Background There has been a surge of interest on velocity-based training (VBT) in recent years. However, it remains unclear whether VBT is more effective in improving strength, jump, linear sprint and change of direction speed (CODs) than the traditional 1RM percentage-based training (PBT). Objectives To compare the training effects in VBT vs. PBT upon strength, jump, linear sprint and CODs performance. Data sources Web of science, PubMed and China National Knowledge Infrastructure (CNKI). Study eligibility criteria The qualified studies for inclusion in the meta-analysis must have included a resistance training intervention that compared the effects of VBT and PBT on at least one measure of strength, jump, linear sprint and CODs with participants aged ≥16 yrs. and be written in English or Chinese. Methods The modified Pedro Scale was used to assess the risk of bias. Random-effects model was used to calculate the effects via the mean change and pre-SD (standard deviation). Mean difference (MD) or Standardized mean difference (SMD) was presented correspondently with 95% confidence interval (CI). Results Six studies met the inclusion criteria including a total of 124 participants aged 16 to 30 yrs. The differences of training effects between VBT and PBT were not significant in back squat 1RM (MD = 3.03kg; 95%CI: -3.55, 9.61; I2 = 0%) and load velocity 60%1RM (MD = 0.02m/s; 95%CI: -0.01,0.06; I2 = 0%), jump (SMD = 0.27; 95%CI: -0.15,0.7; I2 = 0%), linear sprint (MD = 0.01s; 95%CI: -0.06, 0.07; I2 = 0%), and CODs (SMD = 0.49; 95%CI: -0.14, 1.07; I2 = 0%). Conclusion Both VBT and PBT can enhance strength, jump, linear sprint and CODs performance effectively without significant group difference.

The purpose of this study was to construct a geological hazard susceptibility evaluation and analysis model using three types of machine learning models, namely, random forest (RF), support vector machine (SVM), and naive Bayes (NB), and to evaluate the susceptibility to landslides, using the Puge section of the Zemu River valley in the Liangshan Yi Autonomous Prefecture as the study area. First, 89 shallow landslide and debris flow locations were recognized through field surveys and remote sensing interpretation. A total of eight hazard-causing factors, namely, slope, aspect, rock group, land cover, distance to road, distance to river, distance to fault, and normalized difference vegetation index (NDVI), were selected to evaluate the spatial relationship with landslide occurrence. As a result of the analysis, the results of the weighting of the hazard-causing factors indicate that the two elements of rock group and distance to river contribute most to the creation of geological hazards. After comparing all the indices of the three models, the random forest model had a higher correct area under the ROC curve (AUC) value of 0.87, root mean squared error (RMSE) of 0.118, and mean absolute error (MAE) of 0.045. The SVM model had the highest sensitivity to geological hazards. The results of geological hazard prediction susceptibility analysis matched the actual situation in the study area, and the prediction effects were good. The results of the hazard susceptibility assessment of the three models are able to provide support and help for the prevention and control of geological hazards in the same type of areas.