This present study is aimed at deciding the impact of exercises adapted to the ranges of movements of the arm on the performance of javelin throwing. As long as javelin throwing is quite a complex athletic event that presupposes a considerable amount of strength, speed, and biomechanical accuracy, it is crucial to learn whether the exercises designed to target the peculiarities of arm movements can have a positive effect on the performance of javelin throwers. To the study, experimental research with a single group of six youth javelin throwers was carried out. Before and after the eight-week training program, the pre-tests and post-tests were conducted to find the results of training with a specific focus on resistance exercises. Significa

The Co(II), Ni(II) ,Cu(II), Zn(II) ,Cd(II) and Hg(II) complexes of mixed of amino acid (L-Proline ) and Trimethoprim antibiotic were synthesized. The complexes were characterized using solubility, melting point, conductivity measurement ,. and determination the percentage of the metal in the complexes by flame(AAS).Magnetic susceptibility, Spectroscopic Method [FT-IR and UV-Vis]. Draw the proposed structure of the complexes using program , Chem. office 3D(2006). The ligands and there metal complexes were screened for their antimicrobial activity against four bacteria (gram + ve) and (gram -ve){Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus}.The proposed structure of the complexes using program , Chem office 3D(

Mixed ligand complexes of bivalent metal ions, viz ; M= Co(II),Ni(II),Cu(II), Zn(II), Cd (II), and Hg(II) of the composition [M(Anth)2(TMP)] in 1:2:1 molar ratio, (where . AnthrH= Anthranilic acid (C7H7NO2) and Trimethoprime (TMP) = (C14H18N4O3) have been synthesized and characterized by repeated melting point determination, Solubility, Molar conductivity (Λm ),determination the percentage of the metal (M%) in the complexes by (AAS), FT-IR, magnetic susceptibility measurements [μeff (BM)] and electronic spectral data. The two ligands and their metal complexes have been screened for their bacterial activity against selected microbial strains (Gram +ve) & (Gram -ve).

The Co(II), Ni(II) ,Cu(II), Zn(II) ,Cd(II) and Hg(II) complexes of mixed of amino acid (L-Proline) and Trimethoprim antibiotic were synthesized. The complexes were characterized using solubility, melting point, conductivity measurement ,. and determination the percentage of the metal in the complexes by flame(AAS).Magnetic susceptibility, Spectroscopic Method [FT-IR and UV-Vis]. Draw the proposed structure of the complexes using program , Chem. office 3D(2006). The ligands and there metal complexes were screened for their antimicrobial activity against four bacteria (gram + ve) and (gram-ve){Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus}.The proposed structure of the complexes using program , Chem office 3D(20

Mixed ligand complexes of bivalent metal ions, viz ; M= Co(II),Ni(II),Cu(II), Zn(II), Cd (II), and Hg(II) of the composition [M(Anth)2(TMP)] in 1:2:1 molar ratio, (where . AnthrH= Anthranilic acid (C7H7NO2) and Trimethoprime (TMP) = (C14H18N4O3) have been synthesized and characterized by repeated melting point determination, Solubility, Molar conductivity (Λm ),determination the percentage of the metal (M%) in the complexes by (AAS), FT-IR, magnetic susceptibility measurements [µeff (BM)] and electronic spectral data. The two ligands and their metal complexes have been screened for their bacterial activity against selected microbial strains (Gram +ve) & (Gram -ve).

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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