The derivatives formed after the successive acetylation, esterification and nitration reactions to cholic, deoxycholic, and taurocholic acids were identified to be of the following general strucure: Colt, Where RI=NO3, OH, 0=, or CH3COO. R2=H, NO3, OH, 0-=, or CH3COO. R3=H, NO3,01-1, 0=, or CH3COO. R4=OH, NH(CH2)2S03Na, NH(CH2)2S03H, or OMe. By using U.V-visible and I.R spectrophotometry . The number of hydroxyl groups was determined, purity was checked from T.L.C, Most of these derivatives will find pharmaceutical application.

The important parameter used for determining the probable application of miscible displacement is the MMP (minimum miscibility pressure). In enhanced oil recovery, the injection of hydrocarbon gases can be a highly efficient method to improve the productivity of the well especially if miscibility developed through the displacement process. There are a lot of experiments for measuring the value of the miscibility pressure, but they are expensive and take a lot of time, so it's better to use the mathematical equations because of it inexpensive and fast. This study focused on calculating MMP required to inject hydrocarbon gases into two reservoirs namely Sadi and Tanomaa/ East Baghdad field. Modified Peng Robenson Equation of State was

This manuscript presents several applications for solving special kinds of ordinary and partial differential equations using iteration methods such as Adomian decomposition method (ADM), Variation iterative method (VIM) and Taylor series method. These methods can be applied as well as to solve nonperturbed problems and 3rd order parabolic PDEs with variable coefficient. Moreover, we compare the results using ADM, VIM and Taylor series method. These methods are a commination of the two initial conditions.

BMMAM Saleh, EUROPEAN ACADEMIC RESEARCH, 2016

The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic