Sixteen new complexes with the general formula [M(L)2(H2O)2] were prepared resulting from the reaction of the two new Schiff base ligands, which are: - L1= (E)-5-((2-hydroxybenzylidene)amino)-2-phenyl-2,4-dihydro-3H-pyrazol-3-one) L2 = (E)-5-((2-hydroxy-3-methoxybenzylidene)amino)-2-phenylpyrazolidin-3-one) With divalent metal ions (manganese, cobalt, nickel, copper, zinc, cadmium, mercury) and (tetravalent platinum). Ligands was derived from the reaction of the amine (5-amino-2-phenyl-2,4-dihydro-3H-pyrazol-3-one) with Salicylaldehyde and ortho-vanillin, which is linked to the metal ions via the nitrogen atoms are the isomethene group and the oxygen is the hydroxide group of the pyrazoline ring. The two prepared ligands are distinguished by their ability to dissolve in organic solvents such as alcohols, ether, acetone, chloroform, carbon tetrachloride and ethanol, and their inability to dissolve in water. The prepared compounds were characterized using infrared spectroscopy and spectroscopy nuclear magnetic resonance, ultraviolet spectroscopy, micro-analysis of elements (C.H.N), atomic absorption analysis, electrical conductivity of solutions of complexes at a concentration of 1×10-3 , the magnetic susceptibility of the complexes was measured, and the effect of the solvent on the prepared compounds was studied, and from the results of the measurements, the octahedral geometric structure of the prepared complexes was proposed.. The inhibitory ability of the prepared compounds was tested against two types of selected bacteria, Staphylococcus aureus and Escherichia coli, and a type of fungus, Candida. The results showed that the ability of the metal complexes to inhibit is higher than the ligand.
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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Allopurinol derivative were prepared by reacting the (1-chloroacetyl)-2-Hydropyrazolo{3,4-d}pyrimidine-4-oneiwith 5- methoxy- 2-aminoibenzothiazoleiunder certain conditions to obtain new compound ( N- (2-aminoacetyl (5-methoxy) benzothiazole -2yl) (A4), Reaction of 5-(P-dimethyl amine benzene)-2-amino-1,3,4- oxadiazole in the presence of potassium carbonate anhydrous to yield new compound (N-(2- aminoacetyl-5-(P-dimethyl amine benzene )-1,3,4-oxadiazoles-2-yl)(A30) and Azo compound (N-(5-(Azo-2-hydroxy-5-amino benzene)-1,3-Diazol-2yl)Allopurinol(A46). The structure of prepared compounds were confirmed by (FT-IR)
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Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.
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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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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
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Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto
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This paper considers a new Double Integral transform called Double Sumudu-Elzaki transform DSET. The combining of the DSET with a semi-analytical method, namely the variational iteration method DSETVIM, to arrive numerical solution of nonlinear PDEs of Fractional Order derivatives. The proposed dual method property decreases the number of calculations required, so combining these two methods leads to calculating the solution's speed. The suggested technique is tested on four problems. The results demonstrated that solving these types of equations using the DSETVIM was more advantageous and efficient
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This paper considers a new Double Integral transform called Double Sumudu-Elzaki transform DSET. The combining of the DSET with a semi-analytical method, namely the variational iteration method DSETVIM, to arrive numerical solution of nonlinear PDEs of Fractional Order derivatives. The proposed dual method property decreases the number of calculations required, so combining these two methods leads to calculating the solution's speed. The suggested technique is tested on four problems. The results demonstrated that solving these types of equations using the DSETVIM was more advantageous and efficient
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