Background: Force plates are commonly used as a non-fatiguing measure of recovery. However, the recovery time course captured via the force plate assessment of vertical jumps has yet to be established. Therefore, the objective of this systematic review and meta-analysis was to examine the change in vertical jump performance and the time course of recovery following an acute bout of strenuous exercise using force plates. Methods: Peer-reviewed articles (n = 22) published prior to 8 November 2023, were identified by searching three electronic databases (PubMed, Scopus, Web of Science). Studies included in this review met the following criteria: (1) available in English; (2) involved adult participants >18 years of age; (3) measured the change in vertical jump performance over consecutive days using a force plate system. Individual effect sizes (ESs) were calculated by dividing the change in vertical jump at each timepoint (24 h, 48 h, etc.) by the pooled standard deviation (SD), and they were aggregated using a three-level random-effects model. Results: Vertical jump performance decreased following an acute strenuous exercise bout (ES = −0.2639; p < 0.0001) and returned to baseline after 3 days of recovery, with larger decreases observed when assessed using Peak Height rather than Peak Power (ES = −0.4687 vs. ES = −0.1399; p = 0.0393). Older participants showed a larger decrease in vertical jump (β = −0.0489; p < 0.0001). Conclusions: Force plates can be used to evaluate recovery post-exercise, with a decline in performance on Days 1 and 2, and full recovery by Day 3. The findings from this study support the use of force plates for the evaluation of recovery.

The amount of new renewable energy generation is increasing worldwide every year. Among many new renewable energy sources, solar energy generation using solar energy accounts for the highest proportion of new renewable energy generation. There is a variation in power production because solar power generation is more affected by climate conditions compared to power generation using crude oil or oil. In order to accurately predict solar energy generation dependent on climate variables, this study compares the performance of machine learning-based solar power generation prediction models using weather forecast data from the current forecast technology, Numeric Weather Prediction (NWP). In this study, we experimented on two NWP types, and 7 machine learning models depending on 21 photovoltaic(pv) power stations. Based on results, we select the model with the lowest statistical indicators nMAE(%) by region as the optimal model for the region. Finally, experimental results show that the 7-Block ANN model devised in this study is better than conventional machine learning models.

The 3rd Generation Partnership Project (3GPP), which has developed the most successful standard technologies in the mobile communication market, is currently carrying out the 5G standardization of both access network (AN) and core network (CN) in 5G system. The 5G core (5GC) was designed to be commonly connected to multiple access networks in the first phase (Release 15) of normative standardization. In the next phase (Release 16), Access Traffic Steering, Switching and Splitting (ATSSS), one of work items to converge multiple access networks in 5GC, is under standardization. In this paper, we propose an efficient session management in multiple access environment to support ATSSS function for 5G broadband multimedia service. Proposed multi-access (MA) session management for ATSSS achieves radio resource saving and signaling reduction by using existing policy rules and signaling procedures.