• Progress in Medical Physics
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• v.24 no.4
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• pp.271-277
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• 2013

In prostate IMRT planning, the planning target volume (PTV), extended from a clinical target volume (CTV), often contains an overlap air volume from the rectum, which poses a problem inoptimization and prescription. This study was aimed to establish a planning method for such a case. There can be three options in which volume should be considered the target during optimization process; PTV including the air volume of air density ('airOpt'), PTV including the air volume of density value one, mimicking the tissue material ('density1Opt'), and PTV excluding the air volume ('noAirOpt'). Using 10 MV photon beams, seven field IMRT plans for each target were created with the same parameter condition. For these three cases, DVHs for the PTV, bladder and the rectum were compared. Also, the dose coverage for the CTV and the shifted CTV were evaluated in which the shifted CTV was a copied and translated virtual CTV toward the rectum inside the PTV, thus occupying the initial position of the overlap air volume, simulating the worst condition for the dose coverage in the target. Among the three options, only density1Opt plan gave clinically acceptable result in terms of target coverage and maximum dose. The airOpt plan gave exceedingly higher dose and excessive dose coverage for the target volume whereas noAirOpt plan gave underdose for the shifted CTV. Therefore, for prostate IMRT plan, having an air region in the PTV, density modification of the included air to the value of one, is suggested, prior to optimization and prescription for the PTV. This idea can be equally applied to any cases including the head and neck cancer with the PTV having the overlapped air region. Further study is being under process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.317-324
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• 2015

GNSS was firstly proposed for application in weather forecasting in the mid-1980s. It has continued to demonstrate the practical uses in GNSS meteorology, and other relevant researches are currently being conducted. Precipitable Water Vapor (PWV), calculated based on the GNSS signal delays due to the troposphere of the Earth, represents the amount of the water vapor in the atmosphere, and it is therefore widely used in the analysis of various weather phenomena such as monitoring of weather conditions and climate change detection. In this study we calculated the PWV through the meteorological information from an Automatic Weather Station (AWS) as well as GNSS data processing of a Continuously Operating Reference Station (CORS) in order to analyze the heavy snowfall of the Ulsan area in early 2014. Song’s model was adopted for the weighted mean temperature model (T_m), which is the most important parameter in the calculation of PWV. The study period is a total of 56 days (February 2013 and 2014). The average PWV of February 2014 was determined to be 11.29 mm, which is 11.34% lower than that of the heavy snowfall period. The average PWV of February 2013 was determined to be 10.34 mm, which is 8.41% lower than that of not the heavy snowfall period. In addition, certain meteorological factors obtained from AWS were compared as well, resulting in a very low correlation of 0.29 with the saturated vapor pressure calculated using the empirical formula of Magnus. The behavioral pattern of PWV has a tendency to change depending on the precipitation type, specifically, snow or rain. It was identified that the PWV showed a sudden increase and a subsequent rapid drop about 6.5 hours before precipitation. It can be concluded that the pattern analysis of GNSS PWV is an effective method to analyze the precursor phenomenon of precipitation.

• Journal of Chest Surgery
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• v.39 no.9 s.266
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• pp.692-698
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• 2006

Background: Owing to the fact that the average life span has increased and the progress in medical science has been made, the number of patients with chronic renal failure (CRF) who have to take hemodialysis (HD) has been going up gradually. Accordingly, it is considered to be as a significant issue to obtain blood vessels which can be used repetitively and supply enough blood flows. Therefore, there have been various kinds of study on an inosculation rate andfactors influencing it following an arteriovenous fistula (AV fistula) and lots of studies are ongoing for the purpose of escalating the inosculation rate. The authors analyzed the effects of short-term result, age, sex, diabetes and hypertension on arteriovenous inosculations in 134 anatomical snuffbox operated subjects among the patients who have taken an AV fistula at this center. Material and Method: Based on 134 patients who underwent an AV fistula at the department of thoracic surgery of this center from July, 2000 to May, 2004, the difference in arteriovenous inosculation rate was compared and analyzed depending or age (discriminated by 65-year-old), sex and the condition of the presence or absence of diabetes and hypertension. Correlation analyses were conducted for each parameter and statistical tests were performed by using SPSS for windows Release 11.0.1, which were determined to be statistically significant if p value was below 0.05. Result: The total number of operations was 169 including 35 of re-operations. The male/female rate was 70 : 64 (52% : 48%). The average age was $56.3{\pm}12.26$ years and there were 33 (24%) old aged patients above 65-year-old; there were 103 (71%) patients with hypertension and 90 (67%) patients with diabetes. Overall arteriovenous inosculation rate was $93{\pm}2.4%,\;91{\pm}2.7%,\;89{\pm}3.0%$ at 6, 12, 24 months, respectively. The arteriovenous inosculation rate of above 65-year-old patient group was $85{\pm}4.8%,\;80{\pm}5.8%,\;80{\pm}5.8%$ and below 64-year-old patient group's was $85{\pm}4.8%,\;80{\pm}5.8%,\;80{\pm}5.8%$ at given time points, respectively, which showed higher inosculation rate in below 64-year-old patient group with a statistical significance (p=0.0034). However, no statistical significance was found between the patients with hypertension and diabetes and the patients with no complication. In addition, there was no statistical significance in inosculation rate between male and female. Conclusion: The arteriovenous inosculation ratewas higher in the treated patient below 64-year-old than in the treated patient above 65-year-old. Thus it is advantageous for increase in long-term inosculation rate to obtain hemodialysis routes at an early age. The conditions of sex and the presence or absence of diabetes and hyper- tension do not make statistically significant effect on the arteriovenous inosculation rate.

• Tuberculosis and Respiratory Diseases
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• v.46 no.2
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• pp.215-228
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• 1999

Background: Sarcoidosis is a chronic granulomatous inflammatory disease of unknown etiology often involving the lungs and intrathoracic lymph nodes. The natural course of sarcoidosis is variable from spontaneous remission to significant morbidity or death. But, the mechanisms causing the variable clinical outcomes or any single parameter to predict the prognosis was not known. In sarcoidosis, the number and the activity of CD4 + lymphocytes are significantly increased at the loci of disease and their oligoclonality suggests that the CD4 + lymphocytes hyperreactivity may be caused by persistent antigenic stimulus. Recently, it has been known that CD4+ lymphocytes can be subdivided into 2 distinct population(Th1 and Th2) defined by the spectrum of cytokines produced by these cells. Th1 cells promote cellular immunity associated with delayed type hypersensitivity reactions by generating IL-2 and IFN-$\gamma$. Th2 cells playa role in allergic responses and immediate hypersensitivity reactions by secreting IL-4, IL-5, and IL-10. CD4+ lymphocytes in pulmonary sarcoidosis were reported to be mainly Th1 cells. IL-12 has been known to play an important role in differentiation of undifferentiated naive T cells to Th1 cells. And, Moller et al. observed increased IL-12 in bronchoalveolar lavage fluid(BALF) in patients with sarcoidosis. So it is possible that the elevated level of IL-12 is necessary for the continuous progression of the disease in active sarcoidosis. This study was performed to test the assumption that IL-12 can be a marker of active pulmonary sarcoidosis. Methods: We measured the concentration of IL-12 in BALF and in conditioned medium of alveolar macrophage(AM) using ELISA(enzyme-linked immunosorbent assay) method in 26 patients with pulmonary sarcoidosis(10 males, 16 females, mean age: $39.8{\pm}2.1$ years) and 11 normal control. Clinically, 14 patients had active sarcoidosis and 12 patients had inactive. Results: Total cells counts, percentage and number of lymhocytes, number of AM and CD4/CD8 lymphocyte ratio in BALF were significantly higher in patients with sarcoidosis than in control group. But none of these parameters could differentiate active sarcoidosis from inactive disease. The concentration of IL-12 in BALF was significantly increased in sarcoidosis patients ($49.3{\pm}9.2$ pg/ml) than in normal control ($2.5{\pm}0.4$ pg/ml) (p<0.001). Moreover it was significantly higher in patients with active sarcoidosis ($70.3{\pm}14.8$ pg/ml) than in inactive disease ($24.8{\pm}3.l$ pg/ml) (p=0.001). Also, the concentration of IL-12 in BALF showed significant correlation with the percentage of AM(p<0.001), percentage(p<0.001) and number of lymphocyte(p<0.001) in BALF, suggesting the close relationship between the level of IL-12 in BALF and the inflammatory cell infiltration in the lungs. Furthermore, we found a significant correlation between the level of IL-12 and the concentration of soluble ICAM-1 : in serum(p<0.001) and BALF (p=0.001), and also between IL-12 level and ICAM-1 expression of AM(p<0.001). The AM from patients with pulmonary sarcoidosis secreted significantly larger amount of IL-12 ($206.2{\pm}61.9$ pg/ml) than those of control ($68.3{\pm}43.7$ pg/ml) (p<0.008), but, there was no difference between inactive and active disease group. Conclusion : Our data suggest that the BALF IL-12 level can be used as a marker of the activity of pulmonary sarcoidosis.

• Korean Journal of Soil Science and Fertilizer
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• v.8 no.1
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• pp.1-17
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• 1975

Laboratory experiments on the phosphorus adsorption by soil were conducted to evaluate the parameters for determination of phosphorus adsorption capacity of soil, which serve as a basis for establishing the amount of phosphorus required to improve newly reclaimed soil and volcanic ash soil. The calculated Langmuir adsorption maxima varied from 6.2-32.9, 74.7-90.4 and 720-915mg p/100g soil for cultivated soils, non-cultivated soils, and volcanic ash soils respectively. The phosphorus absorption coefficient ranged from 116-179, 161-259 and 1,098-1,205mg p/100g soil for cultivated soils, non-cultivated soils, and volcanic ash soils respectively. The ratio of the phosphorus absorption coefficient to Langmuir adsorption maximum was low in soils of high phosphorus adsorption capacity (1.3-1.5) and high in soils of low phosphorus adsorption capacity (2.2-18.7). Changes in the amount of phosphurus adsorption induced by liming and preaddition of phosphorus were hadly detected by the phosphorus absorption coefficient, which is measured using a test solution with a relatively high phosphorus concentration. The Langmuir adsorption maximum was a more sensitive index of the phosphorus adsorption capacity. The Langmuir adsorption maxima of the non-cultivated soils, which were treated with an amount of calcium hydroxide equivalent to the exchangeable Al and incubated ($25-30^{\circ}C$) for 40 days at field capacity, were lower than the original soils. The change in the adorption maximum on incubation following the liming of soils was insignificant for other soils. The secondary adsorption maximum of soils, which received phosphorus equivalent to the Langmuir adsorption maximum of the limed soils incubated ($25-30^{\circ}C$) for 50 days at held capacity, was 74.5, 5.6 and 23.8% of the primary adsorption maximum for volcanic ash soils, non-cultivated soils, and cultivated soils respectively. The amount of phosphorus adsorbed by soils increased quadratically with the concentration of phosphorus solution added to the soils. The amount of phosphorus adsorbed by 5-g soil samples from 100ml of 100- and 1,000mg p/l solution for the mineral soils and volcanic ash soils respectively was found to be close to the Langmuir adsorption maximum. The amount of the phosphorus adsorbed at these concentrations is defined as a saturation adsorption maximum and proposed as a new parameter for the phosphorus adsorption capacity of the soil. The evaluation of the phosphorus adsorption capacity by the saturation adsorption maximum is regarded as a more practical method in that it obviates the need for the various concentrations used for the determination of the Langmuir adsorption maximum.

• The Korean Journal of Nuclear Medicine
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• v.39 no.4
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• pp.224-230
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• 2005

Purpose: The maximal elastance ($E_{max}$) of myocardium has been established as a reliable load-independent contractility index. Recently, we developed a noninvasive method to measure the regional contractility using gated myocardial SPECT and arterial tonometry data. In this study, we measured regional $E_{max}(rE_{max}$ in the patients who underwent coronary artery bypass graft surgery (CABG), and assessed its relationship with other variables. Materials and Methods: 21 patients (M:F=17:4, $58{\pm}12$ y) who underwent CABG were enrolled. $^{201}TI$ rest/dipyridamole stress $^{99m}Tc$-sestamibi gated SPECT were performed before and 3 months after CABG. For 15 myocardial regions, regional time-elastance curve was obtained using the pressure data of tonometry and the volume data of gated SPECT. To investigate the coupling with myocardial function, preoperative regional $E_{max}$ was compared with regional perfusion and systolic thickening. In addition, the correlation between $E_{max}$ and viability was assessed in dysfunctional segments (thickening <20% before CABG). The viability was defined as improvement of postoperative systolic thickening more than 10%. Results: Regional $E_{max}$ was slightly increased after CABG from $2.41{\pm}1.64 (pre)\;to\;2.78{\pm}1.83 (post)$ mmHg/ml. $E_{max}$ had weak correlation with perfusion and thickening (r=0.35, p<0.001). In the regions of preserved perfusion (${\geq}60%$), $E_{max}$ was $2.65{\pm}1.67$, while it was $1.30{\pm}1.24$ in the segments of decreased perfusion. With regard to thickening, $E_{max}$ was $3.01{\pm}1.92$ mmHg/ml for normal regions (thickening ${geq}40%$), $2.40{\pm}1.19$ mmHg/ml for mildly dysfunctional regions (<40% and ${\geq}20%$), and $1.13{\pm}0.89$ mmHg/ml for severely dysfunctional regions (<20%). $E_{max}$ was improved after CABG in both the viable (from $1.27{\pm}1.07\;to\;1.79{\pm}1.48$ mmHg/ml) and non-viable segments (from $0.97 {\pm}0.59\;to\;1.22{\pm}0.71$ mmHg/ml), but there was no correlation between $E_{max}$ and thickening improvements (r=0.007). Conclusions: Preoperative regional $E_{max}$ was relatively concordant with regional perfusion and systolic thickening on gated myocardial SPECT. In dysfunctional but viable segments, $E_{max}$ was improved after CABG, but showed no correlation with thickening improvement. As a load-independent contractility index of dysfunctional myocardial segments, we suggest that the regional $E_{max}$ could be an independent parameter in the assessment of myocardial function.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.6 no.4
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• pp.307-327
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• 2008

Geochemical study on the rocks and minerals of the Gyeongju low and intermediate level waste repository was carried out in order to provide geochemical data for the safety assessment and geochemical modeling. Polarized microscopy, X-ray diffraction method, chemical analysis for the major and trace elements, scanning electron microscopy(SEM), and stable isotope analysis were applied. Fracture zones are locally developed with various degrees of alteration in the study area. The study area is mainly composed of granodiorite and diorite and their relation is gradational in the field. However, they could be easily distinguished by their chemical property. The granodiorite showed higher $SiO_2$ content and lower MgO and $Fe_2O_3$ contents than the diorite. Variation trends of the major elements of the granodiorite and diorite were plotted on the same line according to the increase of $SiO_2$ content suggesting that they were differentiated from the same magma. Spatial distribution of the various elements showed that the diorite region had lower $SiO_2,\;Al_2O_3,\;Na_2O\;and\;K_2O$ contents, and higher CaO, $Fe_2O_3$ contents than the granodiorite region. Especially, because the differences in the CaO and $Na_2O$ distribution were most distinct and their trends were reciprocal, the chemical variation of the plagioclase of the granitic rocks was the main parameter of the chemical variation of the host rocks in the study area. Identified fracture-filling minerals from the drill core were montmorillonite, zeolite minerals, chlorite, illite, calcite and pyrite. Especially pyrite and laumontite, which are known as indicating minerals of hydrothermal alteration, were widely distributed in the study area indicating that the study area was affected by mineralization and/or hydrothermal alteration. Sulfur isotope analysis for the pyrite and oxygen-hydrogen stable isotope analysis for the clay minerals indicated that they were originated from the magma. Therefore, it is considered that the fracture-filling minerals from the study area were affected by the hydrothermal solution as well as the simply water-rock interaction.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Food Hygiene and Safety
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• v.29 no.2
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• pp.123-130
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• 2014

A simultaneous determination was developed for 9 aminoglycoside antibiotics (amikacin, apramycin, dihydrostreptomycin, gentamicin, hygromycin B, kanamycin, neomycin, spectinomycin, and streptomycin) in meat by liquid chromatography tandem mass spectrometry (LC-MS/MS). Each parameter was established by multiple reaction monitoring in positive ion mode. The developed method was validated for specificity, linearity, accuracy, and precision based on CODEX validation guideline. Linearity was over 0.98 with calibration curves of the mixed standards. Recovery of 9 aminoglycosides ranged on 60.5~114% for beef, 60.1~112% for pork and 63.8~131% for chicken. The limit of detection (LOD) and limit of quantification (LOQ) were 0.001~0.009 mg/kg and 0.006~0.03 mg/kg, respectively in livestock products including beef, pork and chicken. This study also performed survey of residual aminoglycoside antibiotics for 193 samples of beef, pork and chicken collected from 9 cities in Korea. Aminoglycosides were not found in any of the samples.