Search

In some applications, the quality of the process or product is characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is a technique for checking the stability of the relationship over time. Existing linear profile monitoring methods usually assumed the error distribution to be normal. However, this assumption may not always be true in practice. To address this situation, we propose a method for profile monitoring under the framework of generalized linear models when the relationship between the mean and variance of the response variable is known. Two multivariate exponentially weighted moving average control schemes are proposed based on the estimated profile parameters obtained using a quasi-likelihood approach. The performance of the proposed methods is evaluated by simulation studies. Furthermore, the proposed method is applied to a real data set, and the R code for profile monitoring is made available to users. [ABSTRACT FROM AUTHOR]

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMCp, RNMCpm and RNMCpu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMCp and NMCpm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions. [ABSTRACT FROM AUTHOR]

The Shewhart-type control chart is traditionally developed under the normality assumption. In practice, however, this assumption may not hold. Because the skew normal distribution represents a broad distribution class and is more flexible than is the normal distribution, we propose two new control charts to monitor process mean and spread for skew normal distributed data. Moreover, to facilitate practical implementation, tables of charting constants are provided. We conducted simulation studies to compare the false alarm rates, and the results show that new proposed charts perform better than others as skewness increases. Finally, an illustrative example is provided. [ABSTRACT FROM PUBLISHER]

Recent studies have emphasized a role of adaptive immunity, and particularly T cells, in the genesis of hypertension. We sought to determine the T-cell subtypes that contribute to hypertension and renal inflammation in angiotensin II-induced hypertension. Using T-cell receptor spectratyping to examine T-cell receptor usage, we demonstrated that CD8(+) cells, but not CD4(+) cells, in the kidney exhibited altered T-cell receptor transcript lengths in Vβ3, 8.1, and 17 families in response to angiotensin II-induced hypertension. Clonality was not observed in other organs. The hypertension caused by angiotensin II in CD4(-/-) and MHCII(-/-) mice was similar to that observed in wild-type mice, whereas CD8(-/-) mice and OT1xRAG-1(-/-) mice, which have only 1 T-cell receptor, exhibited a blunted hypertensive response to angiotensin II. Adoptive transfer of pan T cells and CD8(+) T cells but not CD4(+)/CD25(-) cells conferred hypertension to RAG-1(-/-) mice. In contrast, transfer of CD4(+)/CD25(+) cells to wild-type mice receiving angiotensin II decreased blood pressure. Mice treated with angiotensin II exhibited increased numbers of kidney CD4(+) and CD8(+) T cells. In response to a sodium/volume challenge, wild-type and CD4(-/-) mice infused with angiotensin II retained water and sodium, whereas CD8(-/-) mice did not. CD8(-/-) mice were also protected against angiotensin-induced endothelial dysfunction and vascular remodeling in the kidney. These data suggest that in the development of hypertension, an oligoclonal population of CD8(+) cells accumulates in the kidney and likely contributes to hypertension by contributing to sodium and volume retention and vascular rarefaction. [ABSTRACT FROM AUTHOR]

RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. Among the many difficulties of RNAseq analysis, correctly adjusting for batch effect is a pivotal one for large-scale RNAseq based studies. The batch effect of RNAseq data is most obvious in microRNA (miRNA) sequencing studies. Using real miRNA sequencing (miRNAseq) data, we evaluated several batch removal techniques and discussed their effectiveness. We illustrate that by adjusting for batch effect, more reliable differentially expressed genes can be identified. Our study on batch effect in miRNAseq data can serve as a guideline for future miRNAseq studies that might contain batch effect.

Traditional wheezes detection methods are based on the frequency and durations of acoustic signal or the location of peaks from successive spectra. In these methods, the discriminative threshold used to identify peaks usually is fixed empirically. Therefore, accuracy of detected wheeze is affected by environment noise and artificial factors. The objective of this study is to classify normal and abnormal (wheezing) respiratory sounds using Cepstral analysis in Gaussian Mixture Models. The sound signal is divided in overlapped segments, which are characterized by a reduced dimension feature vectors using Mel-Frequency Cepstral Coefficients. In this study the ;speaker' is wheeze. During the test phase, an unknown sound is compared to all the GMM models and the classification decision is based on the Maximum Likelihood criterion. In these processes, identification is based on threshold value. If the threshold is bigger than zero, the sound is normal. Otherwise, the sound is wheeze. From experimental results, when the Gaussian mix number is 16, the accuracy of identification of wheeze is up to 90%.