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Anticipating potential environmental risks of offshore hydrogen production powered by offshore wind farm
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Microstructure optimization by combinatorial approach applied to Duplex Medium Manganese steels
No full textInternational audienceThis study introduces a novel combinatorial approach for optimizing the microstructure of duplex medium-manganese (Mn) steels by coupling a controlled thermal gradient with in situ high-energy X-ray diffraction (HEXRD) during intercritical annealing. A temperature gradient (680–720 °C) across a single sample enables real-time monitoring of phase transformations over a broad thermal range in one experiment. Compared to isothermal trials, this method offers high-resolution insight into austenite formation kinetics and phase stability, enabling accurate identification of the optimal temperature window for maximizing retained austenite. The results reveal a narrow optimal range (∼700–710 °C) where retained austenite fractions exceed 30 %, surpassing values from traditional methods. Post-mortem Electron Backscatter Diffraction (EBSD) analysis showed the spatial distribution of stabilized austenite, highlighting the complementary roles of in situ and ex situ characterization. This work demonstrates the potential of gradient-based combinatorial metallurgy to accelerate process optimization and support the design of high-performance third-generation advanced high-strength steels
Crystallization behavior of co-polyesters based on hydroxy fatty acids extracted from tomato peel agro-wastes
No full textInternational audienceThis work investigates the crystallization behavior of bio-based slightly crosslinked polyester networks synthesized from long-chain hydroxy fatty acids. The crystallization kinetics and the melting behavior were assessed using a combination of modulated-temperature differential scanning calorimetry (MT-DSC) and fast scanning calorimetry (FSC). The characterization of the crystalline phases was performed by X-ray diffraction (XRD) and polarized-light optical (POM) microscopy. A methodology based on calorimetric investigations was used, allowing to estimate the theoretical value of the melting enthalpy of the initial cutin monomers as well as of the derived co-polyester networks. The results obtained by this method allowed to estimate the crystalline content . The combination of XRD and calorimetric analyses evidenced the existence of polymorphs with different stabilities over time at room temperature, characterized by a monotropic transition from the metastable crystal phase (α-phase) to more stable crystal phases (β'and β-phases) depending on the crystallization time accorded to the monomers and the crosslinked co-polyesters. This work delves deeper into the complex crystallization behavior of biobased polyesters, which could eventually allow a better control over the properties of these materials
EsurvFusion: An evidential multimodal survival fusion model based on Epistemic random fuzzy sets
No full textInternational audienceMultimodal survival analysis aims to combine heterogeneous data sources to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noise across data sources. Additionally, the exact survival time is often censored (partially known) due to incomplete event observation. To address the above challenges, we propose a novel interpretable evidential multimodal survival fusion model, EsurvFusion. This model is designed to combine multimodal data at the decision level using Epistemic Random Fuzzy Sets that jointly handle both data and model uncertainty while incorporating modality-level reliability. Specifically, EsurvFusion first models unimodal data with newly introduced Gaussian random fuzzy numbers, producing possible unimodal survival predictions along with corresponding aleatory and epistemic uncertainty. It then estimates modality-level reliability through a reliability discounting layer to correct the misleading impact of noisy data modalities. Finally, a multimodal evidence fusion layer is introduced to combine the discounted predictions, revealing modality-level influence based on the learned reliability coefficients. Extensive experiments on four multimodal cancer survival datasets demonstrate the effectiveness of our model in handling highly heterogeneous data, establishing a new state-of-the-art performance on several benchmarks
BUFNet: Boundary-aware and uncertainty-driven multi-modal fusion network for MR brain tumor segmentation
No full textInternational audienceBrain tumor segmentation plays a critical role in the diagnosis and treatment planning of brain tumors. However, achieving accurate segmentation is challenging due to the complex boundaries between different tumor sub-regions. Additionally, many existing methods produce deterministic segmentation results without addressing prediction uncertainty, limiting their reliability and interpretability in clinical applications. To tackle these challenges, this paper proposes a novel Boundary-aware and Uncertainty-driven multi-modal Fusion Network (BUFNet) for MR brain tumor segmentation. Specifically, a boundary-aware mechanism is proposed to extract tumor boundary information, and guide the network by leveraging this information for better discrimination of tumor sub-regions. Furthermore, an effective multi-modal fusion method is proposed to integrate complementary information from multiple MR modalities. To further reduce uncertainty, a novel uncertainty-based segmentation loss function is proposed to improve segmentation performance. Additionally, to enhance clinical interpretation and decision-making, uncertainty quantification is incorporated to provide confidence measures for segmentation results. Experimental results demonstrate the effectiveness of the proposed method, showing superior performance compared to state-of-the-art methods
Bridging gender gaps in entrepreneurship through diverse learning environments and change tolerance
No full textInternational audienceThis study explores how diverse learning environments (DLE) influence entrepreneurial intentions and the moderating role of gender. By integrating the theory of planned behaviour with the concept of change tolerance, we examine how DLE characterised by cultural and institutional diversity impact students’ entrepreneurial outcomes. Our findings indicate that immersive DLE enhance participants’ perceived behavioural control, fostering proactive entrepreneurial mindsets among both male and female students. Contrary to traditional research highlighting significant gender differences in entrepreneurial attitudes, our results suggest that inclusive learning contexts can mitigate these disparities. This study contributes to the understanding of gender issues in entrepreneurship by demonstrating how educational environments can promote equality and enhance self-efficacy. The implications for business schools are substantial, as fostering DLE can nurture a diverse pool of entrepreneurs, ultimately driving economic growth and social development within various cultural contexts
What regime for the additional charges of the article 23.4 of the CMR?
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