The reaction of 2, 4, 6-trihydroxyactophenonemonohydrate with hydrazine monohydrate was realized under reflux in methanol and a few drops of glacial acetic acid were added to give the (intermediate) 2-(1-hydrazono-ethyl)-benzene-1, 3, 5-triol, which reacted with salicylaldehyde in methanol to give a new type (NO) ligand [HL][(2-1-[(2-hydroxy-benzyliidene)-hydrazono]-ethyl) benzene-1, 3, 5-triol. The ligand was reacted with Mcl.(where M-Co, Ni, Cu, and Zn) under reflux in methanol with (l: 1) ratio to give complexes of the general formula [M (HL)]. All compounds have been characterized by spectroscopic methods I" H NMR, IR. UV-Vis, HPLC, atomic absorption] microanalysis along with conductivity measurement. From the above data the proposed mo

الوصف A simple chemistry method approach was used to synthesise new ligand derivate from L-ascorbic acid and its complexes. All of them were water-soluble and are used quite extensively in the medical and pharmaceutical fields. This study synthesised the new ligand derivative from L-ascorbic acid-base using the following steps: A 5, 6-O-isopropylidene-L-ascorbic acid was prepared by reacting dry acetone with L-ascorbic acid followed by reacting it with trichloroacetic acid to yield [chloro (carboxylic) methylidene]-5, 6-O-isopropylidene-L-ascorbic acid in the second stage. In the third stage, the derivative was reacted with (methyl (6-methyl-2-pyridylmethyl) amine to create a new ligand (ONMILA). This novel ligand was identified using

A simple chemistry method approach was used to synthesise new ligand derivate from L-ascorbic acid and its complexes. All of them were water-soluble and are used quite extensively in the medical and pharmaceutical fields. This study synthesised the new ligand derivative from L-ascorbic acid-base using the following steps: A 5,6-O-isopropylidene-L-ascorbic acid was prepared by reacting dry acetone with L-ascorbic acid followed by reacting it with trichloroacetic acid to yield [chloro(carboxylic)methylidene]-5,6-O-isopropylidene-L-ascorbic acid in the second stage. In the third stage, the derivative was reacted with (methyl(6-methyl-2-pyridylmethyl)amine to create a new ligand (ONMILA). This novel ligand was identified using a number

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 study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv