This study aimed to investigate individual trial-to-trial performance in three tests to define adaptive regulation as a key feature of expertise in nine-ball. Thirty-one male players were assigned into the low-skilled (n = 11), intermediate (n = 10), or high-skilled groups (n = 10). The power control, cue alignment, and angle tests were selected to assess participants' ability to control the power applied in shots, strike the ball straight, and understand the ball paths, respectively. Error distance and correction of error distance were identified for each shot using 2D video analysis. Results of one-way analysis of variance showed that the high-skilled group performed better in two out of the three tests than the other two groups (p = .010 for the cue alignment test; p = .002 for the angle test). However, the adaptation effect represented by the decreased error distances across trials was not observed. Pearson correlation revealed only a few significant correlations between the error distance and its correction within each participant in all tests (p < .05), and hence, the hypothesis that "low correction happened after small error and vice versa" is not supported.
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http://dx.doi.org/10.1123/mc.2021-0094 | DOI Listing |
Anal Bioanal Chem
January 2025
Statistical Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899-8980, USA.
Closely related species of Salmonidae, including Pacific and Atlantic salmon, can be distinguished from one another based on nucleotide sequences from the cytochrome c oxidase sub-unit 1 mitochondrial gene (COI), using ensembles of fragments aligned to genetic barcodes that serve as digital proxies for the relevant species. This is accomplished by exploiting both the nucleotide sequences and their quality scores recorded in a FASTQ file obtained via Next Generation (NextGen) Sequencing of mitochondrial DNA extracted from Coho salmon caught with hook and line in the Gulf of Alaska. The alignment is done using MUSCLE (Muscle 5.
View Article and Find Full Text PDFWater Res X
December 2024
Professor, Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.
Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11543, Saudi Arabia.
Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.
View Article and Find Full Text PDFJACC Clin Electrophysiol
December 2024
The Hull Family Cardiac Fibrillation Management Laboratory, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.
Background: Conduction velocity (CV) is a measure of the health of myocardial tissue. It can be measured by taking differences in local activation times from intracardiac electrodes. Several factors introduce error into the measurement, among which ignoring the 3-dimensional aspect is a major detriment.
View Article and Find Full Text PDFNeural Netw
January 2025
Institute of Cognitive Sciences and Technologies, National Research Council, Padova, Italy. Electronic address:
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behaviors could be explained in terms of active inference - either as discrete decision-making or continuous motor control - inspiring innovative solutions in robotics and artificial intelligence.
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