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学术成果

Seeing the Forest and the Trees: Holistic View of Social Distancing on the Spread of COVID-19 in China | Applied Geography

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures’ impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

学术成果

Spatio-Temporal Heterogeneity in the International Trade Resilience during COVID-19 | Applied Geography

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially Mainland China, followed by high-income countries with fast vaccine roll-out (e.g., U.S.), whereas low-income countries (e.g., Africa) show high vulnerability. Our results encourage a comprehensive strategy to enhance international trade resilience when facing future pandemic threats including effective non-pharmaceutical measures, timely development and rollout of vaccines, strong governance capacity, robust healthcare systems, and equality via international cooperation. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.

学术成果

Spatio-Temporal Heterogeneity in the International Trade Resilience during COVID-19: A Complex Network Approach | SSRN

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade and supply chains. The COVID-19 pandemic shows diverse impacts on different economies and markets that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of strengths and vulnerabilities of international trade networks in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world scale, regional scale, and country scale for the pre- and post- COVID-19 outbreak period from 2018 to 2021. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially mainland China, followed by high-income countries (e.g., European Union), whereas low-income countries (e.g., Africa) show high vulnerability. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.

学术成果

Social Eating as a Favor Exchange Facilitator: New Survey Evidence from China | Economic Sociology: Perspectives and Conversations

Social eating – or eating a meal with significant others – is universally important for social networking in society. This article reviews a research program on social eating as a network builder and resource mobilizer for favor exchanges, and presents new survey evidence on patterns of participation in social eating and favor exchanges in China today.

学术成果

International Trade Network Resilience during COVID-19 | AAG 2022 Annual Meeting

The COVID-19 pandemic and subsequent lockdowns have created health and economic crises, leading to unprecedented disruption to world trade and supply chains. The COVID-19 shows diverse impacts on different economies and markets that suffer and recover at different rates and degrees. This research aims to evaluate the strengths and vulnerabilities of international trade networks (ITNs) in the current crisis to evaluate global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches on the monthly ITNs for the pre- and post- COVID-19 outbreak period. The overall findings elicit the hidden spatio-temporal trading disruption, development, and resilience due to the adverse impact of COVID-19 pandemic.

学术成果

影像组学研究成果在《Frontiers in Oncology》上发表

Automated Classification of Papillary Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma Based on a Small Computed Tomography Imaging Dataset Using Deep Learning
This study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.