The snthesis and characterization of cobalt(II), nickel(II), copper(II) and zinc(II) complexes of azo ligand 4-[(5-acetyl-2-aminophenyl)- diazenyl]-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one derived from 4-aminoantipyrine and 4-aminoacetophenone are reported. The nature of the compounds have been studied followed by mole ratio and methods of continuous contrast, Beer′s law followed during a condensation rate (1 × 10-4 – 3 × 10-4 M). The analytical data showed that all the complexes are in 1:2 metal-ligand ratio. An octahedral geometry have been suggested for all the compounds and biological studies of all the complexes were evaluated against different types of antimicrobial strains.

The new bidentate Schiff base ligand namely [(E)-N1-(4-methoxy benzylidene) benzene-1, 2-diamine] was prepared from condensation of 4-Methoxy benzaldehyde with O-Phenylene diamine at 1:1 molar ratio in ethanol as a solvent in presence of drops of 48% HBr. The structure of ligand (L) was characterized by, FT-IR, U.V-Vis., 1H-, 13C- NMR spectrophotometer, melting point and elemental microanalysis C.H.N. Metal complexes of the ligand (L) in general molecular formula [M(L)3], where M= Mn(II), Co(II), Ni(II),Cu(II) and Hg(II); L=(C14H14N2O) in ratio (1:3)(Metal:Ligand) were synthesized and characterized by Atomic absorption, FT- IR, U.V-Vis. spectra, molar conductivity, chloride content, melting point and magnetic susceptibility from the above d

            Semi-parametric regression models have been studied in a variety of applications and scientific fields due to their high flexibility in dealing with data that has problems, as they are characterized by the ease of interpretation of the parameter part while retaining the flexibility of the non-parametric part. The response variable or explanatory variables can have outliers, and the OLS approach have the sensitivity to outliers. To address this issue, robust (resistance) methods were used, which are less sensitive in the presence of outlier values in the data. This study aims to estimate the partial regression model using the robust estimation method with the wavel

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

In this paper we use Bernstein polynomials for deriving the modified Simpson's 3/8 , and the composite modified Simpson's 3/8 to solve one dimensional linear Volterra integral equations of the second kind , and we find that the solution computed by this procedure is very close to exact solution.

Nonlinear diffraction patterns can be obtained by focusing a laser beam through a thin slice of the material. Here, we investigated experimentally the formation of the far field nonlinear diffraction patterns of cw laser beam at 532 nm passing through a quartz cuvette containing multi-wall carbon nanotubes (MWCNT's) suspended in acetone and in DI water at concentrations of 0.030.wt.%, 0.045 wt.%, 0.060 wt.%, and 0.075 wt.%. Our results show that increasing the concentration of both types of suspensions (MWCNTs in acetone and MWCNTs DI water) led to increase in the number of pattern rings which indicates an increase in their nonlinear refractive indices. Moreover, MWCNTs DI water suspension at a concentration of 0.075 wt. % was more effic

The research was performed in order to investigate the prevalence of Eimeria spp in buffalo. Coccidiosis, is a common livestock disease include water buffaloes and nothing is known about the most pathogenic species of Eimeria. Since the highest prevalence of oocyst shedding and incidence of disease occurs in buffalo calves less than one year of age. The omnipresent occurrence and negative effects of the infection on health and buffalo growth output are taken into account. Therefore, both farmers and veterinarians should pay greater attention to infections with Eimeria spp. And there is little analysis of data reported in Iraq and the world regarding Eimeria infection in river buffalo spp.

    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima