Héliodore, Héron, Nilée, Philoxène, Sostrate

peer reviewe

Convolutional neural network-based mapping of material micro-structures to deep material networks for non-linear mechanical response prediction

peer reviewedData-driven approaches make the development of surrogates of complex heterogeneous material responses possible. After being trained using a previously generated data-set during an offline stage, the surrogates can be used as a material law to conduct structural simulations during the online stage. Nevertheless, in view of accounting for the uncertainty and variability of the heterogeneous materials, the surrogates should be able to account for the micro-structure variability, which remains a challenge. Among the possible surrogate candidates, (Interaction-Based-)Deep-Material Networks ((IB-)DMN) offer the advantage that they can extrapolate the response for new material model parameters of the heterogeneous material phases and for arbitrary loading histories outside of their training range. This advantage results from their thermodynamics consistency and from the fact that the IB-DMN learnable parameters represent solely the micro-structure organization and not the phases material response. However, a trained IB-DMN remains an image of a given micro-structure spatial organization realization in terms of clustering etc. A new microstructure realization thus requires a new training process, limiting the interest of the IB-DMN for stochastic multi-scale analyses. In order to address this limitation, we define the learnable or topological arameters of the IB-DMN from a combination of convolution encoder and neural network, with the micro-structure image serving as input data. After training a CNN encoder-decoder, the encoder part allows extracting the feature vectors of the heterogeneous material directly from micro-structure images. These feature vectors then serve as input of a trained feedforward neural network (FNN) that predicts the topological parameters of the IB-DMN, yielding a “Image to IB-DMN” framework. The methodology is first illustrated in the context of Unidirectional (UD) composites, for which Stochastic Volumes Elements (SVEs) serve as images of the micro-structure realizations. In a second step we show that the machine learning tools can be trained by considering simultaneously composite families of different inclusion shapes such as circular, elliptical and squared. Despite training considering only elastic data, the predictions for a complex pressure-sensitive elasto-plastic model remain accurate. These results demonstrate the complementary roles of the two networks: the CNN encoder–decoder efficiently extracts reduced feature vectors from micro-structure images with diverse inclusion geometries, and the FNN accurately maps these features to the topological parameters of the IB-DMN, establishing a robust, end-to-end image-to-model framework capable of generalizing across different micro-structural configurations.This research has been funded by the Walloon Region under the agreement no. 2010092-CARBOBRAKE in the context of the M-ERA.Net Join Call 2020 funded by the European Union under the Grant Agreement no. 958174.9. Industry, innovation and infrastructur

Life cycle assessment

editorial reviewed9. Industry, innovation and infrastructure12. Responsible consumption and production13. Climate actio

Debunking the Colonial Narrative in Belgium: Public Space Decolonization in Brussels and Cultural Objects Restitution to Central African Countries

peer reviewed[CHAPTER] This chapter analyzes contemporary decolonial thoughts and practices in Belgium, linking the nation's colonial period to the persistence of structural racism against people with sub-Saharan African origins. The chapter argues that Belgium's dominant colonial narrative and propaganda, centered on the "civilizing mission," continue to influence society despite the country's collective memory having largely suppressed its colonial history. [BOOK] Revived by the global resonance of the Black Lives Matter movement in 2020, this book adds to the current discussion on the idea of decolonizing Europe. Drawing inspiration from the study of colonialism, postcolonialism and the imperative to decolonize knowledge and practice, the editors bring together a group of scholars approaching these issues through ethnographic inquiry. The volume explores how race, colonial legacies and structural inequality are addressed across diverse European contexts – north, central, eastern and southern – as well as in their entanglements with regions beyond Europe. It offers critical, grounded insights into the possibilities and challenges of decolonial thinking today

Simulating SRSLY: Sensitivity Resolved SubvoxeL sepctroscopY

peer reviewedMagnetic resonance spectroscopy employs localization in an approximately rectangular voxel, which commonly includes contributions from various tissues due to non-rectangular in-vivo tissues geometry. The suggested technique aims to resolve spectral contribution from different spatially distinct regions based on multichannel data, smilar to the imaging Sensitivity Encoding technique. Simulations are performed using spectral data acquired on a phantom and multichannel coil sensitivity profiles. Simulations show feasibility of the suggested spectral separation technique and demonstrate limitations related to chemical shift displacement. Real data does not yet show full spectral separation of the components

Synergistic visible-light photocatalysis by ZnO/black phosphorus nanohybrids immobilized on activated kaolinite

peer reviewedThis study reports the design and synthesis of a novel composite photocatalyst based on ZnO nanoparticles immobilized onto a black phosphorus (BP)-modified activated kaolinite matrix. The fabrication strategy combines solvothermal doping and sol-gel methods to achieve a hybrid 2D/3D semiconductor system. Comprehensive characterization, including XRD, XPS, UV-Vis DRS, PL, SEM, and TEM, confirmed the successful integration of ZnO and BP within the clay structure. The resulting composites exhibited enhanced photocatalytic activity under visible light compared to pure ZnO, as evaluated by the degradation of two polyazo dyes (Direct Red 80 and Chicago Sky Blue 6B). The Activated Clay/BP/ZnO composite showed superior performance, with first-order kinetic constants up to three times higher than those of pure ZnO. This improvement is attributed to improved charge separation, defect engineering, and strong interfacial interactions within the heterostructure. These findings highlight the potential of BP-doped clay-supported ZnO composites as efficient, sustainable photocatalysts for wastewater treatment

Recension de Louis Rouquayrol "Descartes et la culture des esprits" (Honoré Champion, 2025)

editorial reviewe

Europaïsche Rezeption. Schwerpunkt Frankreich

peer reviewe

Troubles à l’ordre nucléaire. Fissures et corrosion sous contrainte dans les réacteurs nucléaires français

peer reviewedLa découverte en 2021 d’un phénomène générique de fissures dans certains tuyaux des réacteurs nucléaires français a entraîné une crise du secteur et mis sous pression tant l’opérateur EDF que les acteurs de la sûreté. En s’appuyant sur ce qu’Andrew Barry nomme la « politique des matériaux », cet article vise à comprendre comment ces fissures microscopiques troublent l’ordre sociomatériel nucléaire basé à la fois sur une politique de standardisation de l’infrastructure et sur des pratiques de contrôle s’appuyant sur des retours d’expérience. En étudiant un phénomène qui ne cesse d’échapper à des acteurs dont la mission est de le discipliner, cet article montre que ces troubles sont liés à un processus continu de singularisation des objets nucléaires. Un tel processus interroge l’écart croissant entre la réalité prescrite par les protocoles d'inspection et de maintenance et la réalité singulière des objets qui se dégradent, dont une partie significative des informations échappe aux pratiques censées permettre de prolonger leur durée de vie

Integrating climate-driven hydropower variability into long-term energy planning: A Bolivian case study under El Niño and La Niña scenarios

peer reviewedAs climate change effects become more evident worldwide, particularly regarding the variation in hydro resources availability, quantifying their potential impacts is critical to enable adequate adaptation strategies and facilitate planning efforts. In this sense, countries heavily reliant on hydropower must assess and integrate the implications of this variability to ensure a reliable electricity supply. Considering Bolivia as a case study, the impact of alternative hydro availability scenarios is evaluated through the analysis of extreme weather conditions associated with El Niño and La Niña events. To this end, a modeling framework is presented that combines global precipitation projections downscaled to a local level, with which three scenarios (Control, El Niño, and La Niña) are developed for 2030, 2040, and 2050. These scenarios are later analyzed using a cost-optimization energy model tailored to Bolivia, developed with PyPSA-Earth, which allows the representation of region-specific conditions with hourly resolution, both for hydro resources availability and electrical components. Results indicate that both El Niño and La Niña events can reduce hydropower availability significantly, by up to 37 % compared to average years, with neither of them being strictly linked to a higher reduction in hydropower generation. Regarding the operation of the system, it is seen that Bolivia’s legacy power plants can handle hydrological variability until 2040. However, the decommissioning of fossil capacity by 2050 significantly increases system vulnerability. As a result, deployment of flexible technologies and battery storage will play a key role in addressing both long-term capacity adequacy and short-term flexibility