Study on the Spatial Differentiation Characteristics and Influencing Factors of China's Economic Resilience under Different Shocks

Recognizing regional economic resilience and its influencing factors under different shocks is necessary to promote stable regional economic development. The article analyzes the regional economic resilience of 31 Chinese provinces under three kinds of shocks, namely, financial crisis, economic downturn, and COVID-19, in terms of the resistance of regional economies to shocks, and examines spatial distribution and main influencing factors. The results of the study found that: (1) The characteristics of regional economic resilience under different shocks are different. During the financial crisis, the strong resilient provinces are distributed in the central and western and northern regions;during the economic downturn, the strong resilient provinces are mainly distributed in the western and central regions;during COVID-19, the strong resilient provinces are mainly distributed in the western and eastern coastal regions. The economic resilience of each province shows significant "high-high" and "low-low" spatial clustering characteristics during the economic downturn and the COVID-19. (2) The main influencing factors of economic resilience in different shocks are different. In the financial crisis, the magnitude of the contribution of the influencing factor is leading industry (0.283) >related diversity (0.197) >foreign trade dependence (0.190);during the economic downturn, the magnitude of the contribution of the influencing factor is population density (0.464) >leading industry (0.427) >related diversity (0.285);the magnitude of the contribution of the impact factor during the COVID-19 was related diversity (0.282) >unrelated diversity (0.274) >leading industry (0.272). (3) In the interaction of impact factors, the strongest explanatory power is found in related diversity, unrelated diversity, and leading industries, which represent the industrial structure. Therefore, there is a need to adjust the industrial structure and improve the regional economic resilience from the shock itself.

One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era.

This paper proposes a sustainable management and decision-making model for COVID-19 control in schools, which makes improvements to current policies and strategies. It is not a case study of any specific school or country. The term one-size-fits-all has two meanings: being blind to the pandemic, and conducting inflexible and harsh policies. The former strategy leads to more casualties and does potential harm to children. Conversely, under long-lasting strict policies, people feel exhausted. Therefore, some administrators pretend that they are working hard for COVID-19 control, and people pretend to follow pandemic control rules. The proposed model helps to alleviate these problems and improve management efficiency. A customized queue model is introduced to control social gatherings. An indoor-outdoor tracking system is established. Based on tracing data, we can assess people's infection risk, and allocate medical resources more effectively in case of emergency. We consider both social and technical feasibility. Test results demonstrate the improvements and effectiveness of the model. In conclusion, the model has patched up certain one-size-fits-all strategies to balance pandemic control and normal life.

An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces

To date, the protracted pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had widespread ramifications for the economy, politics, public health, etc. Based on the current situation, definitively stopping the spread of the virus is infeasible in many countries. This does not mean that populations should ignore the pandemic;instead, normal life needs to be balanced with disease prevention and control. This paper highlights the use of Internet of Things (IoT) for the prevention and control of coronavirus disease (COVID-19) in enclosed spaces. The proposed booking algorithm is able to control the gathering of crowds in specific regions. K-nearest neighbors (KNN) is utilized for the implementation of a navigation system with a congestion control strategy and global path planning capabilities. Furthermore, a risk assessment model is designed based on a “Sliding Window-Timer”algorithm, providing an infection risk assessment for individuals in potential contact with patients.