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Efficient Statistical Significance Approximation for Local Similarity Analysis of High-Throughput Time Series Data Using the Circular Moving Block Bootstrap
Submitted: 11 December 2025 You are already at the latest version To address the challenges of statistical inference for non-stationary traffic flow, this paper proposed an improved block permutation framework tailored to the correlation analysis requirements of traffic volume time series, and developed a statistical significance assessment method for local similarity scores based on the Circular Moving Block Bootstrap (CMBBLSA).
Direct observation of chemical short-range order in a medium-entropy alloy
Abstract Complex concentrated solutions of multiple principal elements are being widely investigated as high- or medium-entropy alloys (HEAs or MEAs)1,2,3,4,5,6,7,8,9,10,11, often assuming that these materials have the high configurational entropy of an ideal solution. However, enthalpic interactions among constituent elements are also expected at normal temperatures, resulting in various degrees of local chemical order12,13,14,15,16,17,18,19,20,21,22.
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