Gao Jie

Introduction: Low fertility has become a global challenge that threatens sustainable social development. In China, fertility intentions among people of marriageable and childbearing age (18–35 years) remain persistently low. A prevailing “risk consciousness” in online culture, together with the tendency for digital activities to substitute for real-world interactions, further weakens young people’s intrinsic motivation to have children. The relationship between simulated gaming environments and real-world fertility desire, therefore, deserves more rigorous investigation. Methods: Drawing on parasocial relationship theory and attachment theory, we collected questionnaire data from 612 game players. We tested the theoretical model using Partial Least Squares Structural Equation Modeling (PLS-SEM). Measurement properties were examined via confirmatory factor analysis, and direct and indirect effects were evaluated using bootstrapped mediation tests. Results: Eleven of the sixteen hypotheses were supported. Game Concentration (GC) positively predicted Identification Friendship (IF), Parasocial Cognition (PC), and Parasocial Emotions (PE), but showed no direct effect on Fertility Desire (FD). Mediation analyses revealed significant indirect effects through the affective pathway. The cognitive path involving PC did not yield substantial indirect effects. Discussion/conclusion: Our findings indicate that simulated-game experiences are linked to FD mainly via affective rather than cognitive pathways. We propose an “Emotional Compensation Hypothesis,” whereby virtual emotional bonds buffer real-world stressors and, in turn, shape reproductive attitudes. This framing positions emotional bonding as a key theoretical lens for exploring the association between virtual gaming experiences and affective orientations toward parenthood, offering preliminary insights into digital-era reproductive attitudes. Future studies should use longitudinal designs and assess the model’s generalizability across diverse contexts.

The measurement of shot length is an essential index for the evaluation of cinematographic research. Given the limitations of existing measurement tools, which require human intervention and are constrained by a narrow measurement scale, this paper proposes an automatic acquisition method of film shot length based on machine vision technology. This method is then applied to scale film calculation and analysis. In order to accurately identify the points of editing in a film and thereby obtain the corresponding shot length, the article examines the most commonly used editing techniques in Chinese film. It determines that the most appropriate method for calculating shot length is video motion target detection. After a comprehensive analysis of the three video motion target detection algorithms, it was determined that the inter-frame difference algorithm exhibited the greatest applicability. Subsequently, experimental tests were conducted on positive and negative datasets to produce the optimal threshold for obtaining shot length using the inter-frame difference algorithm. During the course of the experiments, the inter-frame difference method was also optimized algorithmically with a view to realizing the visual reconstruction of film shot length. Ultimately, the supercomputing center was utilized to complete the operation of the shot length data of 1,600 sample films from 1949 to 2019. This resulted in the formulation of a regularity assertion regarding the shot length of Chinese film over the past seven decades since the founding of the People's Republic of China. The research results show that the average shot length and median shot length of Chinese films have experienced three peaks of fluctuation around 1960, 1980, and 2009 (corresponding to the "Seventeen-Year Period", reform and opening-up, and the opening of the film market), yet they demonstrate an overall downward trend. Since 1997, the annual rate of change in film shot length has been increasing, indicating that with the application of new film technologies, film editing has become more diversified and flexible. These findings hold significant implications for reconstructing the history of Chinese film and objectively evaluating its development.

This study investigates user experience (UX) sharing behaviors in the context of smartphone user interface (UI) design, emphasizing their significance for UI enhancement and effective marketing strategies. Grounded in the Stimulus–Organism–Response (SOR) framework, we examine how design perception attributes—perceived usability, novelty, enjoyment, and brand image—influence UX sharing, with a spotlight on the mediating role of individual motivation. A quantitative analysis (N = 472), Structural Equation Modeling (SEM), and mediation analysis were conducted. Our findings confirm that these components can positively impact UX sharing by bolstering personal expectations and self-efficacy in knowledge sharing, with perceived usability being an exception as it unexpectedly showed a negative association with sharing frequency. Moreover, perceived brand image and individual self-expectancy and self-efficacy enhance sharing outcomes. This research enriches our understanding of the strategic importance of user interface (UI) design in the context of smartphones, furnishing empirical grounding for devising sustainable UI design strategies and productive marketing tactics. Consequently, it bears considerable relevance to both theoretical insights and practical applications.

This study investigates the capabilities and constraints of artificial intelligence (AI) in creating art for emotional conveyance and its influence on art’s evolution. Anchored in Riegl’s concept of “Kunstwollen”, a case analysis method is applied to assess Théâtre D’opéra Spatial, an AI-generated artwork, in terms of emotional engagement, interactive experience, and inventive design. The findings suggest AI art transcends conventional visual limits but struggles with complex emotions’ nuanced portrayal, especially in simulating profound emotional resonance. Nevertheless, a collaborative approach between humans and AI emerges as a promising avenue for AI in emotional artistry. This research underscores AI’s role in broadening artistic horizons and spotlights the intricate balance between technological progress and the emulation of emotional cognition. Future work should enhance AI algorithms for more accurate emotion detection and articulation, striving for equilibrium in human-AI interaction. Such advancements in AI art would not only foster creative productivity but also precisely mirror and evoke human emotions, offering empirical support and strategic insights for seamlessly merging technology and art.