The utilization of recycled materials has emerged as a pivotal strategy for mitigating resource depletion and reducing carbon emissions in the construction industry. However, existing reviews predominantly focus on specific technical aspects, often overlooking the interdisciplinary complexities associated with recycled materials as a systems engineering challenge. This study systematically reviews 1533 documents from the Web of Science Core Collection, integrating quantitative and qualitative analytical approaches to assess the current state and future trajectory of the field, thereby addressing existing research gaps. The findings highlight the substantial evolution of recycled building materials from waste recovery to a multifaceted domain encompassing value assessment, circular economy principles, advanced technologies, interdisciplinary collaboration, and long-term societal benefits. This study identifies six key research themes in recycled building materials: life cycle assessment, biological and natural materials, recycled concrete, recycled asphalt and building infrastructure, construction and demolition waste, and environmental impacts with composite factors. Furthermore, current research is categorized into two primary dimensions: value strategies and technological tools. The analysis of future research directions underscores the potential of AI-driven innovations and their role in enhancing human living environments. However, developing countries continue to face critical challenges, necessitating further interdisciplinary integration and knowledge exchange. Finally, this study proposes a comprehensive and systematic disciplinary framework that offers valuable insights for future strategic planning and technological advancements in the field.

Buildings play a pivotal role in the daily functioning of cities, and the development of smart cities is intricately linked to the sustainable development of architectural practices. However, existing reviews have predominantly concentrated on the development of smart cities, often overlooking the interdisciplinary complexities associated with integrating smart city technologies and sustainable building practices. This study systematically reviews 418 relevant papers from the Web of Science database, employing both quantitative and qualitative analytical methods to assess the current status and future trajectory of the field. Therefore, it bridges a significant gap in the existing literature. The findings underscore the contributions of technologies such as the Internet of Things (IoT), artificial intelligence, and big data in enhancing the sustainability of buildings within smart cities. The key areas of focus include energy management, smart building systems, and resource optimisation. Furthermore, the study identifies emerging research themes, such as smart city buildings, smart energy management, and digital twins, highlighting their potential to optimise building performance and foster sustainability within evolving urban systems. The keywords identified in the current body of research are categorised into six main areas: context, objectives, methods, artificial intelligence, emerging technologies, and opportunities and challenges. Research themes are seen to progress from “performance” to “building” and “sustainability” and from “city” to “city” and “sustainability”. Notably, themes such as “city”, “modelling”, and “design” have evolved into themes centred around the “Internet”. However, with the rapid expansion of digital technologies, scholars must also address several critical challenges, including data security and privacy protection, the complexity of cross-system data coordination, uncertainties in sustainable optimisation processes, and the ethical and societal implications of technology adoption. To ensure the successful and sustainable development of future urban smart buildings, it is essential to establish rigorous data security standards, harmonise technical protocols, implement effective global strategies, and prioritise ethical considerations. In addition, unmanned technologies and their associated systems offer valuable insights into the sustainability of buildings in smart cities. Finally, this study presents a comprehensive and systematic framework that provides invaluable insights for future strategic planning and technological advancements in the field.

Yuxian paper cutting is a distinctive regional form within UNESCO-recognized Chinese paper cutting. Its opera figure images remain difficult to organize at scale because interpretation and cataloguing rely heavily on manual expertise, while automated tools for preliminary annotation and retrieval remain limited. This study formulates a domain-specific object detection task and constructs a curated dataset of 926 original images containing 976 annotated instances across three operational visual categories: armor-clad, robe-wearing, and female-role figures. A YOLOv8n-based detector is adapted by integrating the Spatial and Channel Synergistic Attention (SCSA) module and the Inner-IoU regression strategy. On the test set, the resulting configuration achieves 95.8% precision, 92.9% recall, 98.8% mAP@0.5, and 89.6% mAP@0.5:0.95, exceeding the YOLOv8n baseline by 3.0, 3.6, 2.9, and 2.9 percentage points, respectively. It also achieves a forward-pass throughput of 92.3 FPS under the reported GPU configuration. Among the evaluated configurations, the combined SCSA placement with Inner-IoU achieved the highest overall detection performance under the current dataset and experimental protocol. The results support its use for preliminary visual-category annotation, retrieval, and organization of Yuxian paper-cut opera figure resources. However, the findings are based on a small domain-specific dataset and require further validation across alternative data partitions and broader heritage collections.

Historic commercial buildings are often described as resilient, but the mechanisms that sustain ordinary use remain fragmented across morphology, adaptive reuse, heritage governance, and community research. This Review examines how inherited spatial configurations and actor arrangements jointly shape everyday-use resilience. Using transparent, PRISMA-informed searching, screening, appraisal, and narrative/thematic synthesis, we identified 50 eligible empirical studies from 3314 records. Evidence came from 18 countries or regional settings but remained concentrated in Malaysia (9 studies), Indonesia (8), Türkiye (7), and mainland China (7); no eligible study came from the Americas, and direct African evidence was limited to one Egyptian case. In response to prescreen-sensitivity concerns, all 80 records excluded solely because the deterministic dictionary did not detect a spatial-focus term were independently reassessed; 30 were advanced beyond title/metadata review and 13 ultimately met all eligibility criteria. Four evidence-informed spatial capacities recurred: network permeability, fine-grained and reversible subdivision, climatic and service compatibility, and active public–private interfaces. These capacities did not determine resilience independently. Owners and traders translated capacity into maintenance and incremental adaptation; users sustained social legitimacy; and public, professional, financial, and digital intermediaries enabled or constrained adaptation through regulation, investment, programming, and visibility. Heritage retention was assessed in all 50 studies; use vitality was observed in 40 and assessed prospectively in 10; community benefit was observed or partly assessed in 37, prospectively in 10, and not assessed in 3. The geographically concentrated, English-language, public-API, and full-text corpus limits transferability, while the targeted validation demonstrates why deterministic terminology rules require human sensitivity checks. Everyday-use resilience is therefore presented as an evidence-informed, actor-mediated proposition concerning the conversion of inherited spatial capacity into heritage retention, use vitality, and community benefit.

Understanding the spatial distribution patterns and siting characteristics of traditional villages provides an important basis for informing their conservation and spatial planning. This study employed geographic information system (GIS)-based spatial analysis, principal component analysis (PCA), and clustering analysis to investigate the spatial distribution patterns, environmental associations, and siting typologies of traditional villages in the Taihu Basin. The results revealed that traditional villages in the Taihu Basin exhibited a spatial pattern characterized by large-scale clustering and localized scattered distributions, with their spatial distribution patterns and siting characteristics significantly associated with topographic, climatic, and hydro-ecological conditions. The siting of traditional villages was closely associated with variations in topographic, climatic, and hydro-ecological conditions. Different environmental factors jointly contributed to the formation of differentiated siting patterns of traditional villages. Among these factors, topographic and climatic characteristics showed relatively high contributions to distinguishing overall environmental variations, whereas hydrological conditions reflected the strong spatial association between traditional villages and regional water systems in the Taihu Basin. Based on the results of PCA and clustering analysis, traditional villages in the Taihu Basin were classified into three major siting typologies: mountainous steep-slope humid villages, plain waterfront warm–humid villages and plain gentle-slope sunny villages. This study revealed the environmental adaptation patterns underlying traditional village siting at the basin scale, providing scientific insights for traditional village typology identification, regional conservation, and spatial planning.