Carbon nanospheres (CNSs) were successfully prepared and synthesized by Catalytic Chemical Vapor Deposition (CCVD) by using camphor as carbon source only, over iron Cobalt (Fe-Co) saturated zeolite at temperature between (700 oC and 900 °C), with different concentrations of camphor, and reaction time. The synthesized CNSs were characterized using Scanning Electron Microscopy (SEM), X-ray diffraction spectroscopy (XRD), and Fourier Transform Infrared (FTIR). The carbon spheres in different sizes between 100 nm and 1000 nm were investigated. This work has done by two parts, first preparation of the metallic catalyst and second part formation CNSs by heat treatment.

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

FH Ghanim, Journal of Global Pharma Technology, 2018

The free Schiff base ligand (HL1) is prepared by being mixed with the co-ligand 1, 10-phenanthroline (L2). The product then is reacted with metal ions: (Cr+3, Fe+3, Co+2, Ni+2, Cu+2 and Cd+2) to get new metal ion complexes. The ligand is prepared and its metal ion complexes are characterized by physic-chemical spectroscopic techniques such as: FT-IR, UV-Vis, spectra, mass spectrometer, molar conductivity, magnetic moment, metal content, chloride content and microanalysis (C.H.N) techniques. The results show the formation of the free Schiff base ligand (HL1). The fragments of the prepared free Schiff base ligand are identified by the mass spectrometer technique. All the analysis of ligand and its metal complexes are in good agreement with th

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial