• Journal of Practical Engineering Education
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• v.9 no.2
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• pp.183-190
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• 2017

Amid concentrating on inviting foreign students in the aspect of securing talents as well as advanced educational service industry in the world, the number of foreign students in Korea exceeded over 90,000 in 2015, increased to 104,262 anticipating further increase according to National Statistical Office. The government tried to expand the dormitory supply to the foreign students upon the discretion that short of the basic infrastructure in the universities such as quantitative shortage and facilities of the dormitories limited to lure the foreign students, despite the will of inviting more foreign students by the government, however, the rate of foreign students' staying in the dormitories was low with 36.0% nationwide in 2016, reflecting the difficulties of residence for the foreign students in Metropolitan areas. Hence, this study is to suggest the alternative potential as the cooperative housing for the foreign students with the expanded concept and its foundation methods, upon reviewing the concept and trend of the common dormitory such as universities-cooperative housing with the initiative of public institutions and the common dormitories for the Korean students studying in Seoul, and analyzing the university city of Paris and Tokyo International Exchange Center that are the examples of existing cooperative housing in overseas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.395-397
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• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.

• 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 Technology Innovation
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• v.28 no.3
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• pp.29-53
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• 2020

Existing main path analysis is useful to clarify the backbone of technology developments over the past, but has difficulty in identifying future technology candidates, and also in anticipating changes in the mainstream technology. Our method develops a growth velocity indicator, and combines it with key-route analysis and traversal counts measure in the main path analysis. It enables us to identify rapidly growing paths of future technology candidates, and further to evaluate the relative growth potential of such paths by which can replace the mainstream technology in the main path. Our method can contribute to identifying future technology candidates in a quantitative way by using patents, and broaden the scope of main path analysis research toward foresight. It can be useful for technology strategy in practice. Biofuel technology is exemplified.

• The Korean Fashion and Textile Research Journal
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• v.23 no.1
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• pp.44-56
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• 2021

This study analyzes the fashion video content of vegan YouTubers in order to understand how the vegan message is being conveyed in media. This study collected data with NoxInfluencer and conducted a case study of the vegan fashion YouTube content. We collected 143 videos for analysis as follows. The characteristics of vegan fashion content were divided into six categories. First, fashion know-how consisted of YouTubers' explanation on vegan fashion, from styling to where to buy vegan and fair trade products. Second, fashion haul content showed second hand products as well as certified vegan fashion products. Third, fashion daily life focused on a day in the life of a vegan YouTuber, casually showcasing fashion in real life. Fourth, fashion products reviews were about vegan YouTubers' thoughts and concerns about various vegan fashion brands and products. Fifth, fashion coordination category consisted of Lookbooks according to seasons. Last, the fashion entertainment category showed YouTubers challenging themselves to dress outside of their comfort zone. The content of the message was distinguished by consumption methods and aesthetic interaction. Also, vegan YouTubers were always anticipating the possibility of appealing to a wider demographic. This study differs from existing studies because it analyzed fashion YouTube content in order to understand the spread of a vegan message in the media environment. This study has its significance in suggesting the direction that the vegan community should take in delivering vegan messages in the future.

• Education of Primary School Mathematics
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• v.25 no.4
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• pp.433-457
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• 2022

An effective teacher community helps the participating teachers improve their instructional quality. This study reports a teacher community consisting of 15 elementary school teachers and one teacher educator. This paper analyzed 15 mathematics lessons in which the teachers implemented the five practices for orchestrating productive mathematics discussions by Smith and Stein (2018) based on the grade-specific discussions as well as the whole community's discussions. The results of this study showed that the overall levels of each practice either increased gradually or maintained at the highest Level 4, as mathematics lessons had been implemented. Specifically, the following practices were quite successful: setting goals for a lesson, selecting an appropriate task, anticipating student responses, and selecting student solutions. However, both sequencing and connecting student solutions were implemented at various levels. Monitoring student work tended to remain at Level 2 which included incorrect implementation of the practice. This paper closes with implications related to the skillful implementation of the five practices through a teacher community.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Environmental and Resource Economics Review
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• v.32 no.3
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• pp.149-166
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• 2023

The emissions trading system, introduced to reduce greenhouse gas emissions, experienced a sharp increase in emission allowance prices during the second plan period (2018-2020), which led to an increase in the demand for smooth supply and demand of emission allowances, while suppliers anticipating a shortage of emission allowances in the future did not participate in trading. Therefore, the authority temporarily revised the guidelines to ensure that the amount of allowances carried forward is proportional to the trading volume as a market stabilization measure. Through an optimization process using a dynamic nonlinear mathematical model, this paper analyzes the impact of the government's intervention on the carryover policy on GHG emission reductions and emission allowance market prices. According to the simulation analysis results, banking regulations could cause a decline in prices during the regulation period, even though the initial policy was predicted to be adopted.

• Women's Health Nursing
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• v.29 no.2
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• pp.104-114
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• 2023

Purpose: The purpose of this study was to comprehensively understand and describe the meaning of physical activity for managing menopausal symptoms in middle-aged women. Methods: This study targeted middle-aged women with menopausal symptoms who participated in regular exercise at least three times a week for more than 12 weeks. Nine participants were individually interviewed via in-depth face-to-face interviews, and participatory observation was also employed. Colaizzi's phenomenological qualitative research method was applied for analysis. Results: Participants were asked, "What does it means to participate in physical activity at this time of your life?" Fourteen codes, six themes, and three theme clusters were derived for the meaning of physical activity for managing menopausal symptoms these middle-aged women. The six themes were "reviving the exhausted body and mind," "being free from the yoke of pain," "being settled in life," "finding oneself and becoming altruistic," "striving while anticipating change," and "equipping the body and mind." The three theme clusters were "overcoming my past pain," "taking the initiative for today's life," and "moving towards new change." Conclusion: The narratives revealed that physical activity allowed women to overcome menopausal symptoms, the burden of relationships, and stress, thereby enabling them to make positive changes in their lives and have expectations for the future. Thus, physical activity was a positive force in a healthy menopausal transition for women with menopausal symptoms. The findings of this study can be used to encourage physical activity in peri-menopausal women and to develop physical activity programs for managing menopausal symptoms.