• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.12a
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• pp.14-14
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• 2020

In December 2019, the COVID-19 epidemic was discovered in Wuhan, China, and since has disseminated around the world impacting human health for millions. Herein, in-silico drug discovery approaches were utilized to identify potential candidates as Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) main protease (M^pro) inhibitors. We investigated several databases including natural and natural-like products (>100,000 molecules), DrugBank database (10,036 drugs), major metabolites isolated from daily used spices (32 molecules), and current clinical drug candidates for the treatment of COVID-19 (18 drugs). All tested compounds were prepared and screened using molecular docking techniques. Based on the calculated docking scores, the top ones from each project under investigation were selected and subjected to molecular dynamics (MD) simulations followed by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. Combined long MD simulations and MM-GBSA calculations revealed the potent compounds with prospective binding affinities against M^pro. Structural and energetic analyses over the simulated time demonstrated the high stabilities of the selected compounds. Our results showed that 4-bis([1,3]dioxolo)pyran-5-carboxamide derivatives (natural and natural-like products database), DB02388 and Cobicistat (DB09065) (DrugBank database), salvianolic acid A (spices secondary metabolites) and TMC-310911 (clinical-trial drugs database) exhibited high binding affinities with SARS-CoV-2 M^pro. In conclusion, these compounds are up-and-coming anti-COVID-19 drug candidates that warrant further detailed in vitro and in vivo experimental estimations.

• Journal of Distribution Science
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• v.22 no.1
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• pp.47-59
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• 2024

Purposes: The study aimed at examining the impact of the COVID-19 pandemic on the shift of online consumer purchasing behavior and whether the new behaviors would be maintained after the epidemic season. The study also aims to investigate how online customers change based on perceived risks. Research design and Methodology: The study investigated purchasing behavior of the same 377 online Vietnamese consumers during two periods: (1) during the period of social distancing and (2) one and half year after that, allowing data to be collected in real time, so that consumers do not have to recall their behavior. Results: Purchasing behavior appeared to be more influenced by gender, age and household size. Aged consumers are more concerned about risks than those in the younger group, who only worry about the risks during the pandemic. Consumers in households with two or more people are more concerned about the risks than those living alone. Female appeared to be more influential in both during and after pandemic than male. Conclusions: The findings contribute to clarify shift of online consumer purchasing behavior, which helps business to develop effective marketing strategies and enhance their presence in the e-commerce sector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4145-4162
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• 2021

During the period of epidemic prevention and control, contact tracing systems are developed in many countries, to stop or slow down the progression of COVID-19 contamination. However, the privacy issues involved in the use of contact tracing apps have also attracted people's attention. First, we divide contact tracing techniques into two types: Bluetooth Low Energy (BLE) based and Global Positioning System (GPS) based techniques. In order to clear understand the system structure and its elements, we create data flow diagram (DFD) of each types. Second, we analyze the possible privacy threats contained in various types of contact tracing apps by applying LINDDUN, which is a threat modeling technique for personal information protection. Third, we make a comparison and analysis of various contact tracing techniques from privacy point of view. These studies can facilitate improve tracing and security performance to contact tracing apps through comparisons between different types.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.130-135
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• 2021

Because of the COVID-19 epidemic, many countries have made the obligation to wear masks normal. Wearing masks in public places has become a must. At present, wearing a mask to participate in sports makes it very common. People seek to gain health through exercise but ignore the potential respirato-ry health threat. That is, wearing a mask will cause a decrease in oxygen content in the body. This neg-ative impact becomes more prominent as the wear-ing time and oxygen consumption increase. To pro-tect people from viruses and enjoy a healthy life. This paper proposes a breathing pattern that im-proves blood oxygen saturation while wearing a jogging mask and walking. Namely, shouting breathing pattern. Use a pulse oximeter to measure the blood oxygen saturation of running at different speeds and compare the normal breathing pattern and the shouting breathing pattern. The results show that the shouting breathing pattern has a sig-nificant improvement in the blood oxygen satura-tion of low-speed walking and medium-speed jog-ging.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1393-1407
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• 2023

In this study, we propose a dynamic SEIQRV mathematical model and examine it to comprehend the dynamics of COVID-19 pandemic transmission in the Coimbatore district of Tamil Nadu. Positiveness and boundedness, which are the fundamental principles of this model, have been examined and found to be reliable. The reproduction number was calculated in order to predict whether the disease would spread further. Existing arrangements of infection-free, steady states are asymptotically stable both locally and globally when R₀ < 1. The consistent state arrangements that are present in diseases are also locally steady when R₀ < 1 and globally steady when R₀ > 1. Finally, the numerical data confirms our theoretical study.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.816-827
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• 2021

The purpose of this study is to understand the relationship between fear and anger emotions in the discourse produced by the media during the period of major emerging infectious diseases (SARS, Swine Flu, MERS, and COVID-19) that occurred since 2000 in Korea. The researcher collected editorial articles of the major daily newspaper after a significant epidemic of new infectious diseases and analyzed them using the Extended Parallel Processing Model (EPPM) and text mining techniques. In all epidemic times, fear appears stronger than anger, but the smaller the fear, the greater the risk control message is produced. In detail, fear emerges strongly in the discourse of the risk of infectious diseases or the economic crisis. Anger appears strong when the government's quarantine failures, groups where group infections occurred, and concealing information about infectious diseases. In this process, anger is strongly expressed against the factors that threaten the safety of society. Anger is also an emotion that can justify strong quarantine, but it can be the basis for discourse on minority hate. In this respect, anger is a two-sided emotion, so it must be handled carefully in the media.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.367-376
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• 2022

The purpose of this study is to investigate the current status of work performance of public health doctors(PHDs) involved in quarantine of COVID-19, and to suggest a plan to support PHDs for effective national epidemic prevention and control in the future. As a result of the study, it was found that PHDs mainly performed sample collection, interview, and treatment. 39% of PHDs worked in places without negative pressure facilities, and personal protective equipment and welfare support were poor. In addition, it was investigated that they experienced high-risk infectious diseases, mental distress, exclusion from the decision-making process, conflicts with officials, problems with work guidelines, and lack of prior education. For effective infectious disease management, it is necessary to assign appropriate ranks and to participate in the decision-making process for quarantine, to specify appropriate compensation and regulations, to education, and to support mental health.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.1
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• pp.123-141
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• 2021

PURPOSE: Focusing on the factors that influence the infectious disease emergency response policy (approached by dividing the factors into health policy management and economic policies), both SARS and MERS cases were based on the legal system, manpower, and budget, but there has not been enough learning from the epidemic. This study focused on infectious disease emergency governance, which various studies have neglected despite its social and academic importance. METHODS: The research is based on an analysis of SARS, MERS, and COVID-19 and compares global policies. In this study, infectious disease emergency governance was divided into health policy management and economic factors. This study focused on planning and leadership before and after the outbreak of infectious diseases and how cooperation was achieved to monitor and respond to infectious diseases successfully. RESULTS and CONCLUSION: The limit of this study was that COVID-19 is a currently ongoing infectious disease with high uncertainty. Because it is an ongoing problem, only some data and statistics are reflected, and many limitations prevent a proper comparison under the same criteria as other infectious diseases. In addition, because continuous changes are expected, there is also room for infectious diseases to develop in a completely different pattern from the current situation, and continuous research must be accompanied in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.