Disentangling the effects of the built environment on urban vitality at the scale of community life circles is crucial for informing precise urban planning and design, particularly in the context of urban renewal. However, studies examining the complex relationships and spatial heterogeneity in these effects remain limited, hindering the identification of built environment characteristics that may generate sustainable benefits. Therefore, this study took Xi’an, a typical high-density city in Northwest China, as an example. The eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) method were utilized to reveal threshold effects and spatial correlations between the built environment and community life circles’ vitality across varying buffer zones. The results show that (1) there is a significant spatial correlation between the built environment and the core–periphery structure of community life circles’ vitality. (2) Indicators, such as facility accessibility, the floor area ratio, intersection density, and the residential land use ratio, contribute significantly to community life circles’ vitality. (3) While the micro-built environment and socio-economic factors show limited contributions, their collaboration with the macro-built environment can enhance their individual effects, highlighting the necessity of taking them into account together. These findings provide new insights into supporting community life circles’ vitality through urban planning and design.

Fiber-reinforced composites, such as glass fiber-reinforced polymer (GFRP), are widely used across industries but are susceptible to inclusion defects during manufacturing. Detecting and classifying these defects is crucial for ensuring material integrity. This study classifies four common inclusion defects—metal, peel ply, release paper, and PTFE film—in GFRP using terahertz technology and machine learning. Two GFRP sheets with inclusion defects at different depths were fabricated. Terahertz time-domain signals were acquired, and a cross-correlation-based deconvolution algorithm extracted impulse responses. LSTM-RNN, Bi-LSTM RNN, and 1D-CNN models were trained and tested on time-domain, frequency-domain, and impulse response signals. The defect-free region exhibited the highest classification accuracy. Bi-LSTM RNN achieved the best recall and macro F1-score, followed by 1D-CNN, while LSTM-RNN performed worse. Training with impulse response signals improved classification while maintaining accuracy.

To address the coordination between social and spatial relationships within villages in the loess gully region, this study developed a joint Geospatial–Social Network analytical approach. Taking Caojian Village in Zizhou County, Shaanxi Province as the study case, geospatial and social network models were constructed, respectively, with time cost and social interaction frequency as measurement indicators. The two network models were analyzed using cohesive subgroups and node centrality, respectively. Correlation analysis revealed the dominant role of spatial relationships in shaping social relationships, as well as the reinforcing effect of kinship on cross-geographical connections. Furthermore, the joint analysis of the two networks was used to identify village spatial hierarchies and core areas. On this basis, targeted strategies were proposed for public space allocation, public service facility layout, and site selection for relocated dwellings. Ultimately, this research aims to provide quantitative analysis tools and decision-making support for optimizing the spatial structure of villages in the loess gully region.

In the context of people-oriented and high-quality development, improving the quality of rural public service facilities is an important way to promote rural revitalization. However, at present, there are problems such as imbalanced resource allocation, single service functions, and low service quality in regard to rural public service facilities in general, and the contradiction between supply and demand is becoming increasingly prominent. Only by effectively grasping the correlation between residents’ needs and their satisfaction with such facilities can the precise allocation of service facilities be carried out. In this study, Gaoqing County, Zibo City, Shandong Province, is selected as the empirical case. By applying the Kano model and Importance–Performance Analysis (IPA) through the use of questionnaire surveys and expert interviews, we develop an integrated “demand–satisfaction” analytical framework to propose targeted optimization strategies for rural public service facilities. Based on the Kano model, the essential, desired, and attractive needs of rural residents for various types of facilities were identified. Combining the IPA model, the coupling and coordination relationship between demand and satisfaction was further analyzed, and rural public service facilities were analyzed and categorized accordingly. The research results show that educational institution facilities, healthcare facilities, and market facilities have the highest demand, while cultural, sports, and technological facilities have relatively low demand. Based on the analysis results, this study proposes an optimization strategy for the classification and grading of the “demand–satisfaction” coupling relationship of public service facilities. It provides a scientific basis for the layout optimization of rural public service facilities, and also provides an effective reference for the sustainable development and service quality improvement and upgrading of rural areas.