ATTAPULGITE clay was modified in this study by the graphene oxide sheets and the clay was diagnosed before and after modification using several techniques (Fourier-transform infrared spectroscopy FT-IR, X-ray powder diffraction XRD, Scanning electron microscope SEM , energy dispersive spectroscopy EDX ) ,The surface of the attapulgite clay (before (Ata) after modification by graphene oxide (Ata-GO) ) was applied to adsorption of the Alizarin dye from its water solutions through the application of several kinetic models (pseudo first-order model , pseudo second -order model , intraparticle diffusion model ),It was found that the practical results follow pseudo second -order model. The process of modification on the surface of the mud has imp

The seed propagation is the predominant method of Echinacea propagation, which has been criticized for its time-consuming control over the separation factor and the uncertainty of pathogen-free plants produced by this method. The technology of tissue culture has provided multiple opportunities for the production of secondary metabolites continuously without being restricted to a specific season, due to the possibility of controlling the environmental conditions and the components of the nutrient medium needed by the plant. This study was conducted to investigate the effects of salicylic acid as elicitor and tyrosine as precursor on propagation and some secondary compounds production in coneflower in vitro. The result showed the superiori

he study aimed to purify of Leucine aminopeptidase (LAP) from sera of hyperthyroidism patients and its relation to some thyroid hormones (TSH, T 3 , T 4 ) of subclinical hyperthyroidism and hyperthyroidism patients with lipid peroxidation levels that may be play a role in this diseases. Specimens were collected during the time from Nov 2017 until Jan 2018 from Endocrine and Diabetes Center, blood samples were collected from fifty healthy control and one hundred patients, patients were divided into two groups consisted of (50) with hyperthyroidism and (50) with subclinical hyperthyroidism. The aged for all subjects ranged (15-60) years with body mass ranged ((25- 29) kg/m 2 . The purification is done by addition of ammonium sulfate, dial

This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).