Catalytic combustion can effectively and cleanly convert the chemical energy of fossil fuels into infrared radiation energy. However, there is little research on the use of this technology to cure powder coatings. Therefore, catalytic infrared heating equipment based on a Pt/Al₂O₃ noble metal catalyst was designed, constructed, and tested in this study. The optimal curing parameters for the catalytic infrared curing process for powder coatings were determined via experiments at 220 °C for 3 min and 230 °C for 2 min. As the curing temperature increased and the curing time increased, the mechanical properties of the coating were found to improve. However, the gloss of the coating was reduced and the color darkened. A one-dimensional heat transfer model was developed to investigate the heat transfer process for powder coatings. This study introduced an internal heat source for the first time, and the heat transfer process for polyester-based powder coatings with different substrate thicknesses was numerically simulated. The numerical simulations demonstrated that the efficiency of the heat transfer between the catalytic infrared gas supply and the coating surface was 0.4. When the substrate thickness was 1 mm, the coating was most rapidly cured at 230 °C. When the substrate thickness was ≥2 mm, the most rapid curing occurred at 220 °C.

Nowadays, relevant data collected from hospital buildings remain insufficient because hospital buildings often have stricter environmental requirements resulting in more limited data access than other building types. Additionally, existing window-opening behavior models were mostly developed and validated using data measured from the experimental building itself. Hence, their accuracy is only assessed by the algorithm’s evaluation index, which limits the model’s applicability, given that it is not tested by the actual cases nor cross-verified with other buildings. Based on the aforementioned issues, this study analyzes the window-opening behavior of doctors and patients in spring in a maternity hospital in Beijing and develops behavioral models using logistic regression. The results show that the room often has opened windows in spring when the outdoor temperature exceeds 20 °C. Moreover, the ward windows’ use frequency is more than 10 times higher than those of doctors’ office. The window-opening behavior in wards is more susceptible to the influence of outdoor temperature, while in the doctors’ office, more attention is paid to indoor air quality. Finally, by embedding the logistic regression model of each room into the EnergyPlus software to simulate the CO₂ concentration of the room, it was found that the model has better applicability than the fixed schedule model. However, by performing cross-validation with different building types, it was found that, due to the particularity of doctors’ offices, the models developed for other building types cannot accurately reproduce the window-opening behavior of doctors. Therefore, more data are still needed to better understand window usage in hospital buildings and support the future building performance simulations of hospital buildings.

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies.

With the rapid advancement of urbanization in China, urban challenges such as environmental pollution, ecological degradation, and public health risks have become increasingly prominent. As a critical diagnostic tool for urban development issues, the Urban Physical Examination (UPE) mechanism has been widely adopted across multiple cities. However, the current UPE system faces significant challenges in data checking: (1) the identification of problematic images relies on manual review, where accuracy is vulnerable to staff’s expertise and subjective judgment, leading to inconsistent evaluations; (2) traditional manual screening is inefficient and hard to meet the growing demand for urban health assessments. To enhance both the accuracy and efficiency of issue identification, this study introduces automated image recognition technology to assist the manual inspection process. Focusing on three common UPE indicators—missing surveillance cameras at unit entrances, absent signage at unit entrances, and damaged stair treads. This study utilizes the EfficientNet-B0 convolutional neural network model, comparing three training approaches: fine-tuning of fully connected layers, fine-tuning all layers, and training the model from scratch. Experimental results demonstrate that EfficientNet-B0 achieves outstanding performance across multiple indicators in the housing dimension of UPE, with all accuracy rates exceeding 88.6%. Notably, the accuracy for the stair tread damage/intact indicator surpasses 98%, indicating exceptional discriminative capability. Notably, the total training time for the model was merely 1.15 hours and testing in 0.6 hours, which is only 3% of the manual review time. These outcomes provide a powerful technical means for improving urban management efficiency and safety evaluations.