• The Korean Journal of Gastroenterology
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• v.72 no.6
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• pp.286-294
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• 2018

Background/Aims: The predictive factors of functional dyspepsia (FD) remain controversial. Therefore, we sought to investigate symptom responses in FD patients after Helicobacter pylori (H. pylori) eradication and used predictive factor analysis to identify significant factors of FD resolution at one-year after commencing eradication therapy. Methods: This prospective, multi-center clinical trial was performed on 65 FD patients that met Rome III criteria and had H. pylori infection. Symptom responses and factors that predicted poor response were determined by analysis one year after commencing H. pylori eradication therapy. Results: A total of 63 patients completed the one-year follow-up. When an eradication success group (n=60) and an eradication failure group (n=3) were compared with respect to FD response rate at one year, results were as follows; complete response 73.3% and 0.0%, satisfactory response 1.7% and 0.0%, partial response 10.0% and 33.3%, and refractory response 15.0% and 66.7%, respectively (p=0.013). Univariate analysis showed persistent H. pylori infection (p=0.021), female gender (p=0.025), and medication for FD during the study period (p=0.013) were associated with poor FD response at one year. However, age, smoking, alcohol consumption, and underlying disease were not found to affect response. Finally, multivariate analysis showed that female gender (OR, 4.70; 95% CI, 1.17-18.88) was the sole independent risk factor of poor FD response at one year after commencing H. pylori eradication therapy. Conclusions: Female gender was found to predict poor response in FD patients despite H. pylori eradication. Furthermore, successful H. pylori eradication appears to be associated with FD improvement, but the number of non-eradicated patients was too small to conclude.

• Sleep Medicine and Psychophysiology
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• v.26 no.1
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• pp.23-32
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• 2019

Objectives: Insomnia is not only the most common sleep-related disorder, but also is one of the most important. Knowledge of the comorbidities of insomnia is essential for proper treatment including pharmacological and non-pharmacological methods to prevent disease chronification. This study aimed to determine sleep clinic patients' knowledge of insomnia. Methods: This study recruited 44 patients (24 males and 20 females; mean age $54.11{\pm}16.30years$) from the sleep clinic at National Center for Mental Health. All subjects were asked to complete a self-report questionnaire about their reasons for visiting a sleep clinic and about their knowledge of treatment and comorbidities of insomnia. Results: The reasons for visiting the sleep clinic were insomnia symptoms of daytime sleepiness, irregular sleeping time, nightmares, snoring, and sleep apnea, in that order. Of the responders, 72.7% had a comorbidity of insomnia, and 22.7% showed high-risk alcohol use. In addition, 70.5% of responders chose pharmacological treatment of insomnia as the first option and reported collection of information about treatment of insomnia mainly from the internet and medical staff. More than half (52.3%) of the respondents reported that they had never heard about non-pharmacological treatments of insomnia such as cognitive behavioral treatment (CBT-I) or light therapy. The response rate about comorbidities of varied, with 75% of responders reporting knowledge of the relation between insomnia and depression, but only 38.6% stating awareness of the relation between insomnia and alcohol use disorder. Of the total responders, 68.2% were worried about hypnotics for insomnia treatment, and 70% were concerned about drug dependence. Conclusion: This study showed that patients at a sleep clinic had limited knowledge about insomnia. It is necessary to develop standardized insomnia treatment guidelines and educational handbooks for those suffering from insomnia. In addition, evaluation of alcohol use disorders is essential in the initial assessment of sleep disorders.

• Korean Journal of Environment and Ecology
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• v.33 no.3
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• pp.303-320
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• 2019

Plants interact with various biotic and abiotic environmental factors. It requires much information to understand the traits of a plant species. A shortage of information would restrict the assessment, especially in the evaluation of what kind of factors influence a plant species to face extinction. Polygonatum stenophyllum Maxim. is one of the northern plants of which Korea is the southern distribution edge. The Korean Ministry of Environment had designated it to be the endangered species until December 2015. Although it is comparatively widespread, and a large population has recently been reported, it is assessed to be vulnerable due to the low population genetic diversity. This study evaluated the current distribution of Polygonatum stenophyllum Maxim. We investigated the vegetational environment, population structures, phenology, soil environment, and self-incompatibility based on the results. Lastly, we evaluated the current threats observed in the habitats. The habitats tended to be located in the areas where the masses at the edge of the stream accumulated except for those that were located on slopes of some mountainous areas. Most of them showed a stable population structure and had re-established or recruited seedlings. Polygonatum stenophyllum Maxim. had the difference in time when the shoots appeared above the ground depending on the depth of the rhizome located in the underground. In particular, the seedlings and juveniles had their rhizome located shallow in the soil. Visits by pollinator insects and success in pollination were crucial factors for bearing of fruits by Polygonatum stenophyllum Maxim. The threats observed in the habitat of Polygonatum stenophyllum Maxim. included the expansion of cultivated land, construction of new buildings, and construction of river banks and roads. Despite such observed risk factors, it is not likely that there would be rapid population reduction or extinction because of its widespread distribution with the total population of more than 2.7 million individuals and the new populations established by the re-colonization.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.264-270
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• 2021

In this study we investigated the elution level of lead (Pb), cadmium (Cd), arsenic (As), zinc (Zn), nickel (Ni), antimony (Sb), germanium (Ge), aluminum (Al) and hexavalent chromium (Cr⁶⁺) from 69 home-cooking utensils into a food stimulants. The results of migration testing according to the Korea standards and specifications for utensils, containers and packages showed values the allowable migrantion limits. Al was detected in all 7 utensil materials with the average concentration ranging from 0.002-5.989 mg/L. According to the migration conditions for (180℃, 30 min), the average concentration of Al in paper was 7.2 times higher than 25℃, 10 min (P<0.05). The results of migration testing at 180℃, 30 min were also below the allowable migrantion limits. When comparing with the provisional tolerable weekly intake (PTWI) of Al, the estimated weekly intakes (EWI) accounted for 0.000-0.045% for Al.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.567-574
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• 2022

Coronary artery disease (CAD) and atrial fibrillation (AF) are known to share many risk factors. In particular, in the case of acute coronary syndrome, it may be difficult to clearly determine the diameter of the vessel due to complete occlusion of the vessel and thrombus. Thus, the relationship between the diameter of the coronary arteries was evaluated to be used as a reference data before the treatment of coronary arteries and drug selection in patients with AF. From January 2020 to August 2022, images of coronary angiography (CAG) with AF and normal sinus rhythm (NSR) on electrocardiography were target. In both subjects, images of normal coronary artery without lesions as a result of CAG were used. For all vessels, the diameters of the vessels were measured by dividing them into proximal, middle, and distal parts, and the measured diameters were divided by the average for evaluation. As a result of analyzing the left anterior descending artery diameter, the vessel diameter of the AF patient was 2.24±0.26 mm, which was smaller than that of the NSR patient, 2.86±0.38 mm, and was statistically significant. (p<0.001) As a result of analyzing the left circumflex artery diameter, the vessel diameter of the AF patient was 2.34±0.28 mm, which was smaller than the vessel diameter of the NSR patient, 2.87±0.29 mm, and was statistically significant. (p<0.001) As a result of analyzing the diameter of the right coronary artery, the vessel diameter of the AF patient was 2.68±0.5 mm, which was smaller than the vessel diameter of the NSR patient, 3.35±0.4 mm, and was statistically significant. (p<0.001) Considering that the coronary artery size of AF patients is significantly smaller than the coronary vessel size of NSR patients, it is considered as a useful study to be used as a reference for evaluating coronary artery diameter when the arrhythmia is AF. In particular, it is considered to be a study that can be helpful in diagnosing lesions, using drugs before and after surgery, and choosing to use auxiliary devices such as intravascular ultrasound.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.334-346
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• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.311-330
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• 2023

Development of Carbon Capture and Storage (CCS) technique is becoming increasingly important as a method to mitigate the strengthening effects of global warming, generated from the unprecedented increase in released anthropogenic CO₂. In the recent years, the characteristics of basaltic rocks (i.e., large volume, high reactivity and surplus of cation components) have been recognized to be potentially favorable in facilitation of CCS; based on this, research on utilization of basaltic formations for underground CO₂ storage is currently ongoing in various fields. This study investigated the feasibility of underground storage of CO₂ in basalt, based on the examination of the CO₂ storage mechanisms in subsurface, assessment of basalt characteristics, and review of the global research on basaltic CO₂ storage. The global research examined were classified into experimental/modeling/field demonstration, based on the methods utilized. Experimental conditions used in research demonstrated temperatures ranging from 20 to 250 ℃, pressure ranging from 0.1 to 30 MPa, and the rock-fluid reaction time ranging from several hours to four years. Modeling research on basalt involved construction of models similar to the potential storage sites, with examination of changes in fluid dynamics and geochemical factors before and after CO₂-fluid injection. The investigation demonstrated that basalt has large potential for CO₂ storage, along with capacity for rapid mineralization reactions; these factors lessens the environmental constraints (i.e., temperature, pressure, and geological structures) generally required for CO₂ storage. The success of major field demonstration projects, the CarbFix project and the Wallula project, indicate that basalt is promising geological formation to facilitate CCS. However, usage of basalt as storage formation requires additional conditions which must be carefully considered - mineralization mechanism can vary significantly depending on factors such as the basalt composition and injection zone properties: for instance, precipitation of carbonate and silicate minerals can reduce the injectivity into the formation. In addition, there is a risk of polluting the subsurface environment due to the combination of pressure increase and induced rock-CO₂-fluid reactions upon injection. As dissolution of CO₂ into fluids is required prior to injection, monitoring techniques different from conventional methods are needed. Hence, in order to facilitate efficient and stable underground storage of CO₂ in basalt, it is necessary to select a suitable storage formation, accumulate various database of the field, and conduct systematic research utilizing experiments/modeling/field studies to develop comprehensive understanding of the potential storage site.