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Machine learning applied to X-ray tomography as a new tool to analyse the voids in RRP Nb3Sn wires

Machine learning. Machine learning applied to X-ray tomography as a new tool to analyse the voids in RRP Nb3Sn wires 19-05-2021 Scientists have developed a new tool to investigate the internal features of Nb3Sn superconducting wires, combining X-ray tomographic data acquired at beamline ID19 with an unsupervised machine-learning algorithm. The method provides new insights for enhancing wire performance. Share Interest in niobium-tin (Nb 3Sn) as a material for superconducting wires has recently been renewed because this material has been selected to replace niobium-titanium as the next step in accelerator magnet technology [1]. The design of these magnets relies on the availability of advanced Nb

A physics perspective on wound healing

 E-Mail IMAGE: Fluorescent microscopy image of a proliferating cell front, whose edge is indicated in green. The rat epithelial cells, with cytoplasm (blue) and nuclei (red), form a 2-dimensional culture which can. view more  Credit: © Guillaume Rapin, UNIGE In material physics understanding how systems interact across the interfaces separating them is of central interest. But can physical models clarify similar concepts in living systems, such as cells? Physicists at the University of Geneva (UNIGE), in collaboration with the University of Zurich (UZH), used the framework of disordered elastic systems to study the process of wound healing - the proliferation of cell fronts which eventually join to close a lesion. Their study identified the scales of the dominant interactions between cells which determine this process. The results, published in the journal

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