(1) Background: The purpose of this study was to examine the relationship between expectancy-value components and attitudes toward sportspersonship among Malaysian adolescent field hockey players. This study also examined the effect of expectancy beliefs, task values, and sportspersonship attitude on the motivation of adolescent field hockey players by gender and age group. (2) Methods: The Malay versioned Expectancy Value Model Questionnaire and the Malay versioned Multidimensional Sportspersonship Orientations Scale were administered on 730 respondents (µ = 15.46 ± 1.83 years). (3) Results: The expectancy values and attainment value (r = 0.894), utility value and attainment value (r = 0.833) were highly correlated. There was no significant gender difference in expectancy, task values, and sportspersonship attitude dimensions. The main effect of age group was significant on task values: (2724) = 4.19; = 0.01. The difference was indicated between age groups of 15-16 years and 12-14 years ( = 0.02, = 0.014) under task values variable. (4) Conclusions: There is no significant relationships between sportspersonship attitude (MSOS-M) and of expectancy beliefs and task values (EVMQ-M). To conclude, female and younger players demonstrate lower expectancy beliefs, task values, and sportspersonship attitudes than male and older field hockey players.
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http://dx.doi.org/10.3390/ijerph19084819 | DOI Listing |
Magn Reson Med
December 2024
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
Purpose: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Information Science and Technology, Northeast Normal University, 130117 Changchun, China.
The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features.
View Article and Find Full Text PDFCureus
November 2024
Department of Neurosurgery, Fukushima Medical University, Fukushima, JPN.
Introduction The degree to which each human brain hemisphere governs specific cognitive processes, such as language and handedness (the preference or dominance of one hand over the other), varies across individuals. Research has explored the nature of language laterality in left-handed (LH) individuals, indicating that left-hemisphere dominance for language is commonly observed across both left- and right-handed populations. Advanced imaging techniques, including functional transcranial Doppler sonography and fMRI, have revealed subtle differences in language lateralization between LH and right-handed (RH) individuals, particularly in semantic processing tasks.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
November 2024
The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States.
Purpose: This study aimed to investigate the impact of different model retraining schemes and data partitioning on model performance in the task of COVID-19 classification on standard chest radiographs (CXRs), in the context of model generalizability.
Approach: Two datasets from the same institution were used: Set A (9860 patients, collected from 02/20/2020 to 02/03/2021) and Set B (5893 patients, collected from 03/15/2020 to 01/01/2022). An original deep learning (DL) model trained and tested in the task of COVID-19 classification using the initial partition of Set A achieved an area under the curve (AUC) value of 0.
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