• Journal of Korean Academy of Nursing
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• v.41 no.5
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• pp.633-641
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• 2011

Purpose: The purpose of this study was to propose and test a predictive model that could explain and predict Korean nurses' turnover intentions. Methods: A survey using a structured questionnaire was conducted with 445 nurses in Korea. Six instruments were used in this model. The data were analyzed using SPSS 15.0 and Amos 7.0 program. Results: Based on the constructed model, organizational commitment, and burnout were found to have a significant direct effect on turnover intention of nurses. In addition, factors such as empowerment, job satisfaction, and organizational commitment were found to indirectly affect turnover intention of nurse. The final modified model yielded ${\chi}^2$=402.30, p<.001), ${\chi}^2$/df=2.94, RMSEA=0.07, RMR=0.03, GFI=0.90, AGF=0.87, NFI=0.88, CFI=0.92 and good fit indices. Conclusion: This structural equational model is a comprehensive theoretical model that explains the related factors and their relationship with turnover intention in Korean nurses. Findings from this study can be used to design appropriate strategies to further decrease the nurses' turnover intention in Korea.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.507-527
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• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.2
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• pp.255-268
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• 2018

Subsea tunnel can be highly vulnerable to seawater intrusion due to unexpected high-water pressure during construction. An artificial ground freezing (AGF) will be a promising alternative to conventional reinforcement or water-tightening technology under high-water pressure conditions. In this study, the freezing energy and required time was calculated by the theoretical model of the heat flow to estimate the total amount of refrigerant required for the artificial ground freezing. A lab-scale freezing chamber was devised to investigate changes in the thermal and mechanical properties of sandy soil corresponding to the variation of the salinity and water pressure. The freezing time was measured with different conditions during the chamber freezing tests. Its validity was evaluated by comparing the results between the freezing chamber experiment and the numerical analysis. In particular, the freezing time showed no significant difference between the theoretical model and the numerical analysis. The amount of refrigerant for artificial ground freezing was estimated from the numerical analysis and the freezing efficiency obtained from the chamber test. In addition, the energy ratio for maintaining frozen status was calculated by the proposed formula. It is believed that the energy ratio for freezing will depend on the depth of rock cover in the subsea tunnels and the water temperature on the sea floor.