This work represents the preparation of the starting material, 3-chloro-2-oxo-1,4-dithiacyclohexane (S) using a new method. This material was reacted with, 4-phenylthiosemicarbazide to give (H3NS3) as a tetradentate ligand H3L. New complex of rhenium (V) with this ligand of the formula [ReO(L)] was prepared. New complexes of the general formula [M(HL)] of this ligand when reacted with some metal ions where: M = Ni(II), Cu(II), Cd(II), Zn(II), Hg(II) have been reported. The ligand and the complexes were characterized by infrared, ultraviolet–visible, mass, 1H nuclear magnetic resonance and atomic absorption spectroscopic techniques and by (HPLC), elemental analysis, and electrical conductivity. The proposed structure for H3L with Re (V) i

Compound 4-(((6-amino-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazin-3-yl)methoxy)methyl)- 2,6-dimethoxyphenol (6) was synthesized by multi steps. The corresponding acetonitrile thioalkyl (7) was cyclized by refluxing with acetic acid to afford 4-(((6-amino-7H-[1,2,4]triazolo[3,4- b][1,3,4]thiadiazin-3-yl)methoxy)methyl)-2,6-dimethoxyphenol (8). Two new series of 4-(((6-(3- (4-aryl)thioureido)-7H-[1,2,4]triazolo[3,4-b][1,3,4] thiadiazin-3-yl)methoxy)methyl)-2,6- dimethoxyphenol (9a-c) and of 4-(((6-(substitutedbenzamido)7H-[1,2,4]triazolo[3,4- b][1,3,4]thiadiazin-3-yl)methoxy)methyl)-2,6-dimethoxyphenol (10a-c) were synthesized as new derivatives for fused 1,2,4-trizaole-thiadiazine(8). The antioxidants of newly compounds were evaluated by DPPH

The M(II) complexes [M2 (phen)2 (L)(H2O)2Cl2 ] in (2:1:2 (M:L:phen) molar ratio, (where M(II) =Mn(II), Co(II), Cu(II), Ni(II) and Hg(II), phen = 1,10-phenanthroline; L = 2,2'-(1Z,1'Z)-(biphenyl-4,4'-diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1- ylidene)diphenol] were synthesized. The mixed complexes have been prepared and characterized using 1H and13C NMR, UV/Visible, FTIR spectra methods and elemental microanalysis, as well as magnetic susceptibility and conductivity measurements. The metal complexes were tested in vitro against three types of pathogenic bacteria microorganisms: Staphylococcus aurous, Escherichia coli, Bacillussubtilis and Pseudomonasaeroginosa to assess their antimicrobial properties. From this study shows that all the

The reaction of 2-amino benzoic acid with 1,2-dichloroethane under reflux in methanol and KOH as a base to gave the precursor [H4L]. The precursor under reflux and drops of CH3COOH which reacted with (2mole) from salicycaldehyde in methanol to gave a new type N2O4 ligand [H2L], this ligand was reacted with (MCl2) Where [M= Co (II), Ni(II), Cu(II) and Zn(II)] in (1:1) ratio at reflux in methanol using KOH as a base, to give complexes of the general formula [M(L)]. All compounds have been characterized by spectroscopic methods [1H NMR ( just to the ligand), FTIR, uv-vis, atomic absorption], melting point, conductivity, chloride content, as well as magnetic susceptibility measurements. From the above data, the proposed molecular structu

Four Co(II), (C1); Ni(II), (C2); Cu(II), (C3) and Zn(II), (C4) chelates have been synthesized with 1-(4-((2-amino- 5‑methoxy)diazenyl)phenyl)ethanone ligand (L). The produced compounds have been identified by using spectral studies, elemental analysis (C.H.N.O), conductivity and magnetic properties. The produced metal chelates were studied using molar ratio as well as sequences contrast types. Rate of concentration (1 ×10􀀀 4 - 3 ×10􀀀 4 Mol/L) sequence Beer’s law. Compound solutions have been noticed height molar absorptivity. The free of ligand and metal chelates had been applied as disperse dyes on cotton fabrics. Furthermore, the antibacterial activity of the produced compounds against various bacteria had been investigated. F

    Two local fish Himri Carasobarbus luteus (Heckel, 1843)  and Hishni Liza abu (Heckel, 1843) were stained with Alizarin Red and featured some anatomical qualities which cleared the difference of the muscular and skeletal fabric for each fish. Since clear Histologic differences appeared in these two species, it was intended from this study the possibility of adopting a diagnosis between local fish species by staining bones and tissues.

The reaction of 2-amino benzoic acid with 1,2-dichloroethane under reflux in methanol and KOH as a base to gave the precursor [H₄L]. The precursor under reflux and drops of CH₃COOH which reacted with (2mole) from salicycaldehyde in methanol to gave a new type N₂O₄ ligand [H₂L], this ligand was reacted with (MCl₂) Where [M= Co (II), Ni(II), Cu(II) and Zn(II)] in (1:1) ratio at reflux in methanol using KOH as a base, to give complexes of the general formula [M(L)]. All compounds have been characterized by spectroscopic methods [¹H NMR ( just to the ligand), FTIR, uv-vis, atomic absorption], melting point, conductivity, chloride content, as well as m

5-((2,4-dibromo-6-((cyclohexyl(methyl)amino)methyl)phenyl)diazenyl)quinolin-8-ol azo ligand (L) has been synthesized through the reaction of diazonium salt for 2,4-dibromo-6-((cyclohexyl(methyl)amino)methyl)aniline with 8-hydroxyquinoline. The azo ligand (L) was characterized utilizing spectroscopic techniques, including FTIR, UV-Vis, 1H and 13C NMR, as well as mass spectrometry and micro-elemental analysis (C.H.N). Metal complexes containing Co(II), Ni(II), Cu(II), and Zn(II) were synthesized and analyzed through mass spectrometry, flame atomic absorption, elemental analysis (C.H.N), infrared and UV-Vis spectroscopy, along with measurements of conductivity and magnetic properties. The experimental findings suggested that all met

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system