• Journal of Gastric Cancer
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• v.18 no.4
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• pp.379-391
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• 2018

Purpose: Gastric cancer (GC) patients with peritoneal metastasis (PM) have poor prognosis. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) in combination with systemic chemotherapy is a novel treatment option for patients in stage IV of the disease. Materials and Methods: Between November 2015 and June 2018, prospective data collection was performed in 24 patients with GC and PM (median age, 57; range, 44-75 years). These patients underwent 46 PIPAC procedures with a median number of 2 interventions per patient (range, 1-6). A laparoscopic access was used and a combined therapy of cisplatin and doxorubicin aerosol was administered. Results: The median peritoneal carcinomatosis index before the 1st PIPAC was 14 (range, 2-36), and the median ascites volume in patients before the 1st PIPAC was 100 mL (range, 0-6 mL, 300 mL). Eleven patients, who received 2 or more PIPAC procedures, had decreased and stable volumes of ascites, while only 3 patients displayed increasing volume of ascites. The median overall survival was 121 days (range, 66-625 days) after the 1st PIPAC procedure, while 8 patients who received more than 3 PIPAC procedures had a median survival of 450 days (range, 206-481 days) (P=0.0376). Conclusions: Our data show that PIPAC is safe and well tolerated, and that the production of ascites can be controlled by PIPAC in GC patients. Patients, who received 2 or more PIPAC procedures, reported a stable overall quality of life. Further studies are required to document the significance of PIPAC as a palliative multimodal therapy.

• Journal of radiological science and technology
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• v.41 no.6
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• pp.587-594
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• 2018

The purpose of this study was to develop the indirect dosimetry by calculation (IDC) method for diagnostic X-ray equipment. The experiments were performed with inverter type X-ray tubes: Toshiba (E7252X, Japan) and Varian (RAD-14, USA). For the development method, we first applied the standard quality of X-ray beam shown in the TRS457 document, and second, to produce the constants of trendline for the IDC, the total filtration on X-ray beam was subdivided. Third, in order to increase the precision, the energy region was divided into the high energy region and the low energy region and developed by the IDC. In order to verify the IDC, mean dose (mR) values were measured for three Toshiba X-ray tubes and three Varian X-ray tubes at clinical medical institutions and then compared with the IDC on the 2013. As a result, compared with the previous study, the accuracy of the IDC of this study were improved by 2.71% and 9.91% in Toshiba and Varian X-ray tubes, respectively.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.79-108
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• 2021

Information retrieval in the health field has several challenges. Health information terminology is difficult for consumers (laypeople) to understand. Formulating a query with professional terms is not easy for consumers because health-related terms are more familiar to health professionals. If health terms related to a query are automatically added, it would help consumers to find relevant information. The proposed query expansion (QE) models show how to expand a query using MeSH terms. The documents were represented by MeSH terms (i.e. Bag-of-MeSH), found in the full-text articles. And then the MeSH terms were used to generate LDA (Latent Dirichlet Analysis) topic models. A query and the top k retrieved documents were used to find MeSH terms as topic words related to the query. LDA topic words were filtered by threshold values of topic probability (TP) and word probability (WP). Threshold values were effective in an LDA model with a specific number of topics to increase IR performance in terms of infAP (inferred Average Precision) and infNDCG (inferred Normalized Discounted Cumulative Gain), which are common IR metrics for large data collections with incomplete judgments. The top k words were chosen by the word score based on (TP *WP) and retrieved document ranking in an LDA model with specific thresholds. The QE model with specific thresholds for TP and WP showed improved mean infAP and infNDCG scores in an LDA model, comparing with the baseline result.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Land and Housing Review
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• v.13 no.1
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• pp.55-65
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• 2022

This paper investigates the distinct characteristics of freehold and leasehold households living in the seven largest cities and the other areas. We employ the two-stage logit regression analysis to identify the marginal effects of wealth and income after controlling for the other one. We document the following results. First, households with more net wealth are more likely to reside in their own houses, regardless of living areas. Second, the pure income effect after controlling for wealth and other variables lowers the tendency of freeholders to live in the seven largest cities while increasing the tendency to live in the other areas. Furthermore, the income effects reduce the tendency to live in the former regions. Our results suggest that the pure income effects enhance preferences for a better living environment (e.g., larger spaces, better school districts, etc.), whereas the wealth effect increases the likelihood of freeholds.

• Communications of Mathematical Education
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• v.36 no.4
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• pp.559-587
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• 2022

The purpose of this study is to derive implications for the development of the next curriculum and textbooks by comparing and analyzing the textbooks of the 2015 revised high school mathematics curriculum <Mathematics for Economics> and social studies curriculum <Economics>. In the <Mathematics for Economics> textbooks, economic terms and function notations should be introduced. Additionally, the use of graphs for economic-related functions is different from the use of graphs in mathematics in the <Mathematics for Economics> textbooks. For these reasons, the usage of economic terms, function notations, and function graphs covered in the <Mathematics for Economics> textbooks were compared and analyzed with the usage in the <Economics> textbooks. In the <Mathematics for Economics> textbooks, economic terms that are highly related to mathematics are defined and presented. Contrary to the conventions of mathematics and economics, the function notations in the <Mathematics for Economics> textbooks were used inconsistently because uppercase and lowercase letters were mixed in the function notations. Function graphs in the <Mathematics for Economics> textbooks had differences in the range of values represented by the variables regarding axes and scaling. The <Mathematics for Economics> textbooks did not provide a mathematical interpretation of the translation or slope. In the course of <Mathematics for Economics>, it is necessary to specify considerations for teaching and learning, and assessment in the curriculum to promote students' understanding of mathematics and economics. The descriptions in the curriculum document and textbooks of <Mathematics for Economics> should be supplemented to provide learning opportunities for mathematical interpretation of economics-related contents.

• Korea Trade Review
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• v.48 no.2
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• pp.153-174
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• 2023

Electronic bill of lading, Letter of Guarantee, Surrender B/L, Seaway Bill, etc. have been proposed as alternative tools to overcome the crisis of bill of lading, but the most useful of these is by far the Surrender B/L. However, since the Surrender B/L has various problems due to the absence of a legal basis, studies so far have been conducted focusing on these legal limitations or problems in use of the Surrender B/L. This study tried to empirically identify the factors that affect the spread of Surrender B/L by expanding this point of research view. A hierarchical regression analysis was conducted by acquiring 190 valid samples centered on member companies of the Korea International Freight Forwarders Association. In addition, the R program was used to diagnose the research data and analyze the degree of spread by region. As a result of the empirical analysis, it was found that the utilization of Surrender B/L increased due to the convenience and cost reduction effect of Surrender B/L and the apprenticeship training of forwarding companies. In addition, as a result of regional analysis, the spread of Surrender B/L was notable not only in neighboring regions but also even in areas far from Korea.

• Journal of Nutrition and Health
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• v.35 no.4
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• pp.480-488
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• 2002

This study was performed to document the association between obesity, a major risk factor for chronic diseases, and dietary pattern in Korean elderly. The subjects were 595 men and women aged 60-89 years. The subjects were classified into 4 groups based on BMI: under weight, BMI < 18.5, normal weight, 18.5 $\leq$ BMI 22.9; overweight, 23.0 BMI 24.9; and obese BMI $\geq$ 25.0. Dietary intakes by 24-hr recall, blood pressure, anthropometric parameters and health eating index (HEI) were measured. Underweight group was excluded for data analysis doe to small number of subjects, and age-adjusted measurements were compared among normal, overweight and obese groups. The mean anthropometric values for males and females were 23.7 and 24.8 kg/$m^2$ for BMI, 0.90 and 0.86 for WHR and 140.7 and 138.8 mmHg for SBP, respectively. The mean intakes of energy, Ca, vitamin A, vitamin B$_2$, and vitamin E did not meet Korean RDA for elderly. Intakes of fat and cholesterol were low: the percent energy from fat for male and female subjects were 19.1% and 18.1% and mean cholesterol intakes were 208 mg and 152 mg, respectively. Judging by HEI score, dietary quality was better in females than in male subjects. The mean BMI of normal, over and obese groups were 21.4, 23.9, 26.7 kg/$m^2$ in male subjects and 21.6, 23.9, 27.1 kg/$m^2$ in female subjects. WHR, SBP and TSF were increased with increasing BMI No association was found between BMI and nutrient intakes and/or food consumption pattern. However, a tendency was shown that the overweight group reported higher intakes in most nutrients compared to normal and obese groups. This study implies that with increasing BMI, anthropometric risk factors, such as WHR, TSF, and blood pressure were Increased, however, no significant differences were found in nutrient intakes and food patterns. Energy and fat intakes do not seem to be a cause for obesity in Korean elderly.

• Korea Trade Review
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• v.48 no.4
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• pp.303-320
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• 2023

Recently, import and export companies have been using T/T remittance and Surrender B/L more frequently than L/C when selecting the process and method of trade payment settlement. The new trade and logistics environment is thriving in the era of the Fourth Industrial Revolution (4IR). Document-based trade transactions are undergoing a digitalization as bills of lading or smart contracts are being developed. The purpose of this study is to verify whether exporters choose export payment types based on negotiating factors. In addition, we would like to discuss the application of the characteristics of the new trade and logistics environment. Data for analysis was collected through surveys. The collection method consisted of direct visits to the company, e-mail, fax, and online surveys. The survey distribution period is from February 1, 2023, to April 30, 2023. The questionnaire was distributed in 2,000 copies, and 447 copies were collected. The final 336 copies were used for analysis, excluding 111 copies that were deemed inappropriate for the purpose of this study. The results of the study are shown below. First, among the negotiating factors, the product differentiation of exporters did not significantly affect the selection of export payment types. Second, among the negotiating factors, the greater the purchasing advantage recognized by exporters, the higher the possibility of using the post-transfer method. In addition to analyzing the results, this study suggests that exporters should consider adopting new payment methods, such as blockchain technology-based bills of lading and trade finance platforms, to adapt to the characteristics of the evolving trade and logistics environment. Therefore, exporters should continue to show interest in initiatives aimed at digitizing trade documents as a response to the challenges posed by bills of lading. In future studies, it is necessary to address the lack of social awareness in Korea by conducting advanced research abroad.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.