• Computers and Concrete
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• v.20 no.1
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• pp.65-75
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• 2017

In this study, a novel application of symbolic regression (SR) is employed for the prediction of ultimate shear strength of steel fiber reinforced (SFRC) and glass fiber reinforced (GFRC) corbels without stirrups, for the first time in the literature. A database is created using the test results (42 tests) conducted by the authors of current paper as well as the previous studies available in the literature. A symbolic regression based empirical formulation is proposed using this database. The formulation is unique in a way that it has the capability to predict the shear strength of both SFRC and GFRC corbels. The performance of proposed model is tested against randomly selected testing set. Additionally, a parametric study with a wide range of variables is carried out to test the effect of each parameter on the shear strength. The results confirm the high prediction capacity of proposed model.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.221-234
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• 2013

In this paper, we report bankruptcy risk level forecasting result for automobile parts manufacturing industry. With the premise that upstream supply risk and downstream demand risk could impact on automobile parts industry bankruptcy level in advance, we draw upon industry input-output table to use the economic indicators which could reflect the extent of supply and demand risk of the automobile parts industry. To verify the validity of each economic indicator, we applied simple linear regression for each indicators by varying the time lag from one month (t-1) to 12 months (t-12). Finally, with the valid indicators obtained through the simple regressions, the composition of valid economic indicators are derived using stepwise linear regression. Using the monthly automobile parts industry bankruptcy frequency data accumulated during the 5 years, R-square values of the stepwise linear regression results are 68.7%, 91.5%, 85.3% for the 3, 6, 9 months time lag cases each respectively. The computational testing results verifies the effectiveness of our approach in forecasting bankruptcy risk forecasting of the automobile parts industry.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3879-3887
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• 2012

Background: Development of effective educational strategies should accompany increases in public awareness and the availability of genetic testing for breast cancer (BC). These educational strategies should be designed to fulfill the knowledge gap while considering factors that influence women's interest in order to facilitate decision making. Objective: To determine the possible correlates of Saudi women's interest in BC genes testing including socio-demographics, the level of awareness towards BC genes, the family history of BC and the perceived personal risk among adult Saudi women in Al Hassa, Saudi Arabia. Subjects and methods: This cross-sectional study was carried out during the second BC community-based campaign in Al Hassa, Saudi Arabia. All Saudi women aged ${\geq}18$ years (n=781) attending the educational components of the campaign were invited to a personal interview. Data collection included gathering information about sociodemographics, family history of BC, the perceived personal risk for BC, awareness and attitude towards BC genes and the women's interest in BC genes testing. Results: Of the included women (n=599), 19.5% perceived higher risk for BC development, significantly more among < 40 years of age, and with positive family history of BC before 50 years of age. The participants demonstrated a poor level of awareness regarding the inheritance, risk, and availability of BC genetic testing. The median summated knowledge score was 1.0 (out of 7 points) with a knowledge deficit of 87.8%. The level of knowledge showed significant decline with age (> 40 years). Of the included women 54.7% expressed an interest in BC genetic testing for assessing their BC risk. Multivariate regression model showed that being middle aged (Odds Ratio 'OR'=1.88, confidence intervals 'C.I'=1.14-3.11), with higher knowledge level (OR=1.67, C.I=1.08-2.57) and perceiving higher risk for BC (OR=2.11, C.I=1.61-2.76) were the significant positive correlates for Saudi women interest in BC genetic testing. Conclusion: Saudi women express high interest in genetic testing for BC risk despite their poor awareness. This great interest may reflect the presence of inappropriate information regarding BC genetic testing and its role in risk analysis.

• Journal of The Korean Association For Science Education
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• v.18 no.3
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• pp.327-336
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• 1998

The purposes of this study were to investigate 6th-graders' use of cognitive strategies and metacognitive strategies in usual learning and testing situations, and to compare the difference in the use of the strategies by students' science achievement, learning motivation, and gender. The relationship among these strategies, science achievement, and learning motivation were also examined, and the portion of variance of explanation for achievement score was studied by a multiple regression analysis. The results showed that high-achieving students used more cognitive strategies and metacognitive strategies in usual learning and more cognitive strategies in testing situations than low-achieving students. Highly motivated students used more cognitive and metacognitive strategies than poorly motivated students in all situations. Elementary female students used more learning strategies than male students in usual learning. On the other hand, no gender differences was found to be significant in the use of strategies in testing situations. These learning strategies were significantly correlated with the science achievement and motivation scores. The cognitive strategies in usual learning accounted for the significant portion of the variance of the achievement score. Educational implications are discussed.

• Computers and Concrete
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• v.19 no.6
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• pp.717-724
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• 2017

In this paper, four formulas are proposed via gene expression programming (GEP)-based models and regression analysis (RA) to predict the flexural strength ($f_s$) values of mortars containing different mineral admixtures that are ground granulated blast-furnace slag (GGBFS), silica fume (SF) and fly ash (FA) at different ages. Three formulas obtained from the GEP-I, GEP-II and GEP-III models are constituted to predict the $f_s$ values from the age of specimen, water-binder ratio and compressive strength. Besides, one formula obtained from the RA is constituted to predict the $f_s$ values from the compressive strength. To achieve these formulas in the GEP and RA models, 972 data of the experimental studies presented with mortar mixtures were gathered from the literatures. 734 data of the experimental studies are divided without pre-planned for these formulas achieved from the training and testing sets of GEP and RA models. Beside, these formulas are validated with 238 data of experimental studies un-employed in training and testing sets. The $f_s$ results obtained from the training, testing and validation sets of these formulas are compared with the results obtained from the experimental studies and the formulas given in the literature for concrete. These comparisons show that the results of the formulas obtained from the GEP and RA models appear to well compatible with the experimental results and find to be very credible according to the results of other formulas.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.581-585
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• 2010

As developers and testers today we all suffer from increasing project complexity, the risks of late defect discovery, repetitive manual processes, and the risk of release delays. In this paper, we introduce an effective framework for automated testing to help solve such problems. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework helps automate the distribution, execution and results analysis of test cases. It also aids communication among the various stakeholders, using tables for representing tests and for reporting the results of automatically checking those tests. This paper describes the practices and benefits of using the proposed framework.

• Journal of Korean Academy of Nursing
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• v.45 no.5
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• pp.641-649
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• 2015

Purpose: The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. Methods: For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. Results: Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. Conclusion: It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.

• Journal of Information Technology Applications and Management
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• v.22 no.2
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• pp.201-217
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• 2015

In this paper, we report computational testing result to examine the validity of firm's bankruptcy risk estimation through quantification of supply chain risk. Supply chain risk in this study refers to upstream supply risk and downstream demand risk, To assess the firm's risk affected by supply chain risk, we adopt unit of analysis as industry level. since supply and demand relationships of the firm could be generalized by the industry input-output table and the availability of various valid economic indicators which are chronologically calculated. The research model to estimate firm's risk level is the linear regression model to assess the industry bankruptcy risk estimation of the focal firm's industry with the independent variables which could quantitatively reflect demand and supply risk of the industry. The publicly announced macro economic indicators are selected as the candidate independent variables and validated through empirical testing. To validate our approach, in this paper, we confined our research scope to steel industry sector and its related industry sectors, and implemented the research model. The empirical testing results provide useful insights to further refine the research model as the valid forecasting mechanism to capture firm's future risk estimation more accurately by adopting supply chain industry risk aspect, in conjunction with firm's financial and other managerial factors.

• Korean Journal of Metals and Materials
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• v.49 no.10
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• pp.774-779
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• 2011

S-N fatigue behavior of cast 304 stainless steel was studied at 25, -50 and $-196^{\circ}C$ and at a stress ratio of -1 in uniaxial and bending loading condition. It was found that the resistance to S-N fatigue was greatly improved with decreasing testing temperature. The normalized S-N fatigue curves by tensile strength at three different testing temperatures matched each other, suggesting that tensile strength determines the S-N fatigue resistance of cast 304 stainless steel at low temperatures. The effects of different loading on the resistance to S-N fatigue of cast 304 stainless steel were quantified. The S-N fatigue curves at 25, -50 and $-196^{\circ}C$ were described by using Basquin's law the relationship between the S-N fatigue curve and the testing temperature was obtained by using a simple regression method.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.423-433
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• 2014

Test statistics using cumulative sums of residuals have been widely used in various regression models including generalized linear models(GLM). Recently, Pan and Lin (2005) extended this testing procedure to the generalized linear mixed models(GLMM) having random effects, in which we encounter difficulties in computing the marginal likelihood that is expressed as an integral of random effects distribution. The Gaussian quadrature algorithm is commonly used to approximate the marginal likelihood. Many commercial statistical packages provide an option to apply this type of goodness-of-fit test in GLMs but available programs are very rare for GLMMs. We suggest a computational algorithm to implement the testing procedure in GLMMs by a freely accessible R package, and also illustrate through practical examples.