Analysis of Fluid Balance and Urine Values in Elite Soccer Players: Impact of Different Environments, Playing Positions, Sexes, and Competitive Levels.

Exercise can disrupt the fluid balance, hindering performance and athlete health. Limited data exist on fluid balance responses in varying climates, sexes, and ages. This study aimed to measure and compare fluid balance and urine values among elite soccer players during training at high and low temperatures, examining the differences between sexes, playing positions, and competitive levels within men's soccer. During the 2022-2023 competitive season, a descriptive observational study was conducted on 87 soccer players from an elite Spanish soccer team. The study found that none of the groups exceeded weight loss values of 1.5% of their body mass. Additionally, the soccer players studied experienced higher weight loss, fluid intake, and a higher sweat rate (SR) during summer training compared to winter training. During the summer, male U23-21 soccer players exhibited higher levels of weight loss, fluid intake, and a higher SR compared to female soccer players or the U19-17 male category. No significant differences were found between playing positions. In conclusion, differences in the fluid balance were observed based on the climatic conditions, competitive level, and sex.

A survey on image enhancement for Low-light images.

In real scenes, due to the problems of low light and unsuitable views, the images often exhibit a variety of degradations, such as low contrast, color distortion, and noise. These degradations affect not only visual effects but also computer vision tasks. This paper focuses on the combination of traditional algorithms and machine learning algorithms in the field of image enhancement. The traditional methods, including their principles and improvements, are introduced from three categories: gray level transformation, histogram equalization, and Retinex methods. Machine learning based algorithms are not only divided into end-to-end learning and unpaired learning, but also concluded to decomposition-based learning and fusion based learning based on the applied image processing strategies. Finally, the involved methods are comprehensively compared by multiple image quality assessment methods, including mean square error, natural image quality evaluator, structural similarity, peak signal to noise ratio, etc.