This study detects the presence of an important flavonoid "Casticin" in the fruits of Vitex agnus-castus L. grown in Iraq. The pharmaceutical importance of Casticin arise from its consideration as anti-tumor substance and have cytotoxic effects, and the absence of any study concerning Casticin content of this medicinal plant in Iraq, gave this study its importance. This study concerned with the extraction, identification, isolation and purification  of Casticin from the fruits of Vitex agnus-castus L. The extraction of this compound was carried out using  two methods. Identification of this compound was done by Thin Layer Chromatography (TLC) in which three different solvent system has been tried. This identif

Galantamine was isolated from the bulb part of Narcissus jonquilla L. plant cultivated in Iraq. The compound was identified by different chemical analysis like: Fourier Transforms Infrared spectra (FTIR), High Performance Liquid Chromatography (HPLC) and mass spectroscopy and 1H-NMR.

The aim of the study is to examine the challenges of financing small and medium enterprises in Iraq and subsequently to proffer solutions to mitigate problems. These solutions are achieved by focusing on the role of accounting information on the financial projects in for example, hotel construction, and by providing the necessary accounting information for the concerned parties to finance these projects. In order to highlight the challenges associated with the funding of small and medium enterprises and the role of accounting information in reducing those challenges, a questionnaire was prepared. As the government authorities are the ones responsible for the accomplishment of these projects, a questionnaire form was distributed in the proje

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