Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R²) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R² = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R² = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations.

China’s university dormitories have high population densities, which can result in a large number of casualties because of crowding and stampedes during emergency evacuations. It is therefore important to plan properly for evacuations by mitigating the effect of choke points that create backlogs ahead of time. Accurate computer representations of the structure of a building and behavior of the evacuees are two important factors to obtain accurate evacuation time. In this paper, Agent-Based Modeling (ABM) and Building Information Modeling (BIM) are, respectively, implemented using the Unity platform to simulate the evacuation process. As a case study, the layout of a student dormitory building at Shanghai Normal University Xuhui District, Shanghai, China, is utilized along with the A* algorithm in Unity to explore the impact of evacuation speed and delays in creating choke points. Compared with previous research, the innovation of this study lies in: (1) using Unity software to make simulation of the physical environment both realistic and easy to implement, demonstrating Unity can be a well-developed platform to implement ABM-BIM research that focuses on crowd evacuation. (2) Using these simulations to evaluate different degrees of congestion caused by varying evacuation speeds, thus providing information about possible issues relating to evacuation efforts. Using the results, several recommended measures can be generated to help improve evacuation efficiency.

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings.

The purpose of highly accelerated life test (HALT) is mainly to stimulate the potential defects of products, Therefore, at present, HALT can't quantitatively obtain the reliability index of the product. In this paper, because of the current situation of insufficient utilization of enhanced test data, the methods of data extrapolation and reliability evaluation based on accelerated model are studied. Firstly, the HALT data is extrapolated into failure data under normal stress environment by using the acceleration model, and the extrapolated HALT data are regarded as a set of reliability growth test data. Then, the reliability of the product is evaluated by the reliability growth test evaluation method, thereby realizing the reliability evaluation based on the HALT data.