• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2785-2796
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• 2023

How to generate the precise broad group cross section is important for the fast reactor design. In this study, a fast reactor multi-group cross-section generation code MGGC2.0 are developed in-house for processing ultrafine group MATXS format library. Validation and verification are performed for MGGC2.0 code by applying the benchmarks of ICSBEP handbook, and the results of MGGC2.0 agree well with that of MCNP. The consistent P_N method with critical buckling search is in good agreement that condensed with TWODANT flux and flux moment for the inner core and outer core region. For the radial blanket and reflector, two region approximation method has been applied in MGGC2.0 by using collision Probability Method neutron flux solver. The RBEC-M benchmark was used to verify the power distribution calculation, and the relative error of power distribution comparison with the reference are less than 0.8% in the fuel region and the maximum relative error is 5.58% in the reflector region. Therefore, the precise broad cross section can be generated by MGGC2.0 for fast reactor.

• Asian Pacific Journal of Cancer Prevention
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• 제15권16호
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• pp.6811-6817
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• 2014

Background: Breast cancer risk prediction models are widely used in clinical practice. They should be useful in identifying high risk women for screening in limited-resource countries. However, previous models showed poor performance in derived and validated settings. Therefore, we aimed to develop and validate a breast cancer risk prediction model for Thai women. Materials and Methods: This cross-sectional study consisted of derived and validation phases. Data collected at Ramathibodi and other two hospitals were used for deriving and externally validating models, respectively. Multiple logistic regression was applied to construct the model. Calibration and discrimination performances were assessed using the observed/expected ratio and concordance statistic (C-statistic), respectively. A bootstrap with 200 repetitions was applied for internal validation. Results: Age, menopausal status, body mass index, and use of oral contraceptives were significantly associated with breast cancer and were included in the model. Observed/expected ratio and C-statistic were 1.00 (95% CI: 0.82, 1.21) and 0.651 (95% CI: 0.595, 0.707), respectively. Internal validation showed good performance with a bias of 0.010 (95% CI: 0.002, 0.018) and C-statistic of 0.646(95% CI: 0.642, 0.650). The observed/expected ratio and C-statistic from external validation were 0.97 (95% CI: 0.68, 1.35) and 0.609 (95% CI: 0.511, 0.706), respectively. Risk scores were created and was stratified as low (0-0.86), low-intermediate (0.87-1.14), intermediate-high (1.15-1.52), and high-risk (1.53-3.40) groups. Conclusions: A Thai breast cancer risk prediction model was created with good calibration and fair discrimination performance. Risk stratification should aid to prioritize high risk women to receive an organized breast cancer screening program in Thailand and other limited-resource countries.

• Korean Journal of Radiology
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• 제21권1호
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• Economic and Environmental Geology
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• 제37권5호
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• pp.521-532
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• 2004

The aim of this study is to evaluate the susceptibility of landslides at Kangneung area, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified from interpretation of satellite image and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. Using frequency ratio model which is one of the probability model, the relationships between landslides and related factors such as slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of wood, lithology, distance from lineament and land cover were calculated as frequency ratios. Then, the frequency ratio were summed to calculate a landslide susceptibility indexes and the landslide susceptibility maps were generated using the indexes. The results of the analysis were verified and cross-validated using actual landslide location data. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• The Korean Journal of Pain
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• 제34권4호
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• pp.501-508
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• 2021

Background: Yellow flags are psychosocial factors shown to be indicative of long-term chronicity and disability. The purpose of the study was to evaluate the psychometric properties of the Turkish Yellow Flag Questionnaire (YFQ) in patients with chronic musculoskeletal pain (CMP). Methods: The cross-cultural adaptation was conducted with translation and back-translation of the original version. Reliability (internal consistency and test-retest) was examined for 231 patients with CMP. Construct validity was assessed by correlating the YFQ with the Hospital Anxiety and Depression Scale (HADS), Orebro Musculoskeletal Pain Questionnaire (OMPQ), and Tampa Kinesiophobia Scale (TKS). Factorial validity was examined with both exploratory and confirmatory factorial analysis. Results: The YFQ showed excellent test/retest reliability with an Intraclass correlation coefficient of 0.82. The internal consistency was moderate (Cronbach's alpha of 0.797). As a result of the exploratory factor analysis, there were 7 domains compatible with the original version. As a result of confirmatory factor analysis, the seven-factor structure of YFQ was confirmed. There was a statistically significant correlation between YFQ-total score and OMPQ (r = 0.57, P < 0.001), HADS-anxiety (r = 0.32, P < 0.001), HADS-depression (r = 0.44, P < 0.001), and TKS (r = 0.37, P < 0.001). Conclusions: This study's results provide considerable evidence that the Turkish version of the YFQ has appropriate psychometric properties, including test-retest reliability, internal consistency, construct validity and factorial validity. It can be used for evaluating psychosocial impact in patients with CMP.

• Convergence Security Journal
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• 제22권2호
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• Journal of Korean Academy of Nursing
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• 제35권6호
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• pp.1063-1071
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• 2005

Purpose: This study was done to develop and test validity and reliability of on instrument for predicting nursing intention for SARS patient care. Method: The psychometric properties of a SARS patient care attrition prediction tool, based on the Theory of Planned Behavior, were examined in this study. The Three-phase design involved a) salient beliefs generated from clinical nurses (n=43) b) content validation by expert panel evaluations(n=5) c) face validation by plot testing (n=10) d) and instrument validation in a cross sectional survey (n=299). Psychometric analysis of survey data provided empirical evidence of the construct validity and reliability of the instrument. Result: Principal component analysis verified the hypothesized 6-factor solution, explaining $68.2\%$ of variance, and Alpha coefficients of .7538 to .9389 indicated a high internal consistency of the instrument. Conclusion: The instrument can be used by nurse administrators and researcher to assess clinical nurses' salient beliefs about caring for SARS patients, guide tailored intervention strategies to effective caring, and evaluate the effectiveness of interventions.

• Korean Journal of Child Studies
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• 제35권6호
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• pp.111-133
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• 2014

This study was conducted in order to develop a parenting behavior scale for parents of school-age children and to analyze the scale in terms of both reliability and validity. Data were collected from a sample of mothers of $1^{st}$ to 3rd grade students in four elementary schools located in Seoul. 778 mothers were administered a parenting behavior scale with 123 items, and 779 mothers were asked to verify the validity of the developed scale in which 45 items remained after a series of analyses. Data were analyzed by means of exploratory factor analysis, confirmatory factor analysis, and correlation analysis. The results of factor analysis identified five factors, Warmth, Reasoning, Intrusiveness, Coercion, and Neglect. The Cronbach's ${\alpha}$ of each factor demonstrated results of .82~.86, suggesting that the scale had adequate internal consistency. Concurrent validity was established by using correlations between mothers' parenting behaviors and children's social competence. Moreover, cross-validation was also verified for the five factors. Considering the reliability and validity of this scale, it can clearly serve as a useful tool for assessing parenting behavior which is closely related to child development.

• Journal of The Korean Society of Grassland and Forage Science
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• 제34권3호
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• pp.214-218
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• 2014

This study was conducted to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for the estimation of crude protein (CP) fractions in forage legume mixtures (sudangrass and pea mixture, and kidney bean and potato mixture). A total of 178 samples were collected and their spectral reflectance obtained in the range of 400~2,500 nm. Of these, 50 samples were selected for calibration and validation, and 35 samples were used for calibration of the data set, and the modified partial least square regression (MPLSR) analysis was performed. The correlation coefficient ($r^2$) and the standard error of cross-validation (SECV) of the calibration models in the CP fractions, A, B1, B2, B3, and C, were 0.94 (1.05), 0.92 (0.74), 0.96 (0.95), 0.91 (0.42), and 0.83 (0.38), respectively. Fifteen samples were used for equation validation, and the $r^2$ and the standard error of prediction (SEP) were 0.87 (1.45), 0.91 (0.49), 0.94 (1.13), 0.36 (0.96), and 0.74 (0.67), respectively. This study showed that NIRS could be an effective tool for the rapid and precise estimation of CP fractions in forage legume mixtures.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3494-3515
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• 2022

This paper presents the development of a nodal diffusion code, RAST-V, and its verification and validation for VVER (vodo-vodyanoi energetichesky reactor) analysis. A VVER analytic solver has been implemented in an in-house nodal diffusion code, RAST-K. The new RAST-K version, RAST-V, uses the triangle-based polynomial expansion nodal method. The RAST-K code provides stand-alone and two-step computation modes for steady-state and transient calculations. An in-house lattice code (STREAM) with updated features for VVER analysis is also utilized in the two-step method for cross-section generation. To assess the calculation capability of the formulated analysis module, various verification and validation studies have been performed with Rostov-II, and X2 multicycles, Novovoronezh-4, and the Atomic Energy Research benchmarks. In comparing the multicycle operation, rod worth, and integrated temperature coefficients, RAST-V is found to agree with measurements with high accuracy which RMS differences of each cycle are within ±47 ppm in multicycle operations, and ±81 pcm of the rod worth of the X2 reactor. Transient calculations were also performed considering two different rod ejection scenarios. The accuracy of RAST-V was observed to be comparable to that of conventional nodal diffusion codes (DYN3D, BIPR8, and PARCS).