The authors propose a practice-specificity-based model of arousal for achieving peak performance. The study included 37 healthy male physical education students whom they randomly assigned to a high-arousal (n = 19) or low-arousal group (n = 18). To manipulate participants' level of arousal, the authors used motivational techniques. They used heart rate and the Sport Competition Anxiety Test (R. Martens, 1977) to measure the level of arousal that participants achieved. At the determined and given arousal state, the 2 groups performed the task (basketball free throws) for 18 sessions. Both groups performed a retention test at the 2 arousal levels immediately after the last exercise session, in the posttest, and after 10 days. Results showed that both groups learned the task similarly and achieved their peak performance at their experienced arousal level. When tested at an arousal level that differed from the one that they experienced throughout practice sessions, participants' performance had deteriorated significantly. Performance of the task seemed to have integrated with the arousal level of the participants during the task learning. The findings of this study suggest a practice-specificity-based explanation for achieving peak performance.
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http://dx.doi.org/10.3200/JMBR.39.6.457-462 | DOI Listing |
Sci Rep
December 2024
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Huhhot, 010000, Inner Mongolia, China.
Mongolian patterns are easily damaged by various factors in the process of inheritance and preservation, and the traditional manual restoration methods are time-consuming, laborious, and costly. With the development of deep learning technology and the rapid growth of the image restoration field, the existing image restoration methods are mostly aimed at natural scene images. They do not apply to Mongolian patterns with complex line texture structures and high saturation-rich colors.
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December 2024
Department of Materials and Metallurgical Engineering, Amirkabir University of Technology, Tehran, 15875-4413, Iran.
This study explores the impact of metallic shells by electroforming method on the mechanical behavior of thermoplastic polyurethane (TPU)-based lattice structures. First, the TPU lattice structures were printed by additive manufacturing technique. Then layers of Ni and Cu as a thin shell were dressed on the TPU lattice structures in the electroforming baths of Ni and Cu solutions.
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December 2024
State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210009, China.
The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) in the the onset of postoperative delirium (POD) remains an area of inquiry. We aim to determine whether qEEG could assist in the diagnosis of early POD in cardiac surgery patients. We prospectively studied a cohort of cardiac surgery patients undergoing qEEG for evaluation of altered mental status.
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December 2024
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration.
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December 2024
School of Management, Shenyang University of Technology, Shenyang, 100870, China.
This study presents a novel framework for advancing sustainable urban logistics and distribution systems, with a pivotal focus on fast charging and power exchange modalities as the cornerstone of our research endeavors. Our central contribution encompasses the formulation of an innovative electric vehicle path optimization model, whose paramount objective is to minimize overall operational costs. Integrating V2G technology, we facilitate sophisticated slow charging and discharging management of EVs upon their return to distribution centers, enhancing resource utilization.
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