the field study was conducted in one district of educational-experimental forest at tehran university (kheirood-kenar forest) in the north of iran. eighty-five soil profiles were dug in the site of study and several chemical and physical soil properties were considered. these factors included: soil ph, soil texture, bulk density, organic carbon, total nitrogen, extractable phosphorus and depth ...

We address the definition of symbolic variance and covariance for random interval-valued variables, and present four known symbolic principal component estimation methods using a common insightful framework. In addition, we provide a simple explicit formula for the scores of the symbolic principal components, equivalent to the representation by Maximum Covering Area Rectangle. Furthermore, the ...

This paper provides a comprehensive study of the syndicate structure and its relationship to information asymmetry and loan spread by using principal component analysis on a large set of 40 structure-related variables. A total of six structure components are identified and related to syndicate quality, syndicate members’ heterogeneity or share concentration, lead arranger’s characteristics, lea...

Abstract: Measuring the success of sustainable urban development has been difficult in the past. However, as this has become more important in the past few years, this paper develops an innovative sustainable urban development capacity measurement model based on principal component analysis (PCA) and Grey TOPSIS methodology, which has a significantly more comprehensive measurement, and reduces ...

maryam sadat salamati1, hossein zeinali2 ,ehdi yousefi3     1- ardestan branch, islamic azad university, ardestan, iran   2- esfahan agricultural and natural research center, isfahan, iran   3- assist. prof, payam noor university, isfahan, iran             received: 10 february 2011 accepted: 26 may 2011     * corresponding author: e-mail: [email protected]       abstract     in order to...

We design a binary sensing matrix in compressive imaging to reduce the capture time while maintaining image reconstruction performance, by minimizing the distance between the binary matrix and a modified principal component analysis sensing matrix. OCIS codes: (110.1758) Computational imaging, (100.3010) Image reconstruction technique.