This study aims to reveal the similarities and differences between Iraqi and Malay university learners and their genders in producing the supportive moves of criticism. To this end, 30 Iraqi and 30 Malay university learners have participated in this study. A Discourse Completion Test (DCT) and a Focus Group Interview (FGI) are conducted to elicit responses from the participants. Nguyen’s (2005) classification of criticism supportive moves is adapted to code the data. The data are analysed qualitatively and quantitatively. Overall, the findings unveil that both groups use similar categories of supportive moves, but Iraqis produce more of these devices than Malays in their criticisms. Although both females and males of both groups use id

Proteus mirabilis ? -lactamase of local isolates number 4TF represent karkh side and 20TF represent rusafa side of Baghdad were extracted and purified 23.17, 25.23 fold with yield of 36.66 %, 37.5% and specific activity 11.8, 12.6 of unit/ mg protein by DEAE –cellulose and Sepharose 4B (respectively ).Molecular weight of both enzyme was about 35500 Dalton determined by gel filtration. The study indicated that the isoelectric point of purified ? -lactamase that extracted from isolate number 4TF and 20TF was 5.4.

Two series of Schiff Bases [VI]n and thiazolidin-4-one derivatives[VII]n were synthesized by many steps starting from cyclization of 4- hydroxyacetophenon with thiourea in iodine to yield 1,3-thiazole compound which was reacted with pentoxy bromide in anhydrous potassium carbonate to converted compound[II] and this reacted with Phenol to yield azo compound[III]. The azo compound reacted with ethyl chloro acetate in basic medium to get a new easter compound[IV] which is converted to their acid hydrazid[V]. The later compound condensation with n-alkoxy benzaldehyde to give new Schiff bases[VI]n . Imine group undergoes addition cyclization with thioglycolic acid to get thiazolidinone compounds[VII]n .Also, two new series of Schiff Bases [XII]n

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor

The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint P

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be