• Neonatal Medicine
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• v.18 no.2
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• pp.310-319
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• 2011

Purpose: The aim of this study was to investigate the effect of perinatal risk factors on brain maturation and the relationship of brain maturation and neurodevelopmental outcomes with brain maturation scoring system in brain MRI. Methods: ELBWI infants born at the Seoul National University Children's Hospital from January 2006 to December 2010 were included. A retrospective analysis was performed with their medical record and brain MR images acquired at near full term. We read brain MRI and measured maturity with total maturation score (TMS). TMS is a previously developed anatomic scoring system to assess brain maturity. The total maturation score was used to evaluate the four parameters of maturity: (1) myelination, (2) cortical infolding, (3) involution of glial cell migration bands, and (4) presence of germinal matrix tissue. Results: Images from 124 infants were evaluated. Their mean gestational age at birth was 27.1${\pm}$2.1 weeks, and mean birth weight was 781.5${\pm}$143.9 g. The mean TMS was 10.8${\pm}$2.0. TMS was significantly related to the postmenstrual age (PMA) of the infant, increasing with advancing postmenstrual age (P<0.001). TMS showed no significance with neurodevelopmental delay, and with brain injury, respectively. Conclusion: TMS was developed for evaluating brain maturation in conventional brain MRI. The results of this study suggest that TMS was not useful for predicting neurodevelopmental delay, but further studies are needed to make standard score for each PMA and to re-evaluate the relationship between brain maturation and neurodevelopmental delay.

• Journal of Chest Surgery
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• v.42 no.5
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• pp.604-609
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• 2009

Background: Recently, percutaneous cardiopulmonary support (PCPS) has been widely used to rescue patients in cardiogenic shock or cardiac arrest. However, patients with cardiopulmonary bypass (CPB) weaning failure during open heart surgery still have very poor outcomes after PCPS. We investigated clinical results and prognostic factors for patients who underwent PCPS during open heart surgery. Material and Method: From January 2005 to December 2008, 10 patients with CPB weaning failure during open heart surgery underwent PCPS using the CAPIOX emergency bypass system ($EBS^{(R)}$, Terumo Inc, Tokyo, Japan). We retrospectively reviewed the medical records of those 10 patients. Result: The average age of the patients was $60.2{\pm}16.5$ years (range, $19{\sim}77$ years). The mean supporting time was $48.7{\pm}64.7$ hours (range, $4{\sim}210$ hours). Of the 10 patients, 6(60%) were successfully weaned from the PCPS While 5 (50%) were able to be discharged from the hospital. Complications were noted in 5 patients (50%). In univariate analysis, long aortic cross clamp time during surgery, mediastinal bleeding during PCPS and high level of Troponin-I before PCPS were significant risk factors. All of the discharged patients are still surviving $34{\pm}8.6$ months (range, $23{\sim}48$ months) post-operatively. Conclusion: The use of PCPS for CPB weaning failure during open heart surgery can improve the prognosis. More experience and additional clinical studies are necessary to improve survival and decrease complications.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.23-48
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• 2021

The Korean economy has achieved continuous economic growth for the past several decades thanks to the government's export strategy policy. This increase in exports is playing a leading role in driving Korea's economic growth by improving economic efficiency, creating jobs, and promoting technology development. Traditionally, the main factors affecting Korea's exports can be found from two perspectives: economic factors and industrial structural factors. First, economic factors are related to exchange rates and global economic fluctuations. The impact of the exchange rate on Korea's exports depends on the exchange rate level and exchange rate volatility. Global economic fluctuations affect global import demand, which is an absolute factor influencing Korea's exports. Second, industrial structural factors are unique characteristics that occur depending on industries or products, such as slow international division of labor, increased domestic substitution of certain imported goods by China, and changes in overseas production patterns of major export industries. Looking at the most recent studies related to global exchanges, several literatures show the importance of cultural aspects as well as economic and industrial structural factors. Therefore, this study attempted to develop a forecasting model by considering cultural factors along with economic and industrial structural factors in calculating the import volume of each country from Korea. In particular, this study approaches the influence of cultural factors on imports of Korean products from the perspective of PUSH-PULL framework. The PUSH dimension is a perspective that Korea develops and actively promotes its own brand and can be defined as the degree of interest in each country for Korean brands represented by K-POP, K-FOOD, and K-CULTURE. In addition, the PULL dimension is a perspective centered on the cultural and psychological characteristics of the people of each country. This can be defined as how much they are inclined to accept Korean Flow as each country's cultural code represented by the country's governance system, masculinity, risk avoidance, and short-term/long-term orientation. The unique feature of this study is that the proposed final prediction model can be selected based on Design Principles. The design principles we presented are as follows. 1) A model was developed to reflect interest in Korea and cultural characteristics through newly added data sources. 2) It was designed in a practical and convenient way so that the forecast value can be immediately recalled by inputting changes in economic factors, item code and country code. 3) In order to derive theoretically meaningful results, an algorithm was selected that can interpret the relationship between the input and the target variable. This study can suggest meaningful implications from the technical, economic and policy aspects, and is expected to make a meaningful contribution to the export support strategies of small and medium-sized enterprises by using the import forecasting model.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.44-58
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• 2018

Cities face new challenges not only in natural disasters by climate change but also in social and economic fluctuations. With the existing simple reconstruction method, it is difficult to solve the overall problems that a city or region may face. As a new approach to cope with various changes, the concept of resilience is emerging. Resilience is also one of the themes of recent major urban design projects. Design with the concept of resilience is a new strategy that can deal with various changes of urban space, rather than a temporary trend. The purpose of this paper is to explore the design method by analyzing cases where the concept of resilience is employed. We aim to examine what kind of design strategies are needed for the resilience design and how this design process differ in character, as compared to general design projects. Cases for this study include the "Rebuild by Design" competition held in 2013 and the "Resilient by Design/Bay Area Challenge" competition held in 2017. This paper consists of literature reviews and case studies. The latter is divided into two aspects: content analysis based on the theory of resilience and characteristics of the design process. Cases are analyzed through literature reviews and process characteristics of resilience design in response to the general design process. The main categories for urban resilience used as the framework for analysis include: Urban Infrastructure, Social Dynamics, Economic Dynamics, Health and Wellbeing, Governance Networks, and Planning and Institutions. As a result, the aspects of resilience concepts considered and design strategies undertaken by each team were identified. Each team tried to connect all 6 categories to their design strategies, placing special value on the role of governance, a system that enables collaborative design and project persistency. In terms of the design process, the following characteristics were found: planning the whole project process in the pre-project phase, analyzing predictable socioeconomic risk factors in addition to physical vulnerabilities, aiming for landscape-oriented integrated design, and sustainable implementation strategies with specific operations and budget plans. This paper is meaningful to connect the concept of resilience, which has been discussed in various articles, to design strategy, and to explore the possibility of constructing a practical methodology by deriving the characteristics of the resilience design process. It remains a future task to research design strategies that apply the concept of resilience to various types of urban spaces, in addition to areas that are vulnerable to disasters.

• Tuberculosis and Respiratory Diseases
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• v.60 no.3
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• pp.277-284
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• 2006

Background : Delayed treatment of pulmonary tuberculosis is an important problem because it results in greater mortality and the nosocomial transmission of tuberculosis. This study was conducted to analyze the factors that contribute to the delayed treatment of pulmonary tuberculosis in a university hospital and we wanted to provide basic data for instituting an effective management program for tuberculosis. Methods : we retrospectively reviewed the medical records of 155 patients with smear-positive or culture-positive pulmonary tuberculosis and who were treated between May 1999 and October 1999. A case-control study was performed to analyze the factors. We then tried to follow up the patients in delayed treatment group via telephone for the purpose of assessing the therapeutic interventions. Results : Among 150 patients, 55 (37%) were included in the delayed treatment group. The factors associated with delayed treatment on the univariate analysis included age (61 vs 40 years old; p <0.001), a smear-negative sputum test for acid-fast bacilli (AFB) (85% vs 55%; p <0.001) and no visits to a private clinic before the patient presented to the university hospital (56% vs 36%; p = 0.014). Multivariate analysis revealed that old age (p = 0.001), a smear-negative sputum for AFB (p = 0.001), and lower lobe infiltrate on chest X-ray (p = 0.041) were the independent predictors of delayed treatment. Of the 22 patients who did not receive any treatment, 20 of them 91%) consented to our suggestion of revisiting the hospital. Conclusion : Delayed treatment of patients with pulmonary tuberculosis is not uncommon in a university hospital. Old age, smear-negative for AFB, and lower lobe infiltrate on chest X-ray are the risk factors for delayed treatment. A more systematic management system is required for achieving better control of tuberculosis.

• Journal of Nutrition and Health
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• v.47 no.2
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• pp.113-123
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• 2014

Purpose: Vitamin D has been known to play an important role in the central nervous system and brain functions in the human body, and cumulative evidence has shown that vitamin D deficiency might be linked with various mental health conditions. Epidemiologic studies have shown that vitamin D deficiency may be associated with higher risk of depression in the US and European populations. However, limited information is available regarding the association between vitamin D status and depression in the Korean population. The objective of this study was to examine the associations between vitamin D levels and prevalence of depression. Methods: We conducted a cross-sectional analysis using nationally representative data from the 2008-2010 Korean National Health and Nutrition Examination Survey from which serum 25-hydroxyvitamin D concentrations were available. A total of 18,735 adults who had available demographic, dietary, and lifestyle information were included in our analysis. We defined "depression" with a diagnosis by a physician. "Depressed condition" was defined as having feelings of sadness or depression without diagnosis by a physician. Results: The prevalence of depression was 1.63% and 5.43% in Korean men and women, respectively; 12.5% of men and 26.1% of women were defined as the group having depressed conditions. In multivariate logistic regression models, no significant associations were observed between vitamin D status and prevalence of depression or depressed conditions in Korean men and women. Conclusion: We found no association between vitamin D insufficiency and depression/depressed conditions in Korean adults. Future large prospective studies and randomized controlled trials are needed to confirm this relationship.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• 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 Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• 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.