• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.209-217
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• 2019

This is a descriptive research study undertaken to confirm the relationship between depression, fear-avoidance beliefs, catastrophizing, and the quality of life in the elderly with chronic pain, and how psychological factors affect their quality of life. The subjects were 147 seniors aged 65 years or older, who visited a neuropathy clinic in A city from March 4, 2019 to March 18, 2019. Statistical analysis was achieved by applying t-test, ANOVA, Pearson's correlation coefficients, and multiple regression analyses using the SPSS/WIN 22.0 software. Considering the demographic characteristics of the elderly, quality of life revealed significant differences with respect to age (F=3.464, p<0.001), the presence of the spouse (F=3.464, p<0.001), health condition (t=4.545, p<0.001), and pain degree (F=14.76, p<0.001). Further analysis revealed that factors affecting quality of life in the elderly with chronic pain are depression (${\beta}=-0.25$, p<0.001), pain degree (${\beta}=0.25$, p<0.001), catastrophizing (${\beta}=-0.28,$p<0.001) and health condition $({\beta}=-0.19$, p<0.001), with a total explanation power of 49%. Hence, researches on the negative psychological factors, such as depression and catastrophizing, are required to improve the quality of life for the elderly. In addition, the development of a systematic nursing arbitration program is necessary to positively recognize active pain control and health conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.81-90
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• 2016

The objective of this study was to investigate the effect of an education method applying the flipped learning technique for college students. Both self-directed learning readiness and educational performance before and after applying the flipped learning were examined. After applying the flipped learning technique, teacher-student interaction, learning satisfaction, and learning motivation were identified. The correlation of each variable was examined after applying the flipped learning technique to investigate its influence on learning motivation. A total of 68 second-year nursing students enrolled in E University were analyzed. A difference between before and after applying the flipped learning was analyzed by the paired t-test; a correlation between the variables was analyzed via Pearson's correlation coefficient; and an influence on the dependent variable learning motivation was analyzed using the stepwise multiple regression analysis. The results showed that self-directed learning readiness increased before and after applying the flipped learning technique with statistical significance, and the difference of educational performance was not significant. After an education session applying the flipped learning technique, a learning motivation demonstrated a significantly positive correlation with self-directed learning readiness (r=0.33, p=.006), college student educational performance (r=0.51, p<.001), teacher-student interaction (r=0.72, p<.001), and learning satisfaction (r=0.79, p<.001). A significantly positive correlation was also observed between the other variables. Factors influencing learning motivation were learning satisfaction and teacher-student interaction. The explanatory power for learning motivation in the regression model considering these two variables was 71.3% (F=80.66, p<.001). Therefore, to enhance learning motivation in applying the flipped learning technique, it is necessary to increase learning satisfaction and to establish a strategy that further vitalizes the teacher-student interaction.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.405-415
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• 2011

The primary purpose of this paper is to identify the influencing factors on the new products adoption between countries and generations. For this purpose, a research is developed based on the relevant literature reviews. Data have been collected from 524 persons and were tested by t-test and various statistical methods. The results of this empirical study are summarized as follows. In the cultural factors, the groupism has high discretion in China old generation. The materialism and shopping preference have high discretion in two young generations. There is no difference between the two groups in the distance of power. In innovativeness of personality, Korea and China young generation have high discretion. Innovativeness has high discretion in Korea and China youngs. Cognition and sensory innovativeness are has low discretion in Korea old. In the social risk perception, physiological, functional general and financial risk has high discretion in China old. In risk reducing behavior, the normative taking level and ad, new product adoption has high discretion in Korea and China youngs. But, the influence of others has high discretion in China old generation. The safety and brand reputation are no influences. The findings have a several marketing strategies in generation and countries.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• Korean Journal of Head & Neck Oncology
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• v.9 no.1
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• pp.3-9
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• 1993

The benficial effects for perfusion in the preservation of free flaps have been controversial in the clinical and experimental field until now. This study was undertaken to observe the effect of UW solution. a recently developed. high molecular weight. organ perfusion solution. for protection of ischemic injury in normothermic free myocutaneous flaps. Forty rabbits were used in this sutdy. A 1x2x1cm sized gastrocnemius myocutaneous flap based on the feeding vessel from common femoral artery was made. The author set up the ischemic time for 12 hours in these flaps. The flap was washed out with normal saline(control grop, n=10), urokinase(comparative group I, n=10), UW solution before ischemic time(comparative group II, n=10) and UW solution before ischemic time and pentoxifylline before reperfusion(comparative group III, n=10). Afterthen, reperfusion was made for 12 hours. After this procedure, we checked the degree of ischemia and necrosis of myocutaneous flap by gross finding, electrical stimulation test of muscle, triphenyltetrazolium chloride staining and wet/dry weight ratio. The degree of necrosis of comparative group II and III were lesser than control and urokinase group in gross finding(p<0.05). In the electrical stimulation test of muscle, there was no statistical difference between control($1.76{\pm}1.01$) and urokinase($2.36{\pm}\1.02$) group however the muscular power of comparative group II($3.54{\pm}0.93$) and III($3.49{\pm}1.37gm/mm^2$) demonstrated significantly higher than control group(p<0.05). The ischemic findings were found in seven cases of control group and three cases of urokinase group but there were no ischemic findings in comparative group II and III in TIC stain(p<0.05). In the wet/dry weight ratio of flaps in order to evaluate the tissue edema. there was no statistical difference between control($4.55{\pm}0.29$) and III($3.75{\pm}0.48$) were scored significantly lesser than control and urokinase group (p<0.05). These results suggest that perfusion washout with UW solution improves the viability of normothermic free myocutaneous flap by inhibition of cellular swelling.

• 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 Dental Rehabilitation and Applied Science
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• v.33 no.2
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• pp.71-79
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• 2017

Purpose: Ni-Cr alloy does not contain Beryllium, causing the metal compound to form oxides in the furnace but by using Titanium as a chemical catalyst the forming of the oxides can be controlled, and by controlling the impurities formed on the metal surface, the possibility of the Ni-Cr alloy bond strength being increased can be analysed. Materials and Methods: Titanium was used as a chemical catalyst in the porcelain for the oxidation of beryllium-free metal (Ni-Cr) alloy. The T1 group, which does not use Titanium power as a chemical catalyst is a reference model for comparison. The T2 group and T3 group used 10 g and 20 g of Titanium power, respectively. They are fabricated to observe the shear bond strength and surface properties. There was no significance when One-way ANOVA analysis/Tukey Honestly Significant Difference Test was conducted for statistical analysis among groups (P > 0.05). Results: Results of measuring the three-point flexural bond strength of the Ni-Cr alloy and thickness of the oxide film. Experiment T3 using 20 g Titanium chemical catalyst: $39.22{\pm}3.41MPa$ and $6.66{\mu}m$, having the highest bond strength and thinness of oxide film. Experiment T2 using 10 g Titanium chemical catalyst: $34.65{\pm}1.39MPa$ and $13.22{\mu}m$. Experiment T1 using no Titanium chemical catalyst: $32.37{\pm}1.91MPa$ and $22.22{\mu}m$. Conclusion: The T2 and T3 experiments using Titanium chemical catalyst showed higher bond strength for the Ni-Cr alloy and lower thickness of oxide film than experiment T1, and the titanium catalyst being able to increase bond strength was observed.

• Research in Community and Public Health Nursing
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• v.12 no.1
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• pp.247-260
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• 2001

The objectives of this study are first, to investigate the significant differences in the perception and attitude for sexual role of workers, second, to identify the degree of the perception of women manpower in workplace, third, to identify the perception for sexual harassment in workplace and fourth to analyze the relative important factors that effect on conception for sexual harassment. The survey data were collected by questionnaires on May 2000, and the number of subjects was 300 workers. The SAS-PC program was used for the statistical analysis such as t-test, ANOVA and regression analysis. Major results were follow as: 1. The performance rates of health education for sexual harassment was 66%, and 58.9% of the lecturer of sexual harassment education was occupational health nurse. The 45.2% of workers acquired the information for sexual harassment through massive education in workplace. 2. The perception and attitude of sexual role was relatively positive as 2.3(SD=0.69) of mean values. and the difference by sex, age, marital status. and working period was significant. 3. The perception of the women manpower in workplace was generally positive as 7.9(SD= 3.25) of mean values, and the working experience group of less 10 years and more 10 years old age group showed the significant difference in comparative to other group. 4. The perception of sexual harassment of workplace composed of legal basis, range of victim, place of sexual harassment, type of sexual harassment. misconception of sexual harassment, and coping methods of sexual harassment. Among of these perception. type and coping methods of sexual harassment were shown high perception level. The difference by sex between group for perception of sexual harassment was highly significant. 5. Factors that effect on the perception of sexual harassment was not high for the explanation power of regression, but the age, the type of task, the. perception for women manpower were important variables. Based on this results of study, we recommend that the education of sexual harassment in workplace should be strengthened and specified according to age, working period, type of working task. It also should provide various education and information to workplace. Finally and there should be continuous education opportunity systematically to occupational health nurses who are major lecturers for sexual harassment, therefore they can educate workers more effectively.

• Health Policy and Management
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• v.19 no.4
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• pp.121-139
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• 2009

Objectives : The purpose of this study was to investigate regional commitment index(RGI) of hospital in Korea, and the relationship RGI and hospital characteristics, such as foundation, region, size. Therefore, we are to suggest fundamental information to make and evaluate healthcare resource policy in hospital- and government-level. Methods : The 'Patient Survey 2002(administered by Ministry of Health and Social Welfare(MOHW)' was analyzed. We selected the patient data of the hospitals above 100 beds. Then, we calculated the RGI, number of same cases divided by all cases in each hospital. By using SPSS/win ver 14.0, statistical analysis such as t-test, ANOVA, correlational and regression analysis was carried out. Results : The results are as follows. 1. Overall mean and standard deviation of RGI were revealed as 0.805${\pm}$0.225 in inpatients, and 0.871${\pm}$0.184 in outpatient. The median of inpatients' and outpatients' RGI were 0.890 and 0.933. The RGI of inpatients of private hospitals were revealed significantly higher than that of the public(public: 0.727, private: 0.822). However, outpatients' RGI was not revealed as significantly different. 2. The RGI of general specialty hospitals were significantly lower than others, therefore we could think that more inpatients and outpatients of general specialty hospitals flowed in from others province or metropolitan cities than other hospital types. 3. The RGI of hospitals holding above 400 beds were significantly lower than others in inpatients and outpatients. 5. The RGI of hospitals were significantly different among sixteen province and metropolitan cities. The RGI inpatients of Gwangju and Daejon metropolitan city were lowest sub-group(0.659, 0.664), and the RGI inpatients of Jeju was revealed as highest, 0.979. 6. Available beds, total doctors, and total employees were negatively correlated with RGI of inpatients and outpatients. 7. The significant influencing factors to RGI of inpatients and outpatients were appeared samely such as available beds, wide healthcare region, hospital size, and foundation type. Conclusions : It is considered that RGI of hospital represent competitive power in healthcare market. Also, the competitive advantage and quality of hospital clustered by characteristics could made out by RGI. Therefore, the results of this study would be useful to develop and evaluate hospital policy of individual hospital or local government.