• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.45-73
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• 1995

The current paper presents a system dynamics model which can generate the land use anq transportation system performance simultaneously is proposed. The model system consists of 7 submodels (population, migration of population, household, job growth-employment-land availability, housing development, travel demand, and traffic congestion level), and each of them is designed based on the causality functions and feedback loop structure between a large number of physical, socio-economic, and policy variables. The important advantages of the system dynamics model are as follows. First, the model can address the complex interactions between land use and transportation system performance dynamically. Therefore, it can be an effective tool for evaluating the time-by-time effect of a policy over time horizons. Secondly, the system dynamics model is not relied on the assumption of equilibrium state of urban systems as in conventional models since it determines the state of model components directly through dynamic system simulation. Thirdly, the system dynamics model is very flexible in reflecting new features, such as a policy, a new phenomenon which has not existed in the past, a special event, or a useful concept from other methodology, since it consists of a lots of separated equations. In Chapter I, II, and III, overall approach and structure of the model system are discussed with causal-loop diagrams and major equations. In Chapter V _, the performance of the developed model is applied to the analysis of the impact of highway capacity expansion on land use for the area of Montgomery County, MD. The year-by-year impacts of highway capacity expansion on congestion level and land use are analyzed with some possible scenarios for the highway capacity expansion. This is a first comprehensive attempt to use dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions. The model structure is not very elaborate mainly due to the problem of the availability of behavioral data, but the model performance results indicate that the proposed approach can be a promising one in dealing comprehensively with complicated urban land use/transportation system.

• Korean Journal of Ecology and Environment
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• v.35 no.3 s.99
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• pp.160-171
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• 2002

The impact of summer monsoon on water quality of the lower Nakdong River was evaluated during the summer (June-August) in 1997. Several limnological variables were measured in the interval of $1{\sim}3$ day using an automatic monitoring system (Hydrolab $Recorder^{TM}$) to detect water quality changes caused by rainfall on onehour basis. During the monsoon period (from late June to mid July), 5 times of major rainfall events of >50 mm were recorded in the river basin. Dynamic changes of water quality were observed during the monsoon, and the first rainfall event (June$25{\sim}27$) had a significant influence on the water quality at the lower part of the river. All Parameters were largely changed due to the first rain event, and the changed level was maintained until the end of monsoon period. Nutrient concentrations and turbidity increased and values of the other parameters were declined as a result of water dilution. This rainfall event, Changma, is a meteorological phenomenon caused by the East-Asian monsoon climate. The magnitude and frequency of the rainfall during the early monsoon play an important role in change of water quality and ecosystem characteristics of large river systems.

• Korean journal of food and cookery science
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• v.30 no.6
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• pp.670-678
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• 2014

The purpose of this study was to find the optimal swelling method and condition for seacucumber to improve its taste and texture to accomodate the rapid increase of consumption. Another purpose was to try to determine an easy way to soak dried sea-cucumber under different conditions, and identify the influence of swelling time on the texture of sea-cucumber, in order to reduce preparation time and provide basic data for easy handling. After boiling or steaming for six different periods including 5, 15, 30 and 60 minutes the texture of the sea-cucumbers were compared, For the additive test, the sea-cucumbers were boiling for 30 minutes period with 4 different additives and the textures were compared, Since the texture is an important characteristic of sea-cucumber, there are many variables that affect this property including the, drying and preservation methods. This study provides basic understanding of the influence of the heating method, time and temperature on the swelling of sea-cucumber for handy use at processing sites.

• Current Research on Agriculture and Life Sciences
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• v.14
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• pp.49-60
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• 1996

In order to evaluate soil factors affecting the growth of Italian poplar, 23 areas planted with Italian poplar were surveyed. These 23 areas were classified into 3 categories, river-side, fallow-land and hill-side. The growth performance and soil factors for each area were investigated. The growth of Italian poplar at river-side was shown to be superior to that of fallow-land and fill-side. The rates of growth for fallow-land and hill-side are decreased by 8% and 21% compared to those of river-side, respectively. This suggests that plantation of Italian poplar at hill-side would not be profitable. Soil conditions of high productive area appeared liquid phase 20%, porosity 45%, water holding capacity 35 - 40%, soil hardness $1kg/cm^3$. pH 6 and rich in organic matter and total nitrogen. The results of factor analysis for soil factors affecting to Italian poplar growth that showed eigenvalue over 1 and communality value over 70% explained factor 1 : liquid phase, porosity and water holding capacity, factor 2 : pH and calcium, and factor 3 : soil hardness. This suggests that physical characteristics of soil is more important than chemical characteristics for Italian poplar growth. Multiregerssion analysis was conducted between diameter growth and soil hardness, liquid phase and calcium. The t-values for each independent variables showed significance at 1 - 10% level, but water holding capacity and pH are not significant. It is supposed that sites suitable to Italian poplar were alluvial plain of sandy loam or part of banking soil, well-ventilating soil, lower soil hardness, apposite soil moisture absorbing with about 100cm of ground water level, plentiful organic matters and total nitrogen and little acidity soil.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.205-209
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• 2018

Purpose: Several studies have shown that magnesium plays an important role in modulating N-methyl-D-aspartate (NMDA)-related seizures by blocking NMDA ion channel receptors. Clinicians usually measure total serum magnesium levels instead of biologically active ionized magnesium levels. We compared the serum ionized magnesium ($iMg^{2+}$) level between epileptic children with and without a history of fever-triggered seizure (FTS). Methods: All epileptic children who visited the outpatient clinic or pediatric emergency department at Korea University Guro Hospital between January 2015 and July 2017 were included. Only epileptic children aged 1-8 years who were newly diagnosed within 2 years were included. Results: There were 12 children with FTS and 16 without FTS. Median serum $iMg^{2+}$ level was 0.93 (0.85-1.14, quartile) mEq/L. Serum $iMg^{2+}$ level was significantly lower in epileptic children with FTS (0.86 mEq/L) compared to those without FTS (1.10 mEq/L) (P=0.005). No difference was noted in clinical variables between the two groups. Lower serum $iMg^{2+}$ level significantly increased the risk of having FTS in epileptic children based on multivariable logistic regression analysis (odds ratio [OR]=0.028). Conclusion: Serum $iMg^{2+}$ level was significantly lower in epileptic children with FTS than in those without FTS. Measurement of biologically active serum $iMg^{2+}$ level could be considered in epileptic children with recurrent FTS. A large-scale prospective study is warranted.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.195-209
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• 2021

According to the 2019-2020 social media usage survey conducted by the Seoul e-commerce center, 5 out of 10 consumers have experienced shopping through social media. The cost of traditional advertising media has been reduced and advertising spending on social media has risen by 74%, indicating that social media is becoming a more important marketing element. While the number of users of social media has increased and corporate marketing activities have increased accordingly, research has been conducted in various aspects of marketing such as user motivation for social media, satisfaction, and purchase intention. There was no subdivided study on the differences in the social media usage frequency of consumers in actual purchasing behavior. This study attempted to identify differences in consumer characteristics by cluster in the agrifood purchase situation by grouping them by type according to the frequency of use of social media for consumers who purchase agri-food online. Product involvement, product need, and online purchase channel Consumer characteristics such as demographic distribution, perceived risk, and eating and lifestyle in each cluster were checked for the three agrifood purchase situations including choice, and types for each cluster were presented. To this end, questionnaire data on the frequency of social media use and online agrifood purchase behavior were collected from 245 consumers, and the validity of the measurement variables was secured through factor analysis and reliability analysis. As a result of cluster analysis according to the frequency of social media use, it was divided into three clusters. The first cluster was a group that mainly used open social media, and the second cluster was a group that used both open and closed social media and online shopping malls; The third cluster was a group with low online media usage overall, and the characteristics of each cluster appeared. Through regression analysis, the effect on product involvement, product need, and purchase channel selection when purchasing agri-food online through each of the three clusters was confirmed through regression analysis. As a result of the regression analysis, the characteristic of cluster 1 in the situation of purchasing agri-food online is a male in his 30s living in a rural area who has no reluctance to purchase agri-food on social media or online shopping malls. The characteristics of cluster 2 are mainly consumers who are interested in purchasing health food, and the consumer characteristics are represented. In the case of cluster 3, when purchasing products online, they purchase after considering quality and price a lot, and the consumer characteristics are represented as people who are more confident in purchasing offline than online. Through this study, it is judged that by identifying the differences in consumer characteristics that appear in the agri-food purchase situation according to the frequency of social media use, it can be helpful in strategic judgments in marketing practice on social media customer targeting and customer segmentation.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Sleep Medicine and Psychophysiology
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• v.26 no.1
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• pp.33-43
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• 2019

Objectives: Bipolar disorder is characterized by repetitive relapses that result in psychosocial dysfunctions. The functioning of bipolar disorder patients is related to the severity of symptoms, quality of sleep, drug compliance, and social support. The purpose of this study was to investigate the association between sociodemographic and clinical factors and functional status in bipolar disorder patients. Methods: A total of 52 bipolar disorder patients participated in the study. The following scales were utilized: Korean version of personal and social performance scale (K-PSP), Korean version of Hamilton rating scale for depression (K-HDRS), Korean version of young mania rating scale (K-YMRS), Korean version of pittsburgh sleep quality index (PSQI-K), Korean version of drug attitude inventory (K-DAI), mood disorders insight scale (MDIS), and multidimensional scale of perceived social support (MSPSS). Results: The K-PSP score showed a negative relationship with K-HDRS score (r = -0.387, p = 0.005), but not with K-YMRS score (r = -0.205, p = 0.145). The K-PSP score showed a negative relationship with global PSQI-K score (r = -0.378, p = 0.005) and overall sleep quality (r = -0.353, p = 0.010). The K-PSP scores were positively associated with the KDAI score (r = 0.409, p = 0.003) and MSPSS score (r = 0.334, p = 0.015). The predictive factors for K-PSP were overall sleep quality and social support from family. Conclusion: Our study showed that depressive symptoms were related to overall function in bipolar disorder. Also, our study suggested that improving sleep quality is important in maintaining functional status. Appropriate social support and positive perception toward the drug may lead to the higher level of functioning. This study is meaningful in that the functional status of bipolar disorder patients is analyzed in a multivariate manner in relation to various variables in psychosocial aspects.

• Korean Journal of Family Social Work
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• no.58
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• pp.5-36
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• 2017

This study aims to empirically analyze whether the women's cognitive attitude toward gender role, which is formed through social norms, enforces the gender division of housework. In this study, 4,435 married women aged 18-59 years from the 5th wave dataset of Korean Longutudinal Survey of Women and Family Data were selected for analysis. Using the Structural Equation Model(SEM), we examine the direct effect of "attitude toward behavior" and "subjective norm" on the domestic working hours and whether those two independent variables, such as "attitude toward behavior" and "subjective norm," influence the mediator variable "Behavior Intention" which in turn affect the dependent variable. The study reveals that "attitude toward the gender division of housework" has a statistically significant direct effect on the domestic working hours as well as an indirect effect operating through "behavior intention." And"subjective norm "has only a statistically significant indirect effect on the domestic working hours, operating through "behavior intention." Despite the fact that many women are now aware that various work-life balance policies are avaliable to mitigate the gender inequality of domestic works, it is proven that the gender division of housework becomes worse. The reason behind this is not only because there exist some problems in implementing the institutions themselves, but also because women's deeply internalized self-perception of gender role based on the traditional patriarchal culture somehow exacerbates the gender division of housework. Hence, in order to instill a progressive change in gender division of housework, it is important for women to try to change the way they perceive the stereotypical gender roles as well as for men to treat women equally.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.