• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Journal of the Korean Chemical Society
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• v.44 no.1
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• pp.68-73
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• 2000

In this study, tlhe relationships of high school students' abilities to solve chemistry problems with cognitive variables (logical thinking ability, mental capacity. and learning strategy) and affective variables(self-efficacy, self-concept of ability, learning goal, and attitude toward science) were investigated. The proportion of variance due to the variables for algorithmic and conceptual problem solving ability was studied by a multiple regression analysis. The results indicated that, among the cognitive variables, the logical thinking ability significantly predicted the algorithmic problem solving ability, and the learning strategy was the best predictor of conceptual problem solving ability although not significant. Among the affective variables studied, the self-concept of alility was the significant predictor of both algorithmic and conceptual problem solving abilities. The seif-efficacy was significantly correlated with conceptual problem solving ability, but it had no predictive power.

• Journal of Korean Elementary Science Education
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• v.31 no.2
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• pp.197-207
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• 2012

The purpose of this study was to assess pro-environmental behavior of elementary school students by school year and gender and to investigate the relationships of their emotional affinity toward and intellectual curiosity about life, and experiences about nature and life with the pro-environmental behavior. A total of 436 elementary students participated in the study: 189 3rd grade; 247 6th grade students, and 233 boys; 203 girls. The variables were measured by using a self-report questionnaire with 5-point Likert scale items. The results showed the levels of pro-environmental behavior of the 3rd grade students and the girls were higher than that of the 6th grade and the boys, respectively. Their pro-environmental behavior was significantly related with all measured variables. However, it was found that the direct predictors of their pro-environmental behavior were the intellectual curiosity and the indirect experience, while the emotional affinity and the direct experience did not have a direct impact on the behavior. The predictive power of the intellectual curiosity and the indirect experience for their pro-environmental behavior was higher in 3rd grade than 6th grade. While the indirect experience was a stronger predictor of the boys' pro-environmental behavior than the intellectual curiosity, the curiosity was a stronger predictor of the girls.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.483-497
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• 2020

We used a deep neural network model for the prediction of the stock prices of Kia Motors and Shinsegae as listed in the KOSPI 100. We used an emotional variable derived from online news in addition to the various technical indicators most often used. The emotional variable used as a predictor variable was generated from the average of the emotional scores for companies in the industrial group after building an emotional dictionary specific to each industrial group classified in a social network analysis. The study was conducted with various combinations of predictors and confirmed that good predictive and profitable power could be expected when jointly using technical indicators and an emotional variable based on online news by industrial groups.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4968-4974
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• 2010

Electrical Railroads provide electric power, which can operate vehicles, via feeder wires. And the supplied current returns to the transformer substation through lines and ground net. In domestic cases, the configuration of ground net affecting such a return current mostly uses an exclusive earth method. However, along with the introduction to Gyeongbu HSL(High Speed Line), the concept of Common Earth was applied to Electrical Railroads. In this paper, based on the return currents analyzed to be measured in the electrified sections for the operation of High Speed Rails in existing lines, we analyze the relations between earth methods and return currents in electrified sections. For analysis, we utilize the measured values of return currents measured in track circuits in Gyeongbu HSL, and predictive values of those compared to the earth methods between Gyeongbu HSL and the existing electrified sections.

• Journal of Korean Public Health Nursing
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• v.30 no.1
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• pp.30-41
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• 2016

Purpose: This study was conducted to determine the effects of middle-aged women's social support, self-efficacy, and perceived health status on aging anxiety. Methods: Research subjects were 210 middle-aged women between 40 - 60 years living in G-city. Data were collected from July $21^{st}$ 2014 to August $8^{th}$ using a structured questionnaire and analyzed using t-test, One way ANOVA, and multiple regression analysis using the SPSS/WIN 21.0 program. Results: Aging anxiety showed statistically significant differences in religion (t=2.44, p=.015), satisfaction with married life (F=4.39, p=.014), type of leisure activity (F=8.09, p<.001), and number of friends (F=3.23, p=.024). Aging anxiety showed correlation with social support (r=-.21, p=.003), self-efficacy (r=-.46, p<.001), and perceived health status (r=-.33, p<.001). Factors affecting aging anxiety were self-efficacy (${\beta}=-.33$, p<.001), perceived health status (${\beta}=-.23$, p<.001), leisure activity type (${\beta}=-.16$, p=.010), and religious affiliation (${\beta}=-.17$, p=.038). The predictive power of these variables was 25% (F=18.63, p<.001). Conclusion: Nursing intervention strategies for improving self-efficacy, perceived health status, and leisure activity are required to relieve aging anxiety of middle-aged women.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.279-285
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• 2020

The purpose of this study is to investigate the factors affecting the practice of oral health care in pregnant women. A self-reported questionnaire was filled out by 203 pregnant women who visited the OBGY office located in Daejeon, for the period March 2~30, 2019. In the knowledge of systemic health behavior, non-experiece childbirth was higher than that of experience childbirth(p<0.01). Factors affecting the practice for oral health management of pregnant women, education level, pre-pregnancy oral examination and treatment experience, experience of oral condition change due to pregnancy, and knowledge of oral health management in pregnant women, and predictive power was 26.9%(p<0.05). To expand the target of oral health education for pregnant women and oral health education program that can improve the practice rate should be developed.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2007.05a
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• pp.83-125
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• 2007

Increasing energy costs have caused profitability problems for paper suppliers. Therefore unprofitable lines are being closed down. The actions aiming for improved profits are focused either on cost savings or on increasing the capacity of the remaining machines. The runnability of a paper machine and its total efficiency have a significant effect on energy consumption. Producing one ton of waste paper consumes at least as much energy as producing the same amount of sellable end product. New automation solutions enable significant cost-effective improvements to the total efficiency of a line without large investment projects. The measures focus on minimizing changes, interruptions, interruption recovery times and grade change times. Newest actuators, online quality measurements and wet end analysators create an improvement potential, which can be optimally implemented with the latest machine direction control solutions, based on model predictive control concepts. Equally, drying management is significant to the energy consumption. The newest control strategies optimize the use of various drying actuators for different situations; either by responding to changes as efficiently as possible or by using only the cheapest energy sources in stable situations. An even steam supply, which is vital for paper machines, is achieved with control for the power plant steam network. This makes possible to avoid the delays upon starting the paper machine and assure an even steam supply for the drying section and the actuators. This document describes means which have brought significant energy and raw material savings for paper machines. Metso Automation has provided efficiency improvement packages, which are usually based on optimized control of dry weight and drying in all running conditions. The solutions are based on performance analysis, on which the estimations for improvement potential and the necessary actions are based on. Typically benefits on an annual level have been from hundreds of thousands of euros to over one million euro. For example, variations in dry weight have been decreased more than 50%. The results are presented with a few examples. Additionally, the analysis models, adjustment solutions and the changes in running methods with which the results were achieved, are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3789-3798
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• 2014

This study examined the factors affecting blood donation intention and behavior in college students based on the theory of planned behavior. The subjects were 258 students from one college in G city, and the data was collected by a survey using self-administered questionnaires structured from June 3 to June 14, 2013. The data was analyzed using the SPSS 19.0 program, the variables affecting the blood donation intention were analyzed by stepwise multiple regression, and the variables affecting the blood donation behavior were analyzed by Logistic regression. The factors affecting the blood donation intention were in order the perceived behavioral control, attitude toward the behavior and subjective norm, and the explanatory power was 65.6%. In addition, the factor affecting the blood donation behavior was only the blood donation intention (OR=1.129, p=.010) except for perceived behavioral control. Therefore, the perceived behavioral control, attitude and subjective norm should be considered when developing blood donation programs for college students.