Shaokang Chen

Introduction: Dress can be considered a visible and flexible form of female beautification that may communicate attractiveness as well as social and relational cues. However, less is known about how men interpret dress-based beautification cues when evaluating women as potential romantic partners across different mating contexts. The present study examined whether perceived attractiveness, autonomy, and connectedness derived from female dress jointly predict men’s romantic judgment and whether these associations differ between short-term and long-term relationship contexts. Methods: A between-subjects design was used in which heterosexual male participants ( N = 405) were randomly assigned to either a short-term or long-term romantic evaluation condition. They rated ten female dress stimuli on attractiveness, autonomy, connectedness, and romantic judgment, yielding 4,050 observations. Data was analyzed using cross-classified linear mixed-effects models with all continuous predictors double-centered to remove between-participant and between-stimulus mean differences. Results: Relationship context moderated the associations with attractiveness and autonomy; attractiveness was more influential in short-term judgment, whereas autonomy showed a stronger association in long-term judgment. The interaction for connectedness was in the predicted direction but did not reach statistical significance. Conclusion: These findings suggest that dress-based cues are weighed differently across mating contexts. Attractiveness, autonomy, and connectedness converge to predict romantic judgments, with varying differences depending on the mating context.

Beyond utilitarian purpose, architecture possesses profound aesthetic value, serving not only as structures to inhabit but as spaces to be perceived, interpreted, and appreciated. Unity and variety are among the predictors that influence aesthetics, traditionally viewed as oppositional but jointly predictive of aesthetic preference. The present research investigates how unity and variety jointly predict aesthetic preference in the domain of Chinese carved windows and how these relationships change from isolated elements to window–background compositions. Two quasi-experimental studies were conducted. Study 1 asked participants to rate ten digitally rendered Chinese windows on unity, variety, and aesthetic preference. Study 2 used the same ten windows embedded in either a stylistically congruent Chinese background or an incongruent Western background. Across both studies (N = 797), results showed that unity and variety were negatively correlated yet both positively associated with aesthetic preference, with unity exerting the stronger predictive influence. The trade-off between unity and variety decreased once windows were placed in backgrounds, and stylistically incongruent Western backgrounds reliably suppressed perceived unity and inflated perceived variety. The findings are consistent with the Unity-in-Variety principle and highlight how architectural context influences unity–variety judgments. The studies provide strategies toward a more nuanced, architecture-specific understanding of unity and variety in aesthetic preference.

George Town, the capital of Penang, Malaysia, was inscribed as a UNESCO World Heritage Site in 2008 and is renowned for its multicultural architectural surfaces. However, these historic façades face significant deterioration challenges, particularly biodeterioration caused by weed growth on wall surfaces under hot and humid equatorial conditions. Root penetration is a critical surface defect, accelerating mortar decay and threatening structural integrity. To address this issue, this study proposes YOLOv11-SWDS (Surface Weed Detection System), a lightweight and interpretable deep learning framework tailored for surface defect detection in the form of weed intrusion on heritage buildings. The backbone network was redesigned to enhance the extraction of fine-grained features from visually cluttered surfaces, while attention modules improved discrimination between weed patterns and complex textures such as shadows, stains, and decorative reliefs. For practical deployment, the model was optimized through quantization and knowledge distillation, significantly reducing computational cost while preserving detection accuracy. Experimental results show that YOLOv11-SWDS achieved an F1 score of 86.0% and a mAP@50 of 89.7%, surpassing baseline models while maintaining inference latency below 200 ms on edge devices. These findings demonstrate the potential of deep learning-based non-destructive detection for monitoring surface defects in heritage conservation, offering both a reliable tool for sustaining George Town’s cultural assets and a transferable solution for other UNESCO heritage sites.

Fujian Tulou, a UNESCO World Heritage Site, is highly vulnerable to environmental and anthropogenic stresses, with its earthen walls prone to surface cracking that threatens both structural stability and cultural value. Traditional manual inspection is inefficient, subjective, and may disturb fragile surfaces, highlighting the need for non-destructive and automated solutions. This study proposes a dual-path framework that integrates lightweight crack detection with independent physical simulation. On the detection side, an improved YOLOv12 model is developed to achieve lightweight and accurate recognition of multiple crack types under complex wall textures. On the simulation side, a two-layer RFPA^3D model was employed to parameterize loading conditions and material thickness, reproducing the four-stage crack evolution process, and aligning well with field observations. Quantitative validation across paired samples demonstrates improved consistency in morphology, geometry, and topology compared with baseline models. Overall, the framework offers an effective and interpretable solution for standardized crack documentation and mechanistic interpretation, providing practical benefits for the preventive conservation and sustainable management of Fujian Tulou.

The ‘ Most Advanced, Yet Acceptable ’ ( MAYA ) principle has been widely recognized in design research, emphasizing that both typicality and novelty are key predictors of aesthetic preference and that people prefer a combination of both features in design. However, existing studies do not fully explain why one predictor may have a greater influence on aesthetic preference than the other. This study investigates the role of typicality and novelty in explaining aesthetic preferences across rich (with multiple sub-categories) and poor categories (with few sub-categories). Using furniture as stimuli, a questionnaire survey was conducted with 306 participants with non-design backgrounds. The results indicate that typicality is a stronger predictor for poor categories, while novelty has a greater influence on rich categories. These findings suggest that object category richness influences the weight of typicality and novelty in shaping aesthetic preference, offering further elaboration and nuanced interpretation of the MAYA principle that ‘ Most Advanced, Yet Acceptable ’ is suitable for rich categories while ‘ Most Acceptable, Yet Advanced ’ is for poor categories. The study also highlights the necessity of object categorization when integrating typicality and novelty into product designs. These results provide new insights into the cognitive mechanisms underlying aesthetic preference, with implications for both theoretical development and practical design applications.

Electronic sports show significant user churn caused by a toxic gaming atmosphere, and current GUI-based interventions are insufficient to address the issue. Based on the theoretical framework of Perceiving and Experiencing Fictional Characters, a new hybrid interaction interface and paradigm combined with tangibles is proposed to counter negative mood. To support the frustrated users of Massive Online Battle Arena (MOBA) games, we added AI teammates for better personal performance and social robots for the disclosure of negative mood. We hypothesized that AI teammates’ invisibility and anonymity would mitigate negative emotions; an effect amplified by the presence of social robots. A comparative experiment was conducted with 111 participants. Social robots for emotion-oriented coping improved user mood but AI teammates for problem-oriented coping did so better, although their higher levels of experienced anonymity may not have been preferred. Unexpectedly, conversing with a robot after playing with an AI teammate brought the mood back to that experienced when talking to a robot alone, while increasing the distancing tendencies. With this in mind, AI and social robots can counter the negative atmosphere in MOBA games, positively contributing to game design and empathic human–computer interaction.