AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...
Abstract: Probabilistic latent variable models (PLVMs), such as probabilistic principal component analysis (PPCA), are widely employed in process monitoring and fault detection of industrial processes ...
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 ...
In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...
Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...
PCA, CPCA and PBA all identified three dietary patterns, with a common “traditional southern Chinese” pattern high in rice and animal-based foods and low in wheat products and dairy. Only this pattern ...
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 ...
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 ...
The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...
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