Background: Plyometric training is an effective training method to improve explosive strength. However, the ability to perform plyometric training in the adolescent population is still controversial, with insufficient meta-analyses about plyometric training on lower limb explosive strength in adolescent athletes. Objective: To investigate the influence of plyometric training on the explosive strength of lower limbs in adolescent athletes. Methods: We performed a search of six databases (Web of Science, PubMed, Scopus, ProQuest databases, China National Knowledge Infrastructure (CNKI), and Wan-fang database) from the starting year of inclusion in each database to April 4, 2022. The quality of the included literature was assessed using the Cochrane risk assessment tool, and data were analyzed using the Review Manager 5.4 software. Result: Plyometric training had significant effects on the performance of adolescent athletes in countermovement jump (MD = 2.74, 95% CI: 1.62, 3.85, p < 0.01), squat jump (MD = 4.37, 95% CI: 2.85, 5.90, p < 0.01), standing long jump (MD = 6.50, 95% CI: 4.62, 8.38, p < 0.01), 10-m sprint (MD = −0.04, 95% CI: −0.08, −0.00, p = 0.03), and 20-m sprint (MD = −0.12, 95% CI: −0.20, −0.04, p = 0.03); all had positive and statistically significant effects (p < 0.05). Conclusion: Plyometric training can significantly enhance the explosive strength of lower limbs in adolescent athletes.

Zhang, M, Chen, L, Dai, J, Yang, Q, Huang, Z, He, J, Ji, H, Sun, J, and Li, D. Application of a new monitoring variable: Effects of power loss during squat training on strength gains and sports performance. J Strength Cond Res 38(4): 656–670, 2024—This study aimed to compare the effects of power loss (PL) autoregulated volume (PL10 and PL20) with standardized fixed-load (FL) prescription on strength, sports performance, and lean body mass (LBM). Thirty-five female basketball players from a sports college were randomly assigned to 3 experimental groups (PL10, n = 12; PL20, n = 12; and FL, n = 11, respectively) that performed a resistance training (RT) program with wave-like periodization for 10 weeks using the back squat exercise. Assessments performed before (Pre) and after (Post) intervention included assessed 1 repetition maximum (1RM), body composition, 20-m sprint (T20M), change of direction (COD), and jump performance, including countermovement jump with arm swing, maximum vertical jump, and reactive strength index. Three groups showed significant improvements in strength (effect size [ES]: PL10 = 2.98, PL20 = 3.14, and FL = 1.90; p < 0.001) and jump performance (ES: PL10 = 0.74, PL20 = 1.50, and FL = 0.50; p <0.05–0.001). However, PL10 and PL20 demonstrated different advantages in sports performance compared with FL (group × time interaction, p <0.05). Specifically, PL10 significantly improved COD performance (ES = −0.79 ∼ −0.53, p <0.01), whereas PL20 showed greater improvements in sprint (ES = −0.57, p <0.05) and jump performance (ES = 0.67–1.64, p <0.01–0.001). Moreover, PL10 resulted in similar gains to PL20 and beneficial improvements compared with FL in LBM, despite performing the least repetitions. Overall, the study indicates that power loss–based autoregulation induces greater gains in LBM and sports performance, as well as eliciting a higher efficiency dose response than standardized FL prescriptions, particularly for PL10. Therefore, incorporating PL monitoring in training programs is recommended, and further studies on power-based RT would be worthwhile.

Due to the character of the taekwondo, the adenosine triphosphate–phosphocreatine system provides the energy for each kick, the glycolytic system supports the repeated execution of kicks, and the aerobic system promotes recovery between these movements and the bout. Therefore, taekwondo athletes require high explosive power and anaerobic capacity in order to carry out sustained and powerful attacks. So, the purpose of this study is to compare the effects of APRE and VBRT on lower-limb explosive power and anaerobic capacity in college taekwondo players. A total of 30 taekwondo players completed an 8-week training intervention with autoregulatory progressive resistance exercise (APRE; n = 15) and velocity-based resistance training (VBRT; n = 15). Testing included the one-repetition maximum squat, countermovement jump (CMJ), taekwondo anaerobic intermittent kick test (TAIKT), and 30-s Wingate anaerobic test (WAnT). (1) Intragroup comparisons revealed significant effects for one-repetition maximum squat, peak power of CMJ (CMJ_PP), relative peak power of CMJ (CMJ_RPP), and total number of TAIKT (TAIKT_TN) in both the APRE and VBRT groups. The VBRT group exhibited small effect sizes for time at peak power of WAnT (WAnT_PPT) and moderate effect sizes for peak power of WAnT (WAnT_PP), relative peak power of WAnT (WAnT_RPP), and fatigue index of TAIKT (TAIKT_FI), whereas the APRE group exhibited small effect sizes for TAIKT_FI. (2) Intergroup comparisons revealed no significant effects in any of the results. However, VBRT demonstrated a moderate advantage in WAnT_PP and WAnT_RPP, whereas APRE had a small advantage in CMJ_PP and CMJ_RPP. These findings suggest that APRE improved explosive power (CMJ_PP and CMJ_RPP) more, whereas VBRT improved anaerobic power output (WAnT_PP and WAnT_RPP) more. Both methods were found to have similar effects in improving the anaerobic endurance (WAnT_PPT and TAIKT_TN) and fatigue index (power drop of WAnT and TAIKT_FI).

Recent deep convolution-based stereo matching methods have shown significant progress. However, most state-of-the-art models achieve high accuracy by using 3D convolutions during cost aggregation, which come with more floating-point computations and makes it difficult for deployment in real-time applications. In this paper, we propose an effective and efficient multi-scale aggregation module (without 3D convolution) to build our fast Multi-Scale Residual Fusion Network (MSRFNet). Three lightweight components are involved in our aggregation module: combination block, attention block, and residual fusion block. The combination block combines features with different receptive fields and the attention block emphasizes salient regions in various cost volumes. The residual fusion module focuses on extracting the differences between adjacent cost volumes, using progressively residual aggregation instead of simply stacking or adding. Extensive experiments on Scene Flow and KITTI benchmarks demonstrate that our method achieves competitive accuracy com-pared with state-of-the-art methods while running at 56ms.