Learn More
BACKGROUND Mesenchymal stem cells (MSCs)-based regenerative therapy is currently regarded as an alternative approach to salvage the acute myocardial infarcted hearts. However, the efficiency of MSCs transplantation is limited by lower survival rate of engrafted MSCs. In previous study, we found that 1.0 microg/ml Lipopolysaccharide (LPS) could protect MSCs(More)
In some applications, quality of a process is characterized by the functional relationship between a response variable and one or more explanatory variables. Profile monitoring is for checking the stability of this relationship over time. Control charts for monitoring nonparametric profiles are useful when the relationship is too complicated to be described(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t This paper proposes an exponentially weighted moving average scheme(More)
One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we propose a novel approach, namely DPMFS, to address this issue. The proposed approach is designed 1) to group documents into a set of clusters while the number of document clusters(More)
Recently, monitoring the process mean and variance simultaneously by using a single chart has drawn more and more attention. In this paper, we propose a new single chart that integrates the EWMA procedure with the generalized likelihood ratio (GLR) test statistics for jointly monitoring both the process mean and variance. It can be easily designed and(More)
Finding the appropriate number of clusters to which documents should be partitioned is crucial in document clustering. In this paper, we propose a novel approach, namely DPMFP, to discover the latent cluster structure based on the DPM model without requiring the number of clusters as input. Document features are automatically partitioned into two groups, in(More)