As an essential material carrier of cultural heritage, the accurate identification and effective monitoring of buildings in traditional Chinese villages are of great significance to the sustainable development of villages. However, along with rapid urbanization in recent years, many towns have experienced problems such as private construction, hollowing out, and land abuse, destroying the traditional appearance of villages. This study combines deep learning technology and UAV remote sensing to propose a high-precision extraction method for conventional village architecture. Firstly, this study constructs the first sample database of traditional village architecture based on UAV remote sensing orthophotos of eight representative villages in Beijing, combined with fine classification; secondly, in the face of the diversity and complexity of the built environment in traditional villages, we use the Mask R-CNN instance segmentation model as the basis and Path Aggregate Feature Pyramid Network (PAFPN) and Atlas Space Pyramid Pool (ASPP) as the main strategies to enhance the backbone model for multi-scale feature extraction and fusion, using data increment and migration learning as auxiliary means to overcome the shortage of labeled data. The results showed that some categories could achieve more than 91% accuracy, with average precision, recall, F1-score, and Intersection over Union (IoU) values reaching 71.3% (+7.8%), 81.9% (+4.6%), 75.7% (+6.0%), and 69.4% (+8.5%), respectively. The application practice in Hexi village shows that the method has good generalization ability and robustness, and has good application prospects for future traditional village conservation.

Building façade materials are fundamental to understanding cultural heritage and preserving the historic urban environment. While traditional material analysis requires extensive manual effort, this study introduces an automated methodology of material identification and feature extraction. We construct the Building Façade Material Segmentation (BFMS) database, which surpasses previous datasets in its alignment with architectural standards and the diversity of material categories included. Based on the database, we develop a transformer-based semantic segmentation model that achieves an overall accuracy of 76.5%. Furthermore, the algorithm to extract detailed textures and colors for each material is formulated with K-means clustering and grid statistics. Applied in the Taiping Alley, a historic area in Jingdezhen, the methodology demonstrates robustness and efficiency, yielding valuable guidance for architectural landscape assessment and urban renewal planning. The study promotes the application of cutting-edge deep learning algorithms in urban conservation and contributes to the broader understanding of urban material characteristics.

The Great Wall is an example of linear cultural Heritage (LCH) subjected to a fragile ecological environment and unbalanced economic development. However, no studies have been conducted to assess the sustainable development suitability (SDS) of the Great Wall region. Heritage area development assessments can increase public and government knowledge of the state of heritage areas and aid decision makers in formulating sensible policies or plans to protect and develop heritage areas. The valley is the spatial model of mountain economic development proposed on the basis of a basin combined with the ecological protection, rural development, and cultural inheritance present in mountainous areas. This study chose 169 valley units in the Beijing Great Wall Cultural Belt as research objects. A 15-indicator assessment index system was established based on the ecological, socio-economic, and cultural dimensions. The assessment procedure was enhanced by employing the triangle graphical method, and spatial autocorrelation was used to study the geographical distribution features of the development suitability scores derived from the research region. The results show the following facts: (1) Yanqing, Huairou, Miyun, Pinggu, and Changping have high development suitability results, whereas Mentougou and Changping have low scores. (2) In total, 96 valley units have practical advantages, and 51.5% are comprehensive development types (with advantages in at least two aspects). (3) Valley development suitability scores spatially cluster into seven high- and low-value groups. The ecological carrying capacity of the Badaling Cluster cannot maintain its overheated development. The results match up well with the objective condition of planning in the Beijing Great Wall National Cultural Park. We conclude that the proposed indicator framework and analytical method can be transferred to cases with similar contexts.

As one of the most popular additive manufacturing methods, laser powder bed fusion (L-PBF) builds 3D components with complex geometries layer by layer using alloy powders. This technique has found widespread adoption in various industrial applications, including biomedical and aerospace fields. However, L-PBF encounters challenges related to poor process repeatability and inconsistency in fabricated part quality, which hinder its broader adoption. Various quality improvement methods have been proposed to address these challenges and achieve high-quality, reliable parts. Given the abundance of parameters and the intricate phenomena that occur during the process, machine learning (ML) methods play a critical role in enhancing the quality of L-PBF, providing an optimum solution for improving the quality of manufactured parts. This review paper begins with a comprehensive and straightforward introduction to ML, focusing primarily on different learning approaches. Subsequently, the paper explores different ML methods applied to parameter optimization and in situ monitoring, both contributing to enhanced quality control. In parameter optimization, ML is employed to extract relationships between input parameters and key factors such as melt pool characteristics, porosity, and mechanical properties. Shifting the focus to in situ monitoring, the paper introduces the application of ML in analyzing various sensor data generated throughout the L-PBF process. Accomplished tasks include segmentation, regression, and classification of quality measurement. In summary, this review underscores the critical role of machine learning in addressing challenges associated with L-PBF, providing an optimal solution for quality enhancement.

Infections to dental pulp commonly result in pulpitis and pulp necrosis, and surgical removal of the infected tissues is the only therapeutic approach. Dental pulp injury remains a challenging medical issue due to the limited regenerative capability of dental pulp. In this work, a dental pulp guidance construct (DPGC) with the instructive niche was bioprinted to mimic native teeth for dentin and neovascular-like structure reconstruction. GelMA-Dextran aqueous emulsion was used as an ink for in situ printing of porous DPGC to induce predominant nuclear localization of Yes-associated protein (YAP) in the encapsulated dental pulp stem cells (DPSCs) and enhance their stemness properties. Furthermore, the DPSCs encapsulated in DPGC with microporous structures exhibited enhanced viability, migration, and spreading. Meanwhile, we found that DPGC could promote capillary tube formation and induce neurogenesis. In a mouse subcutaneous implant model, the DPGC consisted of porous structures, such as odontoblasts and newly formed vascular structures, that mimic dental pulp characteristics. This study demonstrated a new strategy to design DPGC with instructive niche for dental pulp regeneration, presenting a potential treatment alternative to root canal therapy.

The Great Wall, as a globally important large-scale linear cultural heritage asset, is an example of the integration of architecture and landscape, demonstrating the interaction and feedback between heritage and the environment. In the context of advocating the holistic protection of cultural heritage and surroundings, this study utilizes landscape character assessment (LCA) to identify the landscape character of the Great Wall heritage area. Taking the heritage area of the Great Wall in Beijing, China, as an example, principal component analysis (PCA), two-step clustering, and the eCognition software were used to identify and describe the landscape character types, and the interaction mechanism between heritage and the environment was further explored through the reclassification process. A total of 20 landscape character types and 201 landscape character areas were identified in the study area, and a deep coupling relationship between heritage and the environment and cultural landscape spatial patterns were found in the core heritage area. The heritage and environmental character of linear heritage areas should be integrated so as to protect, manage, and plan cultural heritage areas at the landscape level. This study identifies and describes the character of the coupling of heritage and the environment in the Great Wall area for the first time, expands the types and methods of landscape character assessment, and carries out the exploration to combine natural and cultural elements of large-scale linear cultural heritage areas.