Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea.

A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

Competitiveness of E Commerce Firms through ESG Logistics

Rapid growth in the e-commerce market, caused by COVID-19, has led to fierce competition. The intense competition in e-commerce market triggers firms to strengthen their competitiveness by providing logistics services. Furthermore, as sustainability becomes important in consumers’ choices of products or services, e-commerce companies’ environmental, social, and governance (ESG) activities are becoming important. Therefore, our purpose of study is to examine the attributes of e-commerce’s competitiveness in the perspective of ESG in the logistics service and to suggest differentiation strategies. We analyzed the importance of each ESG attribute in the logistics through a conjoint analysis. As a result, we found that e-commerce consumers value ESG activities in the order of distribution in the social (9.866%), partnership in the governance (9.637%), operation of distribution center in the social (8.570%), packaging in the environmental (8.320%), operation of distribution center in the environmental (8.262%), purchasing in the social (8.200%), and distribution in the environmental (7.153%). Accordingly, we suggested ESG strategies such as win-win cooperation, opening information on the working environment in delivery and distribution centers, development of a shared logistics platform, preventing COVID-19, and raising consumers’ awareness of eco-friendly delivery.