this study aims to assess the effect of grazing intensity on vegetation structure, soil nutrient concentrations and soil physical properties. the study was carried out in steppe rangelands of saveh, markazi province, iran. four sites with four grazing intensities including very high, high, moderate and non-grazed with the same ecological conditions were selected. to study various vegetation and...

remotely sensed imagery is proving to be a useful tool to estimate water depths in coastalzones. bathymetric algorithms attempt to isolate water attenuation and hence depth from other factors byusing different combinations of spectral bands. in this research, images of absolute bathymetry using twodifferent but related methods in a region in the southern caspian sea coasts has been produced. th...

Algorithm 1 Suggesting Compatible Colors 1: procedure COMPATIBLECOLORS(palette t, index k, #cands Ncand, #samples Nsample, threshold (τ, κ)) 2: . Sampling candidate’s HSVs 3: f ← COMPUTEHUEPROBABILITY(t, k) . Eq. 3 or Eq. 4 4: hi← SAMPLINGFROMHUEPROB( f , Nsample) 5: si ∼N (μs,σs) . §4.2 6: vi ∼N (μv,σv) . §4.2 7: . Compute rating 8: for i = 1→ m do 9: ci← (hi, si,vi) 10: Ccand i ← COMPATIBLECA...

A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems. In the proposed performance monitoring framework, the dynamic PCA based performance benchmark is adopted for performance assessment, while performance diagnosis is carried out using a unified weighted dynamic PCA...

Mahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing rerandomization strategy. However, this may not work well for high-dimensional cases because it balances all orthogonalized covariates equally. We propose using principal component analysis (PCA) to identify proper subspaces in which should be calculated. Not on...

Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus a...

Principal component analysis (PCA) and independent component analysis (ICA) are both based on a linear model of multivariate data. They are often seen as complementary tools, PCA providing dimension reduction and ICA separating underlying components or sources. In practice, a two-stage approach is often followed, where first PCA and then ICA is applied. Here, we show how PCA and ICA can be seen...

Several investigators have successfully used principal component analysis (PCA) in interpreting occupational hygiene data. However, traditional textbooks in occupational hygiene provide no guidance for the application and interpretation of PCA. In this article I briefly review the basics of PCA (for those not statistically inclined), provide some guidelines for performing PCA (and designing stu...

The paper explores new expansions of the eigenvalues for −∆u = λρu in S with Dirichlet boundary conditions by the bilinear element (denoted Q1) and three nonconforming elements, the rotated bilinear element (denoted Qrot 1 ), the extension of Q rot 1 (denoted EQ rot 1 ) and Wilson’s elements. The expansions indicate that Q1 and Qrot 1 provide upper bounds of the eigenvalues, and that EQrot 1 an...

In this paper we examine the use of a mathematical procedure, called Principal Component Analysis, in Recommender Systems. The resulting filtering algorithm applies PCA on user ratings and demographic data, aiming to improve various aspects of the recommendation process. After a brief introduction to PCA, we provide a discussion of the proposed PCADemog algorithm, along with possible ways of co...