Estimation of the unknown parameters in 2-D sinusoidal signal model can be considered as important and difficult problem. Due to the difficulty to find estimate of all the parameters of this type of models at the same time, we propose sequential non-liner least squares method and sequential robust  M method after their development through the use of sequential  approach in the estimate suggested by Prasad et al to estimate unknown frequencies and amplitudes for the 2-D sinusoidal compounds but depending on Downhill Simplex Algorithm in solving non-linear equations for the purpose of obtaining non-linear parameters estimation which represents frequencies and then use of least squares formula to estimate

Mixture experiments are response variables based on the proportions of component for this mixture. In our research we will compare the scheffʼe model with the kronecker model for the mixture experiments, especially when the experimental area is restricted.

     Because of the experience of the mixture of high correlation problem and the problem of multicollinearity between the explanatory variables, which has an effect on the calculation of the Fisher information matrix of the regression model.

     to estimate the parameters of the mixture model, we used the (generalized inverse ) And the Stepwise Regression procedure

A case–control study (80 patients with chronic hepatitis B virus [HBV] infection and 96 controls) was performed to evaluate the association of an IL12A gene variant (rs582537 A/C/G) with HBV infection. Allele G showed a signifcantly lower frequency in patients compared to controls (31.2 vs. 46.9%; probability [p]=0.009; corrected p [pc]=0.027) and was associated with a lower risk of HBV infection (odds ratio [OR]=0.49; 95% confdence interval [CI]=0.29–0.83). A similar lower risk was associated with genotypes CG (17.5 vs. 29.2; OR=0.25; 95% CI=0.08–0.81; p=0.02) and GG (10.0 vs. 16.7; OR=0.25; 95% CI=0.07–0.91; p=0.036), but the pc value was not signifcant (0.12 and 0.126, respec‑ tively). Serum IL35 levels showed signifcant difere

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time t . The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integrated with the FD method t

English

Action films employ many artistic and literary elements that contribute greatly to building the general meaning of the film and push the wheel of the film forward. The element of mystery and suspense is used as two basic elements in action films. The cinematic language in action films depends on global coding, which is not models as it might be. It is based on logic, rather as units that aspire to morphology and not their homogeneity as the physical sense, but as the logical harmony of interpretive authority and enlightenment and in action films as a field of communication and a field in its origin in which the signifier contrasts with the perceptions of the meaning and in it takes a certain number of units preventing each other and thro

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t