• Journal of The Korean Association For Science Education
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• v.29 no.8
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• pp.990-1010
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• 2009

To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.

• Tuberculosis and Respiratory Diseases
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• v.46 no.2
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• pp.215-228
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• 1999

Background: Sarcoidosis is a chronic granulomatous inflammatory disease of unknown etiology often involving the lungs and intrathoracic lymph nodes. The natural course of sarcoidosis is variable from spontaneous remission to significant morbidity or death. But, the mechanisms causing the variable clinical outcomes or any single parameter to predict the prognosis was not known. In sarcoidosis, the number and the activity of CD4 + lymphocytes are significantly increased at the loci of disease and their oligoclonality suggests that the CD4 + lymphocytes hyperreactivity may be caused by persistent antigenic stimulus. Recently, it has been known that CD4+ lymphocytes can be subdivided into 2 distinct population(Th1 and Th2) defined by the spectrum of cytokines produced by these cells. Th1 cells promote cellular immunity associated with delayed type hypersensitivity reactions by generating IL-2 and IFN-$\gamma$. Th2 cells playa role in allergic responses and immediate hypersensitivity reactions by secreting IL-4, IL-5, and IL-10. CD4+ lymphocytes in pulmonary sarcoidosis were reported to be mainly Th1 cells. IL-12 has been known to play an important role in differentiation of undifferentiated naive T cells to Th1 cells. And, Moller et al. observed increased IL-12 in bronchoalveolar lavage fluid(BALF) in patients with sarcoidosis. So it is possible that the elevated level of IL-12 is necessary for the continuous progression of the disease in active sarcoidosis. This study was performed to test the assumption that IL-12 can be a marker of active pulmonary sarcoidosis. Methods: We measured the concentration of IL-12 in BALF and in conditioned medium of alveolar macrophage(AM) using ELISA(enzyme-linked immunosorbent assay) method in 26 patients with pulmonary sarcoidosis(10 males, 16 females, mean age: $39.8{\pm}2.1$ years) and 11 normal control. Clinically, 14 patients had active sarcoidosis and 12 patients had inactive. Results: Total cells counts, percentage and number of lymhocytes, number of AM and CD4/CD8 lymphocyte ratio in BALF were significantly higher in patients with sarcoidosis than in control group. But none of these parameters could differentiate active sarcoidosis from inactive disease. The concentration of IL-12 in BALF was significantly increased in sarcoidosis patients ($49.3{\pm}9.2$ pg/ml) than in normal control ($2.5{\pm}0.4$ pg/ml) (p<0.001). Moreover it was significantly higher in patients with active sarcoidosis ($70.3{\pm}14.8$ pg/ml) than in inactive disease ($24.8{\pm}3.l$ pg/ml) (p=0.001). Also, the concentration of IL-12 in BALF showed significant correlation with the percentage of AM(p<0.001), percentage(p<0.001) and number of lymphocyte(p<0.001) in BALF, suggesting the close relationship between the level of IL-12 in BALF and the inflammatory cell infiltration in the lungs. Furthermore, we found a significant correlation between the level of IL-12 and the concentration of soluble ICAM-1 : in serum(p<0.001) and BALF (p=0.001), and also between IL-12 level and ICAM-1 expression of AM(p<0.001). The AM from patients with pulmonary sarcoidosis secreted significantly larger amount of IL-12 ($206.2{\pm}61.9$ pg/ml) than those of control ($68.3{\pm}43.7$ pg/ml) (p<0.008), but, there was no difference between inactive and active disease group. Conclusion : Our data suggest that the BALF IL-12 level can be used as a marker of the activity of pulmonary sarcoidosis.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• The Korean Journal of Food And Nutrition
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• v.29 no.6
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• pp.835-848
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• 2016

The purpose of this study was to find efficient and customized tools for delivering the benefit of health functional foods (HFFs). Delivery tools which could influence the impact of advertising were images, explanations of ingredients, diagrams of health benefit, patents, and comments from authority. Six advertisements were developed using these tools: "A": relevant image + explanation of ingredients + scientific diagram of efficacy; "B": relevant image + explanation of ingredients; "C": relevant image; "D": irrelevant image; "E": irrelevant image + explanation of ingredient + patent; "F": irrelevant image + explanation of ingredient + comments from authority. To analyze the consumer perceptions on HFFs and advertisement effects, 300 respondents were requested to answer a questionnaire comprising of the following questions: 5 questions of attitudes (necessity of HFFs, trust in HFFs, gathering information, watching advertisements and trust in advertisement claims) and 6 questions on the 6 developed advertisements (attention, understanding, sufficiency of information, sympathy, trust, and purchase). Scoring was done as per the 5 Likert scale. There was a higher proportion of females and the elderly, as compared to males and youngsters. The overall consumer attitudes were positive. Explanation of ingredients, scientific diagram of health benefit, patents and expert comments were helpful factors in increasing the advertisement evaluation by consumer, but the images were not. Advertisement evaluation of consumer did not differ with gender and age. However, differences were observed between some of the consumer attitudes (necessity of HFFs, trust in HFFs, gathering information and trust in advertisements claim) and advertisement evaluations (attention, understanding, sympathy and purchase). Our results indicate that for consumers utilizing the HFFs, advertisements with concrete tools such as diagrams, patent, and expert comments are more helpful. However, for consumers who do not have interest in HFFs, the scientific information was irrelevant. We believe that to maximize the effect of health information in advertisements, consumers should be segmented, and customized tools for each segment needs to be developed.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.23 no.1
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• pp.5-20
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• 1997

War and poverty depress the consumption of cosmetics, while peace and prosperity encourage their proliferation. With the end of World War II, the US, Europe and Japan witnessed rapid growth of their cosmetic industries. The ending of the Cold War has stimulated the growth of the industry in Eastern Europe. Improved economies, and mass communication are also responsible for the fast growth of the cosmetic industries in many Asian nations. The rapid development of the cosmetic industry in mainland China over the past decade proves that changing economies and political climates can deeply affect the health of our business. In addition to war, economy, political climate and mass communication, factors such as lifestyle, religion, morality and value concepts, can also affect the growth of our industry. Cosmetics are the product of the society. As society and the needs of its people change, cosmetics also evolve with respect to their contents, packaging, distribution, marketing concepts, and emphasis. In many ways, cosmetics mirror our society, reflecting social changes. Until the early 70's, cosmetics in the US were primarily developed for white women. The civil rights movement of the 60's gave birth to ethnic cosmetics, and products designed for African-Americans became popular in the 70's and 80's. The consumerism of the 70's led the FDA to tighten cosmetic regulations, forcing manufacturers to disclose ingredients on their labels. The result was the spread of safety-oriented, "hypoallergenic" cosmetics and more selective use of ingredients. The new ingredient labeling law in Europe is also likely to affect the manner in which development chemists choose ingredients for new products. Environmental pollution, too, can affect cosmetics trends. For example, the concern over ozone depletion in the stratosphere has promoted the consumption of suncare products. Similarly, the popularity of natural cosmetic ingredients, the search of non-animal testing methods, and ecology-conscious cosmetic packaging seen in recent years all reflect the profound influences of our changing world. In the 1980's, a class of efficacy-oriented skin-care products, which the New York Times dubbed "serious" cosmetics, emerged in the US. "Cosmeceuticals" refer to hybrids of cosmetics and pharmaceuticals which have gained importance in the US in the 90's and are quickly spreading world-wide. In spite of regulatory problems, consumer demand and new technologies continue to encourage their development. New classes of cosmeceuticals are emerging to meet the demands of increasingly affluent Asian consumers as we enter the 21st century. as we enter the 21st century.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.223-252
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• 2008

In this study, we attempt to propose a choice-based diffusion model with switching cost, which can be used to forecast the dynamic substitution and competition among previous and new products at both individual-level and aggregate level, especially when market data for new products is insufficient. Additionally, we apply the proposed model to the empirical case of substitution and competition among Analog Cable TV that represents previous fixed charged broadcasting service and Digital Cable TV and Internet Protocol TV (IPTV) that are new ones, verify the validities of our proposed model, and finally derive related empirical implications. For empirical application, we obtained data from survey conducted as follows. Survey was administered by Dongseo Research to 1,000 adults aging from 20 to 60 living in Seoul, Korea, in May of 2007, under the title of 'Demand analysis of next generation fixed interactive broadcasting services'. Conjoint survey modified as follows, was used. First, as the traditional approach in conjoint analysis, we extracted 16 hypothetical alternative cards from the orthogonal design using important attributes and levels of next generation interactive broadcasting services which were determined by previous literature review and experts' comments. Again, we divided 16 conjoint cards into 4 groups, and thus composed 4 choice sets with 4 alternatives each. Therefore, each respondent faces 4 different hypothetical choice situations. In addition to this, we added two ways of modification. First, we asked the respondents to include the status-quo broadcasting services they subscribe to, as another alternative in each choice set. As a result, respondents choose the most preferred alternative among 5 alternatives consisting of 1 alternative with current subscription and 4 hypothetical alternatives in 4 choice sets. Modification of traditional conjoint survey in this way enabled us to estimate the factors related to switching cost or switching threshold in addition to the effects of attributes. Also, by using both revealed preference data(1 alternative with current subscription) and stated preference data (4 hypothetical alternatives), additional advantages in terms of the estimation properties and more conservative and realistic forecast, can be achieved. Second, we asked the respondents to choose the most preferred alternative while considering their expected adoption timing or switching timing. Respondents are asked to report their expected adoption or switching timing among 14 half-year points after the introduction of next generation broadcasting services. As a result, for each respondent, 14 observations with 5 alternatives for each period, are obtained, which results in panel-type data. Finally, this panel-type data consisting of $4{\ast}14{\ast}1000=56000$observations is used for estimation of the individual-level consumer adoption model. From the results obtained by empirical application, in case of forecasting the demand of new products without considering existence of previous product(s) and(or) switching cost factors, it is found that overestimated speed of diffusion at introductory stage or distorted predictions can be obtained, and as such, validities of our proposed model in which both existence of previous products and switching cost factors are properly considered, are verified. Also, it is found that proposed model can produce flexible patterns of market evolution depending on the degree of the effects of consumer preferences for the attributes of the alternatives on individual-level state transition, rather than following S-shaped curve assumed a priori. Empirically, it is found that in various scenarios with diverse combinations of prices, IPTV is more likely to take advantageous positions over Digital Cable TV in obtaining subscribers. Meanwhile, despite inferiorities in many technological attributes, Analog Cable TV, which is regarded as previous product in our analysis, is likely to be substituted by new services gradually rather than abruptly thanks to the advantage in low service charge and existence of high switching cost in fixed charged broadcasting service market.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Genetic Medicine
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• v.7 no.2
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• pp.125-132
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• 2010

Purpose: Preimplantation genetic diagnosis (PGD), also known as embryo screening, is a pre-pregnancy technique used to identify genetic defects in embryos created through in vitro fertilization. PGD is considered a means of prenatal diagnosis of genetic abnormalities. PGD is used when one or both genetic parents has a known genetic abnormality; testing is performed on an embryo to determine if it also carries the genetic abnormality. The main advantage of PGD is the avoidance of selective pregnancy termination as it imparts a high likelihood that the baby will be free of the disease under consideration. The application of PGD to genetic practices, reproductive medicine, and genetic counseling is becoming the key component of fertility practice because of the need to develop a custom PGD design for each couple. Materials and Methods: In this study, a survey on the contents of genetic counseling in PGD was carried out via direct contact or e-mail with the patients and specialists who had experienced PGD during the three months from February to April 2010. Results: A total of 91 persons including 60 patients, 49 of whom had a chromosomal disorder and 11 of whom had a single gene disorder, and 31 PGD specialists responded to the survey. Analysis of the survey results revealed that all respondents were well aware of the importance of genetic counseling in all steps of PGD including planning, operation, and follow-up. The patient group responded that the possibility of unexpected results (51.7%), genetic risk assessment and recurrence risk (46.7%), the reproduction options (46.7%), the procedure and limitation of PGD (43.3%) and the information of PGD technology (35.0%) should be included as a genetic counseling information. In detail, 51.7% of patients wanted to be counseled for the possibility of unexpected results and the recurrence risk, while 46.7% wanted to know their reproduction options (46.7%). Approximately 96.7% of specialists replied that a non-M.D. genetic counselor is necessary for effective and systematic genetic counseling in PGD because it is difficult for physicians to offer satisfying information to patients due to lack of counseling time and specific knowledge of the disorders. Conclusions: The information from the survey provides important insight into the overall present situation of genetic counseling for PGD in Korea. The survey results demonstrated that there is a general awareness that genetic counseling is essential for PGD, suggesting that appropriate genetic counseling may play a important role in the success of PGD. The establishment of genetic counseling guidelines for PGD may contribute to better planning and management strategies for PGD.

• Clinical and Experimental Pediatrics
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• v.48 no.3
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• pp.298-305
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• 2005

Purpose : We performed this study to evaluate the mean serum levels of insulin-like growth factor (IGF)-I, insulin-like growth factor binding protein(IGFBP)-2 and IGFBP-3 in healthy Korean children according to age and sex. Methods : Ninety two healthy children, consisting of 42 boys and 50 girls, were classified into five groups according to age : neonate; infancy; early childhood; late childhood; and adolescence. We measured serum levels of IGF-I, IGFBP-2 and IGFBP-3 by enzyme-linked immunosorbent assay(ELISA) and analysed the serum levels according to sex and age group. Results : For boys, the mean serum levels of IGF-I(ng/mL) in neonate, infancy, early childhood, late childhood and adolescence were $41.1{\pm}3.6$, $70.9{\pm}33.7$, $103.5{\pm}97.2$, $89.8{\pm}46.5$ and $51.4{\pm}27.8$, respectively. Those of IGFBP-2(ng/mL) were $8.2{\pm}3.4$, $5.8{\pm}0.4$, $9.3{\pm}4.0$, $9.5{\pm}1.1$ and $7.0{\pm}0.5$, respectively. Those of IGFBP-3(ng/mL) were $559.2{\pm}215.2$, $1,333.3{\pm}692.5$, $2,254.6{\pm}1,513.8$, $2,447.1{\pm}1,464.2$, $1,533.6{\pm}807.4$, respectively. For girls, the mean serum levels of IGF-I(ng/mL) according to five age groups were $53.3{\pm}9.5$, $99.3{\pm}45.8$, $69.6{\pm}51.1$, $106.2{\pm}67.0$ and $145.1{\pm}127.8$, respectively. Those of IGFBP-2 (ng/mL) were $9.1{\pm}7.4$, $5.3{\pm}0.9$, $6.9{\pm}2.0$, $10.5{\pm}3.0$ and $7.9{\pm}1.3$, respectively. Those of IGFBP-3(ng/mL) were $858.2{\pm}433.4$, $1,834.8{\pm}851.3$, $1,404.3{\pm}570.2$, $2,203.5{\pm}899.4$ and $2,029.3{\pm}1,316.7$, respectively. There were significant positive correlations observed between IGF-I and IGFBP-3 levels(r=0.589, P=0.000). Conclusion : IGF-I and IGFBP-3 levels increased as children get older. The peak level of IGFBP-3 was observed in late childhood for both boys and girls, suggesting a current trend of children reaching peak growth velocity before adolescence. The IGFBP-2 level was higher in neonates compare to infancy, suggesting that IGFBP-2 is an important substance for fetal growth.

• Journal of the East Asian Society of Dietary Life
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• v.13 no.5
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• pp.391-407
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• 2003

This study was conducted to investigate the factors affecting the born density of food and nutrition major and non-major university students in Seoul area. Data for food habits, dietary and health-related behavior were obtained by self administered questionnaires. BQI(bone quality index) of the subjects were measured by Quantitative Ultrasound (QUS). The results are summarized as follows: The average height, weight, BMI and osteopenia percentage of the major and non-major male and female student were l74.49cm, 67.05kg, 21.96 and 22.0%; l74.34cm, 65.98kg, 21.69 and 11.8%; l60.76cm, 54.48kg, 21.07 and 40.0%; l61.30cm, 54.22kg, 20.84 and 40.2%, respectively. The BQI of the major and non-major subjects were 108.07 and 110.47 in male student group, and 89.13, 88.18 in female student group, respectively. The T-score and Z-score of bone density of the subjects were not significantly different. Weight and BMI were positively related with BQI in male and female group but the relationship with BMI tended to be stronger in non-major female group than other groups. BQI was positively affected by exercise time, favorite food, and intake of seafood and tea in major and non-major male student group. One-side eating habit and intake of instant foods were negatively related with BQI in both male groups. In major and non-major female student group, exercise time, meal regularity, favorite food, amount of meal, intake of tofu were related with BQI positively and intake of tea and/or meats negatively. The result of this study revealed that desirable food habits, dietary behavior and health-related lifestyle may have a beneficial effect on bone density. They need practically and systematically organized nutrition education on optimum body weight, good eating habits, weight bearing exercise and intakes of good quality nutrient for higher bone density level.