Background: This study adopted a quasi-experimental design to examine the effect of a 7-week mindfulness intervention on the psychological coping ability and shooting performance of college-level male basketball athletes in Macau. Methods: A total of 43 male college basketball athletes in Macau were selected as the participants. Besides the regular basketball training, the intervention group (n = 23) received a 7-week mindfulness training; the weekly mindfulness intervention session lasted around one hour according to the mindfulness training manual for athletes, while the control group (n = 20) did not receive any mindfulness training. Before and immediately after the 7-week intervention, all players performed the following tests: the “Five-Facet Mindfulness Questionnaire”, the “Acceptance and Action Questionnaire”, the “Sport Competition Anxiety Test”, the “Mindfulness Attention Awareness Scale”, and three shooting tests. An independent-sample t-test and a paired-sample t-test were used to analyze the between- and within-group differences. Moreover, a repeated measures ANOVA was used to assess the group, time, and group-by-time effects on psychological skills and shooting performances. Results: The intervention resulted in both significant between-group and within-group differences in mindfulness level, acceptance level, attention level, three-point, and free-throw shooting performances (all p < 0.05, Cohen’s d ranging from 0.565 to 1.117). Conclusion: While further study is necessary, the present study suggests that the 7-week mindfulness training program can significantly improve psychological outcomes and shooting performance in Macau college basketball athletes. Future studies involving competition settings and objective metrics will aid in verifying mindfulness as the prevalent practice among basketball practitioners and athletes.

Functional training has become a popular training method in different sports, yet limited studies have focused on paddle sports. The purpose of this study was to evaluate the effects of functional training on functional movement and athletic performance in college dragon boat athletes. A total of 42 male athletes were divided into 2 groups: a functional training (FT) group (n = 21, 21 ± 1.47 years) and a regular training (RT) group (n = 21, 22 ± 1.50 years). The FT group participated in an 8-week (16-session) functional-training program, while the RT group trained with strength-training sessions. Functional movement screen (FMS), Y-balance test (YBT) and athletic performance evaluations were conducted before and after the intervention. Repeated measure ANOVA and t-test evaluations were employed to examine differences for both groups. The FT group was significantly improved in FMS scores (F = 0.191, p < 0.001) and YBT scores (F = 2.59, p = 0.027), and it also showed significantly improved muscular fitness (pull-ups: F = 0.127, p < 0.001; push-ups: F = 1.43, p < 0.001) and rowing speed (F = 4.37, p = 0.004). It is recommended to include functional training as a part of training and routine exercise, as it appears to be an effective way of improving FMS and athletic performance in paddle sports.

Stock price prediction is a core challenge in quantitative finance. While machine learning has advanced the modeling of complex financial time series, existing methods often rely on single-target predictions, underutilize multidimensional market information, and are disconnected from practical trading systems. To address these gaps, this research develops a hybrid machine learning framework for flexible target forecasting and systematic trading of major American technology stocks. The framework integrates Ensemble Models (AdaBoost, Decision Tree, LightGBM, Random Forest, XGBoost) with Fusion Models (Voting, Stacking, Blending) and introduces a Transfer Learning method enhanced by Dynamic Time Warping to facilitate knowledge sharing across assets, improving robustness. Focusing on ten key stocks, we forecast three distinct momentum indicators: next-day Closing Price Difference, Moving Average Difference, and Exponential Moving Average Difference. Empirical results demonstrate that the proposed Transfer Learning approach achieves superior predictive performance and trading simulations confirm that strategies based on these predicted momentum signals generate substantial returns. This research demonstrates that the proposed hybrid machine learning framework can mitigate the high information entropy inherent in financial markets, offering a systematic and practical method for integrating machine learning with quantitative trading.