This study proposes an intelligent framework for the automated classification and valuation of ceramic artifacts, integrating deep learning and machine learning techniques. An improved YOLOv11 model was constructed to identify key ceramic attributes such as decorative patterns, shapes, and craftsmanship styles. The model achieved a mean Average Precision (mAP@50) of 70.0% and a recall of 91.0%, demonstrating strong capability in detecting complex visual features. Based on the extracted visual attributes, a Random Forest classifier was employed to predict price categories using multi-source auction data, achieving a test accuracy of 99.52%. Feature importance analysis further revealed manufacturing techniques and shape as key predictors of market value. The integrated framework effectively combines visual feature extraction and market-informed valuation, providing a scalable solution for intelligent ceramic appraisal and digital heritage curation. This approach supports both expert and non-expert applications, laying a foundation for future development of intelligent cultural heritage management systems.

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.

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.

Campus buildings often present hidden safety risks such as falls and wheelchair instabilities, which are closely related to architectural layout, material selection, and accessibility design. This study develops YOLOv11-Safe, an attention-enhanced and geometry-aware framework that functions as both a detection model and a spatial diagnostic tool for building safety assessment. The framework integrates a modified SimAM attention mechanism and a normalized Wasserstein distance (NWD) loss to improve detection accuracy in complex indoor environments, trained on a dataset of 1000 annotated images covering fall and wheelchair accident scenarios. To interpret spatial risk patterns, a Random Forest classifier combined with SHAP analysis was applied to quantify the contribution of five architectural–behavioral variables: body–ground contact ratio (BGCR), accessibility index (AI), event duration (D), body posture angle (PA), and spatial density (SD). Results show that BGCR and AI dominate the risk-level prediction, while D, PA, and SD refine boundary conditions. Scene-based verification further demonstrated that the framework accurately localized unsafe features—such as uneven drainage edges and discontinuous handrails—and translated them into actionable design feedback. The proposed approach thus links deep-learning detection with interpretable spatial analysis, offering a quantitative foundation for evidence-based architectural safety optimization in university campuses.

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.

This study examines the psychological mechanisms underlying consumer responses to computer product appearances by employing the Unified Model of Aesthetics (UMA) as an integrative framework. UMA proposes that aesthetic evaluation arises from the reconciliation of evolutionary needs for safety and accomplishment across perceptual, cognitive, and social levels. Building on this model, the study incorporates key attributes—unity, variety, proximity, closure, symmetry, continuity, typicality, novelty, performance, security, connectedness, and autonomy—to analyze their contributions to consumer satisfaction. Data were collected from 211 computer users in China and analyzed using confirmatory factor analysis and structural equation modeling. The results show that perceptual organization cues, including unity, variety, and Gestalt-based principles, enhance satisfaction by supporting coherent and fluent visual processing. Cognitive attributes such as typicality and novelty jointly reinforce the balance between familiarity and advancement, while performance and security strengthen safety-oriented evaluations. Social attributes, including connectedness and autonomy, significantly influence satisfaction by addressing needs for group affiliation and individual expression. Overall, the findings validate UMA as a useful framework for explaining aesthetic responses to high-technology products and highlight the importance of integrating perceptual, cognitive, and social dimensions in product design. These insights provide actionable implications for firms seeking to improve user experience through strategically crafted visual and symbolic features.