• The Journal of Korean Society for School & Community Health Education
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• v.22 no.3
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• pp.41-54
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• 2021

Objectives: This study aimed to confirm the degree of gender sensitivity, gender role conflict, nursing professionalism of nursing students and the factors that affect nursing professionalism. Methods: During Jan. 19 to Feb. 5 in 2021, the structured questionnaire was used for 187 nursing students by on-line research methods. Data were analyzed by descriptive analysis, mean comparison(t-test, ANOVA), correlation analysis(Pearson's correlation coefficient) and multiple regression using SPSS/WIN 25.0. Results: The gender sensitivity had positive relationship with nursing professionalism, and gender role conflict had negative relationship with nursing professionalism. And the prediction factors influencing nursing professionalism were major satisfaction, gender sensitivity and gender role conflict. The total variance was 8.2% by predictors. Conclusions: In order to improve the nursing professionalism of nursing students, various ways to increase the satisfaction level of major should be sought, and program should be prepared to improve gender sensitivity and reduce gender role conflict.

• Food Science and Biotechnology
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• v.14 no.3
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• pp.437-439
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• 2005

Freshness of stored soybean curd as sensitivity ($R_{gas}/R_{air}$) was evaluated at 48-50 hr intervals using electronic nose at regular sequential square-wave temperatures between $4\;-\;10^{\circ}C$. Obtained kinetic data from apparent first principal component score $(PC1)_{app}$ and storage time were used for prediction of freshness. Percentage difference between predicted and actual values of stored soybean curd was less than 8.9% under fluctuating temperature condition.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.5-23
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• 2002

Motivation: Protein-protein interaction plays a critical role in the biological processes. The identification of interacting proteins by bioinformatical methods can provide new lead In the functional studies of uncharacterized proteins without performing extensive experiments. Results: Protein-protein interactions are predicted by a computational algorithm based on the weighted scoring system for domain interactions between interacting protein pairs. Here we propose potential interaction domain (PID) pairs can be extracted from a data set of experimentally identified interacting protein pairs. where one protein contains a domain and its interacting protein contains the other. Every combinations of PID are summarized in a matrix table termed the PID matrix, and this matrix has proposed to be used for prediction of interactions. The database of interacting proteins (DIP) has used as a source of interacting protein pairs and InterPro, an integrated database of protein families, domains and functional sites, has used for defining domains in interacting pairs. A statistical scoring system. named "PID matrix score" has designed and applied as a measure of interaction probability between domains. Cross-validation has been performed with subsets of DIP data to evaluate the prediction accuracy of PID matrix. The prediction system gives about 50% of sensitivity and 98% of specificity, Based on the PID matrix, we develop a system providing several interaction information-finding services in the Internet. The system, named PreDIN (Prediction-oriented Database of Interaction Network) provides interacting domain finding services and interacting protein finding services. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PreDIN system. This system can be also used as a new tool for functional prediction of unknown proteins.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.125-134
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• 2002

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.

• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.1-9
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• 2007

Sensitivity analysis of wind resource micrositing has been performed through the application case at the Antarctic King Sejong station with the most representative micrositing softwares: WAsP, WindSim and Meteodyn WT. The wind data obtained from two met-masts separated 625m were applied as a climatology input condition of micro-scale wind mapping. A tower shading effect on the met-mast installed 20m apart from the warehouse has been assessed by the CFD software Fluent and confirmed a negligible influence on wind speed measurement. Theoretically, micro-scale wind maps generated by the two met-data located within the same wind system and strongly correlated meteor-statistically should be identical if nothing influenced on wind prediction but orography. They, however, show discrepancies due to nonlinear effects induced by surrounding complex terrain. From the comparison of sensitivity analysis, Meteodyn WT employing 1-equation turbulence model showed 68% higher RMSE error of wind speed prediction than that of WindSim using the ${\kappa}-{\epsilon}$ turbulence model, while a linear-theoretical model WAsP showed 21% higher error. Consequently, the CFD model WindSim would predict wind field over complex terrain more reliable and less sensitive to climatology input data than other micrositing models. The auto-validation method proposed in this paper and the evaluation result of the micrositing softwares would be anticipated a good reference of wind resource assessments in complex terrain.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• Atmosphere
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• v.19 no.4
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• pp.331-344
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• 2009

A sensitivity study has been performed using ARPS (Advanced Regional Prediction System) version 5.2.10 in a downslope windstorm case of 12-13 February 2006. The purpose of this study was to find out the role of the inversion layer of temperature mainly in relation to the strength of the downslope winds over the Yeongdong region located downstream of the Taebaek mountains. Under the conditions of N (Brunt-$V{\ddot{a}}is{\ddot{a}}la$ frequency)=0.008 and N=0.016, the effects of the presence of the inversion layer, its variation of height of the layer, and the depth of the layer were identified. The sensitivity experiments suggested that the inversion layer effected the downstream wind speed of the mountains under both conditions of N=0.008 and N=0.016, and notably when the inversion layer was located near the mountain crest the downstream wind speed of the mountains was strong (~ $27ms^{-1}$) only under the condition of N=0.016. In addition, when the atmosphere was rather stable (N=0.016) and the depth of the layer was relatively thin (765 m) the downstream wind speed of the mountains was the strongest (~ $30ms^{-1}$) among the sensitivity experiments.

• Korean Journal of Optics and Photonics
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• v.15 no.5
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• pp.461-468
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• 2004

A finite model eye of visual acuity 24/20 in emmertropia was presented. We determined the image intensity profile on retina using optical transfer function of model eye, and compared with clinical data. The retinal contrast sensitivity function based on the Stiles-Crawford effect, photopic response, diffraction, aberration, retinal contrast sensitivity, and pupil size is calculated. Visual acuity for human eye could be predicted by examining the modulation transfer function of a bar target and retinal contrast sensitivity function. This visual acuity was evaluated for pupil diameters ranging from 1 to 8 mm.