• Wind and Structures
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• v.25 no.3
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• pp.233-259
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• 2017

Investigations on simulated near-surface atmospheric boundary layer (ABL) in an open-jet facility are carried out by conducting experimental tests on small-scale models of low-rise buildings. The objectives of the current study are: (1) to determine the optimal location of test buildings from the exit of the open-jet facility, and (2) to investigate the scale effect on the aerodynamic pressure characteristics. Based on the results, the newly built open-jet facility is well capable of producing mean wind speed and turbulence profiles representing open-terrain conditions. The results show that the proximity of the test model to the open-jet governs the length of the separation bubble as well as the peak roof pressures. However, test models placed at a horizontal distance of 2.5H (H is height of the wind field) from the exit of the open-jet, with a width that is half the width of the wind field and a length of 1H, have consistent mean and peak pressure coefficients when compared with available results from wind tunnel testing. In addition, testing models with as large as 16% blockage ratio is feasible within the open-jet facility. This reveals the importance of open-jet facilities as a robust tool to alleviate the scale restrictions involved in physical investigations of flow pattern around civil engineering structures. The results and findings of this study are useful for putting forward recommendations and guidelines for testing protocols at open-jet facilities, eventually helping the progress of enhanced standard provisions on the design of low-rise buildings for wind.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.37
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• pp.13.1-13.7
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• 2015

Background: This study evaluated Somatosensory evoked potentials (SEP), Quantitative sensory testing (QST), and thermography as diagnostic methods for nerve injury. Methods: From 2006 through 2011, 17 patients (mean age: 50.1 years) from ${\bigcirc}{\bigcirc}{\bigcirc}{\bigcirc}$ Hospital who sought care for altered sensation after dental implant treatment were identified. The mean time of objective assessment was 15.2 months after onset. Results: SEP of Inferior alveolar nerve(IAN) was $15.87{\pm}0.87ms$ on the normal side and $16.18{\pm}0.73ms$ on the abnormal side. There was delayed N20 latency on the abnormal side, but the difference was not statistically significant. In QST, the abnormal side showed significantly higher scores of the current perception threshold at 2 KHz, 250 Hz, and 5 Hz. The absolute temperature difference was $0.55^{\circ}C$ without statistically significance. Conclusion: These results indicate that QST is valuable as an objective method for assessment of nerve injury.

• Safety and Health at Work
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• v.8 no.4
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• pp.393-397
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• 2017

Background: It has been suggested that engaging technology can empower individuals to be more proactive about their health and reduce their health risks. The aim of the present intervention was to study the effects of technology-aided testing and feedback on physical activity and biological age of employees in a middle-sized enterprise. Methods: In all, 121 employees (mean age $42{\pm}10$ years) participated in the 12-month three-arm cluster randomized trial. The fitness measurement process (Body Age) determined the participants' biological age in years. Physical activity was measured with the International Physical Activity Questionnaire Short Form. Results: Physical activity did not change during the intervention. Biological age (better fitness) improved in all groups statistically significantly (p < 0.001), but with no interaction effects. The mean changes (years) in the groups were -2.20 for the controls, e2.83 for the group receiving their biological age and feedback, and -2.31 for the group receiving their biological age, feedback, and a training computer. Conclusion: Technology-aided testing with feedback does not seem to change the amount of physical activity but may enhance physical fitness measured by biological age.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.369-378
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• 2018

Purpose: The aim of this study was to construct a saponin content-predicting model using shortwave infrared imaging spectroscopy. Methods: The experiment used a shortwave imaging spectrometer and ENVI spectral acquisition software sampling a spectrum of 910 nm-2500 nm. The corresponding preprocessing and mathematical modeling analysis was performed by Unscrambler 9.7 software to establish a ginsenoside nondestructive spectral testing prediction model. Results: The optimal preprocessing method was determined to be a standard normal variable transformation combined with the second-order differential method. The coefficient of determination, $R^2$, of the mathematical model established by the partial least squares method was found to be 0.9999, while the root mean squared error of prediction, RMSEP, was found to be 0.0043, and root mean squared error of calibration, RMSEC, was 0.0041. The residuals of the majority of the samples used for the prediction were between ${\pm}1$. Conclusion: The experiment showed that the predicted model featured a high correlation with real values and a good prediction result, such that this technique can be appropriately applied for the nondestructive testing of ginseng quality.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.81-92
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• 2007

This paper consider trinomial group testing concerned with classification of N given units into one of k disjoint categories. In this paper, we propose Bayesian inference for estimating individual category proportions using the trinomial group testing model proposed by Bar-Lev et al. (2005). We compared a relative efficience (RE) based on the mean squared error (MSE) of MLE and Bayes estimators with various prior information. The impact of different prior specifications on the estimates is also investigated using selected prior distribution. The impact of different priors on the Bayes estimates is modest when the sample size and group size we large.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.73-78
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• 2006

In this paper, we propose a software reliability growth model based on the testing domain in the software system, which is isolated by the executed test cases in software testing. In particular, our model assumes an imperfect debugging environment in which new faults are introduced in the fault-correction process, and is formulated as a nonhomogeneous Poisson process(NHPP). Further, it is applied to fault-detection data, the results of software reliability assessment are shown, and comparison of goodness-of-fit with the existing software reliability growth model is performed.

• Journal of Korean Society for Quality Management
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• v.23 no.1
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• pp.15-28
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• 1995

We consider a step-stress life testing which is devised for a two-component parallel system with considerably long life time. To describe such a system, we use an exponential distribution as the survival function. The lift distribution is assumed between the log mean life time and the stress with the cumulative exposure model. The criterion for optimality is to minimize the sum of the variances of the maximum likelihood estimators of the mean life times of each part under the normal stress.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1511-1514
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• 2012

Epistasis that may explain a large portion of the phenotypic variation for complex economic traits of animals has been ignored in many genetic association studies. A Baysian method was introduced to draw inferences about multilocus genotypic effects based on their marginal posterior distributions by a Gibbs sampler. A simulation study was conducted to provide statistical powers under various unbalanced designs by using this method. Data were simulated by combined designs of number of loci, within genotype variance, and sample size in unbalanced designs with or without null combined genotype cells. Mean empirical statistical power was estimated for testing posterior mean estimate of combined genotype effect. A practical example for obtaining empirical statistical power estimates with a given sample size was provided under unbalanced designs. The empirical statistical powers would be useful for determining an optimal design when interactive associations of multiple loci with complex phenotypes were examined.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.385-389
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• 2012

With the development of computer storage and the rapidly growing ability to process large amounts of data, the multivariate control charts have received an increasing attention. The existing univariate and multivariate control charts are a single hypothesis testing approach to process mean or variance by using a single statistic plot. This paper proposes a multiple hypothesis approach to developing a new multivariate control scheme. Plotted Hotelling's $T^2$ statistics are used for computing the corresponding p-values and the procedure for controlling the false discovery rate in multiple hypothesis testing is applied to the proposed control scheme. Some numerical simulations were carried out to compare the performance of the proposed control scheme with the ordinary multivariate Shewhart chart in terms of the average run length. The results show that the proposed control scheme outperforms the existing multivariate Shewhart chart for all mean shifts.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.