Objectives: The objective of this study was to examine the psychometric properties of the Chinese version of the Academic Self-Efficacy Scale (ASES-C) and confirm its measurement invariance across gender identities.
Methods: In this study, 502 university students (29.68% male, 70.
This research discusses an interesting topic, using artificial intelligence methods to predict the score of powerlifters. We collected the characteristics of powerlifters, and then used the reservoir computing extreme learning machine to build a predictive model. In order to further improve the prediction results, we propose a method to optimize the reservoir computing extreme learning machine using the whale optimization algorithm.
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