• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.224-234
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• 2014

This study designed to nursing student's image of professional nurse before clinical practice using Q-methodology. Using Q-methodology, 43 nursing student sorted 34 statements selected professional nurse type. The statement positions on the sorting continuum were scored from +4(representing the highest level of agreement) through 0(at the neutral position on the continuum) through -4(representing the highest level of disagreement). Factor analysis of the data was undertaken using PC QUANL software to determine if any patterns were discernible. The three factors extracted eigenvalues greater than 1.0 and explained approximately 53.94% of the variation in responses(41.63, 7.41 and 4.9% respectively). The categories were labeled 'Nightingale's nursing philosophy type', 'Consideration of patient type' and 'Self-development type'. Twenty-two students who located to factor 1 "Nightingale's nursing philosophy type". Eleven students who located to factor 2 "Consideration of patient type". Three students sho located to the factor 3 "Self-development type". The significant differences found in the subjective structure of nursing student's image of professional nurse. The study results can be utilized in both an effective nursing education and clinical practice.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.2
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• pp.140-146
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• 2010

Monitoring of the dynamic changes at paddy rice agriculture is very important for agricultural sustainability. Field monitoring was performed to evaluate the soil chemical properties of 260 paddy soil samples every four years from 1999 to 2007 in Gyeongnam Province. Soil chemical properties such as pH, organic matter, available phosphate, silicate, exchangeable potassium, calcium, and magnesium contents were analyzed. The contents of exchangeable cations, and available silicate were significantly increased in 2007 compared to 1999. The chemical contents of organic matter, exchangeable potassium, and magnesium were significantly increased in acid sulfate soil, and silty clay loam compared to those of other soil types, and textures. Especially, content of organic matter was significantly increased in hill area compared to other soil topographies, while exchangeable potassium was significantly decreased. Principle component analysis (PCA) of chemical properties in paddy soils was obtained with eigenvalues > 1 summing 39.1%of variance for PC1, 20.4%of variance for PC2, and 59.5%of the total variance in the all of soil chemical properties. Therefore, principal component analysis is more effective for monitoring from chemical properties of paddy soil.