Nanoparticles of copper sulfide have been prepared by simple reaction between using copper nitrate with different concentrations ratio 0.1, 0.3, and 0.5 mM, thiourea by a simple chemical route. The prepared Nano powders have been deposited onto glass substrates by casting method at 60°C. The structure of the product Nano- films has been studied by x-ray diffraction, where the patterns showed that all the samples have a hexagonal structure of covellite copper sulfide with the average crystalline sizes 14.07- 16.51 nm. The morphology has been examined by atomic force microscopy, and field emission scan electron microscopy. The AFM images showed particles with almost spherical and rod shapes with average diameter sizes of 49.11- 90.64 nm.

Estimation of the tail index parameter of a one - parameter Pareto model has wide important by the researchers because it has awide application in the econometrics science and reliability theorem.

Here we introduce anew estimator of ^"generalized median" type and compare it with the methods of Moments and Maximum likelihood by using the criteria, mean square error.

 The estimator of generalized median type performing best over all.

Accurate description of thermodynamic, structural, and electronic properties for bulk and surfaces of ceria (CeO₂) necessitates the inclusion of the Hubbard parameter (U) in the density functional theory (DFT) calculations to precisely account for the strongly correlated 4f electrons. Such treatment is a daunting task when attempting to draw a potential energy surface for CeO₂-catalyzed reaction. This is due to the inconsistent change in thermo-kinetics parameters of the reaction in reference to the variation in the U values. As an illustrative example, we investigate herein the discrepancy in activation and reaction energies for steps underlying the partial and full hydrogenation of acetyl

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.