• Anxiety and mood
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• v.19 no.2
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• pp.77-82
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• 2023

Objective : During the coronavirus disease 2019 (COVID-19) pandemic, hospital avoidance had a significant impact on public health. We investigated the factors associated with hospital avoidance and explored practical strategies hospitals could employ to address this phenomenon. Methods : We conducted a patient experience survey in a general hospital in Korea during the COVID-19 pandemic. Between July 6, 2020, and July 20, 2020, a total of 842 patients who had previously visited hospitals before the COVID-19 outbreak participated. Self-reported hospital avoidance, factors associated with hospital avoidance, and satisfaction with the hospital's infection control policies were the main outcomes. Binary logistic regression analysis was used to identify associated factors. Results : Data indicated that 29.9% (n=252) of the respondents avoided visiting the hospital after the COVID-19 outbreak. Satisfaction with the hospital infection control policy (odds ratio [OR]=2.297, p<0.001), female sex (OR=1.619, p<0.05), and higher educational level (OR=1.884, p<0.001) were associated with hospital avoidance. The "entrance body temperature check" was the most satisfactory policy among the hospital's infection control policies. Conclusion : To manage hospital avoidance during an infectious disease crisis, targeted policies for at-risk groups and hospital policies to reassure and satisfy patients are needed.

• Land and Housing Review
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• v.15 no.2
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• pp.39-56
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• 2024

This study addressed housing issues among various social problems of youth. With a focus on residential independence, this study analyzed the factors that lead youth to achieve residential independence. This study drew on nationwide data from the 'Youth Life Survey (2022)' with a sample size of 12,578. Binary logistic regression analysis was employed, with the dependent variable being residential independence. Key factors were as follows. The probability of residential independence was higher for men than women. Residential independence occurred mainly in non-metropolitan areas compared to metropolitan areas. Findings revealed that greater age, income, and assets facilitate achieving residential independence. In addition, public transport and cultural facilities were important for their residential independence, and it was found that the previous experience of residential independence had a positive effect. Policy implications derived from the findings are as follows. It is required to consider the heterogeneity and diversity of youth rather than implementing unitary policies. To ensure continuity and sustainability of self-reliance, long-term support programs are needed rather than temporary support. Moreover, it is required to offer public support comprehensively, instead of youth relying on support from personal networks, including their parents. An inclusive housing policy should be established to support youth for their residential independence in the future.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.466-478
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• 2022

Objective: ¹⁸F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of ¹⁸F-FDG PET/CT in identifying malignancy in patients with HLH by combining ¹⁸F-FDG PET/CT and clinical parameters. Materials and Methods: Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment ¹⁸F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH). Results: The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of ¹⁸F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity. Conclusion: ¹⁸F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using ¹⁸F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of ¹⁸F-FDG PET/CT images.

• Korean small business review
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• v.31 no.2
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• pp.85-102
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• 2009

With the continued globalization of world markets, transfer pricing has become one of the dominant sources of controversy in international taxation. Transfer pricing is the process by which a multinational corporation calculates a price for goods and services that are transferred to affiliated entities. Consider a Korean electronic enterprise that buys supplies from its own subsidiary located in China. How much the Korean parent company pays its subsidiary will determine how much profit the Chinese unit reports in local taxes. If the parent company pays above normal market prices, it may appear to have a poor profit, even if the group as a whole shows a respectable profit margin. In this way, transfer prices impact the taxable income reported in each country in which the multinational enterprise operates. It's importance lies in that around 60% of international trade involves transactions between two related parts of multinationals, according to the OECD. Multinational enterprises (hereafter MEs) exert much effort into utilizing organizational advantages to make global investments. MEs wish to minimize their tax burden. So MEs spend a fortune on economists and accountants to justify transfer prices that suit their tax needs. On the contrary, local governments are not prepared to cope with MEs' powerful financial instruments. Tax authorities in each country wish to ensure that the tax base of any ME is divided fairly. Thus, both tax authorities and MEs have a vested interest in the way in which a transfer price is determined, and this is why MEs' international transfer prices are at the center of disputes concerned with taxation. Transfer pricing issues and practices are sometimes difficult to control for regulators because the tax administration does not have enough staffs with the knowledge and resources necessary to understand them. The authors examine transfer pricing practices to provide relevant resources useful in designing tax incentives and regulation schemes for policy makers. This study focuses on identifying the relevant business and environmental factors that could influence the international transfer pricing of MEs. In this perspective, we empirically investigate how the management perception of related variables influences their choice of international transfer pricing methods. We believe that this research is particularly useful in the design of tax policy. Because it can concentrate on a few selected factors in consideration of the limited budget of the tax administration with assistance of this research. Data is composed of questionnaire responses from foreign firms in Korea with investment balances exceeding one million dollars in the end of 2004. We mailed questionnaires to 861 managers in charge of the accounting departments of each company, resulting in 121 valid responses. Seventy six percent of the sample firms are classified as small and medium sized enterprises with assets below 100 billion Korean won. Reviewing transfer pricing methods, cost-based transfer pricing is most popular showing that 60 firms have adopted it. The market-based method is used by 31 firms, and 13 firms have reported the resale-pricing method. Regarding the nationalities of foreign investors, the Japanese and the Americans constitute most of the sample. Logistic regressions have been performed for statistical analysis. The dependent variable is binary in that whether the method of international transfer pricing is a market-based method or a cost-based method. This type of binary classification is founded on the belief that the market-based method is evaluated as the relatively objective way of pricing compared with the cost-based methods. Cost-based pricing is assumed to give mangers flexibility in transfer pricing decisions. Therefore, local regulatory agencies are thought to prefer market-based pricing over cost-based pricing. Independent variables are composed of eight factors such as corporate tax rate, tariffs, relations with local tax authorities, tax audit, equity ratios of local investors, volume of internal trade, sales volume, and product life cycle. The first four variables are included in the model because taxation lies in the center of transfer pricing disputes. So identifying the impact of these variables in Korean business environments is much needed. Equity ratio is included to represent the interest of local partners. Volume of internal trade was sometimes employed in previous research to check the pricing behavior of managers, so we have followed these footsteps in this paper. Product life cycle is used as a surrogate of competition in local markets. Control variables are firm size and nationality of foreign investors. Firm size is controlled using dummy variables in that whether or not the specific firm is small and medium sized. This is because some researchers report that big firms show different behaviors compared with small and medium sized firms in transfer pricing. The other control variable is also expressed in dummy variable showing if the entrepreneur is the American or not. That's because some prior studies conclude that the American management style is different in that they limit branch manger's freedom of decision. Reviewing the statistical results, we have found that managers prefer the cost-based method over the market-based method as the importance of corporate taxes and tariffs increase. This result means that managers need flexibility to lessen the tax burden when they feel taxes are important. They also prefer the cost-based method as the product life cycle matures, which means that they support subsidiaries in local market competition using cost-based transfer pricing. On the contrary, as the relationship with local tax authorities becomes more important, managers prefer the market-based method. That is because market-based pricing is a better way to maintain good relations with the tax officials. Other variables like tax audit, volume of internal transactions, sales volume, and local equity ratio have shown only insignificant influence. Additionally, we have replaced two tax variables(corporate taxes and tariffs) with the data showing top marginal tax rate and mean tariff rates of each country, and have performed another regression to find if we could get different results compared with the former one. As a consequence, we have found something different on the part of mean tariffs, that shows only an insignificant influence on the dependent variable. We guess that each company in the sample pays tariffs with a specific rate applied only for one's own company, which could be located far from mean tariff rates. Therefore we have concluded we need a more detailed data that shows the tariffs of each company if we want to check the role of this variable. Considering that the present paper has heavily relied on questionnaires, an effort to build a reliable data base is needed for enhancing the research reliability.

• Korean Chemical Engineering Research
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• v.55 no.3
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• pp.322-331
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• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• The Korean Society of Law and Medicine
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• v.19 no.2
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• pp.235-261
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• 2018

The Medical law stipulates regulations about the physician's duty to inform to contribute to patient's self-determination. This law was most recently revised on December 20, 2016, and came into effect on June 21, 2017. There has been much controversy about this, and it has been questioned whether or not it will be effective for physicians to comply with the duty to inform. Therefore, this study investigated perceptions of physicians of whether they observed the duty to inform and their legal judgment about that duty, and analyzed how the revision of the medical law may have affected the legal cognition of physician's duty to inform. This study was conducted through an online questionnaire survey involving 109 physicians over 2 weeks from March 29 to April 12, 2018, and 108 of the collected data were used for analysis. The questionnaire was developed by revising and supplementing the previous research (Lee, 2004). It consisted of 41 items, including 26 items related to the experience of and legal judgment about the duty to inform, 6 items related to awareness of revised medical law, and 9 items on general characteristics. The data were analyzed using SAS 9.4 program and descriptive statistics, Chi-square test, Fisher's exact test and Binary logistic regression were performed. The results are as follows. • Out of eight situations, the median number of situations that did not fulfill the duty to inform was 5 (IQR, 4-6). In addition, 12 respondents (11%) answered that they did not fulfill the duty to inform in all eight cases, while only one (1%) responded that he/she performed explanation obligations in all cases. • The median number of the legal judgment score on the duty to inform was 8 out of 13 (IQR, 7-9), and the scores ranged from a minimum of 4 (4 respondents) to a maximum of 11 (3 respondents). • More than half of the respondents (n=26, 52%) were unaware of the revision of the medical law, 27 (25%) were aware of the fact that the medical law had been revised, 20(18%) had a rough knowledge of the contents of the law, and only 5(5%) said they knew the contents of the law in detail. The level of awareness of the revised medical law was statistically significant difference according to respondents' sex (p<.49), age (p<.0001), career (p<.0001), working type (p<.024), and department (p<.049). • There was no statistically significant relationship between the level of awareness of the revised medical law and the level of legal judgment on the duty to inform. These results suggest that efforts to improve the implementation and cognition of physician's duty to inform are needed, and it is difficult to expect a direct positive effect from the legal regulations per se. Considering the distinct characteristics of medical institutions and hierarchical organizational culture of physicians, it is necessary to develop a credible guideline on the duty to inform within the medical system, and to strengthen the education of physicians about their duty to inform and its purpose.

• The Journal of Bigdata
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• v.6 no.1
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• pp.83-89
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• 2021

Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.

• Journal of agricultural medicine and community health
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• v.48 no.2
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• pp.103-117
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• 2023

Objectives: This study aims to reveal the relationship between social relations and physical activity in the young-old and old-old elderly. Methods: Data from 2020 National survey of Older Koreans were used, and a total of 10,097 subjects over the age of 65 were included in analysis. The dependent variable was physical activity, and the independent variables were social relations barrier and motivational factors. x²-test and binary logistic regression were performed for data analysis. Results: The physical activity rate in the elderly were 40.8% in the young-old and 29.2% in the old-old. The socio-demographic characteristics affecting physical activity were the young-old elderly were sex, residential area, employment status and household income, and the old-old elderly were sex, age, residential area, education level and household income. The social relations barrier factors affecting physical activity were the young-old elderly were number of close friends, family care, exercise information search and video viewing, and the old-old elderly were household type, number of close friends, participation in exercise education, exercise information search and video viewing. The social relations motivational factors affecting physical activity were the young-old elderly were call with children/relative/friend, participation in sports activity, access time from home to parks, and the old-old elderly were call with children/relative/friend, participation in sports activity, satisfaction with green spaces. Conclusions: It was found that social relations barrier and motivational factors of the elderly are important factors to consider when developing physical activity promotion strategy, and there are also difference between the age of the elderly.