This study investigates the incremental variance in job performance explained by assessment center (AC) dimensions over and above personality and cognitive ability. The authors extend previous research by using meta-analysis to examine the relationships between AC dimensions, personality, cognitive ability, and job performance. The results indicate that the 7 summary AC dimensions postulated by W. Arthur, Jr., E. A. Day, T. L. McNelly, & P. S. Edens (2003) are distinguishable from popular individual difference constructs and explain a sizeable proportion of variance in job performance beyond cognitive ability and personality.
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http://dx.doi.org/10.1037/0021-9010.93.5.1042 | DOI Listing |
J Biomech
January 2025
Exercise Biochemistry Laboratory, Department of Biochemistry and Molecular Biology, Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil.
Understanding intrinsic muscular adaptations more deeply can help clarify their relationships with sports performance. Therefore, the aim of this study was to determine if vastus lateralis muscle architecture, quality and stiffness can explain knee extensor maximal torque and countermovement and squat jump performance of athletes. One hundred and two athletes were evaluated based on the architecture, quality and stiffness of the vastus lateralis at rest.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Shanghai Maritime University, Shanghai 201306, China. Electronic address:
Background And Objective: Inferring large-scale brain networks from functional magnetic resonance imaging (fMRI) provides more detailed and richer connectivity information, which is critical for gaining insight into brain structure and function and for predicting clinical phenotypes. However, as the number of network nodes increases, most existing methods suffer from the following limitations: (1) Traditional shallow models often struggle to estimate large-scale brain networks. (2) Existing deep graph structure learning models rely on downstream tasks and labels.
View Article and Find Full Text PDFSci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
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January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
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January 2025
Civil and Environmental Engineering Department, Khalifa University, Abu Dhabi, UAE.
Estimating spatiotemporal maps of greenhouse gases (GHGs) is important for understanding climate change and developing mitigation strategies. However, current methods face challenges, including the coarse resolution of numerical models, and gaps in satellite data, making it essential to improve the spatiotemporal estimation of GHGs. This study aims to develop an advanced technique to produce high-fidelity (1 km) maps of CO and CH over the Arabian Peninsula, a highly vulnerable region to climate change.
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