• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.3
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• pp.165-177
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• 2013

The purpose of this study was to examine the efficiency of senior welfare centers and the cause of differences among senior welfare centers in that regard, and to investigate influential factors for the differences in efficiency and the size of the influence of the factors. What methods would be effective at assessing the efficiency of senior welfare centers by taking into account their circumstances was reviewed, andpost-hoc analyses were made by using data envelopment analysis(DEA) and DAE/AP Modified prosthetic which were useful tools to evaluate relative efficiency. After 20 senior welfare centers located in Gyeonggi-do were selected, their yearly operating data of 2009 were utilized. The purpose of this study was to examine the efficiency of senior welfare centers. The evaluation data released by the Gyeonggi Welfare Foundation were analyzed by DEA, which is one of nonparametric statistics, and it was possible to obtain significant results on the regional operating efficiency of social welfare centers in 14 metropolitan cities and provinces, the causes and degree of their inefficiency and what areas one could refer to. As the data for the counties were utilized in this study, it's not quite possible to produce accurate results on the relative efficiency of senior welfare centers, but this study could be said to be of significance in that it suggested how to evaluate the overall operating efficiency of senior welfare centers in the counties involving the degree of their operating inefficiency, what improvements should be made and what reference groups there might be and provided information on the usefulness of the DEA model.

• Korean Business Review
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• v.13
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• pp.133-157
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• 2000

This paper investigate to construct a new strategic trading model which contains the broker and overconfident informed trader. Assuming more favorable situation for the broker, this paper construct a two period model. At period I overconfident informed trader and liquidity traders participate to trade. At this time the broker does not execute transaction of his own account. he only transfer customer's order by commission. At period 2, the broker identifies informed trade of previous period and he execute the trade of his own account with liquidity traders. The effects of overconfidence to the expected transaction volume and expected transaction profit, and price variability are summarized as follows: (i) As the degree of overconfidence increases, the expected transaction volume of informed trader increases. Under the restriction of moderate degree of overconfidence, it also increases the expected transaction volume of broker. In sum, overconfidence behavior of informed trader increases the expected transaction volume. (ii) As the degree of overconfidence increases, the both expected profit of informed trader and broker decrease. (iii) As the degree of overconfidence increases, unconditional variances of price for each periods increase. And as the degree of overconfidence increases, the informativeness of prices for each period increase. Finally, some limitations of this paper and direction for further research were suggested.

• Korean Security Journal
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• no.42
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• pp.59-85
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• 2015

The purpose of this study is to establish the structural relationship among organizational ethical climate, job satisfaction, and organizational citizenship behavior. It was intended for the private security guards who work in the security companies in Seoul and Gyeonggi from Jan. 1st, 2014 to Apr. 1st, 2014 to achieve the purpose like this. Purposive sampling was used as the sampling method according to this and sampling of 400 persons was done. However, the samples of 372 persons were finally used in the analysis through the process to check faithless answers, double answers, and abnormal data. The collected data was analyzed according to the purpose of the study by utilizing STATA 13.0 and AMOS 17.0. And for statistic techniques, frequency analysis, descriptive analysis, multivariate normality, confirmatory factor analysis(CFA), Pearson's correlation analysis, and structural equation model analysis were carried out. The conclusion gotten from this study through the data analyses according to the methods and procedure like this is as follow: First, organizational ethical climate has found to have the positive effect on job satisfaction(Non-standard $B=1.427^{***}$). That is, it can be interpreted that organizational ethical climate positively affects superiors, fellow employees, pay, current duties, and chances of promotion. Second, job satisfaction has found not to have the significant effect on organizational citizenship behavior. That is, it can be interpreted that job satisfaction does not affect altruism, conscience, and participation behavior. Third, organizational ethical climate has found to have the positive effect on organizational citizenship behavior (Non-standard $B=.361^{***}$). That is, it can be interpreted that organizational ethical climate positively affects altruism, conscience, and participation behavior. Fourth, the relationship between organizational ethical climate and organizational citizens has found that there is no any indirect effect in the bootstrapping estimation result to establish the indirect effect of job satisfaction. Fifth, the relationship between organizational ethical climate and job satisfaction has found that there are the moderating effects in the analytical result of the moderating effects of person-organization fit. That is, the effects of organizational ethical climate on job satisfaction have found that the groups with higher person-organization fit are more positive than those with lower person-organization fit. Sixth, the relationship between job satisfaction and organizational citizenship behavior has found that there are moderating effects in the analytical result of person-organization fit. That is, the effects of job satisfaction on organizational citizenship behavior have found that the groups with higher person-organization fit are more positive than those with lower person-organization fit.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.5 no.2
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• pp.174-180
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• 2002

Purpose: Ursodeoxycholic acid (UDCA) is known to decrease hepatic injury by promoting the biliary secretion of retained toxic endogenous bile acids in hepatobiliary diseases complicated by total parenteral nutrition (TPN). However, most studies have focused on treatment for complications after TPN. We investigated the preventive role of early administration of UDCA in TPN-induced hepatobiliary complications by a randomized, double-blind, placebo-controlled trial. Methods: Between May 2000 and May 2002, thirteen patients, who were given TPN more than 10 days in the hospital, were assigned randomly to two groups. One was the case group (7 patients) who were given UDCA simultaneously with TPN regimen, and the other, the control group (6 patients) who were given placebo. Their age ranged from 1 day to 13 years. They were affected with diseases impossible for enteral nutrition, such as prematurity, cerebral palsy, chronic diarrhea, anorexia nervosa, pancreatitis, and cyclic vomiting. The duration of TPN ranged from 10 to 70 days. Hematologic parameters including liver function test were measured at regular intervals, and the duration, composition, administration rate, total calorie of TPN were recorded. The serum levels of total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase were compared between groups after cessation of the study. Results: The autoregressive coefficient of the control group was 0.4419 (p=0.0651) in bilirubin, -0.0431 (p=0.7923) in AST, 0.2398 (p=0.2416) in ALT, and 0.2459 (p=0.1922) in alkaline phosphatase by mixed procedure model when the parameters were referred to the case group. Conclusion: The serum level of total bilirubin did not increase in comparison with that of the control group, but statistically insignificant, when both TPN and UDCA were administered simultaneously from the beginning.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.135-151
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• 2021

This research attempts to analyze the factors influencing the entrepreneurial intention of employees in manufacturing field. In particular, key factors of entrepreneurship and personal characteristics explain a significant association with the intention to start-up. And study whether R&D support from public enterprise adjusts intention to entrepreneurial Intention. This study conducted a online survey on 292 small and medium-sized enterprise manufacturing employees in May 2020. Using linear regression model and binary logistic model. The main study results are the following: First, among the key factors(innovativeness, proactiveness, risk-taking) of entrepreneurship, proactiveness hardly influenced the opportunity competency. Second, among the factors(risk-taking propensity, locus of control, tolerance for ambiguity) of personal characteristics, locus of control hardly influenced the opportunity competency. Third, opportunity competency(opportunity recognition and opportunity evaluation) had positive influence to entrepreneurial intention. Fourth, the study investigated the mediated effect of opportunity competency. The result showed that among the factors of entrepreneurship and personal characteristics, only two factors that are proactiveness and locus of control were not mediated by opportunity competency. and opportunity evaluation was acted as a mediator between proactiveness and entrepreneurial Intention, compared with opportunity recognition. Lastly, public enterprise's R&D supporting moderated the entrepreneurial intention). Based on the result, the study showed that first, the key factors of entrepreneurship except for proactiveness and personal characteristics(risk-taking propensity, locus of control, tolerance for ambiguity) except for locus of control affect the intention to start-up, repeatedly. This results are explained that employees have not started a business yet. Second, research on start-up suggests the need to analyze factors differentiated before and after the start-ups. Based on the results, entrepreneurship and personal characteristics show that study on the effects of start-up intentions should be carried out before and after the actual start-up takes place, and can be used as effective data in policies to promoting start-ups in manufacturing field.

• Journal of Climate Change Research
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• v.1 no.1
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• pp.31-50
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• 2010

The Arctic climate change for the Last Glacial Maximum(LGM) occurred at 21,000 years ago (21ka) was investigated using simulation results of atmosphere-ocean coupled models from the second phase of the Paleoclimate Modelling Intercomparison Program(PMIP2). In the analysis, we used seven models, the NCAR CCSM of USA, ECHAM3-MPIOM of German Max-Planxk Institute, HadCM3M2 of UK Met Office, IPSL-CM4 of France Laplace Institute, CNRM-CM3 of France Meteorological Institute, MIROC3.2 of Japan CCSR at University of Tokyo, and FGOALS of China Institute of Atmospheric Physics. All the seven models reproduces the Arctic climate features found in the present climate at 0ka(pre-industrial time) in a reasonable degree in comparison to observations. During the LGM, the atmospheric $CO_2$ concentration and other greenhouse gases were reduced, the ice sheets were expanded over North America and northern Europe, the sea level was lowered by about 120m, and orbital parameters were slightly different. These boundary conditions were implemented to simulated LGM climate. With the implemented LGM conditions, the biggest temperature reduction by more than $24^{\circ}C$ is found over North America and northern Europe owing to ice albedo feedback and the change in lapse rate by high elevation. Besides, the expansion of ice sheets leads to the marked temperature reduction by more then $10^{\circ}C$ over the Arctic Ocean. The temperature reduction in northern winter is larger than in summer around the Arctic and the annual mean temperature is reduced by about $14^{\circ}C$. Compared to low mid-latitudes, the temperature reduction is much larger in high northern altitudes in the LGM. This results mirror the larger warming around the Artic in recent century. We could draw some information for the future under global warming from the knowledge of the LGM.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.175-190
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• 2018

Recently, policy of the marine advanced countries were switched from the preemption using ocean to post-project development. In this study, we suggest improvement and the pending issues when are deducted to the database of the marine spatial information is constructed over the GIS system for the Korean Marine Spatial Planning (KMSP). More than 250 spatial information in the seas of Korea were processed in order of data collection, GIS transformation, data analysis and processing, data grouping, and space mapping. It's process had some problem occurred to error of coordinate system, digitizing process for lack of the spatial information, performed by overlapping for the original marine spatial information, and so on. Moreover, solution is needed to data processing methods excluding personal information which is necessary when produce the spatial data for analysis of the used marine status and minimized method for different between the spatial information based GIS system and the based real information. Therefore, collection and securing system of lacking marine spatial information is enhanced for marine spatial planning. it is necessary to link and expand marine fisheries survey system. It is needed to the marine spatial planning. The marine spatial planning is required to the evaluation index of marine spatial and detailed marine spatial map. In addition, Marine spatial planning is needed to standard guideline and system of quality management. This standard guideline generate to phase for production, processing, analysis, and utilization. Also, the quality management system improve for the information quality of marine spatial information. Finally, we suggest necessity need for the depths study which is considered as opening extension of the marine spatial information and deduction on application model.

• Pediatric Infection and Vaccine
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• v.26 no.1
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• pp.1-10
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• 2019

Purpose: We investigated the distribution and antimicrobial resistance of pneumococcal isolates from hospitalized children at Asan Medical Center for recent 4 years, and aimed to recommend proper choice of empirical antibiotics for pneumococcal infection. Methods: From March 2014 to May 2018, children admitted to Asan Medical Center Childrens' Hospital with pneumococcal infection were subjected for evaluation of minimal inhibitory concentration (MIC) for ${\beta}-lactams$ and macrolide antibiotics. Patient's age, underlying disease, gender were retrospectively collected. Using Monte Carlo simulation model and MIC from our study, we predicted the rate of treatment success with amoxicillin treatment. Results: Sixty-three isolates were analyzed including 20.6% (n=13) of invasive isolates, and 79.4% (n=50) of non-invasive isolates; median age were 3.3 years old, and 87.3% of the pneumococcal infections occurred to children with underlying disease. Overall susceptibility rate was 49.2%, 68.2%, and 74.6% for amoxicillin, parenteral penicillin, and cefotaxime respectively. 23.8% and 9.5% of the isolates showed high resistance for amoxicillin, and cefotaxime. Only 4.8% (n=3) were susceptible to erythromycin. Monte Carlo simulation model revealed the likelihood of treatment success was 46.0% at the dosage of 90 mg/kg/day of amoxicillin. Conclusions: Recent pneumococcal isolates from pediatric patients with underlying disease revealed high resistance for amoxicillin and cefotaxime, and high resistance for erythromycin. Prudent choice of antibiotics based on the local data of resistance cannot be emphasized enough, especially in high risk patients with underlying disease, and timely vaccination should be implemented for prevention of the spread of resistant strains.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.57-70
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• 2021

As COVID-19 spreads, people's interest in smart learning that can do non-face-to-face learning is increasing nowadays. In this study, we aim to empirically analyze how users' thoughts on COVID-19 and the information quality and system quality of smart learning systems affect users' acceptance of smart learning and examine the effect of perceived sensitivity and severity of COVID-19 on the satisfaction and use of smart learning through concerns about the risk of transmission. In addition, we examined the influence of information quality composed of content quality and interaction quality and system quality composed of system accessibility and functionality on the use of smart learning through user satisfaction. To verify the validity of the proposed model, we conducted a survey on 334 users with experience in using smart learning, and performed the analysis using Smart PLS 3.0. According to the analysis results, among information quality and system quality, only functionality has a positive (+) effect on the satisfaction of smart learning, and satisfaction has a positive (+) effect on the usage behavior. However, it is found that accessibility among system quality do not affect satisfaction, and concern about the risk of transmission has a negative effect on satisfaction. This study can provide meaningful guidelines to researchers when researching smart learning to support students' learning in a pandemic situation of a new infectious disease, such as COVID-19. It will also be able to provide useful implications for educational institutions and companies related to smart learning.