Portail des publications scientifiques IMT Mines Alès
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Low-cost adsorption treatment using biochar: Influencing factors and reusability
No full textNational audienceClimate change is causing a rise in interest in water management solutions in the Euro-Mediterranean region due to increased concentrations of organic contaminants in water dueto industrial and agricultural expansion. These organic compounds have harmful effects onhuman health and the environment. However, polluted water has the potential for reuse,minimizing environmental consequences and encouraging water sustainability. Various treatmentsare being tested to remove organic compounds from water, including biological, electromagnetic,and electrostatic treatments, membrane separation technologies, and adsorption.Adsorption is the most promising treatment, efficient and financially viable to be marketedand implemented on a large scale, meeting both economic and environmental requirements.Intensive research is underway to create effective and low-cost adsorbents for eliminatingorganic pollutants. Forest residues from reconstruction activities and timber harvesting cancause problems like forest fires, contributing to climate change. Eliminating these residuesand producing biochar can provide significant advantages to ecosystems. In this context,this research investigates the adsorption efficiency of biochar derived from forest residues inNorthwestern France for the elimination of newly discovered pollutants from water, includingFipronil (FPN), Trimethoprim (TRM), and Sulfamethoxazole (SMX). These pollutantswere chosen for inclusion in this work’s adsorption applications because of their extensivepresence in water, potential harm to human health and ecology, and recognition as emergingcontaminants of concern (watch list of Commission implementing decision (EU) 2022/1307,of 22 July 2022). The tested biochar has shown an effective removal for these pollutantswith an efficiency of 77% and 89% for FPN and TRM after 30 min and 96% for SMX after1 hour, respectively. Pollutant chemisorption on biochar surfaces has been characterizedusing isotherms and kinetic models. After that, the effect of operating parameters such aspH, initial SMX concentration and adsorbent dose has been tested in batch mode on one ofthese pollutants in order to optimize the process. A regeneration study just using water hasalso been done and it revealed a reusability of around 88% in the first cycle which is acceptableand can be improved. This communication aims to improve the quality of reclaimedwastewater for safe and sustainable reuse by using biochar as an adsorbent to eliminatethe hazardous organic material in water, in order to promote circular resource managementpractices and address environmental issues
Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning
No full textInternational audienceBackground/Objectives: Radioligandtherapy (RLT) with [177Lu]Lu-PSMA has been newly introduced as a routine treatment for metastatic castration-resistant prostate cancer (mCRPC). However, not all patients can tolerate the entire therapeutic sequence, and in some cases, the treatment may prove ineffective. In real-world conditions, the aim is to distinguish between patients who fully benefit from treatment (those who respond effectively and tolerate the entire therapeutic sequence) and those who do not respond or cannot tolerate the entire sequence. This study explores predictive factors to distinguish between fully beneficial RLT treatment patients (FBTP) and not fully beneficial RLT treatment patients (NFBTP). The objective was to enhance the understanding of predictive factors influencing RLT effectiveness and to highlight the significance of machine learning in optimizing patient selection for treatment planning. Methods: Data from 25 mCRPC patients, categorized as FBTP (11) or NFBTP (14) to RLT, were analyzed. The dataset included clinical, imaging, and biological parameters. Data analysis techniques, including exploratory data analysis and feature engineering, were used to develop machine learning models for predicting patient outcomes. Results: Imaging data analysis revealed statistically significant differences in the renal uptake intensity of Choline between the two groups. A discordance of FDG+ and PSMA− was identified as a potential indicator of NFBTP. The integration of biological data enhanced the model’s predictive capability, achieving an accuracy of 0.92, a sensitivity of 0.96, and a precision of 0.96. Adding blood parameters like neutrophils, leukocytes, and alkaline phosphatase greatly increased prediction accuracy. Conclusions: This study emphasizes the significance of an integrated approach that merges imaging and biological data, thereby augmenting the predictive accuracy of patient outcomes in RLT with [177Lu]Lu-PSMA. In particular, including Choline PET among the imaging parameters provides unique insights into the predictive factors affecting RLT efficacy. This approach not only deepens the understanding of predictive factors but also underscores the utility of machine learning in refining the patient selection process for optimized treatment planning
Influence of the surface energy of a basalt fiber on capillary wicking and in-plane permeability of reinforcements
No full textInternational audienceThis study evaluates the influence of a thermal treatment of a basalt fiber on capillary wicking tests and in-plane permeability experiments, under several pressure differences. The impact of the treatment was characterized at three scales: microscopic, to determine the fiber surface energy; mesoscopic, to estimate an equivalent capillary pressure (Pcap) of the fabric in spontaneous impregnation; and macroscopic, to determine the saturated (Ksat) and unsaturated (Kunsat) permeability of the fibrous preform at the process scale. Results at the microscopic scale showed that the thermal treatment increased the polarity of the fiber by 22% and decreased its surface roughness. Capillary wicking tests showed that the treated fabric presents a better affinity with water, increasing Pcap by 68%. At the process scale, permeability experiments showed the increase of Ksat and Kunsat after treatment. Finally, results of capillary pressure showed a dominance of capillary effects under the negative pressure difference
Finding the Perfect Match: Smallholder Selection in Short Food Supply Chains
No full textInternational audienceIn the short-food supply chain, retailers have to make critical decisions when choosing which farmer to buy the produce from.Moreover, planning for long periods of time further complicates this task. This work presents a linear program integrated in a rollinghorizon to assist retailers in planning their orders from different suppliers and knowing when to place them. The model is developed toovercome the single-item, multi-sourcing, capacitated lot-sizing problem and plan the long-term orders for the retailer. When tested,the model successfully planned the orders, deliveries, and inventories of the different suppliers while minimizing the cost for theretailer. Finally, when tested for instances of increased complexity, the model managed to find the solutions within an acceptableexecution time
A Deep Learning Method for Radiometric Harmonization of Non-Overlapping Remote Sensing Images
No full textInternational audienceConventional relative radiometric normalization (RRN) methods establish mapping relationships by using overlapping areas between images to achieve radiometric alignment between them. However, these methods become inapplicable when stitching weakly overlapping or non-overlapping images. We propose a novel radiometric harmonization method that addresses radiometric alignment as a style transfer problem using the CycleGAN, a Generative Adversarial Network architecture. We use two non-overlapping image sets to train the model, and the trained model can perform style transfer between the target image set and the non-overlapping image set. The corrected image closely approximates the conventional RRN result using the real reference image (overlapping with the target image), and is significantly better than the conventional RRN results obtained using the non-overlapping pseudo-reference image
Putting a label on someone: impact of schizophrenia stigma on emotional mimicry, liking, and interpersonal closeness
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Étude du mélange d’ABS photo-oxydée et vierge : application au recyclage de l’ABS
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Valorisation des composites en fin de vie dans les matériaux cimentaires
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Could near infrared spectroscopy be the new weapon in our understanding of the cerebral and muscle microvascular oxygen demand during exercise?
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From raw microalgae to bioplastics: Conversion of Chlorella vulgaris starch granules into thermoplastic starch
No full textInternational audienceMicroalgae are emerging as a promising feedstock for bioplastics, with Chlorella vulgaris yielding significant amounts of starch. This polysaccharide is convertible into thermoplastic starch (TPS), a biodegradable plastic of industrial relevance. In this study, we developed a pilot-scale protocol for extracting and purifying starch from 430 g (dry weight – DW) of starch-enriched Chlorella vulgaris biomass. More than 200 gDW of starch were recovered, with an extraction yield and starch purity degree reaching 98 and 87 %, respectively. We have characterized this extracted starch and processed it into TPS using twin-screw extrusion and injection molding. Microalgal starch showed similar properties to those of native plant starch, but with smaller granules. We compared the mechanical properties of microalgal TPS with two controls, namely a commercial TPS and a TPS prepared from commercial potato starch granules. TPS prepared from microalgal starch showed a softer and more ductile behavior compared to the reference materials. This study demonstrates the feasibility of recovering high-purity microalgal starch at pilot scale with high yields, and highlights the potential of microalgal starch for the production of TPS using industrially relevant processes