Search

Purpose: This study aimed to investigate the peak performance characteristics of the world top-8 swimmers and the key factors involved in the journey toward achieving better peak performance. Methods: The results of the world top-8 swimmers from 2001 to 2022 were collected from the World Aquatics performance database. Progression to peak performance was tracked with individual quadratic trajectories (1191 cases). Utilizing k-means clustering to group competitive feature variables, this study investigated key developmental factors through a binary logistic regression model, using the odds ratio (OR) to represent whether a factor was favorable (OR > 1) or unfavorable (OR < 1). Results: Significant differences (P <.001) in the peak age between men (23.54/3.80) and women (22.31/4.60) were noticed, while no significant differences (P >.05) in the peak-performance window for both sexes appeared. Peak performance occurred at later ages for the sprint for both sexes, and women had a longer duration in peak-performance window for sprint (P <.05). Peak-performance occurred at later ages for the breaststroke and butterfly for both sexes (P <.05). Binary logistic regression revealed that high first-participation performance (OR = 1.502), high major-competition performance (OR = 4.165), early first-major-competition age (OR = 1.441), participation frequency above 4 times/year in both phase 2 (4.3–8.0 times/y, OR = 3.940; 8.1–20.0 times/y, OR = 5.122) and phase 3 (4.1–7.5 times/y: OR = 5.548; 7.7–15.0 times/y: OR = 7.526), and a career length of 10 years or more (10–15 y, OR = 2.102; 16–31 y, OR = 3.480) were favorable factors for achieving better peak performance. Conclusions: Peak performance characteristics varied across sex, swimming stroke, and race distance in the world top-8 swimmers. Meanwhile, the research indicated that certain specific developmental factors were key conditions for the world top-8 swimmers to achieve better peak performance in the future. [ABSTRACT FROM AUTHOR]

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]