New mixed ligand complexes of New Schiff base 4,4'- ((naphthalen-1-ylimino) methylene) dibenzene-1,3-diol and 8-hydroxy quinoline: Synthesis, Spectral Characterization, Thermal studies and Biological Activities

The new Hexadentate complexes type [M(H3L3)]K were prepared from the condensation reaction of Diphenylmonoxime and KOH with (Mn(II), Co(II), Ni(II), Cu(II), Zn(II), and Hg(II)) in methanol with 3:1 ligand : metal ratio to give a series of new complexes of the general formula [M(H3L3)]K (where: M(II) = Mn ,Co ,N ,Cu ,Zn and Hg).All compounds have been Characterized by spectroscopic methods [I.R, U.v-Vis, atomic absorption and microanalysis (C.H.N) along with conductivity measurements. The stability constant K and Gibbs free energy ∆G were calculated for [Co (H3L3)] K, [Ni (H3L3)] K and [Cu (H3L3)] K and complexes using spectrophotometer method. The obtained values indicate that these complexes stable in their solution. From the above data

Green biosynthesized selenium nanoparticles from Ralstonia insidiosa show potent activity against multidrug-resistant Acinetobacter baumannii , pathogenic Candida spp. and human prostate cancer cells.

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar