Pyridine-2, 6-dicarbohydrazide comp (2) was synthesized from ethanolic solution of diethyl pyridine-2, 6- dicarboxylate comp (1) with excess of hydrazine hydrate. Newly five polymers (P1-P5) were synthesized from reaction of pyridine-2, 6-dicarbohydrazide comp (2) with five different di carboxylic acid in the presence of poly phosphoric acid (PPA). The antibacterial activity of the synthesized polymers was screened against some gram positive and gram negative bacteria. Antifungal activity of these polymers was evaluated in vitro against some yeast like fungi such as albicans (candida albicans). Polymers P3, P4 and P5 exhibited highest antibacterial and antifungal against all microorganisms under test.

In this study, the thermal buckling behavior of composite laminate plates cross-ply and angle-ply all edged simply supported subjected to a uniform temperature field is investigated, using a simple trigonometric shear deformation theory. Four unknown variables are involved in the theory, and satisfied the zero traction boundary condition on the surface without using shear correction factors, Hamilton's principle is used to derive equations of  motion depending on a Simple Four Variable Plate Theory for cross-ply and angle-ply, and then solved through Navier's double trigonometric sequence, to obtain critical buckling temperature for laminated composite plates. Effect of changing some design parameters such as, ortho

Gas adsorption phenomenon on solid surface has been used as a mean in separation and purification of gas mixture depending on the difference in tendencies of each component in the gas mixture to be adsorbed on the solid surface according to its behaviour. This work concerns to study the possibilities to separate the gas mixture using adsorption-desorption phenomenon on activated carbon. The experimental results exhibit good separation factor at temperature of -40 .

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

Abstract: New copper(II) complexes with mixed ligand benziloxime (BOxH) and furfural-dehydeazine (FA) using classical (with and without solvent) and microwave heating methods have been prepared. The resulting complexes have been characterized using physico-chemical techniques. The study suggested that the ligands formed neutral complexes had general formulas [Cu(FA)(BOXH)(Ac)2] and [Cu(FA)(BOX)(OH)] in neutral (or acidic) and basic medium, respectively. Accordingly, hexa-coordinated mono-nuclear complexes have been investigated by this study and having distorted octahedral geometry. The effect of laser have been studied on solid ligands and solid complexes, no effect have been observed on most compounds through the results of melting poin

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlat