• Journal of Information Technology Services
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• v.20 no.5
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• pp.137-146
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• 2021

Recently, public participation in government policy design has been further expanded and public services perceived by users are expanding. At this time, the role of the digital government and the direction of the service to be pursued are user-centered, and above all, it is necessary to focus on the keywords of pre-emptive, preventive, and customized. In order to propose service quality improvement in the public sector, service user-centered classification and monitoring are integrated and the usability of government documents is improved. It is necessary to identify the needs of whether to provide a path for public participation. In the post-corona era, people are accessing quarantine information from the digital government every day. The government should proactively respond to the acceleration of digital transformation and the non-face-to-face demands of the people who experience non-face-to-face daily life. In order to evolve into a smart organization along with the innovation promotion plan and to provide customized services, it is necessary to use existing guides for institutional and technical improvement, along with new technology and data-based analysis, to strive for change management. The government should seek counter-measures that have advanced one step ahead by incorporating new high-tech IT with user-centered necessary services. This study aims to derive improvement plans to provide user-centered digital government service design when designing public services and collecting public opinions. Based on the e-government development model research and the existing research on user-centered service design in the public sector, institutional and technical measures are provided for the improvement of digital government service design.

• The Journal of Korean Medicine
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• v.43 no.4
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• pp.102-130
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• 2022

Objectives: The persistence and unpredictability of coronavirus disease (COVID-19) and new measures to prevent direct medical intervention (e.g., social distancing and quarantine) have induced various psychological symptoms and disorders that require self-treatment approaches and integrative treatment interventions. To address these issues, the Korean Medicine Mental Health (KMMH) center developed a field manual by reviewing previous literature and preexisting manuals. Methods: The working group of the KMMH center conducted a keyword search in PubMed in June 2021 using "COVID-19" and "SARS-CoV-2". Review articles were examined using the following filters: "review," "systematic review," and "meta-analysis." We conducted a narrative review of the retrieved articles and extracted content relevant to previous manuals. We then created a treatment algorithm and recommendations by referring to the results of the review. Results: During the initial assessment, subjective symptom severity was measured using a numerical rating scale, and patients were classified as low- or moderate-high risk. Moderate-high-risk patients should be classified as having either a psychiatric emergency or significant psychiatric condition. The developed manual presents appropriate psychological support for each group based on the following dominant symptoms: tension, anxiety-dominant, anger-dominant, depression-dominant, and somatization. Conclusions: We identified the characteristics of mental health problems during the COVID-19 pandemic and developed a clinical mental health support manual in the field of Korean medicine. When symptoms meet the diagnostic criteria for a mental disorder, doctors of Korean medicine can treat the patients according to the manual for the corresponding disorder.

• Journal of Korean Academy of Rural Health Nursing
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• v.18 no.1
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• pp.27-39
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• 2023

Purpose: This study is a mixed methods research that was conducted to verify factors affecting the working experience of community-based long-term care hospital workers during the COVID-19 pandemic. Methods: The study was carried out from July 19 to November 3, 2021 for 340 nurses who worked at 10 long-term care hospitals located in G city. Results: As the study results, factors that affected job stress of the workers working at community-based long-term care hospitals included job satisfaction (β=-.27, p<.001), work demand (β=-.25, p<.001), fatigue (β=.19, p=.001), and cooperation and leadership (β=-.12, p=.049). It was found that the participants were struggling with physical and mental stress caused by the increased workload due to the preventative measures taken to stop the infection and spread of COVID-19. Despite this, they accepted their situation as necessary to overcome the pandemic and shared the quarantine guidelines of the government and community health centers while actively responding to prevent the spread of COVID-19 under the leadership of their supervisors. However, they were experiencing psychological and emotional burnout in the prolonged pandemic situation. Conclusion: It is considered necessary to help relieve their stress and provide psychological and mental support by adopting a policy to develop and apply comprehensive programs.

• 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 the Korean Society of Food Science and Nutrition
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• v.38 no.11
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• pp.1625-1632
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• 2009

This study investigated consumer awareness on imported food safety management. The questionnaire explored status for confirmation as imported foods, consideration factors when imported foods were marketed, ways for finding imported food safety information, people responsible for problems in imported foods safety management, and imported food safety management items. Answers to 1065 questionnaires were analyzed using S-Plus 8.0. The principal results were as follows: 35.7% of respondents always confirmed whether it was imported food. The most important imported foods marketed is children food. 55.1% of respondents think inspectors have responsibility for problems of imported foods safety management. The most important factor for improvement of safety is reinforcement of quarantine. The providing of restricted information only after security problem occurs was the reason for non-satisfaction of safety information.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.222-231
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• 2024

Purpose: The purpose of this study is to study how to activate the use of public transportation by identifying the main factors that reduce the use of public transportation due to external influences such as COVID-19 infectious diseases. Method: This study analyzed the connection between the traffic behavior and the characteristics of public transportation use in the metropolitan area changed by COVID-19 with COVID-19 indicators, and analyzed social and environmental factors affecting traffic. Results: It was analyzed that the traffic behavior in the metropolitan area moves from commercial areas to tourist resort areas, the number of COVID-19 deaths affects the use of public transportation, and the lower the deviation between population density, agricultural and forestry areas, and gender ratios due to social and environmental factors, the more significant differences are shown. Conclusion: In the future, it will be able to be activated as a basic analysis model for revitalizing the city's transportation system, regional bases, and various social and economic indicators, such as quarantine of public transportation and social distancing, and can be used as basic data for establishing public transport policy directions according to major influencing factors.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.71-81
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• 2024

In response to the COVID-19 pandemic, the global airport industry is actively introducing 4th Industrial Revolution technology-based systems for quarantine and passenger safety, and test bed construction and prior verification using airport infrastructure and resources are actively being conducted. Analysis of recent cases shows that despite the changing travel patterns of airport users and the diversification of airport service demands, most testbeds construction studies are still focused on suppliers, and task prioritization is also determined by decision makers. There is a tendency to rely on subjective judgment. In order to find practical ways to become a first mover that leads innovation in the aviation industry, this study selected tasks and derived priorities to build testbeds from a service perspective that reflects various customer service needs and changes. Research results using the AHP analysis method resulted in priorities in the order of access transportation and parking services (29.2%), security screening services (23.4%), and departure services (21.8%), and these analysis results were tested in the airport industry. It shows that innovation in testbeds construction is an important factor. In particular, the establishment of smart parking and UAM transportation testbeds not only helps strengthen airports as centers of technological innovation, but also promotes cooperation with companies, research institutes, and governments, and provides an environment for testing and developing new technologies and services. It can be a foundation for what can be done. The results and implications produced through this study can serve as useful guidelines for domestic and foreign airport practitioners to build testbeds and establish strategies.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• The Korean Journal of Air & Space Law and Policy
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• v.24 no.2
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• pp.19-45
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• 2009

The purpose of this paper is to research the contents and issues of the draft legislation of Part VI the Carriage by Act of Korean Commercial Code in respect of the liability of the carrier in the carriage of cargo by air, comparing to the related provisions of the Montreal Convention of 1999. The Montreal Convention in respect of the international carriage by air was adopted in 1999, and Korea has ratified the Montreal Convention in 2007. However, there is now no national legislation in respect of the carriage by air in Korea. Thus, the Ministry of Justice has prepared the draft legislation of Part VI the Carriage by Air of the Korean Commercial Code in July 2008, and the draft legislation is now being reviewed by the National Assembly. The draft provisions of Part VI the Carriage by Air are basically adopting most of the related provisions of the Montreal Convention in respect of the carriage of cargo by air and some draft provisions are applying the related provisions of the Korean Commercial Code in respect of the carriage of cargo by land and sea. In respect of the liability of the carrier in the carriage of cargo by air, the contents of the draft legislation of Part VI the Carriage by air are composed of the provisions in respect of the cause of the liability of the and the application for the non-contractual claim, the limit of liability, the exoneration from liability, the extinguishment of liability, the notice of damage to cargo, the liability of the agents and servants of the carrier, and the liability of the actual carrier and successive carrier. The draft legislation of the Carriage by Air of Korean Commercial Code is different from the provisions of the Montreal Convention is respect of the liability of the carrier in the carriage of cargo by air as follows : the draft Article 913 paragraph 1 provides additionally the riot, civil war and quarantine as the exoneration causes from the liability for damage to the cargo of the carrier in the Article 18 paragraph 2 of the Montreal Convention. In respect of the liability of the carrier in carriage of cargo by air, the draft legislation of Part VI the Carriage by Air does not provide the settlement by arbitration of dispute relating to the liability of the carrier and the requirement of adequate insurance covering the liability of the carrier which are provided in the Montreal Convention. In author's opinion, it is desirable that the above mentioned provisions such as the arbitration and the insurance shall be inserted into the draft legislation of the Carriage by Air of Korean Commercial Code. In conclusion, the legislation of Part VI the Carriage by Air of the Korean Commercial Code shall be made by the National Assembly as soon as possible for the smooth and equitable compensation for damage to cargo arising during the carriage by air.

• Journal of Food Hygiene and Safety
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• v.28 no.3
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• pp.217-221
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• 2013

This study developed predictive models for the kinetic behavior of Staphylococcus aureus on processed cheeses. Mozzarella slice cheese and cheddar slice cheese were inoculated with 0.1 ml of a S. aureus strain mixture (ATCC13565, ATCC14458, ATCC23235, ATCC27664, and NCCP10826). The inoculated samples were then stored at $4^{\circ}C$ (1440 h), $15^{\circ}C$ (288 h), $25^{\circ}C$ (72 h), and $30^{\circ}C$ (48 h), and the growth of all bacteria and of S. aureus were enumerated on tryptic soy agar and mannitol salt agar, respectively. The Baranyi model was fitted to the growth data of S. aureus to calculate growth rate (${\mu}_{max}$; ${\log}CFU{\cdot}g^{-1}{\cdot}h^{-1}$), lag phase duration (LPD; h), lower asymptote (log CFU/g), and upper asymptote (log CFU/g). The growth parameters were further analyzed using the square root model as a function of temperature. The model performance was validated with observed data, and the root mean square error (RMSE) was calculated. At $4^{\circ}C$, S. aureus cell growth was not observed on either processed cheese, but S. aureus growth on the mozzarella and cheddar cheeses was observed at $15^{\circ}C$, $25^{\circ}C$, and $30^{\circ}C$. The ${\mu}_{max}$ values increased, but LPD values decreased as storage temperature increased. In addition, the developed models showed acceptable performance (RMSE = 0.3500-0.5344). This result indicates that the developed kinetic model should be useful in describing the growth pattern of S. aureus in processed cheeses.