KE Sharquie, AA Noaimi, ZT Burhan, Journal of Cosmetics, Dermatological Sciences and Applications, 2016 - Cited by 9

Background: The aim of this study was to evaluate the expression of fibroblast growth factor-2 and Heparanase in salivary pleomorphic adenoma, and to correlate the two studied markers with each other and with clinicopathological parameters including: age, sex, tumor site and histopathological presentation. Methods: Sections of twenty five formalin-fixed paraffin embedded tissue blocks specimens of salivary pleomorphic adenoma were immunostained using monoclonal antibodies (Fibroblast growth factor-2 and Heparanase) to assess their expression in this tumor. Results: The expression of fibroblast growth factor-2 and Heparanase were positive in all pleomorphic adenoma cases (100%). The positive expression of fibroblast growth factor-2 was signi

The present study experimentally and numerically investigated the impact behavior of composite reinforced concrete (RC) beams with the pultruded I-GFRP and I-steel beams. Eight specimens of two groups were cast in different configurations. The first group consisted of four specimens and was tested under static load to provide reference results for the second group. The four specimens in the second group were tested first under impact loading and then static loading to determine the residual static strengths of the impacted specimens. The test variables considered the type of encased I-section (steel and GFRP), presence of shear connectors, and drop height during impact tests. A mass of 42.5 kg was dropped on the top surface at the m

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Learning Disabilities are described as a hidden and puzzling disability. Children with these difficulties have the potential to hide weaknesses in their performance because they are a homogenous group of disorders that consist of obvious difficulties in acquiring and using reading, writing, Mathematical inference. Thus, the research aims to identify the disabilities of academic learning in (reading, writing, mathematics), identify the problems of behavior (general, motor, social). Identify the relationship among behaviour problems. The research also aims to identify the counseling needs to reduce the behavioral problems. The researcher adopted the analytical descriptive method by preparing two main tools for measuring learning disabiliti