• International journal of steel structures
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• v.18 no.4
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• pp.1265-1283
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• 2018

This paper presents a four-variable first-order shear deformation theory considering in-plane rotation of functionally graded plates. In recent studies, a simple first-order shear deformation theory was developed and extended to functionally graded plates. It has only four variables, separating the deflection into bending and shear parts, while the conventional first-order shear deformation theory has five variables. However, this simple first-order shear deformation theory only provides good predictions for simply supported plates since it does not consider in-plane rotation varying through the thickness of the plates. The present theory also has four variables, but considers the variation of in-plane rotation such that it is able to correctly predict the responses of the plates with any boundary conditions. Analytical solutions are obtained for rectangular plates with various boundary conditions. Comparative studies demonstrate the effects of in-plane rotation and the accuracy of the present theory in predicting the responses of functionally graded plates.

• Horticultural Science & Technology
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• v.35 no.4
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• pp.510-522
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• 2017

To better understand the morphological variation for Perilla crop in different areas of China, we studied the morphological variation in 87 accessions (84 cultivated var. frutescens and three cultivated var. crispa) from high latitude (Northeast China) and middle latitude (North and Northwest China) areas of China by examining seven quantitative and 10 qualitative characters. Analysis of the morphological variation determined that there was significant morphological differences in five quantitative traits between cultivated var. frutescens and cultivated var. crispa, including effective number of branches (QN2), number of internodes (QN3), number of branches (QN4), length of the largest inflorescence (QN5), and days from germination to flowering (QN7). However, two quantitative traits-plant height and number of florets of the largest inflorescence-did not show any significant differences between cultivated var. frutescens and cultivated var. crispa. In addition, significant differences for six quantitative traits were found between the accessions of cultivated var. frutescens originating from high and middle latitude areas in China, which included QN2, QN3, QN5, number of florets of the largest inflorescence (QN6), and QN7. Principal components analysis (PCA) identified five quantitative characters [plant height (QN1), QN2, QN3, QN4, QN7] and six qualitative characters [fragrance of plant (QL1), color of reverse side of leaf (QL3), degree of pubescence (QL5), color of flower (QL6), shape of leaf (QL7), and hardness of seed (QL10)] that contributed to the positive direction on the first axis. The other quantitative and qualitative characters contributed to the negative direction on the first axis. Most accessions of cultivated var. frutescens and cultivated var. crispa were clearly separated by the first axis. In addition, most accessions of cultivated var. frutescens are from high latitude and middle latitude areas that were clearly separated by the first axis, except for several accessions. The findings from this study will provide useful information towards understanding the morphological variation of Perilla crop according to geographical distribution in high and middle latitude regions of China.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.24-33
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• 2001

As we know, the quality of processes is technically depicted by variation, a product or process with the best quality must naturally require the variation as less as possible. The variation is usually reduced with many ways, say, by adjusting parameters settings under robust design with many turns expensive experiments. So ones are trying to reach the robustness by detecting cheap and simple methods. In this paper, a both practical and simple technique for quality improvement, namely reducing the variation, by data classification is studied. First, all possible system factors are included, which may dominate the variation law. And then we make use of the past observations and their classification as well as boxplot charts to find out the internal rule between the variation and the system factor. Next, adjust the location of the system factor according to the rule so that the variation could, to some extent, be lessened. Finally, two typical quality improvement cases based on data classification are presented.

• Restorative Dentistry and Endodontics
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• v.37 no.3
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• pp.175-179
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• 2012

The aim of this study was to present a method for endodontic management of a maxillary first molar with unusual C-shaped morphology of the buccal root verified by cone-beam computed tomography (CBCT) images. This rare anatomical variation was confirmed using CBCT, and nonsurgical endodontic treatment was performed by meticulous evaluation of the pulpal floor. Posttreatment image revealed 3 independent canals in the buccal root obturated efficiently to the accepted lengths in all 3 canals. Our study describes a unique C-shaped variation of the root canal system in a maxillary first molar, involving the 3 buccal canals. In addition, our study highlights the usefulness of CBCT imaging for accurate diagnosis and management of this unusual canal morphology.

• Korean Journal of Mathematics
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• v.22 no.1
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• pp.57-69
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• 2014

We obtain a formula for the conditional Wiener integral of the first variation of functionals and establish several integration by parts formulas of conditional Wiener integrals of functionals on a function space. We then apply these results to obtain various integration by parts formulas involving conditional integral transforms and conditional convolution products on the function space.

• The Pure and Applied Mathematics
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• v.16 no.4
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• pp.369-382
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• 2009

In this paper we establish various relationships among the generalized integral transform, the generalized convolution product and the first variation for functionals in a Banach algebra S($L_{a,b}^2$[0, T]) introduced by Chang and Skoug in [14]. We then derive an inverse integral transform and obtain several relationships involving inverse integral transforms.

• 대한핵의학회:학술대회논문집
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• 2000.05a
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• pp.53-53
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• 2000

• The Korean Journal of Zoology
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• v.28 no.1
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• pp.9-20
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• 1985

Univariate and multivariate analyses of morphometric characters were performed with samples of striped field mice, Apodemus agrarius coreae Thomas, collected from Mt. Taebaek area, Mt. Wolak area, Mt. Palgong area, and Chongju area. It was confirmed that the samples of A. agrarius coreae were similar with one another to be grouped into a single subspecies. Moreover, clinal variation related with the altitude of localities where samples were collected was revealed in the first axis resulted from discriminant analysis and in the length of tail vertebrae.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.429-440
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• 2008

In this paper, we develop the noninformative priors when the parameter of interest is the common coefficient of variation in two inverse Gaussian distributions. We want to develop the first and second order probability matching priors. But we prove that the second order probability matching prior does not exist. It turns out that the one-at-a-time and two group reference priors satisfy the first order matching criterion but Jeffreys' prior does not. The Bayesian credible intervals based on the one-at-a-time reference prior meet the frequentist target coverage probabilities much better than that of Jeffreys' prior. Some simulations are given.

• International Journal of Quality Innovation
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• v.4 no.1
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• pp.148-159
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• 2003

Variation is the archenemy of quality. To reduce or control the variation in a complex production unit, firstly we need to identify the location of the root cause of the variation. This paper discusses the detection of variability and the techniques used for reduction of variation for a production unit consisting of many processes. In the first part of this paper, the background of variability detection in production systems is introduced which is then followed by a weighted network corresponding to correlation matrix of all processes. Based on the network and clustering criterion of maximum spanning tree, a classification of all processes is derived. Furthermore, the variation of each process in a class is determined by residual analysis. In the last part, the use of methods of robust design for the processes with a larger variability is discussed.