• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.645-650
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• 1997

An oxide fuel small size core (1200 MWt) was analyzed in comparison with a large size core (3600 MWt) in order to evaluate the size effects on transient safety performances of liquid-metal reactors (LMRs). in the first part of the study, main static safety parameters (i.e., Doppler coefficient, sodium void effect, etc.) of the two cores were characterized, and the second part of the study was focused on the dynamic behavior of the cores in two representative transient events: the unprotected loss-of-flow(ULOF) and the unprotected transient overpower (UTOP). Margins to fuel molting and sodium boiling have been evaluated for these representative transients. Results show that the small core has a generally better or equivalent level of safety performances during these events.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1645-1648
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of recurrent neural networks model (RNNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of RNNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1439-1442
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• 2010

Time-lag recurrent neural networks model (Time-Lag RNNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$) and mean relative humidity ($RH_{mean}$). And, for the performances of Time-Lag RNNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of Time-Lag RNNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE using Time-Lag RNNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using Time-Lag RNNM.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1560-1563
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of support vector machine neural networks model (SVM-NNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of SVM-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1346-1349
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• 2010

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.59-64
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• 2019

The purpose of this research is to examine the relationship between corporate social responsibility (CSR), green supply chain management (GSCM) practices, and business performances. After reviewing the extensive literature, we developed a research model including five constructs: CSR, GSCM practices, environmental, economic and operational performances. We conducted the statistical analyses based on the primary data collected from a survey questionnaire, responded by 93 different company managers in the Republic of Guatemala. Furthermore, we utilized structural equation modeling to analyze the data and to test the hypotheses. The results of the analyses showed that there is a significant influence of CSR on the adoption of GSCM practices. It was also found that GSCM practices have a significant influence on environmental, economic and operational performances. In addition, environmental performance has a significant impact on economic and operational performance. Finally, GSCM has a mediating role on the relationship between CSR and environmental and economic performance, but not with operational performance.

• International journal of thyroidology
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• v.10 no.1
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• pp.14-23
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• 2017

Background and Objectives: To evaluate and compare the diagnostic performances of grayscale ultrasound (US) and quantitative parameters obtained from texture analysis of grayscale US and elastography images in evaluating patients with diffuse thyroid disease (DTD). Materials and Methods: From September to December 2012, 113 patients (mean age, $43.4{\pm}10.7years$) who had undergone preoperative staging US and elastography were included in this study. Assessment of the thyroid parenchyma for the diagnosis of DTD was made if US features suggestive of DTD were present. Nine histogram parameters were obtained from the grayscale US and elastography images, from which 'grayscale index' and 'elastography index' were calculated. Diagnostic performances of grayscale US, texture analysis using grayscale US and elastography were calculated and compared. Results: Of the 113 patients, 85 (75.2%) patients were negative for DTD and 28 (24.8%) were positive for DTD on pathology. The presence of US features suggestive of DTD showed significantly higher rates of DTD on pathology, 60.7% to 8.2% (p<0.001). Specificity, accuracy, and positive predictive value was highest in US features, 91.8%, 84.1%, and 87.6%, respectively (all ps<0.05). Grayscale index showed higher sensitivity and negative predictive value (NPV) than US features. All diagnostic performances were higher for grayscale index than the elastography index. Area under the curve of US features was the highest, 0.762, but without significant differences to grayscale index or mean of elastography (all ps>0.05). Conclusion: Diagnostic performances were the highest for grayscale US features in diagnosis of DTD. Grayscale index may be used as a complementary tool to US features for improving sensitivity and NPV.

• Journal of Korean Academy of Nursing
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• v.34 no.5
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• pp.838-848
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• 2004

Purpose: This study was to describe nurses' research activities, perceptions and performances of evidence-based practice and barriers to the use of research evidence in nursing practice in Korea. Method: A cross-sectional survey design was used. A questionnaire, except for Barriers Scale, was developed for the study. Data was collected from a convenient sample of 437 registered nurses working at research and education oriented university hospitals. Result: Nurses' research-related activities were relatively low compared to previous studies. Also perceptions and performances of evidence based nursing practice were low. Preferred informational resources for clinical decision making were identified as ward manuals/clinical guidelines, manager/senior nurses, and literature/research. The major barriers to research utilization were a lack of implication for practice along with inadequate facilitation to implement research evidence and difficulty understanding research written in English. Priorities of barriers factor were Administrator, Communication, Adopter, and Research. Conclusion: The findings provide directions for future training, education, and managerial policy to achieve successful evidence based nursing practice.

• Information Systems Review
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• v.12 no.1
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• pp.59-79
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• 2010

According to resource based view, knowledge is regarded as a salient factor to improve an organization's efficiency in the current fast-changing business environment. Knowledge management (KM) may encourage employees to share and exchange knowledge in the organization in order to improve and sustain a competitive advantage over other companies. The proposed research model examines the impacts of KM performances' antecedents on an employee's absorptive capacity. This study identifies KM performances as employee's satisfaction about KM and shared knowledge quality. This study considers KM performances as the major determinants that enhance his/her absorptive capacity. This study also investigates the key antecedents of KM performances. The research model posits extrinsic reward, intrinsic reward, and relational reward as the KM performances' antecedents. Furthermore, this study examines the difference of the antecedents' effects in terms of firm's type. The proposed research model was tested by using survey data collected from 1,103 employees of 2 public enterprises and 907 employees of 5 private enterprises. The findings of this study showed that employee's satisfaction about KM and shared knowledge quality play a significant role in enhancing employee' absorptive capacity. Extrinsic reward only significantly influences employee's satisfaction about KM, whereas both intrinsic and relational rewards serve as the salient antecedents of improving both KM performances. The results also shed light on the moderating role of firm's type. Theoretical and practical implications of this study are discussed.