Insight into students' athletic behavior patterns is critical to efficient resource deployment and enhanced engagement within higher education. This study develops a clustering method for university student sports behavior based on a hybrid algorithm of K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The hybrid algorithm first uses K-means for data pre-segmentation to identify dense core regions, then uses DBSCAN to fine-tune cluster boundaries. Experimental data come from a university's smart sports platform, covering the exercise behavior records of 5,000 to 10,000 students over an academic year, including features such as exercise frequency, single-session duration, and activity preferences. The hybrid algorithm achieved a silhouette coefficient of 0.71 and successfully identified four typical groups with 89.3% accuracy. Compared with the single K-means and DBSCAN algorithms, this hybrid method improves both clustering accuracy and noise handling capabilities. The research results provide data support for the design of personalized sports curricula and dynamic venue scheduling.
Despite growing interest in positive psychology and AI-enhanced language education, limited research has examined how teachers' character strengths shape student outcomes in AI-assisted EFL classrooms. To address this gap, this study examined the relationship between English as a Foreign Language (EFL) teachers' character strengths and students' motivation and well-being in classrooms integrating Artificial Intelligence (AI) tools. A total of 572 EFL learners participated in the study and completed three validated instruments: the Teachers' Character Strengths Questionnaire (TCSQ), the Students' Motivation Questionnaire (SMQ), and the Students' Well-Being Questionnaire (SWQ). Data were analysed using SPSS version 27 and AMOS version 24 through correlational and structural equation modelling (SEM) analyses. The results revealed significant positive correlations between teachers' character strengths and both students' motivation and well-being. Specifically, the character strengths of humanity, transcendence, and wisdom emerged as the strongest predictors of students' intrinsic motivation and emotional well-being in AI-assisted learning environments. These findings highlight the central role of teachers' positive psychological attributes in fostering learner motivation and emotional balance when technology is incorporated into classroom instruction. Theoretically, the study supports the integration of positive psychology and self-determination theory within AI-mediated educational settings. Practically, it underscores the importance of teacher training programmes that promote character development alongside technological competence. Implications for policy, pedagogy, and future research are discussed in light of these findings.
Parenting stress has become an increasingly critical issue in adult development. In this context, the present study investigated the association between ambivalent sexism and parenting stress, as well as the mediating role of the parent-grandparent coparenting relationship. A questionnaire containing validated scales measuring ambivalent sexism, parent-grandparent coparenting relationship, and parenting stress, was administered to 236 pairs of dual-earner couples. Data were analyzed using the actor-partner interdependence mediation model. The results indicated that husbands scored significantly higher than wives on hostile sexism, while no gender differences were found in benevolent sexism, coparenting relationship, or parenting stress. The actor-partner interdependence mediation model demonstrated good model fit and revealed significant actor effects for both spouses, whereas partner effects were only for husbands. The parent-grandparent coparenting relationship served as a significant mediator in these associations. The current study contributes to theoretical studies on ambivalent sexism and parenting stress while also offering important practical implications for encouraging fertility and reducing parenting stress.
Higher-order Weyl semimetals (HOWSMs), characterized by the coexistence of two-dimensional Fermi arc surface states and one-dimensional topological hinge states, establish the fundamental connection between Weyl physics and higher-order topology. However, previous studies have exclusively focused on HOWSMs within orientable manifolds. Here, we report the realization of HOWSMs on nonorientable manifolds through the introduction of Z(2) gauge fields. The key feature is that the Fermi arc surface states and higher-order hinge states coexist within identical k(z) topological regions, which is fundamentally distinct from conventional HOWSMs. At last, we propose an experimental scheme based on acoustic resonator arrays, which may stimulate similar research works in other systems such as circuit, photonic, and cold-atom systems. Our work highlights the subtle yet crucial interplay between HOWSMs and their underlying manifolds.
CoCo-Prussian blue analogue nanocubes were firstly synthesized via a co-precipitation method and subsequently converted into CoSe2 nanocubes through a high-temperature selenization. The core-shell-structured CoSe2@MoS2 electrocatalyst was then fabricated via a hydrothermal process. The resulting material exhibits outstanding hydrogen evolution reaction performances in both acidic and alkaline electrolytes, achieving overpotentials of 229 and 247 mV at the current density of 10 mA cm(-2), respectively, with the corresponding Tafel slopes of 79 and 115 mV dec(-1). Notably, the CoSe2@MoS2 catalyst maintains a high catalytic activity after extended cycles. The enhanced catalytic activity and durability are primarily ascribed to the core-shell architecture, wherein MoS2 nanosheets uniformly anchored on the surface of CoSe2 nanocubes effectively suppress the self-agglomeration of MoS2 nanosheets, thus providing abundant active sites.
