• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6205-6210
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• 2014

Background: The aim of this study was to investigate Iranian cancer patient perceptions of their prognosis, factors that influence perceptions of prognosis and the effect this has on patient level of hope. Materials and Methods: Iranian cancer patients (n=200) completed self-report measures of their perceptions of their prognosis and level of hope, in order to assess the relationship between the two and identify factors predictive of perceptions by multiple linear regression analysis. Results: Cancer patients perceived of their prognosis positively (mean 11.4 out of 15), believed their disease to be curable, and reported high levels of hope (mean 40.4 out of 48.0). Multiple linear regression analyses demonstrated that participants who were younger, perceived they had greater family support, and had higher levels of hope reported more positive perceptions of their cancer prognosis. Conclusions: Positive perceptions of prognosis and its positive correlation with hope in Iranian cancer patients highlights the importance of cultural issues in the disclosure of cancer related information.

• Journal of Veterinary Clinics
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• v.20 no.1
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• pp.7-11
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• 2003

The selection of a test as a reference with no perfect sensitivity and specificity may lead to bias, yielding distortion of the diagnostic performance. This means it is inappropriate to use imperfect diagnostic tests as a reference method to identify infected patients in clinical environments. In this study, diagnostic performance of rapid urease test, polymerase chain reaction (PCR), and histology of gastric biopsy specimens for diagnosing Helicobacter pylori infection separately and in combination was estimated by using non-linear regression. Based on this approach, the sensitivity, specificity and likelihood ration positive and negative values for each test were as follows: urease test 99.9%, 99.9%, 99.9%, 99.6%, respectively; PCR 88.6%, 99.9%, 99.9%, 70.5%, respectively; histology 78.3%, 97%, 78.3%, 97%, respectively. Predictive values for positive and negative changes with varying Combination of three diagnostic tests employed in the study gives no substantial benefit for practitioners to screen infected patients, and urease test or PCR represents an appropriate single test in clinical environments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3A
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• pp.139-148
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• 2012

Circular concrete-filled tubes (CFTs) are composite members, which consists of a steel tube and concrete infill. CFTs have been used as building columns and bridge piers due to several advantages such as their strength-to-size efficiency and facilitation of rapid construction. Extensive experimental studies about CFT have been conducted for past decades. However experimental results alone are not sufficient to support the engineering of these components. Complementary advanced numerical models are needed to simulate the behavior of CFT to extend the experimental research and develop predictive tools required for design and evaluation of structural systems. In this study, a finite element modeling technique for CFT was developed. The confinement effects, and behavior of CFT subjected various types of loading predicted by the proposed finite element model for CFT were verified by comparing with test results.

• Genomics & Informatics
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• v.11 no.4
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• pp.263-271
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• 2013

We investigated the contribution of genetic variations of KLF5 to basal metabolic rate (BMR) and resting metabolic rate (RMR) and the inhibition of obesity in Korean children. A variation of KLF5 (rs3782933) was genotyped in 62 Korean children. Using multiple linear regression analysis, we developed a model to predict BMR in children. We divided them into several groups; normal versus overweight by body mass index (BMI) and low BMR versus high BMR by BMR. There were no differences in the distributions of alleles and genotypes between each group. The genetic variation of KLF5 gene showed a significant correlation with several clinical factors, such as BMR, muscle, low-density lipoprotein cholesterol, and insulin. Children with the TT had significantly higher BMR than those with CC (p=0.030). The highest muscle was observed in the children with TT compared with CC (p=0.032). The insulin and C-peptide values were higher in children with TT than those with CC (p=0.029 vs. p=0.004, respectively). In linear regression analysis, BMI and muscle mass were correlated with BMR, whereas insulin and C-peptide were not associated with BMR. In the high-BMR group, we observed that higher muscle, fat mass, and C-peptide affect the increase of BMR in children with TT (p < 0.001, p < 0.001, and p=0.018, respectively), while Rohrer's index could explain the usual decrease in BMR (adjust $r^2$=1.000, p < 0.001, respectively). We identified a novel association between TT of KLF5 rs3782933 and BMR in Korean children. We could make better use of the variation within KLF5 in a future clinical intervention study of obesity.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.503-515
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• 2018

In this research, the three-dimensional structural and colorimetrical modeling of yarn-dyed woven fabrics was conducted based on the Kubelka-Munk theory (K-M theory) for their accurate color predictions. In the K-M theory for textile color formulation, the absorption and scattering coefficients, denoted K and S, respectively, of a colored fabric are represented using those of the individual colorants or color components used. One-hundred forty woven fabric samples were produced in a wide range of structures and colors using red, yellow, green, and blue yarns. Through the optimization of previous two-dimensional color prediction models by considering the key three-dimensional structural parameters of woven fabrics, three three-dimensional K/S-based color prediction models, that is, linear K/S, linear log K/S, and exponential K/S models, were developed. To evaluate the performance of the three-dimensional color prediction models, the color differences, ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and ${\Delta}E_{CMC(2:1)}$, between the predicted and the measured colors of the samples were calculated as error values and then compared with those of previous two-dimensional models. As a result, three-dimensional models have proved to be of substantially higher predictive accuracy than two-dimensional models in all lightness, chroma, and hue predictions with much lower ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and the resultant ${\Delta}E_{CMC(2:1)}$ values.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• Journal of Internet of Things and Convergence
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• v.7 no.2
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• pp.31-40
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• 2021

Objectives: This study was conducted to investigate the degree of verbal abuse, emotional response, nursing professionalism, clinical practice stress during clinical practicim of nursing students. Methods: A sample of convenience was 106 nursing students, and a questionnaire was used to measure their verbal abuse, emotional response, nursing professionalism, clinical practice stress. Data were analyzed by descriptive statistics, t-test, one-way ANOVA, and multiple linear regression. Results: A significant positive correlation was found among verbal abuse, emotional response, clinical practice stress(r=.683, r=.573). Grade of which the participant was in, verbal abuse(𝛽=.487), emotional response(𝛽=.240) were significant predictive variables of which accounted for 49% of the variance in clinical practice stress. Conclusions: The results from this study can provide basic data on the development of strategies for nursing college students to cope with verbal abuse and to manage stress under clinical practice

• Animal Bioscience
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• v.36 no.7
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of the Korean GEO-environmental Society
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• v.25 no.7
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• pp.5-19
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• 2024

During the blasting process, a fracture zone is formed in the vicinity of the blast hole. Any damage that extends beyond the excavation boundary line necessitates the implementation of an additional support system to assure safety. Typically, fracture zone radius is estimated from blast hole pressure using theoretical methods due to its simplicity. However, linear charge concentration (kg/m) is used for tunnel blasting. This paper compiles Swedish experimental datasets to estimate the radius of fracture zones based on linear charge concentration. Further numerical analyses are performed in LS-DYNA for coupled single-hole blasting. The Riedel-Hiermaier-Thoma (RHT) model has been selected as the constitutive model for this investigation. The numerical model is validated against small-scale laboratory tests. Parametric studies are conducted to predict fracture zones in granite and sandstone rocks using two kinds of explosives, PETN and AFNO. The analyses evaluate ten types of blast hole sizes, ranging from 17 to 100 mm. The results indicate that granite has a larger fracture zone than sandstone, and the PETN explosive predicts more damage than ANFO. Smaller blast holes exhibit smaller fracture zones in comparison to larger blast holes. Wave propagation is more rapidly attenuated in granite than in sandstone. Subsequently, the predicted fracture zone outcomes are compared with the empirical dataset. Fracture zones of medium blast hole diameter align well with the experimental data set. A predictive equation is derived from the data set, which may be used to evaluate blast design to manage fracture zones beyond the excavation line.