The purpose of my thesis is to synthesis two new bidentate ligands which were used to prepare series of metal complexes by reacting the ligands with (M+2 = Mn, Co, Ni, Cu, Cd and Hg) Succinyl chloride was used as starting material to synthesis two bidentate ligands (L1) and (L2) by reaction it with 4-chloroaniline (L1) and (4-aminoacetophenone) (L2) in dichloromethane as a solvent, that are: (L1) = N1,N4-bis (4-chloro phenyl ) succinamide (L2) =N1,N4-bis(4-acetylphenyl)succinamide The new ligands were characterize by using spectroscopic study (Fourier-transform infrared spectroscopy (FT-IR), electronic spectra ( UV-Vis) ,nuclear magnetic resonance(1H,13C-NMR), Mass spectra ,Elemental microanalysis (C.H.N.S) and thermal analysis (TGA) , which showed a match with the molecular formulas of these ligands. A series of metal complexes (containing six complexes for each ligand) were synthesized from adding some metal ions (M+2 = Mn, Co, Ni, Cu, Cd and Hg) to the ligand with molecular formulas: ]Cl22(H2O)M(L1)2]Cl2 , [2(H2O)M(L2)2] All complexes that synthesized in this investigation were characterized by solubility, melting point, Fourier-transform infrared spectroscopy, electronic spectra, molar conductivity, magnetic susceptibility measurements, element microanalysis and flame atomic absorption, According to the ola inedresults an octahedral geometric structure of the prepared complexes was proposed. The biological activity of the prepared compounds against three types of bacteria, Escherichia coli (G-), and Pseudomonas (G-) Staphylococcus aureus (G+) were examined, the prepared compounds showed good activity and different from the selected bacteria.
This paper deals with constructing mixed probability distribution from mixing exponential
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The main objective of this work is to introduce and investigate fixed point (F. p) theorems for maps that satisfy contractive conditions in weak partial metric spaces (W.P.M.S), and give some new generalization of the fixed point theorems of Mathews and Heckmann. Our results extend, and unify a multitude of (F. p) theorems and generalize some results in (W.P.M.S). An example is given as an illustration of our results.
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The plethora of the emerged radio frequency applications makes the frequency spectrum crowded by many applications and hence the ability to detect specific application’s frequency without distortion is a difficult task to achieve.
The goal is to achieve a method to mitigate the highest interferer power in the frequency spectrum in order to eliminate the distortion.
This paper presents the application of the proposed tunable 6th-order notch filter on Ultra-Wideband (UWB) Complementary Metal-Oxide-Semiconductor (CMOS) Low Noise
In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I
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As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Chloroacetamide derivatives (2a-g) have been prepared through reaction of chloroacetyl chloride(1) (which prepared by the reaction of chloroacetic acid with thionyl chloride) with primary aromatic amines and sulfa compounds to afford compounds (2a-g) which then reacted with p-hydroxy benzaldehyde via Williamson reaction to obtaine the new compounds 2-(4-formyl phenoxy)-N-aryl acetamide (3a-g). Finally , compounds (3a-g) will be use as a good synthon to prepare the Schiff bases represented by compounds 2-(4-aryliminophenoxy)-N-arylacetamide (4a-g). through , reaction with some primary aromatic amine. All the prepared compounds were investigated by the available physical and spectroscopic methods.
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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
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