Some coordination complexes of Co(??), Ni(??), Cu(??), Cd(??) and Hg(??) are reacted in ethanol with Schiff base ligand derived from of 2,4,6- trihydroxybenzophenone and 3-aminophenol using microwave irradiation and then reacted with metal salts in ethanol as a solvent in 1:2 ratio (metal: ligand). The ligand [H4L] is characterized by FTIR, UV-Vis, C.H.N, 1H-NMR,13C-NMR, and mass spectra. The metal complexes are characterized by atomic absorption, infrared spectra, electronic spectra, molar conductance, (C.H.N for Ni(??) complex) and magnetic moment measurements. These measurements indicate that the ligand coordinates with metal (??) ion in a tridentate manner through the nitrogen and oxygen atoms of the ligand, octahedral structures

Some coordination complexes of Co(??), Ni(??), Cu(??), Cd(??) and Hg(??) are reacted in ethanol with Schiff base ligand derived from of 2,4,6- trihydroxybenzophenone and 3-aminophenol using microwave irradiation and then reacted with metal salts in ethanol as a solvent in 1:2 ratio (metal: ligand). The ligand [H4L] is characterized by FTIR, UV-Vis, C.H.N, 1H-NMR,13C-NMR, and mass spectra. The metal complexes are characterized by atomic absorption, infrared spectra, electronic spectra, molar conductance, (C.H.N for Ni(??) complex) and magnetic moment measurements. These measurements indicate that the ligand coordinates with metal (??) ion in a tridentate manner through the nitrogen and oxygen atoms of the ligand, octahedral st

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

A new method, simple and sensitive was utilized in determining mebeverine – HCl (MB-HCl) (3, 4-Dimethoxy benzoic acid ethyl 2, 4 methoxy4-phenyl-1-methyl ethyl amino-butyl ester) in pure and pharmaceutical formulations via utilization this multiple continuous flow cell. The method is dependent on genesis for complex of ion pair(4-((3, 4-dimethoxybenzoyl) oxy)-N-ethyl-N-(1-(4-methoxyphenyl) propan-2-yl) butan-1-aminium-2-hydroxy-3,5- dinitrobenzoate) among mebeverine–HCl (MB-HCl) and 3,5-Dinitrosalicylic acid (3,5-DNSA) in ammonium acetate middle to configure a whiteish yellow precipitate compound via utilizing multiple continuous flow cell that works as a solo flow cell with 4S×3 – 3D analyzer. Optimum parameters were studied to rai

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

Incremental sheet forming (ISF) process offers a high degree of flexibility in the manufacturing of different sheet parts, which makes it an ideal candidate for prototype parts as well as efficient at fabricating various customized products at low production costs compared to traditionally used processes. However, parts produced in this process exhibit notable geometrical inaccuracy and considerable thickness reduction. In this paper, the single point incremental sheet forming variant of the process has been implemented to manufacture a highly customized cranial implant starting from the computed tomography (CT) scan data of the patient's anatomy. A methodology, from the modeling to the realization of the implant, is presented and discus