Inthis study new derivatives of Schiff bases and nucleoside analogues have been synthesized from the starting material D-glucose after a series of reactions. Derivative 1 was prepared from D-glucose then react with P-bromoacetophenone gave derivative 2 was reacted with dimethyl sulfoxide and acetic anhydride for dehydration a molecule of water gave 3. The spiro ring was prepared at 3-position from the reaction of 3 derivative with 1-phenyl-2–thioureagave 4. The protection group at 1 position was removed by using acetic acid fllowed by periodate oxidation to obtain 6. Reaction of 6 with hydrazide derivative at once and dtriazole derivative at another gave 8 and 9 respectively. Compound 6 was reduced to gave derivative 7. The 1-hydroxylgrou

Synthesis of a new class of Schiff-base ligand with a tetrazole moiety to form polymeric metal complexes with Co^II, Ni^II, Zn^II, and Cd^II ions has been demonstrated. The ligand was synthesised by a multi-steps by treating 5-amino-2-chlorobenzonitrile and cyclohexane -1,3-dione, the 5,5'-(((1E,3E)-cyclohexane-1,3-diylidene)bis(azanylylidene))bis(2-chlorobenzonitrile) was obtained. The precursor (M) was prepared  from the reaction 5,5'-(((1E,3E)-cyclohexane-1,3-diylidene)bis(azanylylidene))bis(2-chlorobenzonitrile) with NaN₃ to obtained (1E,3E)-N¹,N³-bis(4-chloro-3-(1H-tetrazol-5-yl)phenyl)cyclohexane-1,3-diimine (N). By reacting the precursor (M) with CS₂

The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita

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 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

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

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.

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