• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.254-261
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• 2022

Background: The use of systemic steroids for 6+ weeks in children is associated with decreased bone mineral content (BMC) and density (BMD). However, the effects of a shorter duration of use on BMD are unknown. Purpose: To determine the effect of the use of systemic steroids for 2-6 weeks on BMD and BMC in pediatric patients. Methods: Twenty-five pediatric patients (21 with tuberculosis, 2 with systemic juvenile idiopathic arthritis, 1 with inflammatory bowel disease, 1 with autoimmune hemolytic anemia) who received systemic steroids for 2-6 weeks and 25 age- and sex-matched controls were enrolled. BMC, BMD, and z scores of the whole body (WB), lumbar spine (LS), nondominant distal radius (DR), and total body less the head (TBLH) were determined by dual-energy x-ray absorptiometry at baseline, the end of steroid therapy or 6 weeks (whichever was earlier; first follow-up), and at the end of 3 months from baseline (second follow-up) in patients and at baseline in controls. The values were adjusted for confounding variables. Continuous and categorical variables were compared using Student t test and the chi-square test or Fisher exact test, respectively. Pairwise comparisons employed Bonferroni correction. Results: Statistically significant decreases in BMC, BMD, and all z scores were observed. BMC declined by 5.37%, 2.08%, 1.82%, and 2.27%, and 11.42%, 3.75%, 3.34%, and 4.17% for WB, LS, DR, and TBLH, respectively, at the first and second follow-ups, respectively. Similarly, BMD declined by 2.01%, 2.31%, 2.18%, and 1.70% and 4.59%, 3.76%, 3.14%, and 3.50% for the WB, LS, DR, and TBLH, respectively, at the first and second follow-ups, respectively. A significant negative correlation was found among bone densitometric parameters, duration, and cumulative dose. Conclusion: The use of systemic steroids for 2-6 weeks in pediatric patients decreased the BMD and BMC of trabecular and cortical bones, an effect that persisted after discontinuation.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2690-2697
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• 2024

With advancements in machine learning technologies, artificial neural networks (ANNs) are being widely used to improve the performance of gamma-ray spectroscopy based on NaI(Tl) scintillation detectors. Typically, the performance of ANNs is evaluated using test datasets composed of actual spectra. However, the generation of such test datasets encompassing a wide range of actual spectra representing various scenarios often proves inefficient and time-consuming. Thus, instead of measuring actual spectra, we generated virtual spectra with diverse spectral features by sampling from categorical distribution functions derived from the base spectra of six radioactive isotopes: ⁵⁴Mn, ⁵⁷Co, ⁶⁰Co, ¹³⁴Cs, ¹³⁷Cs, and ²⁴¹Am. For practical applications, we determined the optimum counting time (OCT) as the point at which the change in the Kullback-Leibler divergence (ΔKLDV) values between the synthetic spectra used for training the ANN and the virtual spectra approaches zero. The accuracies of the actual spectra were significantly improved when measured up to their respective OCTs. The outcomes demonstrated that the proposed method can effectively determine the OCTs for gamma-ray spectroscopy based on ANNs without the need to measure actual spectra.

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.420-433
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• 2016

The purpose of this study is to improve the calibration matrixes of 2-D and 3-D convective rainfall rates (CRR) using the brightness temperature of the infrared $10.8{\mu}m$ channel (IR), the difference of brightness temperatures between infrared $10.8{\mu}m$ and vapor $6.7{\mu}m$ channels (IR-WV), and the normalized reflectance of the visible channel (VIS) from the COMS satellite and rainfall rate from the weather radar for the period of 75 rainy days from April 22, 2011 to October 22, 2011 in Korea. Especially, the rainfall rate data of the weather radar are used to validate the new 2-D and 3-DCRR calibration matrixes suitable for the Korean peninsula for the period of 24 rainy days in 2011. The 2D and 3D calibration matrixes provide the basic and maximum CRR values ($mm\;h^{-1}$) by multiplying the rain probability matrix, which is calculated by using the number of rainy and no-rainy pixels with associated 2-D (IR, IR-WV) and 3-D (IR, IR-WV, VIS) matrixes, by the mean and maximum rainfall rate matrixes, respectively, which is calculated by dividing the accumulated rainfall rate by the number of rainy pixels and by the product of the maximum rain rate for the calibration period by the number of rain occurrences. Finally, new 2-D and 3-D CRR calibration matrixes are obtained experimentally from the regression analysis of both basic and maximum rainfall rate matrixes. As a result, an area of rainfall rate more than 10 mm/h is magnified in the new ones as well as CRR is shown in lower class ranges in matrixes between IR brightness temperature and IR-WV brightness temperature difference than the existing ones. Accuracy and categorical statistics are computed for the data of CRR events occurred during the given period. The mean error (ME), mean absolute error (MAE), and root mean squire error (RMSE) in new 2-D and 3-D CRR calibrations led to smaller than in the existing ones, where false alarm ratio had decreased, probability of detection had increased a bit, and critical success index scores had improved. To take into account the strong rainfall rate in the weather events such as thunderstorms and typhoon, a moisture correction factor is corrected. This factor is defined as the product of the total precipitable waterby the relative humidity (PW RH), a mean value between surface and 500 hPa level, obtained from a numerical model or the COMS retrieval data. In this study, when the IR cloud top brightness temperature is lower than 210 K and the relative humidity is greater than 40%, the moisture correction factor is empirically scaled from 1.0 to 2.0 basing on PW RH values. Consequently, in applying to this factor in new 2D and 2D CRR calibrations, the ME, MAE, and RMSE are smaller than the new ones.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.26 no.3
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• pp.277-285
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• 2016

Objectives: This study was performed in order to evaluate whether 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) could be detected among residents living near Camp Caroll in Waegwan and whether serum concentrations of dioxins, including 2,3,7,8-TCDD, and organochlorine pesticides (OCPs) are associated with length of residence. Methods: Study subjects totaled 113 (for dioxins) and 190 (for OCPs) adults who were selected from participants in a medical investigation. Serum concentrations of dioxins and OCPs were measured using HRGC/HRMS. Information on length of residence was obtained through questionnaires. Results: 2,3,7,8-TCDD was not detected in serum among all subjects. When length of residence was classified as a categorical variable, after adjusting for confounding variables, only residents living in Waegwan for 40 years or longer tended to have high total TEQ values and 2,3,4,7,8-PeCDF with marginal significances. There was no dose-response relation between length of residence and serum concentrations of these chemicals. In multiple regression models with continuous values of the length of residence, total TEQ value and 1,2,3,4,6,7,8-HpCDF were positively associated with length of residence. However, they explained about 3-5% of total variations of serum concentrations of these compounds, while age, consumption of fatty fish, body mass index, alcohol drinking, and cigarette smoking were main variables affecting serum concentrations of dioxins or OCPs. Conclusions: In the current study, high concentrations of certain compounds were mainly observed among persons who lived in Waegwan for at least for 40 years without a dose-response relation. Therefore, it seems difficult to conclude that length of residence meaningfully contributed to the current serum concentrations of dioxins or OCPs among residents in Waegwan. However, considering the half-life of 2,3,7,8-TCDD and indirect exposure routes, the limitations of the current study design should be considered in the interpretation of the study findings.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.2055-2060
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• 2015

Purpose: To present information about prognostic factors of gastric cancer patients treated in our Erzurum center including age, gender, tumour location, pathological grade, stage and the effect of treatment on survival. Materials and Methods: This retrospective study was performed on patients who applied to our clinic and diagnosed as gastric cancer. Age and gender of the patients, primary location, histopathological characteristics, TNM stage of the gastric cancers (GCs), treatment applied, oncological treatment modalities and survival outcomes were studied. A univariate analysis of potential prognostic factors was performed with the log-rank test for categorical factors and parameters with a p value < 0.05 at the univariate step were included in the multivariate regression. Results: A total of 228 patients with a confirmed diagnosis of gastric cancer were included in the study with a male/female ratio of 1.47. Median follow-up period was estimated as 22.3 (range, 3 to 96) months. When diagnosis of the patients at admission was analysed, stage III patients were most frequently encountered (n=147; 64.5%). One hundred and twenty-six (55.3%) underwent surgical treatment, while 117 (51.3%) were given adjuvant chemotherapy. Median overall survival time was 18.0 (${\pm}1.19$) months. Mean overall survival rates for 1, 2, 3 and 5 years were $68{\pm}0.031%$, $36{\pm}0.033%$, $24{\pm}0.031%$and $15.5{\pm}0.036%$, respectively. Univariate variables found to be significant for median OS in the multivariate analysis were evaluated with Cox regression analysis. A significant difference was found among TNM stage groups, location of the tumour and postoperative adjuvant treatment receivers (p values were 0.011, 0.025 and 0.001, respectively). Conclusions: This study revealed that it is possible to achieve long-term survival of gastric cancer with early diagnosis. Besides, in locally advanced GC patients, curative resection followed by adjuvant concomitant chemoradiotherapy based on the McDonald regimen was an independent prognostic factor for survival.

• Journal of Preventive Medicine and Public Health
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• v.49 no.5
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• pp.275-287
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• 2016

Objectives: C-reactive protein (CRP), an inflammatory biomarker, has been widely used as a preclinical marker predictive of morbidity and mortality. Although many studies have reported a positive association between CRP and mortality, uncertainty still remains about this association in various populations, especially in rural Korea. Methods: A total of 23 233 middle-aged participants (8862 men and 14 371 women) who were free from cardiovascular disease, cancer, and acute inflammation (defined by a CRP level ${\geq}10mg/L$) were drawn from 11 rural communities in Korea between 2005 and 2011. Blood CRP concentration was analyzed as a categorical variable (low: 0.0-0.9 mg/L; intermediate: 1.0-3.0 mg/L; high: 3.1-9.9 mg/L) as well as a continuous variable. Each participant's vital status through December 2013 was confirmed by death statistics from the National Statistical Office. Cox proportional hazard models were used to assess the independent association between CRP and mortality after adjusting for other risk factors. Results: The total quantity of observed person-years was 57 975 for men and 95 146 for women, and the number of deaths was 649 among men and 367 among women. Compared to the low-CRP group, the adjusted hazard ratio for all-cause mortality of the intermediate group was 1.17 (95% confidence interval [CI], 0.98 to 1.40) for men and 1.27 (95% CI, 1.01 to 1.61) for women, and the corresponding values for the high-CRP group were 1.98 (95% CI, 1.61 to 2.42) for men and 1.41 (95% CI, 1.03 to 1.95) for women. Similar trends were found for CRP evaluated as a continuous variable and for cardiovascular mortality. Conclusions: Higher CRP concentrations were associated with higher mortality in a rural Korean population, and this association was more prominent in men than in women.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.1
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• pp.28-41
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• 2012

The User-Oriented Public Transit Service provide public transit service through media devices, such as mobile, BIT and In-vehicle display devices, which considered user's individual characteristics and specific preference anytime and anywhere. The objective of this study is to develop and improve the services in the three media devices. This study applied user-satisfaction analysis in evaluating the service in Busan, analyze factor affecting the level of service user's satisfaction, and draw remedies based on the analysis results. The user's satisfaction average values in each media are 3.29 mobile, 3.62 BIT, and 4.05 In-vehicle display devices. Overall satisfaction average value 3.54 on the service showed a positive reaction from the survey participants. The important factor affected on general satisfaction of the User-Oriented Public Transit Service is "In-vehicle display devices" scored .632 (standardized coefficient) by categorical regression analysis. But users prefer to further improve the service environment rather than to add service information because they are already contented with the information they are getting. Furthermore, this study suggested ways of improving the User-Oriented Public Transit Service based on the satisfaction analysis results from the user's perspective.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.399-410
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• 2016

In case of the application of Hazen-Williams C for design, operation or maintenance of water supply system, field situations always should be reflected on the factors. In this study, the relationships between C factors and influencing factors are analyzed using statistical techniques with 174 measured C factor data collected in periodic inspection for safety diagnosis in multi-regional water supply systems. To analyze their relationships, cross analysis, one-way ANOVA, correlation analysis were conducted. Analysis results showed that C factors had high correlations with both of elapsed year and pipe diameter and were relatively highly affected by coating material among influencing factors with the categorical type. On the other hand, elapsed year, pipe diameter and water type were meaningful influencing factors according to the results of multiple regression analysis. The Cluster analysis revealed that C factors had a tendency of being fundamentally classified on the basis of the elapsed year of about 20 years and the pipe diameter of 1500mm. Although C factors were generally greatly affected by elapsed year, size of pipe diameter relatively had an large influence on values of them in case of large diameter pipes. Lastly, It can be suggested that C factor estimation formulas using multiple regression analysis and clustering analysis in this study, can be applied as decision standards of C factor in multi-regional water supply systems.