Yiwen Liu

Accurate occupancy information is critical for optimizing energy efficiency in buildings. Hybrid machine learning models have demonstrated great potential in previous studies; however, their application in passive ultra-low-energy buildings remains underexplored. This study conducts an empirical evaluation of real-time occupancy rate prediction using a CNN-ResNet-RF hybrid model based on multi-source environmental and behavioral data from a passive ultra-low-energy educational building. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Residual Networks (ResNet) to enhance deep feature representation, and Random Forests (RF) for ensemble-based generalization. Indoor CO₂ concentration exhibits the strongest linear correlation with occupancy rate (r = 0.54), indicating a meaningful association with occupancy dynamics. The model demonstrates strong predictive performance on the test set, with a coefficient of determination (R²) of 0.964, a root mean square error (RMSE) of 0.054, and a residual prediction deviation (RPD) exceeding 5. Compared with baseline models such as CNN, RF, and CNN-RF, the proposed framework exhibits generally lower prediction errors and improved stability. Further lightweight compression experiments reveal that the structured compact CNN-ResNet-RF-25 variant achieves even better accuracy (R² = 0.9748, RMSE = 0.0449, RPD = 6.327) while substantially reducing model complexity, demonstrating strong deployment potential in resource-constrained environments.

The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core databases of Web of Science, PubMed, Google Scholar, and PsycINFO, and the knowledge graph software, this paper presents a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related studies has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of the research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change in the modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in the urban environment, the commercial space within the campus requires continuous energy input. Its energy-efficient layout aligns with the principles of sustainable development. This paper uses the university campus as a case study to examine energy-efficient commercial space layout and community practices for environmental protection. Various factors influence the layout of inter-community commercial spaces, and the parameters for measuring the layout structure are diverse, considering the large sample size. Employing machine learning and big data processing to quantify development indicators across various industries and optimize their structure, resource allocation, and energy use has emerged as a viable tool for sustainable urban planning practices. This research seeks to utilize machine learning and data-driven optimization techniques to formulate a comprehensive framework for the sustainable allocation and design of business service spaces within communities. Firstly, we conduct a comprehensive investigation, which includes data collected by applying questionnaire surveys and field research, to assess and model the factors influencing the spatial layout of commercial services on university campuses. Secondly, the AEL machine learning model is constructed by combining the analytic hierarchy process to determine subjective weights, the entropy weight method to calculate objective weights, and the Lagrange algorithm to determine comprehensive weights. Thirdly, we assess and improve the layout of commercial service spaces. Then, by training and testing the Neural Network Model, we apply cases to ensure the accuracy of the machine learning calculation results. Qualitative analysis elucidates the varying factors influencing the sustainable layout of different commercial spaces. Quantitative analysis indicates that, within university campuses, the distance between commercial service spaces and residence halls is a crucial factor in fostering a more sustainable layout. Other significant factors include their location along major student routes and proximity to teaching areas. This study makes contributions not only to the specific field of optimizing commercial service space in communities but also to the broader discourse on sustainable urban development. It advances our understanding of the complex dynamics involved in crafting urban environments that are both efficient and environmentally friendly. Beyond theoretical considerations, the study provides practical solutions and recommendations applicable to implementing tangible improvements in resource allocation. These contributions aim to foster urban environments that are not only environmentally conscious but also economically viable.