• Geomechanics and Engineering
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• v.15 no.2
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• pp.783-791
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• 2018

In consideration of the mesoscopic structure of soil-rock mixtures in which the rock aggregates are wrapped by soil at normal temperatures, a two-layer embedded model of single-inclusion composite material was built to calculate the shear modulus of soil-rock mixtures. At a freezing temperature, an interface ice interlayer was placed between the soil and rock interface in the mesoscopic structure of the soil-rock mixtures. Considering that, a three-layer embedded model of double-inclusion composite materials and a multi-step multiphase micromechanics model were then built to calculate the shear modulus of the frozen soil-rock mixtures. Given the effect of pore structure of soil-rock mixtures at normal temperatures, its shear modulus was also calculated by using of the three-layer embedded model. Experimental comparison showed that compared with the two-layer embedded model, the effect predicted by the three-layer embedded model of the soil-rock mixtures was better. The shear modulus of the soil-rock mixtures gradually increased with the increase in rock regardless of temperature, and the increment rate of the shear modulus increased rapidly particularly when the rock content ranged from 50% to 70%. The shear modulus of the frozen soil-rock mixtures was nearly 3.7 times higher than that of the soil-rock mixtures at a normal temperature.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.357-365
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• 2011

This paper investigates a penalized likelihood method for estimating the parameter of normal mixtures in multivariate settings with full covariance matrices. The proposed model estimates the number of components through the addition of a penalty term to the usual likelihood function and the construction of a penalized likelihood function. We prove the consistency of the estimator and present the simulation results on the multi-dimensional nor-mal mixtures up to the 8-dimension.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.837-849
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• 2011

A self-organizing network is designed to estimate parameters of normal mixtures. SOMN achieves fast convergence and low possibility of divergence even when sample sizes are small, while PMLE eliminate unnecessary components. The proposed network effectively combines the good properties of SOMN and PMLE. Simulation verifies that the proposed network eliminates unnecessary components in normal mixtures when sample sizes are relatively small.

• The Journal of Korean Medicine
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• v.26 no.4
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• pp.62-73
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• 2005

Objectives: This study was designed to identify the ergogenic effects of Red ginseng and Paeonia radix mixtures and optimal ratios. Materials and Methods: This study was conducted by administering treatments of Red ginseng and Paeonia radix mixtures to rats and by measuring the time to exhaustion by treadmill running. Results: The treatment of Red ginseng and Paeonia radix mixtures to the rats increased the time to exhaustion by treadmill running. The most potent inhibition of Red ginseng and Paeonia radix mixtures on the 5-HT synthesis and the TPH expression in the dorsal raphe was observed at the dose of 200 mg/kg and the optimal ratio of Red ginseng and Paeonia radix for the maximum efficacy was 50:50. Under normal conditions (not exercise), long-term treatment of Red ginseng and Paeonia radix mixtures did not affect the 5-HT synthesis and the TPH expression in the dorsal raphe, suggesting that Red ginseng and Paeonia radix mixtures does not alter serotonin level in the normal rats. The suppressive effect of Red ginseng and Paeonia radix mixtures on the 5-HT synthesis and the TPH expression during exercise is a possible ergogenic mechanism of these mixtures. Conclusions : Red ginseng and Paeonia radix mixtures reduce exercise-induced fatigue, and have the effect of acting as ergogenic aids on the time to exhaustion by treadmill exercise and on 5-HT synthesis and TPH expression.

• Geomechanics and Engineering
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• v.15 no.1
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• pp.659-668
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• 2018

A series of direct shear tests were implemented on three different types of specimens (i.e., clean Perth sand, sand containing 10, 20 and 30% bentonite, sand containing 1, 3 and 5% slag, and sand containing 10, 20 and 30% bentonite with increasing percentages of added slag (1%, 3% and 5%). This paper focuses on the shear stress characteristics of clean sand and sand mixtures. The samples were tested under different three normal stresses (100, 150 and 200 kPa) and three curing periods of no curing time, 7 and 14 days. It was observed that the shear stresses of clean sand and mixtures were increased with increasing normal stresses. In addition, the use of slag has improved the shear strength of the sand-slag mixtures; the shear stresses rose from 128.642 kPa in the clean sand at normal stress of 200 kPa to 146.89 kPa, 154 kPa and 161.14 kPa when sand was mixed with 1%, 3% and 5% slag respectively and tested at the same normal stress. Internal friction angle increased from $32.74^{\circ}$ in the clean sand to $34.87^{\circ}$, $37.12^{\circ}$ and $39.4^{\circ}$ when sand was mixed with 1%, 3% and 5% slag respectively and tested at 100, 150, and 200 kPa normal stresses. The cohesion of sand-bentonite mixtures increased from 3.34 kPa in 10% bentonite to 22.9 kPa, 70.6 kPa when sand was mixed with 20% and 30% bentonite respectively. All the mixtures of clean sand, different bentonite and slag contents showed different behaviour; some mixtures exhibited shear stress more than clean sand whereas others showed less than clean sand. The internal friction angle increased, and cohesion decreased with increasing curing time.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.109-130
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• 2018

Several measures of multivariate skewness for scale mixtures of skew-normal distributions are derived. As a special case, those of multivariate skew-t distribution are considered in detail. Furthermore, the similarities, differences, and behavior of these measures are explored for cases of some specific members of the multivariate skew-normal and skew-t distributions using a simulation study. Since some measures are vectors, it is better to take all measures in the same scale when comparing them. In order to attain such a set of comparable indices, the sample version is considered for each of the skewness measures that are taken as test statistics for the hypothesis of t distribution against skew-t distribution. An application is reported for the data set consisting of 71 total glycerol and magnesium contents in Grignolino wine.

• International Journal of Concrete Structures and Materials
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• v.8 no.3
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• pp.251-258
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• 2014

The use of sustainable technologies such as supplementary cementitious materials, and/or recycled post-consumer environmental wastes is widely used in concrete industry in the last decade. This paper presents the results of a laboratory investigation of normal concrete containing sustainable technologies. Twenty one mixtures (21) were prepared with different combinations of silica fume, fly ash, olive's seed ash, and corncob ash (CCA). Fresh and hardened concrete properties were measured, as expected the inclusion of the sustainable technologies affected both fresh and hardened concrete properties. Based on the results obtained in this study and the analyses conducted, the following observations were drawn: replacing the cement by olive's seed ash or CCA has a significant effect on fresh concrete workability. Olive's seed ash increased the slump by more than 200 % compared to the control mixtures. The compressive strength of mixtures containing olive's seed ash showed by 45 and 75 % decrease compared to the control mixtures. The 28 days compressive strength of mixtures produced by CCA of 10 % replacement decreased by 41 % compared to the control mixture.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.459-469
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• 2012

This article proposes a couple of parallel implementations of the self-organizing network for normal mixtures. In principle, self-organizing networks should be able to be implemented in a parallel computing environment without issue. However, the network for normal mixtures has inherent problem in being operated parallel in pure sense due to estimating conditional expectations of the mixing proportion in each iteration. This article shows the result of the parallel implementations of the network using Java. According to the results, both of the implementations achieved a faster execution without any performance degradation.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.143-155
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• 2009

Best subset selection procedure based on mutual information (MI) between a set of explanatory variables and a dependent class variable is suggested. Derivation of multivariate MI is based on normal mixtures. Several types of normal mixtures are proposed. Also a best subset selection algorithm is proposed. Four real data sets are employed to demonstrate the efficiency of the proposals.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.1-11
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• 2008

For a large data set the high computational cost of estimating the parameters of normal mixtures with the conventional EM algorithm is crucially impedimental in applying the algorithm to the areas requiring high speed computation such as real-time speech recognition. Simulations show that the binned EM algorithm, being compared to the standard one, significantly reduces the cost of computation without loss in accuracy of the final estimates.