Long-Term Urban Epidemic and Disaster Resilience: The Planning and Assessment of a Comprehensive Underground Resilience Core

This study utilizes the enclosed and stable environment of underground space for long-term sustainable planning for urban epidemics and disasters. Owing to the COVID-19 epidemic, cities require long-term epidemic-disaster management. Therefore, this study proposed a strategy for integrating multiple functions to plan a comprehensive Underground Resilience Core (URC). A planning and assessment methods of URC were proposed. With this methodology, epidemic- and disaster- URCs were integrated to construct a comprehensive-URC in underground spaces. The results show: (1) Epidemic-resilient URCs adopting a joint progressive approach with designated hospitals can rapidly suppress an epidemic outbreak. (2) The regularity of the morphology of underground spaces determines the area of the URC. Bar-shaped underground spaces have the potential for planning disaster-URCs. (3) The URC planning efficiency ranking is as follows: Bar shapes lead overall, T shapes are second under seismic resilience, and Cross shapes are second under epidemic resilience. (4) The potential analysis of planning a comprehensive-URC in the underground parking in Chinese cities showed that the recovery time can be advanced from 29% to 39% and the comprehensive resilience can be improved by 37.63%. The results of this study can serve as sustainable urban planning strategies and assessment tools for long-term epidemic-disaster management.

Comparation and Sentiment Analysis Method of Chinese and Western Media's COVID-19 Reports on Various Countries

Media plays an important role in the information society and therefore, it should be fair, just, and objective. Media has been influencing the public's cognition of COVID-19 and their views of the actions taken by the government of different countries in 2020. After analyzing more than 260 thousand reports collected from search engines and published by both the Chinese and Western media about 10 countries, we found that Western media has prejudice when reporting the epidemic in China, and there exist obvious abnormal features when they reporting the epidemic in the United States. In addition, compared to Western media, Chinese media are more consistent and objective with the actual development of the epidemic in different countries. © 2022, Science Press. All right reserved.

Subject-Independent Slow Fall Detection with Wearable Sensors via Deep Learning

One of the major healthcare challenges is elderly fallers. A fall can lead to disabilities and even mortality. With the current Covid-19 pandemic, insufficient resources could be provided for the care of elderlies, and care workers often may not be able to visit them. Therefore, a fall may get undetected or delayed leading to serious harm or consequences. Automatic fall detection systems could provide the necessary detection and warnings for timely intervention. Although many sensor-based fall detection systems have been proposed, most systems focus on the sudden fall and have not considered the slow fall scenario, a typical fall instance for elderly fallers. In this paper, a robust activity (RA) and slow fall detection system is proposed. The system consists of a waist-worn wearable sensor embedded with an inertial measurement unit (IMU) and a barometer, and a reference ambient barometer. A deep neural network (DNN) is developed for fusing the sensor data and classifying fall events. The results have shown that the IMU-barometer design yield better detection of fall events and the DNN approach (90.33% accuracy) outperforms traditional machine learning algorithms.