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.

Accurate forecasting of an electric load is vital in the effective management of a power system, especially in flourishing regions. A new hybrid model called logarithmic spiral firefly algorithm-support vector regression (LS-FA-SVR) is proposed to promote the performance of electric load forecasting. The new hybrid model is acquired by combining the support vector regression, firefly algorithm, and logarithmic spiral. Half-hourly electric load from five main regions (NSW, QLD, SA, TAS, and VIC) of Australia are used to train and test the proposed model. By comparing the model results with observed data on the basis of the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE), the performance of the proposed hybrid model is the most outstanding among all the considered benchmark models. Hence, the results of this study show that the hybrid model LS-FA-SVR is preferable and can be applied successfully because of its high accuracy.

Ecological restoration and brownfield reuse are important issues in the current field of urban sustainable development and environmental protection. This paper adopts bibliometric and network analysis methods, using more than 600 literature from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases as research objects, to analyze the research trends, main researchers, contributions of different disciplines, and research hotspots in ecological restoration and brownfield reuse. Based on the results of quantitative analysis, this paper reviews the main research theories, methods, and technologies of ecological restoration and brownfield reuse at home and abroad, as well as response strategies in different regions and future prospects under challenges. Among the top 10 disciplines in terms of disciplinary contribution, the discipline with the highest relevance in the research articles of CNKI is “Environmental Science and Technology”, accounting for approximately 47.24%. The discipline with the highest relevance in the research articles of WOS is “Building Science and Engineering”, accounting for approximately 61.21%. In terms of research theories and methods, emphasis is placed on the application of ecological engineering, landscape ecology, land economics, and sustainable development methods. At the same time, adaptive management methods are emphasized, aiming to achieve a balance between ecological protection and urban development. In terms of response strategies for different regions, the main strategies for ecological restoration and brownfield reuse are proposed from five main aspects: reducing soil erosion and controlling water pollution, restoring ecological communities and enhancing biodiversity, landscape reshaping and spatial transformation, tourism development, and leisure space design. Based on the current challenges in technology, law, funding, management, and society, research prospects for strengthening interdisciplinary integration, digital drive, interdisciplinary collaboration, and multi-party cooperation in the future are proposed. It can be seen that research in this field is no longer just a simple ecological issue, but a comprehensive social problem.

Old city courtyards are crucial elements of Beijing’s ancient capital. However, existing ones face heating problems. This study focuses on renovated and original-style courtyards. By employing ENVI-met and DeST software, we comprehensively analyzed the courtyard’s thermal environment, ventilation, indoor conditions, and energy consumption. Findings reveal that both types have thermal discomfort. Original courtyards are colder in winter and hotter in summer due to wind and radiation. They possess better ventilation but a higher winter heating load. Both require winter heating, with the original ones having a larger unit area load because of envelope heat loss and ventilation differences. Their direct electric heating consumptions, 187.6 kWh/m² and 229.6 kWh/m², respectively, surpass ordinary residences. This study defines issues for future green and low-carbon courtyard work.

As an aspect of garden space, tea space is an important part of traditional culture and inherited culture. This paper takes 210 representative literati painting images of Ming and Qing dynasties as the research object, combining historical materials, the analysis of painted images and mathematical statistics to transform the representative paintings into visual plan form, and then identifies and extracts the environmental elements and behavior activities in the paintings to build the factor base. It is determined that the main sources of tea activities are Hall Style, Water Pavilion Style, High Pavilion Style, Grass Pavilion and Terraces Style and Combination Style. The analysis results show the following: (1) The tea events mainly occur in outdoor gardens and indoor and outdoor spaces, with windows as climate boundaries. (2) Regarding the use of furniture, indoor spaces tend to use wooden furniture, while outdoor spaces use stone furniture and natural stone, or portable wooden furniture. (3) In terms of indoor and outdoor spaces with tea activities, indoor and outdoor spaces often borrow landscape elements from each other, and screens and partitions are often used as a means of space division in terms of space separation. The findings of this study lay a theoretical foundation for the design of traditional garden tea spaces, and provide a reference for the fine design of contemporary architecture and garden tea spaces.

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings.