• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.43-53
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• 2022

This paper describes an attempt to improve and optimize the operational safety level of a domestic research reactor by conducting a probabilistic safety assessment (PSA) under full-power operating conditions. The PSA was undertaken to assess the level of safety at an operating research reactor in Korea, to evaluate whether it is probabilistically safe and reliable to operate, and to obtain insights regarding the requisite procedural and design improvements for achieving safer operation. The technical objectives were to use the PSA to identify the accident sequences leading to core damage, and to conduct sensitivity analyses based thereon to derive insights regarding potential design and procedural improvements. Based on the dominant accident sequences identified by the PSA, eight types of sensitivity analysis were performed, and relevant insights for achieving safer operation were derived. When these insights were applied to the reactor design and operating procedure, the risk was found to be reduced by approximately ten times, and the safety was significantly improved. The results demonstrate that the PSA methodology is very effective for improving reactor safety in the full-power operating phase. In particular, it is a highly suitable approach for identifying the deficiencies of a reactor operating at full power, and for improving the reactor safety by overcoming those deficiencies.

• Journal of Korean Neurosurgical Society
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• v.29 no.12
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• pp.1620-1627
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• 2000

Objectives : DAMA cases were analyzed to examine what the main casual factors of DAMA were and how to deal with these cases effectively in hospital with the DAMA interdisciplinary team including medical social worker whose role is to perform psycho-social assessment, family counsel, to evaluate family's DAMA need. Patients and Methods : The content analysis of medical record and social work record were reviewed in 37 cases referred by medical doctor to DAMA team. These cases were reported by patients' self discharge request or family's request for discharge from September 1998 to February 2000. The DAMA team consists of Assistant Director of Hospital as team leader, medical staff in-charge, social worker, QI nurse, other staff members who are not involved in direct treatment for patient, and administrative clerk. Results : The results of content analysis are as follows : 1) The most causal factors of DAMA consist of combination of more than 2 factors. 2) The major decision-maker is revealed to be son and daughter of patient. 3) In 59.4% of cases, family was not informed of patients' prognosis, alternatives, the consequence of DAMA at all. 4) In cases of DAMA report, the rapid intervention of social worker is carried out. Conclusion : In this study, we propose the interdisciplinary team approach to make decision legitimately and ethically for DAMA. The suggestions from this study are as follows : 1) To deal with DAMA case properly, the interdisciplinary team approach should be considered. 2) The criteria for DAMA case should be formed carefully. For the explicit selection of DAMA case, preliminary system for high-risk patient screening is recommended. 3) The medical social worker is available for the psycho-social problems of the patient once family members. For the effective family counselling, discharge planning and nursing home placement, the participation of medical social worker should be mandatory.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.71-78
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• 2019

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.349-359
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• 2017

During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.

• Fire Science and Engineering
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• v.20 no.3 s.63
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• pp.65-70
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• 2006

From the beginning of the construction stage, the fire protection regulation for the nuclear power plants conforms to the design requirements for the acquisition of the license permit. This regulation is based on the plant status of the normal operation, but it is not enough to be used as an application standard for fire protection at the transient mode of the plant and the outage time for refueling as well as for the plant decommissioning. While the advanced fire protection requirement that has been developed in America recently suggests the performance-based requirement and management rule applicable to the overall life time of the plant, it simply represents the conceptual application. It means that it can not be treated as appropriate standards because it does not deal with the qualitative and quantitative approach in specific ways. By the way, with the use of the performance-based fire risk analysis, the dynamic behavior of the heat and smoke at the fire compartment of the nuclear power plants can be analyzed and the thermal effect to the safety-related equipment and cables can be evaluated as well. At this paper, it suggests the ways to change the applicable fire protection regulations and the required evaluation items for the fire risk resulted from the plant configuration change with an intent to introduce the state-of-the-art quantitative fire risk analysis technology at the domestic nuclear power plants.

• The Journal of Korean Medicine
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• v.30 no.3
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• pp.98-105
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• 2009

Objective: Increased aortic and carotid arterial augmentation index (AI) is associated with the risk of cardiovascular disease. The most widely used approach for determining central arterial AI is by calculating the aortic pressure waveform from radial arterial waveforms using a transfer function. But how the change of waveform by applied pressure and the pattern of the change rely on subject's characteristics has not been recognized. In this study, we use a new method for measuring radial waveform and observe the change of waveform and the deviation of radial AI in the same position by applied pressure. Method: Forty-six non-patient volunteers (31 men and 15 women, age range 21-58 years) were enrolled for this study. Informed consent in a form approved by the institutional review board was obtained in all subjects. Blood pressure was measured on the left upper arm using an oscillometric method, radial pressure waves were recorded with the use of an improved automated tonometry device. DMP-3000(DAEYOMEDI Co., Ltd. Ansan, Korea) has robotics mechanism to scan and trace automatically. For each subject, we performed the procedure 5 times for each applied pressure level. We could thus obtain 5 different radial pulse waveforms for the same person's same position at different applied pressures. All these processes were repeated twice for test reproducibility. Result: Aortic AI, peripheral AI and radial AI were higher in women than in men (P<0.01), radial AI strongly correlated with aortic AI, and radial AI was consistently approximately 39% higher than aortic AI. Relationship between representative radial AI of DMP-3000 and peripheral AI of SphygmoCor had strongly correlation. And there were three patterns in change of pulse waveform. Conclusion: In this study, it is revealed the new device was sufficient to measure how radial AI and radial waveform from the same person at the same time change under applied pressure and it had inverse-proportion to applied pressure.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3805-3809
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• 2014

Background: In Mexico, breast cancer (BCa) is the leading type of cancer in women. Cytochrome P450 (CYP450) is a superfamily of major oxidative enzymes that metabolize carcinogens and many antineoplastic drugs. In addition, these enzymes have influence on tumor development and tumor response to therapy. In this report, we analyzed the protein expression in patients with BCa and in healthy women. Links with some clinic-pathological characteristic were also assessed. Materials and Methods: Immunohistochemical analyses were conducted on 48 sets of human breast tumors and normal breast tissues enrolled in Hospital Militar de Especialidades de la Mujer y Neonatologia and Hospital Central Militar, respectively, during the time period from 2010 to 2011. Informed consent was obtained from all participants. Statistical analysis was performed using ${\chi}^2$ or Fisher exact tests to estimate associations and the Mann Whitney U test for comparison of group means. Results: We found a significant CYP3A4 overexpression in BCa stroma and gland regions in comparison with healthy tissue. A significant association between protein expression with smoking, alcoholism and hormonal contraceptives use was also observed. Additionally, we observed estrogen receptor (ER) and progesterone receptor (PR) positive association in BCa. Conclusions: We suggest that CYP3A4 expression promotes BCa development and can be used in the prediction of tumor response to different treatments. One therapeutic approach may thus be to block CYP3A4 function.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.14 no.3
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• pp.235-243
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• 2016

In the near future, many countries, including the Republic of Korea, will face a significant increase in low level radioactive waste (LLW) from nuclear power plant decommissioning. The purpose of this paper is to look at blending as a method for enhancing disposal options for low-level radioactive waste from the decommissioning of nuclear reactors. The 2007 U.S. Nuclear Regulatory Commission strategic assessment of the status of the U.S. LLW program identified the need to move to a risk-informed and performance-based regulatory approach for managing LLW. The strategic assessment identified blending waste of varying radionuclide concentrations as a potential means of enhancing options for LLW disposal. The NRC's position is that concentration averaging or blending can be performed in a way that does not diminish the overall safety of LLW disposal. The revised regulatory requirements for blending LLW are presented in the revised NRC Branch Technical Position for Concentration Averaging and Encapsulation (CA BTP 2015). The changes to the CA BTP that are the most significant for NPP operation, maintenance and decommissioning are reviewed in this paper and a potential application is identified for decommissioning waste in Korea. By far the largest volume of LLW from NPPs will come from decommissioning rather than operation. The large volumes in decommissioning present an opportunity for significant gains in disposal efficiency from blending and concentration averaging. The application of concentration averaging waste from a reactor bio-shield is also presented.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.