The present work involved two steps: the first step include Mannich reaction was carried out on 2- mercaptobenzimidazole using formaldehyde and different secondary amine or amide to gives the compounds(2-16). The secnd step include preparation of (Ethylbenzimidazoly-2-mercaptoacetate)(17) from the reaction of 2- mercaptobenzimidazole with ethylchloroacetate than prepared hydrazide derivative[18]from reaction of compound(17) with hydrazinehydrate. Followed Preparation of shiff bases(19-24) and there reaction with mercaptoacetic acid to give a new compounds containing thiazolidinderivetives(25-30).Structure confirmation of all prepared compound were proved using FTIR and element analysis (C.H.N.S) and mesurmentedmelting poi

        In this paper, an estimate has been made for parameters and the reliability function for Transmuted power function (TPF) distribution through using some estimation methods as proposed new technique for white, percentile, least square, weighted least square and modification moment methods. A simulation was used to generate random data that follow the (TPF) distribution on three experiments (E₁ , E₂ , E₃)  of the real values of the parameters, and with sample size (n=10,25,50 and 100) and iteration samples (N=1000), and taking reliability times (0< t < 0) . Comparisons have been made between the obtained results from the estimators using mean square error (MSE). The results showed the

KE Sharquie, AA Noaimi, MS Abass, American Journal of Dermatology and Venereology, 2019 - Cited by 4

Lithium-rich layered oxide cathodes have attracted considerable attention due to their high energy density, but have suffered from voltage drop, structural instability, and limited electrical conductivity. In this study, the electrochemical performance of the lithium-rich cathode material Li[Li0.20Mn0.54Ni0.13Co0.13]O2 was evaluated after modification by zinc doping and composition with graphene oxide or graphene. The zinc-doped powders were synthesized by the sol-gel method, while the graphene-based composites were prepared by the hydrothermal route. The structural, morphological and electrochemical characteristics of the modified materials were examined using X-ray diffraction, Fourier transform infrared spectroscopy, field emission scann

This project sought to fabricate a flexible gas sensor based on a short functionalized multi-walled carbon nanotubes (f-MWCNTs) network for nitrogen dioxide gas detection. The network was prepared by filtration from the suspension (FFS) method and modified by coating with a layer of polypyrrole conductive polymer (PPy) prepared by the oxidative chemical polymerization to improve the properties of the network. The structural, optical, and morphological properties of the f-MWCNTs and f-MWCNTs/PPy network were studied using X-ray diffraction (XRD), Fourie-transform infrared (FTIR), with an AFM (atomic force microscopy). XRD proved that the structure of f-MWCNTs is unaffected by the synthesis procedure. The FTIR spectra verified the existence o

A simple straightforward mathematical method has been developed to cluster grid nodes on a boundary segment of an arbitrary geometry that can be fitted by a relevant polynomial. The method of solution is accomplished in two steps. At the first step, the length of the boundary segment is evaluated by using the mean value theorem, then grids are clustered as desired, using relevant linear clustering functions. At the second step, as the coordinates cell nodes have been computed and the incremental distance between each two nodes has been evaluated, the original coordinate of each node is then computed utilizing the same fitted polynomial with the mean value theorem but reversibly.

The method is utilized to predict

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca