• Journal of Nutrition and Health
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• v.52 no.1
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• pp.47-57
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• 2019

Purpose: This study was conducted to investigate the relationship between atopic dermatitis and dietary fat and fatty acid (FA) intakes in 9 ~ 11 year old children. Methods: We analyzed data from the combined 2013 ~ 2015 KNHANES (Korean National Health and Nutrition Examination Survey). Subjects were divided into two groups according to atopic dermatitis (AD); with AD and without AD. Data pertaining to macronutrients and FA intakes were obtained by a single 24-h dietary recall. Food sources were identified based on the amounts of total fat and FA consumption according to each food. The associations between each FA intake and atopic dermatitis were analyzed using simple and multiple logistic regression analyses. Age, sex, body mass index (BMI) and income levels were adjusted as covariates. Results: Of the participants, 17.69% suffered from atopic dermatitis. Children with AD had significantly lower fat percentages of total energy and higher carbohydrate percentages of total energy than normal children. Percentages of energy and intakes of polyunsaturated fatty acid (PUFA), n-3 FA and n-6 FA in children with AD were significantly lower than those in normal children. In the FA, linoleic acid, ${\gamma}$-linoleic acid and ${\alpha}$-linolenic acid levels of children with AD were significantly lower than those of normal children. However, the P/S ratio and n-6/n-3 ratio did not differ significantly between children with AD and normal children. Soybean oil was the main contributor to PUFA, n-3 FA and n-6 FA in both groups, while perilla seed oil and mackerel were the major food sources of n-3 FA in children with atopic dermatitis. Atopic dermatitis was significantly correlated with low-fat and high-carbohydrate diets. The adjusted odds ratios were 0.966, 0.776 and 0.963 for PUFA, n-3 FA, and n-6 FA intakes, respectively. Conclusion: The present study provides reliable evidence regarding the relationship between fat and FA intakes and AD in Korean children 9 ~ 11 years of age.

• Journal of the Korean Orthopaedic Association
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• v.53 no.6
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• pp.505-512
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• 2018

Purpose: Many reconstruction methods have been attempted after an en-bloc resection of the proximal humerus. In particular, the introduction of reverse shoulder arthroplasty (RSA) has made a breakthrough in the functional recovery of the shoulder. Nevertheless, RSA has limitations when the humeral bone stock loss is significant. In addition, it is unclear if RSA is effective in patients showing failure with non-operative treatment of a proximal humeral tumor. Materials and Methods: A reconstruction was performed using an overlapping allograft-RSA composite for 11 patients with a failed proximal humeral construct. Delayed RSA was performed on 6 patients with failed non-operative treatment. The pre- and postoperative Musculoskeletal Tumor Society (MSTS) score and the complications were addressed. Results: Overlapping allograft-RSA composite afforded a stable construct in 11 failed proximal humeral reconstructions and the patient's chief complaints were resolved. The mean time to the union of overlapped allograft-host junction was 5.5 months. Average preoperative MSTS score of 20.3 point increased to 25.7 point, postoperatively. Four of the six patients who had RSA within 4 years from the index operation showed arm elevation of more than $90^{\circ}$ whereas the remaining 5 patients showed some disability. The complications include one case each of dislocation and aseptic infection, which were resolved by changing the polyethylene liner and scar revision, respectively. None of the 6 patients who underwent delayed RSA after the failure of non-operative treatment showed arm elevation more than $90^{\circ}$. Conclusion: An overlapping allograft-RSA composite is a simple and reliable reconstructive modality in patients with massive bone loss. In patients with metastatic cancer necessitating a surgical resection at presentation, early conversion to RSA is recommended to secure functional recovery.

• Atmosphere
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• v.32 no.1
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• pp.61-70
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• 2022

This study evaluates the economic values for the several first precipitation events during Changma period. The selected three years are 2015, 2019, and 2020, where average precipitation amounts across the 58 Korean stations are 12.8, 20.1 and 13.3 mm, respectively. The four categories are used to assess the values including air quality improvement, water resource acquisition/accumulation, drought mitigation, and forest fire prevention/recovery. Economic values for these three years are estimated 50~150 billion won. Among the four factors considered, the effect of air quality improvement is most highly valued, amounting to 70 to 90% of the total economic values. Wet decomposition of air pollution (PM₁₀, NO₂, CO, and SO₂) is the primary reason. The next valuable element is water resource acquisition, which is estimated 9~15 billion won. Effects of drought mitigation and fire prevention are deemed relatively small. This study is the first to estimate the value of the precipitation events during Changma onset. An analysis for more Changma years will be performed to achieve a more reliable estimate.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.