The Normalized Difference Vegetation Index (NDVI) is commonly used as a measure of land surface greenness based on the assumption that NDVI value is positively proportional to the amount of green vegetation in an image pixel area. The Normalized Difference Vegetation Index data set of Landsat based on the remote sensing information is used to estimate the area of plant cover in region west of Baghdad during 1990-2001. The results show that in the period of 1990 and 2001 the plant area in region of Baghdad increased from (44760.25) hectare to (75410.67) hectare. The vegetation area increased during the period 1990-2001, and decreases the exposed area.

Rheumatoid arthritis is a chronic inflammatory autoimmune disease its etiology is unknown. The classical autoimmune diseases, have adaptive immune genetic associations with autoantibodies and major histocompatibility complex (MHC) class II such as rheumatoid arthritis (RA), diabetes mellitus type two (DM II). Serum of99 males suffering from RA without DMII as group (G1), 45 males suffering from RA with DM II as group (G2) and 40 healthy males as group (G3) were enrolled in this study to estimation of alkaline phosphates (ALP), C-reactive protein (CRP) and Pentraxin-3(PTX). Results showed a highly significant increase in PTX3 levels in G1 and G2 compared to G3 and a significant decrease in G1comparing to G2. Results also revealed a significa

The study aimed to investigate the effect of different times as follows 0.5, 1.00, 2.00 and 3.00 hrs, type of solvent (acetone, methanol and ethanol) and temperature (~ 25 and 50)ºc on curcumin percentage yield from turmeric rhizomes. The results showed significant differences (p? 0.05) in all variables. The curcumin content which were determined spectrophotometrically ranged between (0.55-2.90) %. The maximum yield was obtained when temperature, time and solvent were 50ºC, 3 hrs and acetone, respectively.

In this paper we estimate the coefficients and scale parameter in linear regression model depending on the residuals are of type 1 of extreme  value distribution for the largest values . This can be regard as an improvement for the studies with the smallest values . We study two estimation methods ( OLS  & MLE ) where we resort to Newton – Raphson (NR) and Fisher Scoring methods to get MLE estimate because the difficulty of using the usual approach with MLE . The relative efficiency criterion is considered beside to the statistical inference procedures for the extreme value regression model of type 1 for largest values . Confidence interval , hypothesis testing for both scale parameter and regression coefficients

A mathematical model has been introduced to investigate the effect of nuclear reaction constant ( A ), probability of the BEC ground state occupation Ω i, nD is the number density of deuteron (d) and the overall number of nuclei ND on the total nuclear d-d fusion rate (R). Under steady-state of the condensates of Bose-Einstein, the postulate of quantum theory and Bose-Einstein theory were applied to evaluate the total nuclear (d-d) fusion rate trapping in Nickel-metal The total nuclear fusion rate trapping predicts a strong relationship between astrophysical S-factor and masses of Nickel. The reaction rate trapping model was tested on three reaction d(d,p)T, d(d, n)3He and d(d, 4He)Q = 23.8MeV respectively. The reaction rate has described

The objective of this study is to examine the properties of Bayes estimators of the shape parameter of the Power Function Distribution (PFD-I), by using two different prior distributions for the parameter θ and different loss functions that were compared with the maximum likelihood estimators. In many practical applications, we may have two different prior information about the prior distribution for the shape parameter of the Power Function Distribution, which influences the parameter estimation. So, we used two different kinds of conjugate priors of shape parameter θ of the <

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

In this study, structures damage identification method based on changes in the dynamic characteristics
(frequencies) of the structure are examined, stiffness as well as mass matrices of the curved
(in and out-of-plane vibration) beam elements is formulated using Hamilton's principle. Each node
of both of them possesses seven degrees of freedom including the warping degree of freedom. The
curved beam element had been derived based on the Kang and Yoo’s thin-walled curved beam theory
in 1994. A computer program was developing to carry out free vibration analyses of the curved
beam as well as straight beam. Comparing with the frequencies for other researchers using the general
purpose program MATLAB. Fuzzy logic syste