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Badminton, a dynamic racquet sport demanding agility and power, features the overhead forehand smash as a pivotal offensive shot. Utilizing 3D motion analysis, this research delves into the intricate biomechanical facets underpinning this pivotal shot, with a dual focus on both novice and proficient players. Through a comparative analysis of these two player cohorts, the investigation aims to elucidate the fundamental factors influencing the quality of the forehand smash. Our findings reveal that skilled players exhibit significant improvements in smash quality, including a 60.2% increase in shuttlecock speed, reduced clearance height, and flight angle at release. These enhancements are associated with specific determinants, such as consistent positioning, racket angle at impact, and range of motion (ROM) in various joints. More crucially, full-body tension-arc formation and a four-segment whip-like smash contribute to these improvements. Unique to the whip-like smash is the rapid trunk and shoulder rotations in early whip-like control inducing passive elbow flexion and wrist over-extension, enhancing the stretch-shortening cycle (SSC) effect of muscles for a more powerful smash. Emphasizing this uniqueness and the determinants simplify smash learning, potentially boosting training effectiveness. This research contributes to a deeper understanding of badminton’s biomechanics and offers practical implications for coaches and players to enhance their forehand smashes, especially among beginners.

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes.