• Korean Circulation Journal
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• v.54 no.11
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• pp.710-723
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• 2024

Background and Objectives: There are limited national data on the trends and outcomes of patients hospitalized with acute myocardial infarction (AMI) during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to evaluate the impact of early COVID-19 pandemic on the trends and outcomes of AMI using the National Inpatient Sample (NIS) database. Methods: The NIS database was queried from January 2019 to December 2020 to identify adult (age ≥18 years) AMI hospitalizations and were categorized into ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) based on International Classification of Diseases, Tenth Revision, Clinical Modification codes. In addition, the in-hospital mortality, revascularization, and resource utilization of AMI hospitalizations early in the COVID-19 pandemic (2020) were compared to those in the pre-pandemic period (2019) using multivariate logistic and linear regression analysis. Results: Amongst 1,709,480 AMI hospitalizations, 209,450 STEMI and 677,355 NSTEMI occurred in 2019 while 196,230 STEMI and 626,445 NSTEMI hospitalizations occurred in 2020. Compared with those in 2019, the AMI hospitalizations in 2020 had higher odds of in-hospital mortality (adjusted odds ratio [aOR], 1.27; 95% confidence interval [CI], [1.23-1.32]; p<0.01) and lower odds of percutaneous coronary intervention (aOR, 0.95 [0.92-0.99]; p=0.02), and coronary artery bypass graft (aOR, 0.90 [0.85-0.97]; p<0.01). Conclusions: We found a significant decline in AMI hospitalizations and use of revascularization, with higher in-hospital mortality, during the early COVID-19 pandemic period (2020) compared with the pre-pandemic period (2019). Further research into the factors associated with increased mortality could help with preparedness in future pandemics.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Preventive Medicine and Public Health
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• v.46 no.5
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• pp.271-281
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• 2013

Objectives: Emerging evidence indicates that sleep duration is associated with health outcomes. However, the relationship of sleep duration with long-term health is unclear. This study was designed to determine the relationship of sleep duration with mortality as a parameter for long-term health in a large prospective cohort study in Korea. Methods: The study population included 13 164 participants aged over 20 years from the Korean Multi-center Cancer Cohort study. Information on sleep duration was obtained through a structured questionnaire interview. The hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality were estimated using a Cox regression model. The non-linear relationship between sleep duration and mortality was examined non-parametrically using restricted cubic splines. Results: The HRs for all-cause mortality showed a U-shape, with the lowest point at sleep duration of 7 to 8 hours. There was an increased risk of death among persons with sleep duration of ${\leq}5$ hours (HR, 1.21; 95% CI, 1.03 to 1.41) and of ${\geq}10$ hours (HR, 1.36; 95% CI, 1.07 to 1.72). In stratified analysis, this relationship of HR was seen in women and in participants aged ${\geq}60$ years. Risk of cardiovascular disease-specific mortality was associated with a sleep duration of ${\leq}5$ hours (HR, 1.40; 95% CI, 1.02 to 1.93). Risk of death from respiratory disease was associated with sleep duration at both extremes (${\leq}5$ and ${\geq}10$ hours). Conclusions: Sleep durations of 7 to 8 hours may be recommended to the public for a general healthy lifestyle in Korea.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.535-549
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• 2022

Damage to forests, such as broken or falling trees, has increased due to the increased intensity and frequency of abnormal climate events, such as strong winds and heavy rains. However, it is difficult to respond to them in advance based on prediction since structural defects such as cavities and bumps inside trees are difficult to identify with a visual inspection. Non-invasive sonic tomography (SoT) is a method of estimating internal defects while minimizing physical damage to trees. Although SoT is effective in diagnosing internal defects, its accuracy varies depending on the species. Therefore, it is necessary to analyze the reliability of its measurement results before applying it in the field. In this study, we measured internal defects in wood by cross-applying destructive resistance micro drilling on old Pinus densifloraSiebold & Zucc. and Ginkgo bilobaL., which are representative tree species in Korea, to verify the reliability of SoT and compared the evaluation results. The t-test for the mean values of the defect measurement between the two groups showed no statistically significant difference in pine trees and some difference in ginkgo trees. Linear regression analysis results showed a positive correlation with an increase in defects in SoT images when the defects in the drill resistance graph increased in both species.

• Journal of Environmental Health Sciences
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• v.48 no.6
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• pp.315-323
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• 2022

Background: Socioeconomical disadvantaged communities are more vulnerable to environmental chemical exposure and associated health effects. However, there is limited information on chemical exposure among vulnerable populations in Korea. Objectives: This study investigated chemical exposure among underprivileged populations. We measured urinary metabolites of phthalates in urban disadvantaged communities and investigated their correlations with residential environment factors and relative socioeconomic vulnerability. Methods: Urine samples were collected from 64 residents in a disadvantaged community in Seoul. A total of eight phthalate metabolites were analyzed by liquid chromatography-mass spectroscopy. Analytical method used by the Korean National Environmental Health Survey (KoNEHS) was employed. Covariate variance analysis and general linear regression adjusted with age, sex and smoking were performed. Results: Several phthalate metabolites, namely monomethyl phthalate (MMP), monoethyl phthalate (MEP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono-n-butyl phthalate (MnBP) had higher levels than those reported in the adults of 4th KoNEHS. Notably, the MnBP level was higher in the lower socioeconomic group (geometric mean [GM]=47.3 ㎍/g creatinine) compared to non-recipients (GM=31.9 ㎍/g creatinine) and the national reference level (GM=22.0, 28.2 and 32.2 ㎍/g creatinine for adults, 60's and 70's, respectively.). When age, sex and smoking were adjusted, MEP and MnBP were significantly increased the lower socioeconomic group than non-recipients (p=0.014, p=0.023). The lower socioeconomic group's age of flooring were higher than non-recipients, not statistically significant. Conclusions: These results suggest that a relatively low income and aged flooring could be considered as risk factors for increased levels of phthalate metabolites in socioeconomic vulnerable populations.

• Journal of Korean Neurosurgical Society
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• v.65 no.6
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• pp.834-840
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• 2022

Objective : C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), and white blood cell (WBC) count are inflammatory markers used to evaluate postoperative infections. Although these markers are non-specific, understanding their normal kinetics after surgery may be helpful in the early detection of postoperative infections. To compliment the recent trend of reducing the duration of antibiotic use, this retrospective study investigated the inflammatory markers of patients who had received antibiotics within 24 hours after surgery according to the Health Insurance Review & Assessment Service guidelines and compared them with those of patients who had received antibiotics for 5 days, which was proven to be non-infectious. Methods : We enrolled 74 patients, divided into two groups. Patients underwent posterior lumbar interbody fusion (PLIF) at a single institution between 2019 and 2020. Group A included 37 patients who received antibiotics within 24 hours after the PLIF procedure, and group B comprised 37 patients who had used antibiotics for 5 days. A 1 : 1 nearest-neighbor propensity-matched analysis was used. The clinical variables included age, sex, medical history, body mass index, estimated blood loss, and operation time. Laboratory data included CRP, ESR, and WBC, which were measured preoperatively and on postoperative days (POD) 1, 3, 5, and 7. Results : CRP dynamics tended to decrease after peaking on POD 3, with a similar trend in both groups. The average CRP level in group B was slightly higher than that in group A; however, the difference was not statistically significant. Multiple linear regression analysis revealed operation time, number of fused levels, and estimated blood loss as significant predictors of a greater CRP peak value (r²=0.473, p<0.001) in patients. No trend (a tendency to decrease from the peak value) could be determined for ESR and WBC count on POD 7. Conclusion : Although slight differences were observed in numerical values and kinetics, sequential changes in inflammatory markers according to the duration of antibiotic administration showed similar patterns. Knowledge of CRP kinetics allows the assessment of the degree of difference between the clinical and expected values.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.69-85
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• 2016

Successful relationship with loving partners is one of the most important factors in life. In psychology, there have been some previous researches studying the factors influencing romantic relationships. However, most of these researches were performed based on statistical analysis; thus they have limitations in analyzing complex non-linear relationships or rules based reasoning. This research analyzes commitment and persistence in heterosexual involvement according to styles of loving using a datamining technique as well as statistical methods. In this research, we consider six different styles of loving - 'eros', 'ludus', 'stroge', 'pragma', 'mania' and 'agape' which influence romantic relationships between lovers, besides the factors suggested by the previous researches. These six types of love are defined by Lee (1977) as follows: 'eros' is romantic, passionate love; 'ludus' is a game-playing or uncommitted love; 'storge' is a slow developing, friendship-based love; 'pragma' is a pragmatic, practical, mutually beneficial relationship; 'mania' is an obsessive or possessive love and, lastly, 'agape' is a gentle, caring, giving type of love, brotherly love, not concerned with the self. In order to do this research, data from 105 heterosexual couples were collected. Using the data, a linear regression method was first performed to find out the important factors associated with a commitment to partners. The result shows that 'satisfaction', 'eros' and 'agape' are significant factors associated with the commitment level for both male and female. Interestingly, in male cases, 'agape' has a greater effect on commitment than 'eros'. On the other hand, in female cases, 'eros' is a more significant factor than 'agape' to commitment. In addition to that, 'investment' of the male is also crucial factor for male commitment. Next, decision tree analysis was performed to find out the characteristics of high commitment couples and low commitment couples. In order to build decision tree models in this experiment, 'decision tree' operator in the datamining tool, Rapid Miner was used. The experimental result shows that males having a high satisfaction level in relationship show a high commitment level. However, even though a male may not have a high satisfaction level, if he has made a lot of financial or mental investment in relationship, and his partner shows him a certain amount of 'agape', then he also shows a high commitment level to the female. In the case of female, a women having a high 'eros' and 'satisfaction' level shows a high commitment level. Otherwise, even though a female may not have a high satisfaction level, if her partner shows a certain amount of 'mania' then the female also shows a high commitment level. Finally, this research built a prediction model to establish whether the relationship will persist or break up using a decision tree. The result shows that the most important factor influencing to the break up is a 'narcissistic tendency' of the male. In addition to that, 'satisfaction', 'investment' and 'mania' of both male and female also affect a break up. Interestingly, while the 'mania' level of a male works positively to maintain the relationship, that of a female has a negative influence. The contribution of this research is adopting a new technique of analysis using a datamining method for psychology. In addition, the results of this research can provide useful advice to couples for building a harmonious relationship with each other. This research has several limitations. First, the experimental data was sampled based on oversampling technique to balance the size of each classes. Thus, it has a limitation of evaluating performances of the predictive models objectively. Second, the result data, whether the relationship persists of not, was collected relatively in short periods - 6 months after the initial data collection. Lastly, most of the respondents of the survey is in their 20's. In order to get more general results, we would like to extend this research to general populations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.643-650
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• 2021

The purpose of this study was to evaluate the impact of lecture content on the overall academic satisfaction in non-contact online classes. The study was conducted using an online survey of 107 university students attending metropolitan, local and foreign universities for a week from August 25th, 2020 to September 2nd, 2020. The analysis methods used included descriptive statistics and chi-square distribution, Fisher's exact test, linear by linear association, and logistic regression. The result of the study showed a significant decrease in Junior by 0.025 times compared to Senior (p<.05). Furthermore, a significant decrease in the impact of recorded lectures by 0.036 times compared to a hybrid of face-to-face and online lectures (p<.05). Compared to the response 'No', the number of student's responses of 'Yes' increased significantly by 31.358 times (p<.05). Additionally, a significant increase was seen in teaching methods by 19.709 times, and academic satisfaction by 7.989 times(p<.05). In conclusion, the results imply that the quality of lecture content is also important to improve the student's satisfaction with school life, but overall management is required in the areas of appropriate teaching methods, appropriate tuition, and evaluation methods.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.4
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• pp.159-168
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• 2015

Forty-four surface sediments from Doam Bay were analyzed for total organic carbon (TOC), total nitrogen (TN), total metal (Al, Fe, Mn, Cr, Cu, Ni, Pb, Zn) and further chemical partitioning of metals were carried out in some samples. The TOC (0.32~3.10%) and TN (0.03~0.26%) values of the samples were similar to those of other coastal area. The C/N ratios ranged from 7.9 to 11.9 with an average 9.3 which revealed that contribution of terrestrial organic matters was relatively rare. Contents of analysed metals showed a level lower than threshold effects level (TEL) in sediment quality guidelines. Based on the chemical speciation of metals, the lattice fractions were found in the order Cr > Cu > Ni > Zn > Pb > Mn, while Mn and Pb are the ratio of the non-lattice fractions accounted for more than 50%. The average baseline values were obtained relative cumulative frequency curves and linear regression analysis. The respective baseline concentrations for Cu, Ni, Pb, Zn, Cr and Mn were 11.8, 23.1, 26.8, 76.6, 56.7, 585 mg/kg, respectively. Based on geoaccumulation index ($I_{geo}$) with a baseline values of Mn showed that face the contamination phase from estuarine stations. However, in case of Zn and Pb, although there is no sign of contamination, it could be release from sediment when there is a change in the environment, which is caused from the high ratio of non-lattice fractions.