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https://www.selleckchem.com/products/NVP-AUY922.html As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for silent speech recognition could be systemically explored. A total of 120 closely-spaced electrodes were placed on the facial and neck muscles to collect the high-dees. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.We present morphological and compositional analysis of phase-separated Pt-Ni alloy nanoparticles (NPs) formed by ns pulsed laser dewetting. The PtNi NPs obtained by the pulsed laser dewetting consist of phase-separated multiple domains including Pt3Ni, PtNi and PtNi3 phases with various crystal orientations as revealed by transmission electron microscopy, which is in contrast to thermal dewetting resulting NPs of a uniform composition. A

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