All new compounds synthesized by many reactions starting from a product the compounds [I]a,b from reaction of 3-phenylenediamine or 4-phenylenediamine with chloroacetyl chloride, then the compounds [I]a,b reacted with potassium thiocyanate to yield compounds [II]a,b. While the compounds[III]a,b yield from reacted the compounds [I]a,b with sodium azide then the compounds [III]a,b reacted 1,3-dipolar cycloaddition reaction with acrylic acid to give compounds [IV]a,b and the later compounds reacted with phenylene diamine to product benzimidazole compounds [V]a,b . In addition to synthesized acid chloride compounds [VI]a,b by reacted the compounds [IV]a,b with thionyl chloride .Finally reacted the compounds [VI]a,b with different aromatic amine

Abstract

        The critical success factors of the means of the most modern in determining the main directions for organizations to achieve competitive advantage. and can be a critical success factors in organizations that overlap in the functional areas of the organization. that successful organizations use these factors to get to the uniqueness and distinction. as the entrance of critical success factors with the capacity Evaluative phase correction because discovery increases the perception of managers of what is important to the organization and using them to get to the Strategic Entrepreneurship. as it begins in terms of permanence of success and

Background: Sex variations in coronary artery disease (CAD) are well documented. However, sex differences in coronary artery calcium (CAC) and its role in the detection of coronary artery stenosis remain controversial. Objective: To assess the impact of sex variation on coronary artery calcification and its efficacy in predicting coronary artery stenosis. Methods: This is a cross-sectional observational study including 230 consecutive patients with suspected CAD (120 men and 110 women) referred for coronary computed tomography angiography (CCTA). The study analyzed sex-based differences in the sensitivity and specificity of coronary artery calcification (CAC) for detecting moderate to severe stenosis across various coronary arteries

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