• Journal of Korean Dental Science
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• v.17 no.2
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• pp.92-104
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• 2024

Managing multiple non-carious cervical lesions (NCCLs) with gingival recession and dentin hypersensitivity can be challenging. Herein, we present two cases of successful treatment procedure for multiple NCCLs with gingival recession and dentin hypersensitivity using an envelope coronally advanced flap with CTG and composite resin restoration. Through the combined approach of restorative and periodontal procedure, both patients showed adequate extent of gingival coverage and esthetic outcome based on the Modified Root Coverage Esthetic Score (MRES) at 6 months postoperatively. Also, dentin hypersensitivity was reduced effectively during the follow up period. Although the pocket depth slightly increased in patient 1, possibly due to the amount of restoration located sub-gingivally, pocket depth remained within 3 mm. This suggest that re-establishing the clinical CEJ and performing partial restoration is advantageous for periodontal tissue and is expected to contribute to maintain gingival height in the long term. These case reports emphasize the efficacy of the combined approach for treating multiple NCCLs with gingival recession and dentin hypersensitivity, highlighting the importance of careful restoration planning for optimal clinical and aesthetic outcomes.

• Journal of Korean Academy of Nursing
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• v.38 no.6
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• pp.853-865
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• 2008

Purpose: The aim of this study was to develop and to analyze the task of gerontological nurse practitioners (GNPs) in Korea. Methods: The definition of GNP and job description was developed based on developing a curriculum (DACUM) by 7 panels who have experienced in DACUM analysis and gerontological nursing. One hundred sixty nurses who were working at long term care facilities were participated. The questionnaire included frequency, importance, and difficulty of duties, tasks, and task elements. The data were collected in November 2006, analyzed by descriptive statistics. Results: The job description of GNPs in Korea revealed 5 duties, 23 tasks, and 86 task elements. On the all five duties, the highest duty in frequency and in importance was professional nursing care ($3.25{\pm}0.35$, $3.49{\pm}0.29$). But the highest duty in difficulty was research ($3.24{\pm}0.46$). 'Prevent health problem ($3.42{\pm}0.43$, $3.56{\pm}0.33$)', 'Teach other staffs ($2.83{\pm}0.77$, $3.39{\pm}0.43$)', 'Develop the evidence-based standards ($2.43{\pm}0.76$, $3.22{\pm}0.43$)', 'Develop the self ($2.81{\pm}0.65$, $3.26{\pm}0.42$)', and 'Participate the team activities' were the highest score in frequency and in criticality of tasks. 'Provide emotional support to older adults and families ($3.16{\pm}0.41$)', 'Counsel older adults and their families ($3.14{\pm}0.49$)', 'Do clinical research ($3.32{\pm}0.49$)', 'Quality insurance ($3.25{\pm}0.49$)', and 'Build collaborative system ($3.18{\pm}0.47$)' were perceived the most difficult tasks. Conclusion: The political efforts for the legislation of role and task of GNPs were needed.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of The Korean Association For Science Education
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• v.42 no.3
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• pp.371-381
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• 2022

This study causally investigates whether high school student with high science learning motivation becomes to achieve more or vice versa, and also how those two factors affect STEM career motivation. Research participants were 1st year students in a high school at Seoul. We surveyed their science learning motivation three times in the same time interval in the fall semester of 2021, and once a STEM career motivation in the third period. We collected data from 171 students with their mid-term and final exam scores, with which, we constructed and fitted an autoregressive cross-lagged model. The research model shows high measurement stability and fit indices. All the autoregressive and cross-lagged paths were statistically significant. However, standardized regression coefficients were larger in path from motivation to achievement compared to the opposite. Only science learning motivation shows significant direct effect on STEM career motivation, rather than achievement. For indirect effects, the first science learning motivation affected the final exam score and STEM career motivation, and the final exam score affected STEM career motivation. However, the final exam score did not have a total effect toward STEM career motivation. The result of this study shows reciprocal and cyclic causality between science learning motivation and achievement - in comparison, the effect of motivation for the opposite is larger than that of achievement. Also the result of this study strongly reaffirms the importance of science learning motivation. Instructional implications for strengthening science learning motivation throughout a semester was discussed, and a study for the longitudinal effect of science learning motivation and achievement in high school student toward future STEM vocational life was suggested.