Atrium spaces are widely applied in university buildings. However, achieving effective energy reduction while maintaining adequate daylighting and indoor comfort remains a major challenge at the early design stage. This study identifies key building form design variables significantly influencing atrium daylighting, energy use, and thermal comfort, including building orientation, atrium width-to-depth ratio, atrium aspect ratio, atrium bottom area ratio, and skylight–roof ratio. A multi-objective optimization (MOO) framework is proposed to balance daylight performance, energy consumption, and thermal comfort under fixed envelope parameters. Using typical single- and double-atrium teaching buildings in cold regions as case studies, this research adopts Useful Daylight Illuminance (UDI), Energy Use Intensity (EUI), and Discomfort Time Percentage (DTP) as key indicators to evaluate the interactions between design parameters and building performance. Based on the Pareto-optimal results for the studied prototypes, a south-by-west orientation, moderately slender atrium proportions, relatively compact atrium bottom areas, and medium skylight–roof ratios together yield a balanced performance. Compared with the reference to the initial solution, the optimized solutions reduce EUI by up to 5.66% while also improving UDI and DTP. These results are intended as quantitative references and optimization for early-stage geometric forms design of atrium teaching buildings in cold regions.

The site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain logistics parks sites. Traditional site selection methods lacking quantitative standards for assessing the reliability of results. In response, this study introduces Bayesian probability theory to construct a Bayesian network model. This model selects and quantifies influencing factors for site selection, establishing a scientifically evaluation indicator system. Subsequently, utilizing K-means clustering analysis to develop a site selection model. The reliability of clustering results is verified using Bayesian discriminant analysis. Furthermore, a city within the first-class cluster is selected to construct a comprehensive suitability evaluation indicator system for cold-chain logistics park location using Geographic Information System (GIS) technology. Jiangsu Province is chosen as the study area to validate the model, and the analysis demonstrates that Suzhou is the most suitable location for establishing a cold-chain logistics park. The comprehensive suitability evaluation further divides Suzhou into five distinct zones, from which the optimal site is identified and confirmed. Overall, the established site selection model provides a scientific and reliable approach for selecting and determining optimal sites.

This study investigates young adults’ residential floor plan preferences through the Traditional 2D environment and immersive virtual environments and explores whether immersive virtual environments can more effectively identify young users’ expressed preferences under simulated pre-occupancy evaluation conditions. It further examines the relationship between subjective preferences and quantitative floor plan attributes. Eight small and medium-sized apartment units currently on the market in Fukuoka, Japan, were selected as case samples. First, a quantitative evaluation framework was established by combining space syntax analysis and principal component analysis, through which floor plan attributes were reduced to three interpretable dimensions: privacy, comfort, and functionality. Second, a within-subject experiment was conducted in which 20 young participants evaluated the same eight floor plans under both 2D and immersive virtual reality conditions using the Residential Spatial Satisfaction Questionnaire. The results show that the virtual reality condition produced significantly higher ratings for privacy, comfort, functionality, and overall satisfaction than the 2D condition. In addition, immersive virtual reality yielded greater plan discriminability and clearer group-level preference rankings, indicating that it more effectively reveals differences among alternative housing schemes. However, the preference rankings derived from subjective evaluation in virtual reality showed only limited agreement with the rankings generated by the principal component analysis-based physical framework, suggesting that objectively optimized spatial attributes do not automatically correspond to users’ expressed residential floor plan preferences. Overall, the findings indicate that current housing design logic may not fully respond to the experiential needs of potential young users, and that early-stage residential design evaluation should place greater emphasis on user-centered assessment. Compared with traditional 2D representation, immersive virtual reality provides a more effective medium for pre-occupancy housing evaluation and preference-based design decision making.

The site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain logistics parks sites. Traditional site selection methods lacking quantitative standards for assessing the reliability of results. In response, this study introduces Bayesian probability theory to construct a Bayesian network model. This model selects and quantifies influencing factors for site selection, establishing a scientifically evaluation indicator system. Subsequently, utilizing K-means clustering analysis to develop a site selection model. The reliability of clustering results is verified using Bayesian discriminant analysis. Furthermore, a city within the first-class cluster is selected to construct a comprehensive suitability evaluation indicator system for cold-chain logistics park location using Geographic Information System (GIS) technology. Jiangsu Province is chosen as the study area to validate the model, and the analysis demonstrates that Suzhou is the most suitable location for establishing a cold-chain logistics park. The comprehensive suitability evaluation further divides Suzhou into five distinct zones, from which the optimal site is identified and confirmed. Overall, the established site selection model provides a scientific and reliable approach for selecting and determining optimal sites.