The [2-hydroxy-1, 2-diphynel-ethanone oxime] was reacted with 1, 2-dichloroethan to give the new ligand [H2L]. this ligand was reacted with some metal ions (Co (II), Ni (II), Cu (II), Zn (II) and Cd (II) in methanol as a solvent to give a series of new (1: 1) complexes of the general formula [M (HL)] Cl,(where: M= Co (II), Ni (II), Cu (II), Zn (II) and Cd (II)) are isolated All compounds have been characterized by spectroscopic methods [IR, UV-Vis] atomic absorption. Chloride content along with conductivity measurements. From the above data the proposed molecular structure for (Co, Cu, Ni, Zn and Cd) complexes adopting a tetrahedral structure

The [2-hydroxy -1,2-diphynel-ethanone oxime] was reacted with 1,2- dichloroethan to give the new ligand [H2L].this ligand was reacted with some metal ions (Co(II),Ni(II),Cu(II),Zn(II) and Cd(II) in methanol as a solvent to give a series of new (1:1)complexes of the general formula [ M(HL)]Cl ,( where : M= Co(II),Ni(II),Cu(II),Zn(II) and Cd(II)) are isolated All compounds have been characterized by spectroscopic methods [ I.R , U.V -Vis ] atomic absorption . Chloride content along with conductivity measurements. From the above data the proposed molecular structure for (Co, Cu, Ni, Zn and Cd) complexes adopting a tetrahedral structure.

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

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

This work includes the synthesis and identification of ligand {3-((4-acetylphenyl)amino)-5,5-dimethylcyclohex2-en-1-one} (HL* ) by the treatment of 5,5-dimethylcyclohexane-1,3-dione with 4-aminoacetophenone under reflux. The ligand (HL* ) was identified via FTIR, Mass spectrum, elemental analysis (C.H.N.), 1H and 13C-NMR spectra, UV-Vis spectroscopy, TGA and melting point. The complexes were synthesized from ligand (HL* ) mixed with 3-aminophenol (A) and metal ion M(II), where M(II) = (Mn, Co, Ni, Cu, Zn and Cd) at alkaline medium to produce complexes of general formula [M(L* )(A)] with (1:1:1) molar ratio. These complexes were detected via FT-IR spectra, UV-Vis spectroscopy as well as elemental analysis (A.A) and melting point, conductivit

This work includes the synthesis and identification of ligand {3-((4-acetylphenyl)amino)-5,5-dimethylcyclohex2-en-1-one} (HL* ) by the treatment of 5,5-dimethylcyclohexane-1,3-dione with 4-aminoacetophenone under reflux. The ligand (HL* ) was identified via FTIR, Mass spectrum, elemental analysis (C.H.N.), 1H and 13C-NMR spectra, UV-Vis spectroscopy, TGA and melting point. The complexes were synthesized from ligand (HL* ) mixed with 3-aminophenol (A) and metal ion M(II), where M(II) = (Mn, Co, Ni, Cu, Zn and Cd) at alkaline medium to produce complexes of general formula [M(L* )(A)] with (1:1:1) molar ratio. These complexes were detected via FT-IR spectra, UV-Vis spectroscopy as well as elemental analysis (A.A) and melting point, conductivit

This work investigates the utilization of waste papers (natural and industrial) i.e (citrus aurantium and papers A4) mortars containing specified contents 0.5%, 1%, 1.5% of waste papers were prepared and cured. Mechanical characteristics such as compressive and bending strengths, hardness and water absorption were determined for the mortars mixed with the waste papers and compared with those obtained from the pure mortars. Results showed that the addition of waste paper leads to increase the hardness to (69 - 68.5) shore D for (natural and industrial) wastes materials respectively comparing with pure specimen 66 shore D. The compressed strength of the mortar cement specimen cured for 28 days from 13 MPa to (17-18) MPa for (natural and in

Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl^-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their perfor

Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated usin