Coupling reaction of 2-amino benzoic acid with phenol gave the new bidentate azo ligand. The prepared ligand was identified by Microelemental Analysis, FT-IR and UV-Vis spectroscopic technique. Treatment of the prepared ligand with the following metal ions (CoII, NiII, CuII and ZnII) in aqueous ethanol with a 1:2 M:L ratio and at optimum pH, yielded a series of neutral complexes of the general formula [M(L)2]. The prepared complexes were characterized using flame atomic absorption, (C.H.N) Analysis, FT-IR and UV-Vis spectroscopic methods as well as magnetic susceptibility and conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods, Beer's law obeyed over a concentr

A series of new coumarin and N-amino-2-quinolone derivatives have been synthesized. The reaction of coumarin (1) with excess of  Hydrazine hydrate 98% yielded 1-amino-2-quinolone (2), Compound (2) was reacted with different Sulfonyl chloride to yield Sulfonamides [ N-(2-oxoquinolin-1(2H)-yl) methane sulfonamide (3), N-(2-oxoquinolin-1(2H)-yl) Benzene sulfonamide (4) and 4-methyl-N-(2-oxoquinolin-1(2H)-yl) benzene sulfonamide (5) ], while  reaction of  2-(4-methyl-2-oxo-2H-chromen-7-yloxy) acetic acid (8) with different amines yielded compounds [  2-(4-methyl-2-oxo-2H-chromen-7-yloxy)-N-(2-oxoquinolin-1(2H)-yl) acetamide (9) and N-(5-methyl-1,3,4-thiadiazol-2-yl)-2-(4-methyl-2-oxo-2H-chromen-7-yloxy)acetamide (10) ] th

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

Abstract: To study the effect of nickel chloride on bone composition of mice, a number of biophysical and biochemical parameters have been made use. The animals were divided into control and experimental and further subdivided into three groups I, II and III according to the dose of nickel chloride (NiCl2) administered to them i.e. 5.8, 12.8 and 28.2 mg/kg body weight, respectively. Femur bones were obtained by sacrificing the animals three weeks after weaning them once a week. The percentage loss between the wet weight and dry weight of femur in control animals was found to be 32.5+1.5 .In the three experimental groups I,II and III, the percentage loss was 30.4+1.4, 35.3+2.3 and 38.9+2.2 respectively. The percentage loss between the wet we

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

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

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

In recent years , the interest in gold (III) species have gained more and more attention for cancer chemotherapy , this was stimulating by the possibility to develop new agents with mode of action and clinical profile different from the established platinum metalodrugs.

With this frame, recently new square planar Au(III)  complexes (Au(L)(L')_n); where L=SCH₂COO^- ; L'=HSCH₂COO^- had been synthesized with S/O – donor ligands.

In this article and by the aim to replace, one of (L') ligand by anion chloride ligand (which supposedly more relevant for the biodistribution of the compound than for its pharmacodynamic effects), new complex (Au(L')

Three azo compounds were synthesized in two different methods, and characterized by FT-IR, HNMR andVis) spectra, melting points were determined. The inhibitory effects of prepared compounds on the activity of human serum cholinesterase have been studied in vitro. Different concentrations of study the type of inhibition. The results form line weaver-Burk plot indicated that the inhibitor type was noncompetitive with a range (33.12-78.99%).