• Korean Journal of Culture and Social Issue
• /
• v.12 no.5_spc
• /
• pp.133-147
• /
• 2006

Korean society is rapidly becoming an ageing society. The Korean may have to live longer than did their parents. Increasing life expectancy and changing social structure, Korean people are getting interested in quality of life, and well-being is becoming a matter of concern. And, the Korean is fully aware of the importance of health for well-being or good life. This concern about health may bring about specific behaviors related to health. Although health care expenditures of Korea are currently smaller than those of other developed countries, it is continuously increasing. Large portion of increased amount of health care expenditures is to spend for disease prevention and expansion of long-term care facilities. Constructs of well-being of the Korean, not living in western culture, may be different from those of people living in western society. Health is not top-ranked to importance for quality of life in previous studies. It does not mean health isn't determinant factor for good life or well-being. Health is an essential element for well-being. It has been proved in several researches which examined poor quality of life caused by certain diseases and management of health-related quality of life. Some theories relate to health-seeking behaviors suggested the health belief or the attitude toward health, intention to do health behavior, perceived behavioral control, and self efficacy as important factors which could predict health-related behaviors. With getting older, people decline in physical and physiological functions and become vulnerable to chronic diseases. Quality of life depends on how to adjust to these changes in senescence. Social supports, especially supports from offspring, are very important to quality of life in senescence, because supports from offspring have influence on pride of the older, they may be afraid of disclose the conflict with their offspring. Avoiding self-disclosure exclude other source of social supports and harm individual's health, therefore psychological intervention is needed to. Increasing life expectancy of the Korean, Korean government has to provide numerous long-term care facilities as well as psychosocial supports. The Korean, so far, does not recognize that psychologist could render great service to promoting individual or community health and improving individual's quality of life. It is highly expected that psychologists take actively interested and involve in health related to quality of life.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.111-124
• /
• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.10 no.1
• /
• pp.27-35
• /
• 1990

As a part of studies on potassium(K) behavior in grassland with respect to magnesium(Mg) balance of ruminants, seasonal variation of K and Mg contents of forages including native gasses grown in grazing pasture and meadow were investigated. During an experimental period from April to October of 1984, two times of grazings were carried out in the orchardgrass (Dacfylis glomerata L.) and the tall fescue (Festuca arundinacea Schreb.)dominant grazing pastures, and forage plants (forages and native grasses) were sampled monthly and also K and Mg contents were determined without separating into individual plant species (Experiment 1). All the plant species grown in the two meadows which situated in the grazing pastures were harvested five times during the same period, separated into individual plant species, and botanical composition (SDR, ) and K and Mg contents of the plant species were determined (Experiment 2). The results obtained were as follows: 1. During the experimental period in the orchardgrass grazing pasture K contents of the forage plants were the highest in spring, and the seasonal variation of the contents in the orchardgrass pasture (1.5-5.8 % in a dry matter basis) was more significant than that of forage plants in the tall fescue grazing pasture (3.0- 3.8 %). 2. The Mg contents of forage plants in the orchardgrass grazing pasture ranged under 2.0 mg/g DW from Arpil until July, and the contents in the orchardgrass pasture (1.5-3.1 mg/g DW) was in the lower range than that of forage plants in the tall fescue pasture (2.0-3.8 mg/g DW). (Experiment I). 3. Orchardgrass was the dominant species in the orchardgrass meadow until July, but several species of native grasses were observed from summer (July) and native grasses such as Digitaria adscendens and Echinochlw crus-galli became dominant in autumn (October). 4. Seasonal variation of K contents of orchardgrass was in the range of 3.9-5.9 %, and the contents was higher in spring (May) and in autumn (October). The variation of white clover (Trifolium repens L.) was in the range of 3.6-5.0 %, that of tall fescue 3.8-4.8 %, and that of Italian ryegrass (Lolium multiflorum Lam.) 2.7-3.5 %, respectively. 5 . Seasonal variation of Mg content of white clover was in the range of 2.9-3.7 mg, that of tall fescue 2.0- 3.3 mg, and that of orchardgrass 1.6-2.8 mg/g DW, respectively. The variation of the contents of Italian ryegrass was in the range of 1.3-1.9 mg/g DW. And Mg contents of the forage plants were higher in summer(July) 6. In autumn (October and November ) native grasses such as D. adscendens and E. crus-galli contained lower K contents (1.7-3.9 %), but higher Mg contents (3.2-10.1 mg/g DW) than the forages contained. (Experiment 2) From the results above, it is known that K contents ranged higher in younger forages in viewpoint of growth stage and higher in spring and autumn, and that Mg contents ranged lower in spring. Therefore, the mineral imbalance or hypomagnesaemic (grass) tetany can be triggered in spring or autumn, and more frequently by such plant species as orchardgrass and Italian ryegrass with lower Mg and/or higher K contents than by tall fescue. And it is suggested that the dominant native grasses in autumn such as D. adscendens and E. emsgalli can contribute to the prevention of the tetany with higher Mg and lower K contents.

• Strategy21
• /
• s.46
• /
• pp.239-276
• /
• 2020

The failure of early economic sanctions aimed at hurting the overall economies of targeted states called for a more sophisticated design of economic sanctions. This paved way for the advent of 'smart sanctions,' which target the supporters of the regime instead of the public mass. Despite controversies over the effectiveness of economic sanctions as a coercive tool to change the behavior of a targeted state, the transformation from 'comprehensive sanctions' to 'smart sanctions' is gaining the status of a legitimate method to impose punishment on states that do not conform to international norms, the nonproliferation of weapons of mass destruction in this particular context of the paper. The five permanent members of the United Nations Security Council proved that it can come to an accord on imposing economic sanctions over adopting resolutions on waging military war with targeted states. The North Korean nuclear issue has been the biggest security threat to countries in the region, even for China out of fear that further developments of nuclear weapons in North Korea might lead to a 'domino-effect,' leading to nuclear proliferation in the Northeast Asia region. Economic sanctions had been adopted by the UNSC as early as 2006 after the first North Korean nuclear test and has continually strengthened sanctions measures at each stage of North Korean weapons development. While dubious of the effectiveness of early sanctions on North Korea, recent sanctions that limit North Korea's exports of coal and imports of oil seem to have an impact on the regime, inducing Kim Jong-un to commit to peaceful talks since 2018. The purpose of this paper is to add a variable to the factors determining the success of economic sanctions on North Korea: preventing North Korea's evasion efforts by conducting illegal transshipments at sea. I first analyze the cause of recent success in the economic sanctions that led Kim Jong-un to engage in talks and add the maritime element to the argument. There are three conditions for the success of the sanctions regime, and they are: (1) smart sanctions, targeting commodities and support groups (elites) vital to regime survival., (2) China's faithful participation in the sanctions regime, and finally, (3) preventing North Korea's maritime evasion efforts.