Complexes of (Co2+, Ni2+, Cu2+, Zn2+, Cd2+ and Hg2+) with the ligand Ethyl cyano (2-methyl carboxylate phenyl azo acetate) (ECA) have been prepared and characterized by FTIR, (UV-Visible), Atomic absorption spectroscopy, Molar conductivity measurements and magnetic moments measurements. The following general formula has been suggested for the prepared complexes [M(ECA)2]Cl2 where M = (Co2+, Ni2+, Cu2+ ,Zn2+, Cd2+, Hg2+) and the geometry is octahedral.

chloride or poluacrulic acid with different primary amines to mesuring==================================638one hundred three patinents with rheumatic symptoms were include in this study and their sera tested for

The current research aims at identifying the new marketing directions that in the picture because if the changes and challenges that face the business nowadays, these effect on the future of the new product.

The problem of the research concentrate on the new marketing activities and how to make it appropriate between the product and the market's need, and to fulfill the needs and desire of the customer and it's importance for achieving the economical goals of the organization for a long relationship with the customer.

The resume of the research is about the impact of nowadays marketing and it's effect on knowing the customer instead of concentrating on the important roll of mark

The present work involved synthesis of serval new substituted tetrazole via Schiff bases for trimethoprim drug by two steps. The first step involved direct reaction of different ketones and aldehydes with trimethoprim producing the corresponding Schiff bases (1-10), whereas the second step, involved preparation new tetrazoles derivatives (11-20) through reaction of the ready Schiff bases (in the first step) with sodium azidein in dioxin. The prepared compounds were characterized by UV, FT-IR, and some of them by 13C-NMR, 1H-NMR spectroscopy and physical properties.

Preparation of epoxy/ TiO2 and epoxy/ Al2O3 nanocomposites is studed and investigated in this paper. The nano composites are processed by different nano fillers concentrations (0, 0.01, 0.02 ,0.03, 0.04 ,0.05 ,0.07 and 0.1 wt%). The particles sized of TiO2,Al2O3 are about 20–50 nm.Epoxy resin and nano composites containing different shape nano fillers of (TiO2:Al2O3 composites),are shear mixing with ratio 1 to 1,with different nano hybrid fillers concentrations( 0.025 ,0.0 5 ,0.15 ,0.2, and 0.25 wt%) to Preparation of epoxy/ TiO2- Al2O3 hybrid composites. The mechanical properties of nanocomposites such as bending ,wearing, and fatigue are investigated as mechanical properties.

The optimization of artificial gas lift techniques plays a crucial role in the advancement of oil field development. This study focuses on investigating the impact of gas lift design and optimization on production outcomes within the Mishrif formation of the Halfaya oil field. A comprehensive production network nodal analysis model was formulated using a PIPESIM Optimizer-based Genetic Algorithm and meticulously calibrated utilizing field-collected data from a network comprising seven wells. This well group encompasses three directional wells currently employing gas lift and four naturally producing vertical wells. To augment productivity and optimize network performance, a novel gas lift design strategy was proposed. The optimization of

The present work is devoted to investigate the performance of a homemade Y-shape catalytic microreactor for degradation of dibenzothiophene (DBT), as a model of sulphur compounds including in gas oil, utilizing solar incident energy. The microchannel was coated with TiO2 nanoparticles which were used as a photocatalyst. Performance of the microreactor was investigated using different conditions (e.g., DBT concentration, LHSV, operating temperature, and (H₂O₂/DBT) ratio). Our experiments show that, in the absence of UV light, no reaction takes place. The results revealed that outlet concentration of DBT decreases as the mean residence time in the microreactor increases. Also, it was noted that operating temperature s

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables