• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2721-2728
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• 2012

Background: There are limited data evaluating the cost-effectiveness of gastric cancer screening using endoscopy or upper gastrointestinal x-ray in the general population. Objective: To evaluate the cost-effectiveness of population-based screening for gastric cancer in South Korea by decision analysis. Methods: A time-dependent Markov model for gastric cancer was constructed for healthy adults 30 years of age and older, and a deterministic sensitivity analysis was performed. Cost-utility analysis with multiple strategies was conducted to compare the costs and effects of 13 different screening alternatives with respect to the following eligibility criteria: age at the beginning of screening, screening interval, and screening method. The main outcome measurement was the incremental cost-effectiveness ratio. Results: The results revealed that annual endoscopic screening from ages 50-80 was the most cost-effective for the male population. In the females, biennial endoscopy screening from ages 50-80 was calculated as the most cost-effective strategy among the 12 screening alternatives. The most cost-effective screening strategy may be adjustable according to the screening costs and the distribution of cancer stage at screening. The limitation was that effectiveness data were obtained from published sources. Conclusions: Using the threshold of $19,162 per quality-adjusted life year on the basis of the Korean gross domestic product (2008), as suggested by the World Health Organization, endoscopic gastric cancer screening starting at the age of 50 years was highly cost-effective in the Korean population. The national recommendation for gastric cancer screening should consider the starting age of screening, the screening interval, and the screening modality.

• Journal of the Korean Society of Food Culture
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• v.9 no.4
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• pp.379-394
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• 1994

The purpose of this study was to identify the correlation between the factors influencing on housewives' decision-making processes of organic foods and the relating variables, and the 5 stages of decision-making processes of the EBK model is utilized in this study. The sample was selected from 411 housewives living in Seoul from 1st of September through 20th of September, 1993. Frequency, Percentage, Mean, Factor analysis, One-way ANOVA, Duncan's multiple range test, t-Test, Correlation, Multiple regression analysis and Path analysis were measured. Major results are as follows: 1. Purchasing motivation of the organic foods were in order of the health care, nutritive value and taste care. 2. The major informations source for the knowledge of organic foods were in order of TV/radio, newspaper/magazine, recommendations informations and advice through a family/friends/acquaintances. 3. Evalution criteria in shopping of organic foods, the total degree of consideration over the purchasing factors of organic foods was fairly high level: consumers thought much of the sanitation/freshness, nutritive value and the food safety. In this regard opinion leaders was dominantly mass media. Consumers have a tendency to purchase organic foods in consideration of their children and husband. 4. Major place to purchase organic foods are super markets and department stores. And When shopping organic foods, housewives by all means confirm the check points in their own mind, which were expiry date, manufactured date and packing condition, but unexpectedly manufactured company was out of concern. 5. Housewives usually satisfy with decision after purchasing organic foods, while they were fairly unsatisfied with the price, quality, incomplete description for ingredients and manufactured date. 6. The variables influencing to the sincerity when selecting the most desired organic foods is how be cares about the natural freshness of the foods and the types of residents in order. Another interesting tendency is the richer they are very considerate to decide. It is to say the people who cares more about the natural freshness is the sincerer when making decision and also the class who lives in the apartment house enjoying high income do not easily accept the product quality.

• Journal of Korea Water Resources Association
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• v.46 no.10
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• pp.977-987
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• 2013

This study aims to develop a multiple criteria decision making (MCDM) approach for flood vulnerability assessment which considers uncertainty. The flood vulnerability assessment procedure consists of three steps: (1) use the Delphi process to determine the criteria and their corresponding weights-the adopted criteria represent the social, economic, and environmental circumstances related to floods, (2) construct a fuzzy data matrix for the flood vulnerability criteria using fuzzification and standardization, and (3) set priorities based on the number of assessed vulnerabilities. This study uses a modified fuzzy TOPSIS method based on ${\alpha}$-level sets which considers various uncertainties related to weight derivation and crisp data aggregation. Further, Spearman's rank correlation analysis is used to compare the rankings obtained using the proposed method with those obtained using fuzzy TOPSIS with fuzzy data, TOPSIS, and WSM methods with crisp data. The fuzzy TOPSIS method based on ${\alpha}$-cut level sets is found to have a higher correlation rate than the other methods, and thus, it can reduce the difference of the rankings which uses crisp and fuzzy data. Thus, the proposed flood vulnerability assessment method can effectively support flood management policies.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.131-158
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• 2009

We examine the effects of environmental or organizational factors on the performance of TLOs(technology transfer offices) in the PRIs(Public research institutes) using SFA(Stochastic Frontier Analysis), a technique for estimating the efficiency of DMUs(decision making units). In SFA, independent variables are assumed to determine the efficient production technique(production frontier) or affect the efficiency of DMUs. Previous researchs show that input variables such as number of personnel, R&D expenditure affect the production frontier while environmental or organizational variables affect the efficiency. We tried to estimate various types of models to find out whether environmental or organizational variables affect output variables differently from the previous research. Main empirical findings are as follows. First, R&D expenditure tends to increase all output variables considered. Second, environmental factors such as type of institutions and location of institutions affect the level of outputs. Third, organizational factors such as reward system for technology transfer also appear to affect the output variables. Fourth, environmental or organizational variables affect the production frontier directly rather than affect the efficiency of DMUs. Lastly, the efficiency of each DMU appear to be 1 or near to 1. Since almost all DMUs are equally efficient, it may not be effective to evaluate technology transfer activities of PRIs by efficiency criteria. We believe that this research should be complemented by additional data. More general types of production function need to be considered, and new techniques with concepts like output distance functions need to be developed to analyse multiple outputs simultaneously.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.3 no.1
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• pp.86-93
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• 2003

Background : The SCL began screening of newborns and high risk group blood spots with tandem mass spectrometry (MS/MS) in April 2001. Our goal was to determine approximate prevalence of metabolic disorders, optimization of decision criteria for estimation of preventive effect with early diagnosis. This report describes the ongoing effort to identify more than 30 metabolic disorders by MS/MS in South Korea. Methods : Blood spot was collected from day 2 to 30 (mostly from day 2 to 10) after birth for newborn. Blood spot of high risk group was from the pediatric patients in NICU, developmental delay, mental retardation, strong family history of metabolic disorders. One punch (3.2 mm ID) of dried blood spots was extracted with $150{\mu}L$ of methanol containing isotopically labelled amino acids (AA) and acylcarnitines (AC) internal standards. Butanolic HCl was added and incubated at $65^{\circ}C$ for 15 min. The butylated extract was introduced into the inlet of MS/MS. Neutral loss of m/z 102 and parent ion mode of m/z 85 were set for the analyses of AA and AC, respectively. Diagnosis was confirmed by repeating acylcarnitine profile, urine organic acid and plasma amino acid analysis, direct enzyme assay, or molecular testing. Results : Approximately 31,000 neonates and children were screened and the estimated prevalence (newborn/high risk group), sensitivity, specificity and recall rate amounted to 1:2384/1:2066, 96.55%, 99.98%, and 0.73%, respectively. Confirmed 28 (0.09%) multiple metabolic disorders (newborn/high risk) were as follows; 13 amino acid disorders [classical PKU (3/4), BH4 deficient-hyperphenylalaninemia (0/1), Citrullinemia (1/0), Homocystinuria (0/2), Hypermethioninemia (0/1), Tyrosinemia (1/0)], 8 organic acidurias [Propionic aciduria (2/1), Methylmalonic aciduria (0/1), Isovaleric aciduria (1/1), 3-methylcrotonylglycineuria (1/0), Glutaric aciduria type1 (1/0)], 7 fatty acid oxidation disorders [LCHAD def. (2/2), Mitochondrial TFP def. (0/1), VLCAD def. (1/0), LC3KT def. (0/1). Conclnsion : The relatively normal development of 10 patients with metabolic disorders among newborns (except for the expired) demonstrates the usefulness of newborn screening by MS/MS for early diagnosis and medical intervention. However, close coordination between the MS/MS screening laboratory and the metabolic clinic/biochmical geneticists is needed to determine proper decision of screening parameters, confirmation diagnosis, follow-up scheme and additional tests.

• Journal of Korean Academy of Nursing
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• v.29 no.5
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• pp.1087-1102
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• 1999

Diabetes mellitus, a universal and prevalent chronic disease, is projected to be one of the most formidable worldwide health problems in the 21st century. For those living with diabetes, there is a need for self-care skills to manage a complex medical regimen. Self-efficacy which refers to one's belief in his/her capability to monitor and perform the daily activities required to manage diabetes has be found to be related to self-care. The concept of self-efficacy comes from social cognitive theory which maintains that cognitive mechanism mediate the performance of behavior. The literature cites several research studies which show a strong relationship between self-efficacy and self-care behavior. Meta-analysis is a technique that enables systematic review and quantitative integration of the results from multiple primary studies that are relevant to a particular research question. Therefore, this study was done using meta-analysis to quantitatively integrate the results of independent research studies to obtain numerical estimates of the overall effect of a self-efficacy with diabetic patient on self-care behaviors. The research proceeded in three stages : 1) literature search and retrieval of studies in which self-efficacy was related to self-care, 2) coding, and 3) calculation of mean effect size and data analysis. Seventeen studies which met the research criteria included study population of adults with diabetes, measures of self-care and measures of self-efficacy as a predictive variable. Computation of effect size was done on DSTAT which is a statistical computer program specifically designed for meta-analysis. To determine the effect of self-efficacy on self-care practice homogeneity tests were conducted. Pooled effect size estimates, to determine the best subvariable for composite variables, metabolic control variables and component of self-efficacy and self-care, indicated that the effect of self-efficacy composite on self-care composite was moderate to large. The weighted mean effect size of self-efficacy composite and self-care composite were ＋.76 and the confidence interval was from ＋.66 to ＋.86 with the number of subjects being 1,545. The total for this meta-analysis result showed that the weighted mean effect sizes ranged from ＋.70 to ＋1.81 which indicates a large effect. But since reliabilities of the instruments in the primary studies were low or not stated, caution must be applied in unconditionally accepting the results from these effect sizes. Meta-analysis is a useful took for clarifying the status of knowledge development and guiding decision making about future research and this study confirmed that there is a relationship between self-efficacy and self-care in patients with diabetes. It, thus, provides support for nurses to promote self-efficacy in their patients. While most of the studies included in this meta-analysis used social cognitive theory as a framework for the study, some studies use Fishbein & Ajzen's attitude model as a model for active self-care. Future research is needed to more fully define the concept of self-care and to determine what it is that makes patients feel competent in their self-care activities. The results of this study showed that self-efficacy can promote self-care. Future research is needed with experimental design to determine nursing interventions that will increase self-efficacy.

• Journal of Korean Port Research
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• v.10 no.2
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• pp.17-29
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• 1996

It is required to consider pricing and non-pricing factors and external economy in order to achieve the objects of physical distribution system in a port. Recently, among the three factors, much attention has been paid to non-pricing factor in the system. Although physical distribution service in a port(PDSP)has been frequently mentioned in documents and literature related to port and shipping studies, few study on it has not been systematically and scientifically made due to the following problems; $\circ$ there are not proper criteria to evaluate level and quality of PDSP and as a result it is difficult to set up a unified standard for doing so. $\circ$ algorithms to evaluate problems with complex and ambiguous attributes and multiple levels in PDSP are not available. This thesis aims to establish a paradigm to evaluate PDSP and to abvance existing decision making methods to deal with complex and ambiguous problems in PDSP. To tackle the first purpose, extensive and thorough literature survey was carried out on general physical distribution service, which is a corner stone to handle PDSp. In addition, through interviews and questionnaire to the expert, it have extracted 82 factors of physical distribution service in a port. They have been classified into 6 groups by KJ method and each group defined by the expert's advice as follows; a. Potentiality b. Exactness c. safety d. Speediness e. Convenience f. Linkage Prior to the service evaluation, many kinds of its attributes must be identified on the basis of rational decision owing to complexity and ambiguity inherent in PDSP. An analytical hierarchy process (AHP) is a method to evaluate them but it is not applicable to PDSP that have property of non-additivity and overlapped attributes. Therefore, probablility measure can not be used to evaluate PDSP but fuzzy measure is required. Hierarchical fuzzy integral method, which is merged AHP with fuzzy measure, is also not effective method to evaluate attributes because it has vary complicated way to calculate fuzzy measure identification coefficient of attributes. A new evaluation algorithm has been introduced to solve problems with multi-attribute and multi-level hierarchy, which is called hierarchy fuzzy process(HFP).Analysis on ambiguous aspects of PDSP under study which is not easy to be defined is prerequisite to evaluate it. HFP is different from algorithm existed in that it clarified the relationship between fuzzy measure and probability measure adopted in AHP and that it directly calculates the family of fuzzy measure from overlapping coefficient and probability measure to treat and evaluate ambiguous and complex aspects of PDSP. A new evaluation algorithm HFP was applied to evaluate level of physical distribution service in the biggest twenty container port in the world. The ranks of the ports are as follows; 1. Rotterdam Port, 2. Hamburg Port, 3. Singapore Port, 4. Seattle Port, 5. Yokohama Port, 6. Long beach Port, 7. Oakland Port, 8. Tokyo Port, 9. Hongkong Port, 10. Kobe Port, 11. Los Angeles Port, 12. New york Port, 13. Antwerp Port, 14. Felixstowe Port, 15. Bremerhaven Port, 16. Le'Havre Port, 17. Kaoshung Port, 18. Killung Port, 19. Bangkok Port, 20. Pusan Port

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.