Principal component analysis (PCA) integrates multiple clinical indicators into a single score, providing a holistic assessment. Existing clinical indicators often fail to fully reflect health ...
He was a community pillar with a dark underside and possibly other victims. By Kwame Anthony Appiah Kwame Anthony Appiah has been The New York Times Magazine’s Ethicist columnist since 2015 and ...
The N’Guérédonké deposit, Faranah Province (Republic of Guinea), is part of the Leonian-Liberian crystalline shield, consisting of Archean granitoids and greenstone formations with a syn-tectonic ...
Abstract: In this work, the possibility of applying machine learning (ML) techniques to analyze and predict radio wave propagation losses in urban environments is explored. Thus, from a measurement ...
Polycystic ovary syndrome (PCOS) is a common, but clinically heterogeneous, condition. This study explores PCOS subtypes using two orthogonal statistical analyses of biochemical and anthropometric ...
Nuclear imaging for industrial process analysis and non-destructive component testing has been around for longtime, but progression and innovation in this field has been limited and not as advanced ...
A single component failure can bring an entire system to a halt, leading to costly downtime, safety risks and compromised performance. For mechanical engineers, designing critical components isn’t ...
Abstract: Robust tensor principal component analysis (RTPCA) based on tensor singular value decomposition (t-SVD) separates the low-rank component and the sparse component from the multiway data. For ...
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