A fast evacuation from buildings to emergency shelters is necessary and important after the occurrence of a disaster. We investigated the variations in physical behaviors and cognition processes while finding emergency shelter. The on-site emergency-shelter-finding experiments were conducted in Beijing, China. Participants performed the task by using a wearable eye-tracking device. We aimed to assess three eye metrics: fixation counts, mean fixation duration, and visual attention index, to perform cognitive searching analysis for the environmental elements. The results showed that most people spend more fixation time on digital maps (297.77 ± 195.90 ms) and road conditions (239.43 ± 114.91 ms) than signs (150.90 ± 81.70 ms), buildings (153.44 ± 41.15 ms), and plants (170.11 ± 47.60 ms). Furthermore, most participants exhibit hesitation and retracing behaviors throughout the wayfinding process. The participants with relatively rich disaster experience and a proactive personality exhibit better performance in the shelter-finding task, such as a shorter retracing distance (p = 0.007) and nearer destination (p = 0.037). Eye metrics, together with the questionnaire, can mirror the complexity and heterogeneity of evacuation behavior during emergency shelter-finding. In addition, this also provides insights for the optimization of guidance sign systems and improvements in emergency management.

This study presents a theoretical study on the super thin and conductive thermal absorber with built-in corrugated channels on the basis of previous field experiments. The flow and heat transfer characteristics of the corrugated channels are simulated to identify the factors affecting photovoltaic/thermal (PV/T) system efficiency. The influences of the structural parameters such as the corrugation number, the corrugation area, and the flow channel width on the water outlet temperature and heat collection are discussed in order to support the structural optimization design of the hybrid PV/T system. The simulation results were validated to be in good agreement with experimental results. The results indicate that increasing inlet water velocity leads to a decrease in the outlet temperature. It was found that the corrugation area and the flow channel width have impacts on the outlet temperature of the hybrid PV/T collector panel. When the flow channel width of the absorber plate is reduced from 4 mm to 3 mm, the outlet temperature attained is between 298 and 302 K, and the heat collection is in the range of 16.2–51.4 MJ/h. This led to an increase in the amount of heat collected by 18.6%.

Passenger density is a major factor influencing indoor climate conditions and the corresponding energy consumption in the transfer halls of metro transportation hubs. In this study, passenger flow in a subway transfer hall in Beijing was measured across three typical seasons. A differential equation model was established to account for the characteristics of passenger density. Based on this, a passenger density prediction model was developed and utilized for analysis. The results show that passenger density is primarily affected by operation time and the surrounding population composition, with a relatively weak correlation to outdoor weather conditions. Additionally, passenger density differs significantly between weekdays and weekends, with the weekday peak being 2.5 times higher than that on weekends. During the four peak hours on weekdays, both in the morning and evening, passenger density exceeds the design capacity, resulting in insufficient fresh air supply. However, for most of the weekday hours and for all hours on weekends, passenger density remains below the design capacity. This indicates a substantial potential for energy savings by adjusting the fresh air supply according to actual passenger density.

Ground source heat pump (GSHP) systems play a pivotal role in the decarbonization of heating, ventilation, and air conditioning systems. However, their long-term performance depends heavily on operational control strategies capable of addressing system dynamics, varying load conditions, and the increasing integration with renewable and distributed energy resources. Data-driven methods have emerged as promising approaches for improving the operational flexibility, efficiency, and sustainability of GSHP systems. Existing review studies mainly focus on predictive modeling and provide limited systematic analysis of the interactions among predictive modeling, control, and optimization, particularly in hybrid and networked energy systems. This review shows recent advances in data-driven approaches for GSHP operational control. The reviewed studies demonstrate that data-driven control strategies can improve operational performance and enable real-time decision-making under dynamic conditions, particularly when predictive models are integrated with first-principles models and optimization algorithms. Beyond conventional standalone applications, this review highlights the transition toward hybrid GSHP configurations and integrated energy systems, including fifth-generation district heating and cooling networks. Within these systems, operational objectives shift from maintaining local ground thermal balance and maximizing equipment efficiency to achieving coordinated energy exchange, load flexibility, economic performance, and carbon-aware network operation. Despite substantial methodological advances, challenges related to data quality, boundary uncertainty, model transferability, and computational scalability continue to limit large-scale deployment of data-driven control strategies. Finally, future directions for developing intelligent, interpretable, and scalable data-driven control frameworks for next-generation low-carbon buildings and district energy systems are outlined.