Zhejiang Industry Polytechnic College

The integration of multi-modal wearable smart devices is revolutionizing sports science, facilitating a paradigm shift from subjective coaching to a quantitatively-driven, personalized athletic development model. This study designs, implements, and validates a sophisticated wearable system for monitoring and optimizing the training of university-level track and field athletes. We present a custom-designed system architecture integrating high-frequency Inertial Measurement Units (IMUs) and electrocardiography (ECG) sensors, synchronized via a proprietary protocol. The software system employs edge computing for low-latency biomechanical feature extraction using an Extended Kalman Filter and a gradient-boosting regression model on the cloud backend for fatigue prediction. A 12-week randomized controlled trial was conducted with 50 athletes. Results demonstrated that the experimental group achieved statistically significant improvements in running economy (a 6.5% reduction in VO₂ at 16 km/h), a 10.3% enhancement in a novel Movement Quality Index, and a 45% reduction in time-loss injuries. The study concludes that a deeply integrated, multi-modal wearable system, underpinned by robust data processing pipelines and machine learning, can profoundly enhance training precision, performance outcomes, and athlete health.

Online education in creative disciplines often misaligns with industry innovation. This study proposes an artificial intelligence (AI)-generated content-driven closed-loop model that synchronizes classroom creativity with industry demands via a unified digital platform. Codeveloped with three universities and 10 creative enterprises, the framework integrates a dual-mentor mechanism and real-time semantic tuning to establish a dynamic industry–academia ecosystem. Results show a 71% reduction in prototype iteration time, enhanced creative translation, and improved collaboration. Key scaling factors include algorithm compatibility, assessment fairness, and resource sustainability. An “open-incremental-collaborative” optimization strategy and a concrete AI governance framework covering transparency, bias mitigation, privacy, and intellectual property is proposed. This provides an empirically validated blueprint for AI-generated content-enhanced data-driven education.

We investigated the mediating effects of self-control and attachment in the relationship between parenting style and aggressive behavior among Chinese adolescents. A total of 617 adolescents from Jiangsu, Henan and Hunan provinces completed Parenting Style Scale、 Aggressive Behavior Scale、 Self-Control Scale and Attachment Sale. The results were follows: (1) There were significant correlations between parenting style, aggressive behavior, self-control, and attachment. (2) Both self-control and attachment mediated the relationship between positive parenting style and aggressive behavior. (3) Self-control and attachment played a significant chain mediating role in the relationship between positive parenting style and aggressive behavior. Thus both parents' positive and negative parenting styles can directly associate adolescents' aggressive behavior. In addition, parents' positive parenting style can also indirectly associate adolescents' aggressive behavior through the separate mediating and the chain mediating roles of self-control and attachment.

ABSTRACT Rational utilization of the guiding effect of crustal stress on explosive crack can significantly enhance blasting efficiency. This paper modified the calculation models for the crushed zone and fractured zone in tunnels at different burial depths and validated the models using numerical simulation methods. The influencing factors of blasting crack extension in deep and shallow buried roadways were analyzed, and the guiding significance of crack extension characteristics on the design of blasthole mesh parameters was discussed. The results show that the error in the theoretical calculations is controlled within 6%, indicating a high degree of accuracy. The longest radial main cracks appeared in the directions of 75°–90°, 70°–72°, and 74° in the blast holes for burial depths H ≤ h q , h q < H ≤ H P , and H > H P , respectively. Blasting yields optimal results when the blasthole alignment follows the direction of main fracture propagation.

This study systematically investigates the application and effectiveness of Artificial Intelligence (AI) in collegiate Physical Education and Training (PET) through a mixed-methods design. The experimental group employed AI tools including computer vision-based motion analysis, wearable fitness trackers, and machine learning-driven personalized platforms, while the control group received conventional instructor-led training. Quantitative results revealed that the experimental group achieved significantly greater improvements in technical action standardization (25.3% mean score increase), 1000m run time (28.5 seconds reduction vs. 12.3 seconds), and standing long jump distance (15.2cm vs. 6.7cm increase). Student satisfaction was markedly higher in the AI-assisted group (4.52±0.38 vs. 3.21±0.45). Qualitative analysis of interviews with 10 instructors and 20 students identified key themes: enhanced assessment objectivity, personalized training adaptability, and practical barriers such as equipment cost and technical complexity. The study provides empirical evidence and practical insights to support the integration of AI in PET.