• Nutrition Research and Practice
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• v.12 no.3
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• pp.251-257
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• 2018

BACKGROUND/OBJECTIVES: There are few studies that forecast the future prevalence of obesity based on the predicted prevalence model including contributing factors. The present study aimed to identify factors associated with obesity and construct forecasting models including significant contributing factors to estimate the 2020 and 2030 prevalence of obesity and abdominal obesity. SUBJECTS/METHODS: Panel data from the Korea National Health and Nutrition Examination Survey and national statistics from the Korean Statistical Information Service were used for the analysis. The study subjects were 17,685 male and 24,899 female adults aged 19 years or older. The outcome variables were the prevalence of obesity (body mass index ${\geq}25kg/m^2$) and abdominal obesity (waist circumference ${\geq}90cm$ for men and ${\geq}85cm$ for women). Stepwise logistic regression analysis was used to select significant variables from potential exposures. RESULTS: The survey year, age, marital status, job status, income status, smoking, alcohol consumption, sleep duration, psychological factors, dietary intake, and fertility rate were found to contribute to the prevalence of obesity and abdominal obesity. Based on the forecasting models including these variables, the 2020 and 2030 estimates for obesity prevalence were 47% and 62% for men and 32% and 37% for women, respectively. CONCLUSIONS: The present study suggested an increased prevalence of obesity and abdominal obesity in 2020 and 2030. Lifestyle factors were found to be significantly associated with the increasing trend in obesity prevalence and, therefore, they may require modification to prevent the rising trend.

• Journal of Research and Publication Ethics
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• v.2 no.2
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• pp.11-16
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• 2021

Purpose: The purpose of this study is to analyze the research trends of domestic research ethics and to draw implications for the limitations of existing research and future development directions through this. Research design, data and methodology: To this end, this study first examined the concept of research ethics and research area to derive an analysis frame. In addition, 72 academic papers published in domestic journals from 2000 to November 2020 were analyzed according to the research period, research area, research subject, research field, research method, and the nature of the journal. Results: As a result of the analysis, it is judged that social interest in research ethics led to academic interest after the 2005 Hwang Woo-seok incident. These characteristics show that the interest of academia is changing and increasing significantly when issues related to research ethics are raised socially. Of the 72 papers to be analyzed, 29 (40.3%) conceptual and theoretical studies on research ethics and 43 (59.7%) studies on practical measures were surveyed. Conclusions: In the case of published journals on research ethics, the proportion of publications in humanities and social sciences and pedagogical journals was high, and in research methods, literature and theoretical studies were the highest.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.53-65
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• 2022

The fashion industry is facing numerous sustainability-related challenges due to growing consciousness about the egregious extent of global environmental problems. This study examines research trends on sustainable fashion based on five macro trends related to sustainable innovation in the fashion industry. Using the content analysis and network analysis methods, 115 research papers published in domestic and international journals from 2017 to 2021 were collected and analyzed. The study conclusions are as follows. First, majority of domestic papers(55.41%) focused on circular economy, whereas other topics such as consumer awareness(1.35%) and corporate social responsibility(2.70%), are yet to be thoroughly examined; majority of international papers(53.65%) focused on sharing economy and collaborative consumption, whereas other topics such as technological innovation(2.44%), are yet to be thoroughly examined. Second, domestic papers have found that many brands(68.57%) are applying the concept of circular economy, whereas international papers have found that many brands(51.56%) are applying the concept of sharing economy and collaborative consumption. The study results provide useful data for corporate management in the fashion industry.

• Spatial Information Research
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• v.20 no.3
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• pp.39-50
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• 2012

The objective of this study is the quantitative analysis of climate change effects by performing several statistical analyses with hydrometeorological data sets for past 30 years in Geum river watershed. Temperature, precipitation, relative humidity data sets were collected from eight observation stations for 37 years(1973~2009) in Geum river watershed. River level data was collected from Gongju and Gyuam gauge stations for 36 years(1973~2008) considering rating curve credibility problems and future long-term runoff modeling. Annual and seasonal year-to-year variation of hydrometeorological components were analyzed by calculating the average, standard deviation, skewness, and coefficient of variation. The results show precipitation has the strongest variability. Run test, Turning point test, and Anderson Exact test were performed to check if there is randomness in the data sets. Temperature and precipitation data have randomness and relative humidity and river level data have regularity. Groundwater level data has both aspects(randomness and regularity). Linear regression and Mann-Kendal test were performed for trend test. Temperature is increasing yearly and seasonally and precipitation is increasing in summer. Relative humidity is obviously decreasing. The results of this study can be used for the evaluation of the effects of climate change on water resources and the establishment of future water resources management technique development plan.

• The Research Journal of the Costume Culture
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• v.22 no.2
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• pp.273-289
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• 2014

The purpose of this study is to identify the determinants of the susceptibility of global consumer culture. As determinants, materialism and self monitoring as psychological variables and fashion clothing product knowledge as clothing-related variable were included. It was hypothesized that both psychological variables and clothing-related variable influence susceptibility of global consumer culture. Data were gathered by surveying university students in Seoul metropolitan area, using convenience sampling, and 311 questionnaires were used in the statistical analysis. In analyzing data, exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS were conducted. Factor analysis of susceptibility of global consumer culture revealed four dimensions, 'social prestige' factor, 'quality perception' factor, 'conformity to others' factor, and 'conformity to consumption trend' factor. In addition, factor analysis of self monitoring revealed three dimensions, 'center-oriented attention' factor, 'situation-appropriate self-presentation' factor, and 'strategic displays of self-presentation' factor. The results showed that all the fit indices for the variable measures were quite acceptable. In addition, the overall fit of the model suggests that the model fits the data well. Tests of the hypothesized path show that all variables except for the one factor of self monitoring, 'center-oriented attention', and materialism influence all the factors of susceptibility of global consumer culture. The implications of these findings and suggestions for future study are also discussed.

• Journal of dental hygiene science
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• v.24 no.3
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• pp.160-170
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• 2024

Background: This study aimed to analyze trends in qualitative research within the field of dental hygiene, focusing on papers published in Korean journals from 2000 to 2023. As dental hygienists play a crucial role in preventive oral health, understanding the breadth and depth of qualitative research in this field is essential for advancing practice and education. Methods: This descriptive survey research study analyzed 23 qualitative studies using the Consolidated Criteria for Reporting Qualitative Research (COREQ) as a framework. Studies were selected through a comprehensive search of Korean databases. The analysis covered research topics, participant types, methodological approaches, and adherence to COREQ domains, including "Research Team and Reflexivity," "Study Design," and "Analysis and Findings." Results: The analysis revealed that most studies employed a phenomenological methodology (36.4%). Additionally, 87.0% of the studies mentioned Institutional Review Board (IRB) approval and only 8.7% utilized qualitative data analysis software. The studies primarily focused on oral care for the elderly, communication, and the experiences of dental hygienists. Furthermore, 95.7% of the studies included participant quotations, but only 56.5% checked data saturation. Conclusion: This study highlights the need for a more diverse methodological approach in dental hygiene research. Journals should also emphasize strict adherence to IRB guidelines and encourage the use of qualitative data analysis software to enhance the rigor of research. By strengthening the systematic foundation of qualitative research in dental hygiene, the field can better address clinical challenges and expand the understanding of dental hygienists' work environments.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.85-101
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• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.

• KNOM Review
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• v.22 no.3
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• pp.1-12
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• 2019

Recently, researching using big data and AI has emerged as a major issue in the ICT field. But, the size of big data for research is growing exponentially. In addition, users of data transmission of existing network method suggest that the problem the time taken to send and receive big data is slower than the time to copy and send the hard disk. Accordingly, researchers require dynamic and flexible network technology that can transmit data at high speed and accommodate various network structures. SDN/NFV technologies can be programming a network to provide a network suitable for the needs of users. It can easily solve the network's flexibility and security problems. Also, the problem with performing AI is that centralized data processing cannot guarantee real-time, and network delay occur when traffic increases. In order to solve this problem, the edge-computing technology, should be used which has moved away from the centralized method. In this paper, we investigate the concept and research trend of SDN, NFV, and edge-computing technologies, and analyze the trends of data central network technologies used by combining these three technologies.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.507-512
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• 2004

Research that establish new cadastral survey model that use GPS to introduce GPS observation technique in cadastral survey and research that develop connection technologies are gone abuzz. The purpose of this research is to keep in step in such trend and grasp present condition and performance of surveying connection to common use GPS data processing software, and analyze data processing algorithm, and develop suitable GPS data processing software in our real condition regarding GPS data processing and result of control point calculation. This research studies analysis common use software and error occurrence by data processing method that college and company have. Also, It analyzes algorithm that is applied to existing GPS data processing software. After that we study algorithm that is most suitable with cadastral survey and then develop cadastral survey calculation software for new cadastral control points

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.25-37
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• 2009

This Study constructs a dashboard system to synthetically and systematically monitor national R&D information based on data warehouse. Managing the national R&D statistics and trend is important since it provides data for policies and decision making for national R&D. Many agencies related to national R&D information collect the basic R&D statistic data which provides the basis of logical decision making and R&D policies. The data has not well been used. The data has not been consistently collected nor managed. The raw data has not been organized nor processed to meet various demands. The needs has been arisen for a consistent national R&D monitoring system to increase the relevance, accessibility and efficiency of data for various users. This study selects 25 key indicators based on the user requirements and designs data warehouse for supporting the indicators using star schema. The dashboard system is developed in this study provides the infrastructure of monitoring national R&D information and analytic environment of supporting statistical analysis and time-series data analysis.