上海体育学院

E-commerce chatbots increasingly provide product advice during online shopping, yet it remains unclear how culturally framed recommendation language shapes impulsive buying. Drawing on cultural dimensions theory, cultural priming theory, and constructivist interpretation, this study conceptualizes collectivistic and individualistic persuasive styles as message-level cultural language cues rather than as measures of consumers’ stable cultural orientation. Across four experiments with 880 participants, we show that a collectivistic persuasive style increases impulsive buying by making recommendations more likely to be interpreted as socially supported and behaviorally acceptable choices, which strengthens social conformity. By contrast, an individualistic persuasive style reduces impulsive buying by making recommendations more likely to be interpreted as choices requiring confirmation against personal needs, usage situations, and product-feature fit, which strengthens self-referencing. The findings further identify two boundary conditions. Human labeling strengthens the collectivistic pathway through social conformity by making group-oriented language more likely to be treated as socially relevant advice, whereas AI labeling shifts source perception toward algorithmic advice and strengthens the individualistic pathway through self-referencing by increasing attention to personal needs, usage situations, and product-feature fit. Low algorithm accessibility strengthens the collectivistic pathway, whereas high algorithm accessibility strengthens the individualistic pathway. These findings show that chatbot recommendations do more than transmit product information. They shape how consumers interpret the perceived source, social relevance, and personal applicability of recommendations, thereby influencing whether recommendations are quickly accepted as purchase decisions.

Magnetocardiography (MCG) provides a non-invasive method to record the magnetic fields produced by the heart’s electrical activity, which is essential for diagnosing cardiac conditions. However, the analysis of MCG signals is challenging due to their high dimensionality and complex geometric structure. This study aims to develop an advanced method for clustering and classifying MCG signals by integrating transformer networks with Riemannian manifold analysis, which leverages the strengths of both approaches to enhance signal analysis. We propose a novel pipeline consisting of three main modules: preprocessing and feature extraction, where MCG signals are prepared and initial features are extracted; transformer network processing, where a transformer architecture is used to learn higher-level representations from the embedded features; and Riemannian manifold analysis and clustering/classification, where the learned representations are analyzed on a Riemannian manifold to perform clustering and classification tasks. Our method demonstrated superior performance in clustering and classifying MCG signals compared to traditional methods. The transformer network effectively captured complex patterns in the signals, while the Riemannian manifold analysis preserved the geometric structure, leading to more accurate and interpretable results. The integration of transformer networks and Riemannian manifold analysis provides a powerful framework for MCG signal analysis, with the potential to improve the diagnosis of cardiac conditions and could be extended to other areas of medical signal analysis.

With the rise of short video platforms, algorithmic recommendation systems, while enabling personalized content distribution, have been criticized for fueling social media opinion polarization. This study empirically explores how algorithms drive such polarization on mainstream short video platforms. Integrating user behavior, content distribution patterns, and interactive networks, it examines algorithms' filtering and amplification in information flow. Through a theoretical model and multi-source data, it reveals how algorithms shape viewpoint exposure and extreme opinion spread, identifying varying polarization impacts across algorithms and platforms. Via flowcharts and data models, it elaborates on coupling mechanisms among content push, interaction, and differentiation. Finally, targeted algorithm optimization suggestions reduce polarization risks, aiding governance and healthy online ecosystems.

The study aims to explore the evolution patterns of women's volleyball blocking techniques through a longitudinal analysis of changes in blocking techniques during the women's volleyball semifinals and finals from the 2008 Beijing Olympic Games to the 2024 Paris Olympic Games. Performance data for blocking were derived from video analysis of 20 matches (71 sets). From this sample 4196 blocking actions were coded. The recorded variables included the number of blockers, the blocking positions, the opponent's offensive situation, and blocking effectiveness. Results showed that the number of blockers and blocking effectiveness had changed. The occurrence of triple blockers decreased significantly (p = 0.01), while double blockers became the dominant blocking formation. Significant differences were observed across different periods for both blocking errors (p < 0.05) and ineffective blocking (p < 0.05), whereas blocking points increased, suggesting a continuous improvement in blocking techniques proficiency. Additionally, no significant differences were found in relation to blocking positions or the opponent's offensive situation. The study's findings suggest that blocking in volleyball is gradually evolving from a simple defensive technique into an active scoring method, further highlighting the growing importance of this skill in the sport.

In the context of a global trend emphasizing livable environments and cultural heritage preservation, the "internal-external imbalance" in the visual quality of traditional village streetscapes has become a bottleneck constraining the development of China's "Beautiful and Livable Countryside". Current research on rural streetscape features fails to effectively integrate neurocognitive mechanisms with local cultural attributes. Furthermore, its reliance on linear models limits the capture of complex nonlinear relationships. This study takes She Village, a traditional village in the rapidly urbanizing Yangtze River Delta region of China, as a case study. We established an indicator system comprising 23 streetscape features by integrating neurocognitive mechanisms and local cultural attributes. This indicator system adheres to the principles of human visual processing, offering a theoretical foundation for streetscape feature selection. We compared an Ordinary Least Squares (OLS) linear model with five machine learning models and selected XGBoost model. This study uncovered nonlinear mechanisms underlying the effect of streetscape features on visual quality in traditional rural contexts, which differ from predominantly urban-focused studies. We further constructed the "dual-hierarchy" theory of streetscape visual quality. The findings demonstrate: The dominant factors affecting rural streetscape visual quality are, in sequence, culture, building, coordination of style, green, road, fence, streetlight, and color diversity. This confirms the critical role of cultural attributes in rural visual quality. It reveals threshold effects and interaction effects that influence the visual quality of rural streetscapes, which differ from those in urban areas. The thresholds for diminishing marginal benefits of green and road elements are 0.1 and 0.05, respectively. Building view index has a critical threshold at 0.6; beyond this value, its effect on visual quality shifts from positive to negative. Cultural attributes (coordination of style and culture) interact with building, green, Std-depth, and sky. Based on k-means clustering, we formulated targeted, precise improvement strategies for traditional villages focusing on optimizing streetscape configuration, emphasizing cultural characteristics, and strengthening threshold control. This study provides a reference method for identifying and optimizing rural streetscape visual quality in other regions, promoting the sustainable development of rural human settlements.