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, particularly Graph Neural Networks (GNNs), which show great promise. These methods have already demonstrated performance at least comparable to current Track Finding algorithms, with significant room for further
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, United States) demonstrated net en-ergy gain in inertial fusion for the first time, marking a major scientific advancement. This breakthrough has since spurred numerous international projects aimed at developing
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of emerging quantum networks. In the last few years, color centers in the 2D material hexagonal boron nitride (hBN) have established them-selves as excellent quantum emitters, bringing new perspectives
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-turbine capable of converting their energy into electricity. Ultimately, this strategy could significantly reduce the net energy consumption of the process and contribute to the development of low-carbon
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cross-border network — the Upper Rhine Biodiversity Cluster — to share knowledge. This work will lead to analytical tools, action plans, training for professionals, and exhibitions for the general public
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novel eco-UHPFRC mixes with conventional and textile reinforcement, the implementation of these solutions to full-scale case studies, and the study on their life-cycle impacts at the network scale
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framework, covering a wide spectrum from RANS approaches to high-fidelity LES with automatic mesh adaptation. In parallel, recent work has led to the development of multi-fidelity modeling strategies