• Journal of Korean Clinical Nursing Research
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• v.16 no.3
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• pp.25-37
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• 2010

Purpose: The purpose of this study was to examine potential factors related to the management of cancer pain, that is, hospital institutional factors as well as personal aspects of nurses. Methods: This study was a descriptive research study in which 229 RNs working in 2 tertiary medical institutions in Seoul and 4 secondary medical institutions in Seoul, Incheon and Gyeonggi were surveyed. Results: It was found that nurses' knowledge about pain intervention, their working division and their knowledge about the use of analgesics had different effects on their pharmacologic interventions. These 3 variables explained 14.5% of the variance regarding pharmacologic interventions. On the other hand, nurses' knowledge about pain interventions and nursing organization were variables affecting non-pharmacologic interventions by the nurses. These two variables explained 22.1% of the variance regarding non-pharmacologic interventions by the nurses. Conclusion: The findings indicate that nursing organization, one of hospital institutional factors, had significant effects on non-pharmacologic interventions. Therefore, to increase effective pain management by nurses, an organizational system should be established such as placement of nurse practitioners, improvement of nurses' autonomy in pain management, and development and distribution of standardized guidelines.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.183-194
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• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• Asia Marketing Journal
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• v.16 no.1
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• pp.149-168
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• 2014

This study investigates the effect of perceived risk on attitude toward mobile Social Network Services (SNSs). First, we understand that perceived risk of SNSs is a multidimensional concept, and we study the relationship between attitude and perceived risk such as social risk, performance risk, and privacy risk in SNS environments. Subsequently, the relationships between these multidimensional concepts of perceived risk and attitude are investigated. The result indicates that social, performance, and privacy risk have negative effects on attitude. In addition, the moderated effect of individual characteristic variables such as hedonic value and self-construal are confirmed as mitigating factors that alleviate the negative impact of perceived risk. The Findings show that customers who perceive SNSs to be risky are more likely to have a negative attitude toward SNSs. However, the negative impact of perceived risk on their attitude toward SNSs is alleviated in customers with high hedonic value. Similarly, the negative impact of perceived risk on their attitude toward SNS is weaker with customers in interdependent self-construal. This paper presents effective segmentation variables, such as consumer's motivation (hedonic value) and psychological variable (self-construal), which mitigate the risk perception of customers. Therefore, it provides practical guidelines for the marketing managers in terms of who to target and what kind of strategies to implement in terms of these segmentation variables to approach consumers more efficiently.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.85-98
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• 2015

As we enter the technology and knowledge-intensive era, standardization has become a key element to increase the sustainability of companies. However, standardization activities have not been proceeded well due to lack of relevant information, financial and human resources and complexity of the authentication process. Thus, in this paper, we analyze the effect of understanding the internal and external standard trends surrounding companies and recognizing the difficulties of standardization process on the needs of standard/standardization education. We suggest an experiment model and associated variables for analysis based on the previous studies. The analysis results from 1,000 companies that belong to telecommunication industry and electrical and electronics industry showed positive relationships among these three variables. Furthermore, we propose major policy directions for increasing standardization effect.

• The Korean Journal of Health Service Management
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• v.11 no.2
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• pp.129-142
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• 2017

Objectives : The purpose of this study was to identify the main variables of difference in high school students' oral health promotion behaviors among adolescents and to improve their academic and oral health promotion behaviors. Methods : The research subjects consisted of 311 high school students in Jeonju. Results : The adequacy of the hypothetical model accounted for 46.9 % of the oral health promotion behavior. The Redundancy of all variables showed the value of the positive values, indicating that the Goodness of fit was greater than the optimum value of the model, and the model of the PLS was a desirable model. The effects of perceived benefits, self efficacy, and social support on oral health promotion behaviors were found to be higher in oral health promotion behaviors. Conclusions : This study is expected to have a significant impact on the perception of the oral health promotion for adolescents in the future and will contribute to the expansion and generalization of Pender's oral health promotion model.

• Journal of the Korean Home Economics Association
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• v.36 no.5
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• pp.1-15
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• 1998

The purpose of this study is to examine the effects of the extent of information search on consumer satisfaction and to provide, ultimatly, effective consumer search for consumer satisfaction. Data for this study were collected from 452 computer purchasers who living in Seoul. This study has focused on new computer purchasers. The amount of internal information was measured by three categories, such as the number of computer purchase, the frequency, duration of computer use and the degree of computer education. In addition, the amount of prepurchase search was measured by threecategories, such as neutral information, commercial information and private information. The research results shows that the variables affecting on the level of consumer satisfaction are objective search, the degree of computer education, the frequency and duration of computer use, the number of computer purchase. Meanwhile, commercial information search and private information search are not significant affecting variables on the level of consumer satisfaction.

• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.132-141
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• 2003

As the technology has improved and demands of customers have varied, a lot of products are getting diverse and intricate. Consequently, the enterprise that produce products have to simultaneously consider the various variables for the very products. There are some scheme, such as Multivariate control chart and Demerit control chart, designed to simultaneously monitor the variables in the process. In this paper, we present an effective method for process control using the Demerit-CUSUM control chart in the process where nonconforming units or nonconformities are occured by various types. In addition, we show interpretation method for abnormal signal in order to quickly detect the assignable causes as Demerit-CUSUM control chart signals abnormality. we compare performance of Demerit control chart and Demerit-CUSUM control chart using example again used in the existing studies, and present result of performance accoriding to changing sample size and parameter.

• The KSFM Journal of Fluid Machinery
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• v.10 no.2 s.41
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• pp.22-31
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• 2007

This paper describes the shape optimization of rotor blade in a transonic axial compressor rotor. Three surrogate models, Kriging, radial basis neural network and response surface methods, are introduced to find optimum blade shape and to compare the characteristics of object function at each optimal design condition. Blade sweep, lean and skew are considered as design variables and adiabatic efficiency is selected as an objective function. Throughout the shape optimization of the compressor rotor, the predicted adiabatic efficiency has almost same value for three surrogate models. Among the three design variables, a blade sweep is the most sensitive on the object function. It is noted that the blade swept to backward and skewed to the blade pressure side is more effective to increase the adiabatic efficiency in the axial compressor Flow characteristics of an optimum blade are also compared with the results of reference blade.