ABSTRACT
Residential segregation (RS) is a global phenomenon that has become an enduring and important topic in international academic research. In this review, using RS as the search term, 2520 articles from the period 1928–2022 were retrieved from the Scopus database and were visually analyzed using CiteSpace software. The results revealed the following: (1) The United States and its institutions have made outstanding contributions to RS research, while various scholars (e.g., Johnston, Massey, Forrest, Poulsen, and Iceland) have laid the foundation for RS research. (2) Mainstream RS research originates from three fields—psychology, education, and social sciences—while the trend of multidisciplinary integration is constantly increasing. (3) The research hotspots of RS include racial difference, sociospatial behavior, income inequality, mixed income communities, guest worker minorities, typical district segregation, occupational segregation, health inequalities, metropolitan ghetto, and migrant–native differential mobility. Furthermore, (4) gentrification, spatial analysis, school segregation, health disparity, immigrant, and COVID-19 have become new themes and directions of RS research. Future research should pay more attention to the impact of multi-spatial scale changes on RS as well as propose theoretical explanations rooted in local contexts by integrating multidisciplinary theoretical knowledge. © 2022 by the authors.
ABSTRACT
Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development and people's daily life. Taking for example the virus transmission that may occur after college students return to school, we analyze the quantitative influence of the key factors on the virus spread, including crowd density and self-protection. One Campus Virus Infection and Control Simulation (CVICS) model of the novel coronavirus is proposed in this article, fully considering the characteristics of repeated contact and strong mobility of crowd in the closed environment. Specifically, we build an agent-based infection model, introduce the mean field theory to calculate the probability of virus transmission, and microsimulate the daily prevalence of infection among individuals. The experimental results show that the proposed model in this article efficiently simulates how the virus spreads in the dense crowd in frequent contact under a closed environment. Furthermore, preventive and control measures, such as self-protection, crowd decentralization, and isolation during the epidemic, can effectively delay the arrival of infection peak, reduce the prevalence, and, finally, lower the risk of COVID-19 transmission after the students return to school. IEEE
ABSTRACT
This paper proposes a detection and location method for a special culture carrier for Nucleic Acid Detection in COVID-19. In order to reduce the pollution caused by manual operation and promote the automation of Nucleic Acid Detection, we use the image processing and machine vision techniques to detect and locate the target culture carrier. By using the OpenCV library functions, we can complete the detection process of image processing algorithm. Based on the recognition and size measurement of the existing training carrier, the appropriate threshold is obtained through many experiments to complete the detection and location of the target culture carrier. Finally, we gather 10 pictures of images for detection and evaluation, the feasibility and efficiency of this method is demonstrated through the actual detection operation. © 2020 IEEE.