background: effective leadership is essential to passing through obstacles facing the health field. the current health care system in iran has major problems and gaps in the field of effective leadership. the aim of this study was to evaluate hospital managers’ leadership style through self-assessment and to determine the correlation between leadership styles with healthcare executives’ leaders...

It has been proved that global and local structure are both important for process monitoring, but principal component analysis (PCA) and locality preserving projections (LPP) can not consider them simultaneously in the process of dimension reduction. This article proposes a novel method named local and global structure preserving projections with Bayes classification (LGSPP-Bayes). The original...

The family of inverse regression estimators recently proposed by Cook and Ni (2005) have proven effective in dimension reduction by transforming the highdimensional predictor vector to its low-dimensional projections. In this article, we propose a general shrinkage estimation strategy for the entire inverse regression estimation family that is capable of simultaneous dimension reduction and var...

We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss-Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The a...

The tip position control of a single-link flexible manipulator is considered in this paper. The cross-sectional dimension of the manipulator is tuned by genetic algorithms such that the first vibration mode dominates the higher order vibrations. A sliding mode controller of reduced dimension is employed to control the manipulator using the rigid and vibration measurements as feedback. Control e...

Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method,MAVE (minimum average variance estimation). However, it is not robust to outliers in the dependent variable because of the use of least-squares...

This paper proposes a distributed PCA algorithm, with the theoretical guarantee that any good approximation solution on the projected data for k-means clustering is also a good approximation on the original data, while the projected dimension required is independent of the original dimension. When combined with the distributed coreset-based clustering approach in [3], this leads to an algorithm...

Generalized function matrix projective lag synchronization of uncertain complex dynamical networks with different dimension of nodes via adaptive control method is investigated in this paper. Based on Lyapunov stability theory, adaptive controller is obtained and unknown parameters of both the drive network and the response network are estimated by adaptive laws. In addition, the three-dimensio...

let r be an associative ring with identity, c(r) be the category of com-plexes of r-modules and flat(c(r)) be the class of all at complexes of r-modules. we show that the at cotorsion theory (flat(c(r)); flat(c(r))−)have enough injectives in c(r). as an application, we prove that for each atcomplex f and each complex y of r-modules, exti (f,x)= 0, whenever ris n-perfect and i > n.

Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good n...