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Kinematic motor variability is extensively studied in occupational, clinical and sports biomechanics, but the consistency of most motor variability metrics have never been reported.
The reported data on between- and within-subject variability can be used to design future studies using nonlinear motor variability metrics on kinematics data.
Conventional SRAM variability metrics require including test structures that cannot be used to investigate cell bit fails in functional SRAM arrays.
Between- and within-subject consistencies of these variability metrics were quantified by variance components estimated using a nested random effects model.
In this study, we investigated the potential to reliably separate cropland, pasture, natural savanna vegetation, and other relevant land cover classes employing Landsat-derived spectral temporal variability metrics for a savanna landscape in the Brazilian Cerrado.
In order to better understand the surplus value and limitations of spectral temporal variability metrics for classification purposes, we analyzed four datasets of different temporal depth, using 344 Landsat scenes across four footprints between 2009 and 2012.
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By combining metrics into a multimetric index, overall long-term variability may be reduced because metrics with lower variability (e.g., EPT richness) may dampen the influence of highly variable metrics (e.g., % intolerant).
When pupil diameter increased to 6 mm, this bias changed in the myopic direction and the variability between metrics also increased.
The aim of the present study was to evaluate time and frequency domain and nonlinear heart rate variability (HRV) metrics for stress level assessment using a short-time window.
Electrocardiogram data were recorded continuously, and heart rate (HR) and heart rate variability (HRV) metrics were derived.
We analyzed time and frequency domain heart rate variability (HRV) metrics at 5-min long epochs using the manufacturer's Pathfinder software system.
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