ergonomics-Based Assembly Line Balancing
No full textInternational audienceWith the emergence of Industry 5.0, the scientific discussion around production systems is evolving significantly, rather than focusing only on enhancing production. The new paradigm brings human-centric values and sustainability into the spotlight, placing ergonomics at the center of how production systems are designed and evaluated. Integrating ergonomics metrics, such as RULA and REBA, into optimization models becomes essential to prevent musculoskeletal disorders while maintaining industrial performance. Many attempts have been made to integrate these ergonomic metrics into optimization models in the literature, either as objective functions or as constraints, yet none have evaluated how alternative integration strategies affect optimization results or how these methods could be extended to real-time applications
Transcranial Alternating Current Stimulation (tACS) for patients with Post-Stroke Anomia: Preliminary Data on Picture Naming Performance
No full textInternational audienceThe present study evaluated the effectiveness of transcranial alternating current stimulation (tACS) treating patients with post-stroke anomia using a picture-naming task and a Single-Case Experimental Design (SCED). A right-handed 38-year-old woman with a left-hemisphere stroke and a left-handed 54-year-old man with a right-hemisphere stroke underwent an eight-week treatment program. Specifically, they participated in a picture-naming task three times a week, alternating between sessions with and without tACS stimulation every two weeks. Electroencephalography (EEG) measurements were taken at the end of each two-week period, and behavioral data were collected before, during and after the treatment. EEG and behavioral assessments were also conducted at one- and three-month follow-ups. Picture-naming performance was significantly faster during tACS sessions compared to sessions without tACS. By the end of the intervention, both participants demonstrated improved accuracy and speed, with positive effects also observed in behavioral measures. EEG analysis showed that post-treatment brain activity resembled that of healthy individuals performing similar tasks. Patients’ improvements in picture-naming and behavioral tests showed that the positive effects remained stable even after three months. Thus, preliminary data suggest that tACS might be a promising intervention for anomia, with lasting effects. Large-scale studies are needed to confirm these findings
Estimation of the surface tension and dispersive and polar components of polymers as a function of temperature for composite manufacturing applications
No full textInternational audienceUnderstanding adhesion between fiber and matrix at elevated temperatures is essential for improving the mechanical performance of polymer-based composites, especially with thermoplastic matrices. However, detailed characterization of polymer surface tension and its polar and dispersive components as a function of temperature remains limited. In this work, reliable methods were set using the Wilhelmy plate and pendant drop approaches to investigate these properties against temperature. First, experimental procedures were developed, optimized, and validated through cross-comparison with reference liquids of known surface tension and components. Accurate and reproducible measurements were secondly achieved across a range of elevated temperatures for liquid polymers (polyethylene glycol, bio-based epoxy) and for molten thermoplastics (polypropylene, polylactic acid). The results reveal a linear decrease in surface tension with increasing temperature and contribute to a better understanding of fiber wetting phenomena. Additionally, a procedure was set to determine polymer dispersive and polar components as a function of temperature. Due to the volatility and thermal limitation of n-hexane used in interfacial tension measurements, alternative probe liquids were systematically evaluated. Silicone and paraffin oil were identified and validated as suitable replacements, enabling reliable measurements with polymers at high temperatures. These key findings demonstrate robust methodology for high-temperature surface characterization and provide essential data to understand fiber–matrix adhesion under realistic processing conditions
Toward a UAF-Based Multi-View Territorial Architecture: An MBSoSE Approach to Model Critical Infrastructures
No full textInternational audienceThis work treats the territory as a socio-technical System-of-Systems and proposes a Model-Based Systel of System Engineering (MBSoSE) modeling approach based on the Unified Architecture Framework (UAF) to prepare for and manage risks affecting critical infrastructures. It supports the definition of a minimal yet extensible UAF-territory profile that makes public missions and values explicit, allocates capabilities to essential services, and renders interdependencies between sectors such as energy, water, communications, and health queryable. An operational approach, structured into strategic, operational, functional, logical, and then physical resources views, ensures end-to-end traceability, anchors resilience in orchestration policies and measurable thresholds, and integrates parameters andmeasurements for what-if analyses. Current limitations lie in the absence of a complete case study and the variability ofpractices and datasets; an experiment on a reference textbook case territory with baseline scenarios and shared acceptancecriteria is planned. In the medium term, the objective is to provide interoperable, simulable models to compare mitigationoptions, prioritize investments, and objectify decision support in land-use planning, preparedness, and crisis response
Elucidating conduction regimes and Joule heating performance in carbon-cement composites modified with acetylene black
No full textInternational audienceDeveloping multifunctional, self-heating cementitious composites is a key strategy for creating sustainable infrastructure and mitigating the environmental damage caused by de-icing salts. We fabricated composites using Portland cement and high-structure acetylene black as a conductive nanofiller (0–7 wt%) and analyzed their performance through systematic thermo-electric measurements, scanning electron microscopy, and a two-regime percolation model. The results demonstrate a percolation threshold () at approximately 2.5 wt% AB, above which the electrical conductivity increases by eight orders of magnitude to nearly 1 S/m. Our analysis reveals a distinct microstructural transition from a rapid-onset percolation regime (low exponent ) to a complex network consolidation regime (higher exponent ). In Joule heating tests, the composites achieved a maximum temperature increase () of approximately 50 °C. Crucially, the maximum temperature gain plateaus at filler concentrations above 5 wt%, revealing a complex, non-linear relationship between electrical conductivity and steady-state thermal output. A quantitative comparison with literature benchmarks reveals that the AB-based composite possesses a remarkably high intrinsic heating efficiency, orders of magnitude greater than other carbon-based systems. This work provides a quantitative framework that directly links filler content, microstructure, and multifunctional performance in cement-nanocarbon composites, offering a basis for optimizing these materials to achieve both rapid heating and energy efficiency for practical applications
Enhanced Methane Production in Anaerobic Membrane Bioreactors: The Role of In-Situ Electro-Stimulation
No full textInternational audienceThis study compared the 280-day performance of a granular anaerobic membrane bioreactor (GAnMBR) and its electro-assisted variant (eGAnMBR) treating domestic wastewater at ambient temperature across permeate fluxes of 1.7–5.9 L/m2/h. Both systems achieved >95 % chemical oxygen demand removal. At moderate flux, eGAnMBR improved methane recovery by around 15 % versus the control and produced biogas up to 94 % methane. Lower effluent methane saturation suggested reduced dissolved-methane losses and lower greenhouse-gas risk. Despite slightly higher resistance from near-electrode cake, >95 % of fouling remained reversible; targeted design and operational optimization to limit cake and enhance local shear should support scale-up. Both systems remained net-energy-positive, with energy recovery above 0.76 kWh/m3 at the highest loading and clearer eGAnMBR benefits at moderate loading and shorter hydraulic retention time. Overall, electro-stimulated GAnMBR improved methane valorization and process robustness while preserving a net-positive energy balance, indicating a practical path to scalable, low-carbon municipal treatment
Halloysite Nanotubes Reinforcement for Value‐Added Composites Based on Acrylonitrile–Butadiene–Styrene Waste From Electrical and Electronic Equipment
No full textInternational audienceThis study explores the reinforcement of recycled e‐waste ABS with halloysite nanotubes (HNT) to improve its mechanical, thermal, and fire‐retardant properties, while potentially mitigating some risks associated with its reuse. Samples of e‐waste ABS filled with 5 wt.% HNT were melt‐compounded and characterized using SEM–EDX, XRD, ATR‐FTIR, TGA, PCFC, MFR, and tensile measurements. Commercial ABS and recycled e‐waste ABS were also tested under the same conditions for comparison. The results demonstrate that HNT enhances the thermal stability of e‐waste ABS, increasing the degradation temperature (T 5 % ) by nearly 11% compared with unfilled e‐waste ABS. The peak heat release rate (pHRR), an indicator of fire intensity, slightly decreases, likely due to the physical barrier effect of the nanotubes. Mechanical properties are also improved, with Young's modulus increasing by 12% over commercial ABS while maintaining comparable tensile strength. SEM analysis confirms a good interfacial adhesion between HNT and e‐waste ABS matrix, supported by ATR‐FTIR and XRD data. The study highlights the potential of HNT as a reinforcing additive to upgrade recycled e‐waste ABS, improving its performance and expanding its applications while addressing some of the challenges posed by its inherent toxicity. Further research is needed to assess the long‐term environmental impacts of modified e‐waste ABS composites
Régionalisation de modèles neuronaux appliquée aux aquifères fracturés et karstiques
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Brain-to-Speech: Prosody Feature Engineering and Transformer-Based Reconstruction
No full textInternational audienceThis chapter presents a novel approach to brain-to-speech (BTS) synthesis from intracranial electroencephalography (iEEG) data, emphasizing prosody-aware feature engineering and advanced transformer-based models for high-fidelity speech reconstruction. Driven by the increasing interest in decoding speech directly from brain activity, this work integrates neuroscience, artificial intelligence, and signal processing to generate accurate and natural speech. We introduce a novel pipeline for extracting key prosodic features directly from complex brain iEEG signals, including intonation, pitch, and rhythm. To effectively utilize these crucial features for natural-sounding speech, we employ advanced deep learning models. Furthermore, this chapter introduces a novel transformer encoder architecture specifically designed for brain-to-speech tasks. Unlike conventional models, our architecture integrates the extracted prosodic features to significantly enhance speech reconstruction, resulting in generated speech with improved intelligibility and expressiveness. A detailed evaluation demonstrates superior performance over established baseline methods, such as traditional Griffin-Lim and CNN-based reconstruction, across both quantitative and perceptual metrics. By demonstrating these advancements in feature extraction and transformer-based learning, this chapter contributes to the growing field of AI-driven neuroprosthetics, paving the way for assistive technologies that restore communication for individuals with speech impairments. Finally, we discuss promising future research directions, including the integration of diffusion models and real-time inference systems