Due to the urgent need to develop technologies for continuous glucose monitoring in diabetes individuals, poten tial research has been applied by invoking the microwave tech niques. Therefore, this work presents a novel technique based on a single port microwave circuit, antenna structure, based on Metamaterial (MTM) transmission line defected patch for sensing the blood glucose level in noninvasive process. For that, the proposed antenna is invoked to measure the blood glu cose through the field leakages penetrated to the human blood through the skin. The proposed sensor is constructed from a closed loop connected to an interdigital capacitor to magnify the electric field fringing at the patch center. The proposed an tenna sensor i

New metal complexes of some transition metal ions [Fe(III) , Co(II) , Ni(II) and Cu(II)] of two previously prepared ligands HLI=(P-methyl anilino)- P-methoxy phenyl acetonitrile and HLII =(P-methoxy anilino)-P- methoxy phenyl acetonitrile were synthesized. The two ligands were prepared by Strecker's procedure which included the reaction of Pmethoxybenzaldehyde with p-toluidine and P-anisidine respectively. The structures of the new metal complexes were characterized by atomic absorption , i.r and U.V.-visible spectra . Magnetic susceptibilities and conductivity measurements in DMF of metal complexes were also studied. These ligands coordinate as abidentate molecules through nitrogen atoms of amino group and nitrile group except the

  New bidentate dithiocarbamate ligand (NaL) namely [Sodium-2-(((3-methyl -4- “(2,2,2-tri fluoro ethoxy) pyridin-2”-yl) methyl) sulfinyl)-1H-benzoimidazole -1-carbodithioate] was prepared. This free ligand was synthesized from the reaction of a (RS)-2-([3-methyl -4-(2,2,2-tri fluoroethoxy) pyridin-2-yl] methyl sulfinyl)-1H benzoimidazole, CS2 and NaOH in methanol as solvent. From reaction of dithiocarbamate salt (NaL) with metal ions (M); Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Pd(II)”, have obtained  the DTC complexes at general molecular formula [M(L)2(H2O)2] and [Pd(L)2]. To characterize the ligand and its complexes, used different analyses methods such FTIR, UV-Vis, elemental microanalysis, atomic absor

New bidentate dithiocarbamate ligand (NaL) namely [Sodium-2-(((3-methyl -4- “(2,2,2-tri fluoro ethoxy) pyridin-2”-yl) methyl) sulfinyl)-1H-benzoimidazole -1-carbodithioate] was prepared. This free ligand was synthesized from the reaction of a (RS)-2-([3-methyl -4-(2,2,2-tri fluoroethoxy) pyridin-2-yl] methyl sulfinyl)-1H benzoimidazole, CS2 and NaOH in methanol as solvent. From reaction of dithiocarbamate salt (NaL) with metal ions (M); Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Pd(II)”, have obtained the DTC complexes at general molecular formula [M(L)2(H2O)2] and [Pd(L)2]. To characterize the ligand and its complexes, used different analyses methods such FTIR, UV-Vis, elemental microanalysis, atomic absoreption, magnetic susceptibil

Coupling reaction of 4-aminoantipyrene with 8-hydroxyqunoline gave the new bidentate azo ligand 5-(4-antipyrene azo)-8-hydroxyqunoline. Treatment of this ligand with the following metals ions (MnII, CoII, NiII, CuII and ZnII) in aqueous ethanol with a 1:2 M:L ratio yielded a series of neutral complexes of the general formula [M(L)2Cl2]. The prepared complexes were characterized using flame atomic absorption, FT.IR, UV-Vis spectroscopic as well as magnetic susceptibility and conductivity measurements. Chloride ion content were also evaluated by (Mohr Method). From above data, the proposed molecular structure for these complexes as octahedral geometry.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve