• Journal of Radiation Protection and Research
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• v.38 no.2
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• pp.106-118
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• 2013

In this paper, we analyzed the Cognition of irradiated food and its relation with self-efficacy. The most important variables described behaviors based on health choices compared with the choice to choose irradiated food items. According to the survey, 33.1% of respondents said that the reason why irradiated food is considered to be a health risk is because "radiation is dangerous". 27.9% of respondents answered that "eating irradiated food is like eating a radioactive substance", 21.1% said radiated food is comparable to a "genetic variation in food" while 10.1% said "food goes bad during the irradiation process". On this basis, it is reasonable to conclude that respondents have a misunderstanding of irradiated food without reference to the general theory of irradiated knowledge. In this respect, it would be helpful to provide education showing that irradiated food is not related to eating harmful or genetically modified food to help high school students create informed opinions of irradiated food. In terms of relevance with health-specific self-efficacy, experience of acquiring information about irradiated food was marked at r=0.148 (p<0.01), experience of purchasing irradiated food was marked at r=0.077 (p<0.05), experience of eating irradiated food was marked at r=0.113 (p<0.01) while knowledge of irradiated food, attitude towards irradiated food and behavior was marked at r=0.103 (p<0.01), r=0.076 (p<0.05) and r=0.105 (p<0.01) respectively. This shows that self-efficacy is high when one has experience of acquiring information about irradiated food, purchasing or eating irradiated food resulting in a high level of knowledge, attitude and behavior. Education which serves to improve the level of self-efficacy needs to be provided along with an educational program which will increase the public's understanding of irradiated food. It is expected that if this education which increases the level of self-efficacy is provided together with correct information of irradiated food, behavior to choose and eat irradiated food will also improve.

• Journal of Sasang Constitutional Medicine
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• v.10 no.1
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• pp.1-11
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• 1998

Constitution means to combine the function into the body. It also includes the genetic factor and the environmental factor. Sasang Constitutional Medicine has the organized and unique ideas of the theory and the practice. So it is widly used in korea. But It has a problem to be popular. That's the objectification of Sasang constitution. There are so many studies to classify Sasang constitution analytic and objectively. I try to classify the studies to identify Sasang constitution objectively and check the problems. 1. The morphologic studies of Sasang Constitution are dividied into the figure and the face-speech through the morphologics and the phonetics. We need to standardize and make a simple method through computer software. 2. The study of the mind is used by the questionnaire about the telent, the character, and the greed each constitution. Recently QSCC II computer software is made and is used at many clinics in Korea. We need to recheck children and Taeyangin. 3. The symptom and the disease are subjective. So the study of them is usually used by some diagnostic equipments. But these have to be revaluated on the view of Oriental Medicine and Sasang Constitutional Medicine and try to study the objectification of Sasang constitution. And then we must try to objectify Sasang constitutional disease. 4. Disease and symptoms have to be studied much more about the mechanism and the relation. The study of medicine was done the way of the homeopathy using EAV. But that had some problems. So it should depend on the new science.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of The Korean Association For Science Education
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• v.26 no.7
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• pp.813-825
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• 2006

The purpose of this study was to identify students' conceptions and their conceptualization traits in terms of the explanation of subjects: human, animals, and plants. A questionnaire was specially developed to make sure students' conceptions of evolution, students' explanation patterns with the five evolutionary explanations: creationism, internal will explanation, theological explanations, use and disuse explanation and natural selection after mutation and perceived ideas of evolution in terms of the genetic characters of human, animals and plants. 1,540 elementary, middle and high school students participated in responding to the questions. The data was collected and analyzed cross-sectionally by age. Results showed that students' evolutionary explanations were different among each subject: human, animals and plants. Students had concentrated with more 'theological explanations' than 'internal will explanation' and 'use and disuse explanation' about plant evolution. 'Natural selection after mutation explanation' was less represented in explaining human characters. This result showed that the anthropocentric thoughts had influenced students' evolution conceptions. Accordingly, as student's age, 'theological explanation' and 'internal will explanation' became least used in explaining the theory of creation. 'Use and disuse explanation' attained more representation in their explanation. In addition, the diversity of students' evolutionary explanations was getting less attention as age increases. Among youngers, 'theological explanation', 'internal will explanation' and 'use and disuse explanation' showed an even number of responses. The elders responded with more 'use and disuse explanation'. This result let us infer that 'use and disuse explanation' was strongly adhered to by student cognitive structures. Many students recognized that evolution was a kind of scientific hypothesis with little evidence. They have had a little interest in evolution and conceptualized it through informal educational sources. This study mentions that to teach evolution more effectively, teachers should make 'use and disuse explanation' adhere strongly to students' cognitive structure.