• Physical Therapy Rehabilitation Science
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• v.8 no.2
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• pp.99-110
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• 2019

Objective: The aim of the present study was to develop a valid and reliable scale that measures the healthy life styles among young adults. Design: A methodological study design was employed to develop and validate the Healthy Lifestyle Screening Tool (HLST). Methods: The validity and reliability of the HLST were established in accordance with DeVellis' 8 steps guideline for tool development. The question items were generated based on literature reviews and interviews, which were then classified into 12 categories. The HLST was administered to 272 students attending a Korean university. The reliability was tested using Cronbach's alpha. The validity of the scale was examined with the mean inter-item correlations (MIIC) and factor analysis, and was also examined for content validity by experts. Results: The reliability of the HLST was found to be acceptable, as indicated by a Cronbach's alpha of 0.71. In the validity test, items with less than 80% "agreement" ratings on the content validity index by experts were revised. The MIIC values were greater than 0.25. A factor analysis of 36 items extracted 9 factors (i.e., four items per factor), which together explained 50.4% of the variance. The HLST consists of 36 items that measure 9 factors based on a 4-point Likert rating scale, with 4 items per factor, as follows: sunlight, water, air, rest, exercise, nutrition, temperance, trust, and general physical condition. High scores on the HLST are indicative of a healthy lifestyle (HL). Conclusions: The HLST is a valid and reliable scale that can be used to measure HL among young adults. Identification of HL by using the HLST can provide guidance to integrated therapeutic approaches along with conventional physical therapy.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.100-105
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• 2019

As IoT Technology develops and Era of Hyperconnectivity comes, various kinds of customized services became available. As a next-generation display, a smart mirror accesses multimedia devices and provides various services, so it can serve as a social learning tool for the children and the old ones, as well as adults who need information. Smart Mirror must be able to identify users for individualized services. However, since the Smart Mirror is an easily accessible device, there is a possibility that information such as an individual's pattern and habit stored in the smart mirror may be exposed to the outside. Also, the other possibility of leakage of personal location information is through personal schedule or appointment stored in the smart mirror, and another possibility that privacy can be violated is through checking the health state via personal photographs. In this research, we propose a system that identify users by the information the users registered about their physique just like their face, one that provides individually customized service to users after identifying them, and one which provides minimal information and service for unauthenticated users.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.823-835
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• 2021

Identification of factors influencing regional population is critical for establishing government's population policies as well as for improving residents' social, economic and cultural well-being in the region. In this study we analysed the data from 2019 Population Housing Survey in Korea to identify the factors affecting the population size in each of the three regions: Seoul, metropolitan cities, and provincial regions. We applied a Bayesian quantile regression to account for asymmetry and heteroscedasticity of data. The analysis results showed that the effects of factors vary greatly between the three regions of Seoul, metropolitan cities, and provincial regions as well as between sub regions within the same region. These results suggest that population-related variables have very heterogeneous characteristics from region to region and therefore it is important to establish customized population policies that suit regional characteristics rather than uniform population policies that apply to every region.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.312-318
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• 2021

Lack of knowledge and digital skills is a threat to the information security of the state and society, so the formation and development of organizational culture of information security is extremely important to manage this threat. The purpose of the article is to assess the state of information security of the state and society. The research methodology is based on a quantitative statistical analysis of the information security culture according to the EU-27 2019. The theoretical basis of the study is the theory of defense motivation (PMT), which involves predicting the individual negative consequences of certain events and the desire to minimize them, which determines the motive for protection. The results show the passive behavior of EU citizens in ensuring information security, which is confirmed by the low level of participation in trainings for the development of digital skills and mastery of basic or above basic overall digital skills 56% of the EU population with a deviation of 16%. High risks to information security in the context of damage to information assets, including software and databases, have been identified. Passive behavior of the population also involves the use of standard identification procedures when using the Internet (login, password, SMS). At the same time, 69% of EU citizens are aware of methods of tracking Internet activity and access control capabilities (denial of permission to use personal data, access to geographical location, profile or content on social networking sites or shared online storage, site security checks). Phishing and illegal acquisition of personal data are the biggest threats to EU citizens. It have been identified problems related to information security: restrictions on the purchase of products, Internet banking, provision of personal information, communication, etc. The practical value of this research is the possibility of applying the results in the development of programs of education, training and public awareness of security issues.

• Biomolecules & Therapeutics
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• v.29 no.3
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• pp.249-262
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• 2021

The most effective way to control newly emerging infectious disease, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, is to strengthen preventative or therapeutic public health strategies before the infection spreads worldwide. However, global health systems remain at the early stages in anticipating effective therapeutics or vaccines to combat the SARS-CoV-2 pandemic. While maintaining social distance is the most crucial metric to avoid spreading the virus, symptomatic therapy given to patients on the clinical manifestations helps save lives. The molecular properties of SARS-CoV-2 infection have been quickly elucidated, paving the way to therapeutics, vaccine development, and other medical interventions. Despite this progress, the detailed biomolecular mechanism of SARS-CoV-2 infection remains elusive. Given virus invasion of cells is a determining factor for virulence, understanding the viral entry process can be a mainstay in controlling newly emerged viruses. Since viral entry is mediated by selective cellular proteases or proteins associated with receptors, identification and functional analysis of these proteins could provide a way to disrupt virus propagation. This review comprehensively discusses cellular machinery necessary for SARS-CoV-2 infection. Understanding multifactorial traits of the virus entry will provide a substantial guide to facilitate antiviral drug development.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1565-1570
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• 2019

Many possibilities of Big Data has been discussed widely for several years. And the importance of protecting personal information has been emphasized more strongly. During the process of integrating several personal information for the improvement of usability of Big Data, there are many problems occured like the likelihood of the identification of one person, the level of personal infomation used to create personalized services in the companies making and using Big Data. In this study, I summarize GDPR(General Data Protection Regulation) of EU, CCPA(California Consumer Privacy Act) of USA and domestic Big Data 3 Acts Amendment proposals. Also I discuss re-identifcation of de-identificated information, social costs of the usage agreement of personal information, possible problems in construction and combination of private and public big data, political suggestions about settlement of regulatory environment.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.25-37
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• 2022

The government and private companies are endeavoring to help the digital healthcare industry grow. This includes easing regulations on the big data industry such as the amendment of the Data 3 Act. Despite these efforts, however, there have been constant demands for the amendment of laws related to the medical field and for securing medical data transmissions. In this paper, the Data 3 Act of Korea and the legal system related to healthcare are examined. Then the legal, institutional, and technical aspects of the strategies are compared to understand the issues and implications. Based on this, a legal and institutional strategy suitable for the digital healthcare industry in Korea is suggested. Additionally, a direction to improve social perception along with technical measures such as safe de-identification processing and data transmission are also proposed. This study hopes to contribute to the spread of various convergent industries along with the digital healthcare industry.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.45-55
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• 2022

This study explores the issues of "eco-friendly consumption" through online media posts, and aims to identify changes in it. Total 6,812 blog posts on Naver, that included the words "eco-friendly consumption" and "eco-friendly lifestyle," published between 2005 and 2020, in five-year intervals, were analyzed. The results illustrated that consumption issues began with the identification of the causes of environmental problems however, "eco-friendly consumption" gradually gained importance, until it developed into preparing standards and alternatives for proper "eco-friendly consumption." In 2020, "eco-friendly consumption" values and ideal consumption practices were expanded into social movements. However, there is relatively little discussion on controlled and modest spending. Therefore, for the future direction of "eco-friendly consumption," it is necessary to thoroughly examine and highlight the agenda of controlled and modest living from a higher perspective.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.