A new azo (LH) ligand was prepared by coupling reaction between, diazonium salt of Sulfamethoxazole, and 8-hydroxyquinoline in a process called diazotization process resulting in azo-ligand [4-((8-hydroxyquinolin-7-yl)- N(4-methylisoxazol-3-yl) benzene sulfonamide]. The azo ligand was identified by using spectroscopic techniques to detect and characterize the formation of ligand and complexes of Ni2+, Pt4+, Pd2+, and Rh3+ metal ions, and to determine the chelating behavior of ligand and also its bind position. All complexes have a [1:1] [M-ligand] ratio and all complexes are nonelectrolytes and most of the complexes have octahedral geometry, while Pd2+complex gave square planer geometry and Ni2+ complex indicate tetrahedral geometry. Thermal decomposition TGA and DSC results reveal the presence of coordinated water molecules in the complexes. Antioxidant activities of these compounds were evaluated against (DPPH) radical and were compared with the standard natural antioxidant, ascorbic acid. The findings show that these compounds exhibit excellent radical scavenging activities. The geometries were detected depending on Ultra Violet-visible (UV-Vis) technique and according to the Fourier Transform Infrared Spectroscopy (FT-IR) and Liquid Chromatography-mass (LC-Mass) studies; we can also detect the chelating behavior of ligand. While the conductivity properties can be detected by conductivity measurements. In addition, element micro analysis and atomic absorption gave compatible results with theoretically calculated results, and many other techniques support the formation of ligand and occurrence of coordination including (Proton and Carbon-nuclear magnetic resonance (1H & 13C-NMR) and magnetic quantifications
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
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