• Journal of Korea Trade
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• v.25 no.2
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• pp.111-133
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• 2021

Purpose - Although remittance payment in international trade settlements has played a bigger role in recent years, scant research is being done. This study is to zero in on analyzing determinants of international trade payments focused on remittance by constructing a payment prediction model. Design/methodology - This study categorizes the types of trade payments into advance remittance, post remittance, linked remittance, letter of credit, and mixed payment, and analyzes these after constructing a logit model. For empirical analysis, 147 survey data were collected for export manufacturers in Korea, and binominal logistic regression analysis was used to analyze the type of payment method the exporter chooses for trade transactions. Findings - The likelihood of choosing advance remittance increased as the exporters had non-recovery experiences with payments, and decreased as the market power of importers increased. The possibility of post remittance increased when the export amount was large and the character of the buyer was reliable. In the case of linked remittance, it was highly likely to be selected when payment efficiency was important in trade settlement. In addition, when competition among companies in the global market is intense and market uncertainty is high, the possibility of using a letter of credit decreases. It was also found that the greater the export amount, the greater the possibility of choosing advance remittance, and even if the transaction period was longer, exporters using a letter of credit continued to use it. Originality/value - Despite the high proportion of remittances in international trade settlements, it has been hard to find studies that reflect the practical characteristics of remittances. This study classified the types of remittance into advance remittance, post remittance, and linked remittance, and built a trade payment prediction model by adding a letter of credit and mixed payment. In addition, the originality of this study is recognized in that a logistic model was constructed and meaningful results were derived.

• Health Policy and Management
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• v.30 no.3
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• pp.399-408
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• 2020

Background: The purpose of this study was to examine the impact of the regional characteristics on the accessibility of emergency care and the impact of emergency medical accessibility on the patients' prognosis and the emergency medical expenditure. Methods: This study used the 13th beta version 1.6 annual data of Korea Health Panel and the statistics from the Korean Statistical Information Service. The sample included 8,119 patients who visited the emergency centers between year 2013 and 2017. The arrival time, which indicated medical access, was used as dependent variable for multi-level analysis. For ordinal logistic regression and multiple regression, the arrival time was used as independent variable while patients' prognosis and emergency medical expenditure were used as dependent variables. Results: The results for the multi-level analysis in both the individual and regional variables showed that as the number of emergency medical institutions per 100 km² area increased, the time required to reach emergency centers significantly decreased. Ordinal logistic regression and multiple regression results showed that as the arrival time increased, the patients' prognosis significantly worsened and the emergency medical expenses significantly increased. Conclusion: In conclusion, the access to emergency care was affected by regional characteristics and affected patient outcomes and emergency medical expenditure.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.113-126
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• 2011

The smelting and the continuous casting of steel are important processes that determine the quality of steel products. Especially most of quality defects occur during solidification of the steel continuous casting process. Although quality control techniques such as six sigma, SQC, and TQM can be applied to the continuous casting process for improving quality of steel products, these techniques don't provide real-time analysis to identify the causes of defect occurrence. To solve problems, we have developed a detection model using decision tree which identified abnormal transactions to have a coarse grain structure. And we have compared the proposed model with models using neural network and logistic regression. Experiments on steel data showed that the performance of the proposed model was higher than those of neural network model and logistic regression model. Thus, we expect that the suggested model will be helpful to control the quality of steel products in real-time in the continuous casting process.

• The Journal of Korean Medicine
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• v.28 no.2 s.70
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• pp.22-33
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• 2007

Uterine myoma is a benign tumor of smooth muscle in the uterine wall. Recently, in Oriental medicine, concerns about uterine myoma patients have increased. We analyzed the medical records for 944 patients, including 257 uterine myoma patients, who visited Dongeui University Oriental Medical Center from May 2001 to June 2006. We investigated the DSOM (Diagnosis System of Oriental Medicine) symptom scores which effect uterine myoma patients using stepwise logistic regression model. Logistic regression analysis indicated as follows: In the control group composed of 558 outpatients, 18 items of DSOM were associated with myoma, 9 positively and 9 negatively, and the results showed that the correct rate was equal to 81.1%, sensitivity 72.8%, and specificity 84.9%. In 129 clinical trials data, 33 items of DSOM were associated with myoma, 18 positively and 15 negatively, and the results showed that the correct rate was equal to 85.8%, sensitivity 84.8%, and specificity 87.6%. In 687 outpatient and clinical trials data, 18 items of DSOM were associated with myoma, 10 positively and 8 negatively, and the results showed that the correct rate was equal to 82.8%, sensitivity 70.8%, and specificity 87.3%.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.27-35
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• 2004

A study was conducted to investigate an optimal vibration frequency for mobile phones with just noticeable difference(JND). The just noticeable difference, Weber's law, is the minimum amount by which stimulus intensity must be changed in order to produce a noticeable variation in sensory experience. In order to find the optimal vibration frequency, sixteen frequencies ranged from 24Hz to 603Hz were selected. Subjects then wereasked to differentiate a pair of vibration frequencies. For the analysis, the psychometric function to determine the optimal vibration frequency and the logistic regression to validate the determined frequency were used. The results show that the 2nd order polynomial equations were best fitted for the JND psychometric function and the optimal mobile phone vibrations were determined at 140Hz, 151 Hz, and 160Hz. With the ogive-shaped psychometric function developed by the logistic regression, the results of this study was validated that the determined vibration frequencies (140Hz, 151 Hz, and 160Hz) were optimal mobile phone vibration frequencies.

• Journal of Preventive Veterinary Medicine
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• v.41 no.3
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• pp.129-132
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• 2017

The current study identified risk factors associated with porcine circovirus type 2 (PCV2) infection on pig farms in the Republic of Korea using a multinomial logistic regression model to evaluate the PCV2 infection status of pigs at different growth stages. Compulsory disinfection of visitors (odds ratio [OR]: 0.019, 95% confidence interval [CI]: <0.001-0.378, p=0.0095), compulsory registration of visitors (OR: 0.002, 95% CI: <0.001-0.184, p=0.0070), regular blood testing (OR: 0.012, 95% CI: <0.001-0.157, p=0.0007), and running on-farm biosecurity learning programs for workers (OR: 0.156, 95% CI: 0.040-0.604, p=0.0072 and OR: 0.201, 95% CI: 0.055-0.737, p=0.0155, respectively) were identified as factors which could reduce the risk of PCV2 infection. However, visitation by a regular veterinarian (OR: 32.733, 95% CI: 3.768-284.327, p=0.0016) was associated with PCV2 infection.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.41-49
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• 2021

The causes of climate change are natural and artificial. Natural causes include changes in temperature and sunspot activities caused by changes in solar radiation due to large-scale volcanic activities, while artificial causes include increased greenhouse gas concentrations and land use changes. Studies have shown that excessive carbon use among artificial causes has accelerated global warming. Climate change is rapidly under way because of this. Due to climate change, the frequency and cycle of infectious disease viruses are greater and faster than before. Currently, the world is suffering greatly from coronavirus infection-19 (COVID-19). Korea is no exception. The first confirmed case occurred on January 20, 2020, and the number of infected people has steadily increased due to several waves since then, and many confirmed cases are occurring in 2021. In this study, we conduct a study on climate change before and after COVID-19 using weather data from Korea to determine whether climate change affects infectious disease viruses through logistic regression analysis. Based on this, we want to classify before and after COVID-19 through a logistic regression model to see how much classification rate we have. In addition, we compare monthly classification rates to see if there are seasonal classification differences.

• The Journal of Korean Medicine
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• v.42 no.1
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• pp.12-25
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• 2021

Objectives: The aim of this study was to investigate influencing factors of using Korean medicine services (KMS) using the 2017 Korean Medicine Utilization Survey (KMUS). Methods: Demographic statistics of the survey were summarized and influencing factors of the KMS experience and the intention to visit KMS were analyzed using logistic regression model with complex sample design. Influencing factors were specified based on Andersen's behavioral model of health care utilization and factors associated with individual recognitions of KMS. Additionally, using the ordinary logistic regression model without complex sample design, the survey data were analyzed to compare the results. Results: In the logistic regression analysis, sex, age, health condition, presence of chronic disease, a degree of knowledge about Korean Medicine, and a view about herbal medicine safety were statistically significant both in the KMS experience, and the intention to visit KMS. Marital status was statistically significant in the KMS experience, while family income, a view about the cost of KMS were statistically significant in the intention to visit KMS. Conclusion: Individual recognitions of KMS and enabling components should be considered when establishing KMS policies. In addition, future studies analyzing KMUS need to take into account the complex sample design features of the survey to avoid statistically misleading results.

• Smart Media Journal
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• v.12 no.10
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• pp.29-37
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• 2023

Human lifestyle is affected by the agricultural development in the last 12,000 years ago. The development of agriculture is one of the reasons that global population surged. To ensure sufficient food production for supporting human life, pesticides as a more effective and economical tools, are extensively used to enhance the yield quality and boost crop production. This study investigated the factors that affect crop production and whether the factors of pesticide usage are the most important factors in crop production using the dataset from Kaggle that provides information based on crops harvested by various farmers. Logistic regression is used to investigate the relationship between various factors and crop production. However, the logistic regression is unable to deal with predictors that are related to each other and identifying the greatest impact factor. Therefore, causal discovery is applied to address the above limitations. The result of causal discovery showed that crop condition is greatly impacted by the estimated insects count, where estimated insects count is affected by the factors of pesticide usage. This study enhances our understanding of the influence of pesticide usage on crop production and contributes to the progress of agricultural practices.

• Journal of Navigation and Port Research
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• v.48 no.2
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• pp.109-115
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• 2024

To develop a shipping company insolvency prediction model, we sampled shipping companies that closed between 2005 and 2023. In addition, a closed company and a normal company with similar asset size were selected as a paired sample. For this study, data of a total of 82 companies, including 42 closed companies and 42 general companies, were obtained. These data were randomly divided into a training set (2/3 of data) and a testing set (1/3 of data). Training data were used to develop the model while test data were used to measure the accuracy of the model. In this study, a prediction model for Korean shipping insolvency was developed using financial ratio variables frequently used in previous studies. First, using the LASSO technique, main variables out of 24 independent variables were reduced to 9. Next, we set insolvent companies to 1 and normal companies to 0 and fitted logistic regression, LDA and QDA model. As a result, the accuracy of the prediction model was 82.14% for the QDA model, 78.57% for the logistic regression model, and 75.00% for the LDA model. In addition, variables 'Current ratio', 'Interest expenses to sales', 'Total assets turnover', and 'Operating income to sales' were analyzed as major variables affecting corporate insolvency.