Apium graveolens has been utilized for a multitude of purposes due to its diverse pharmacological characteristics. On the other hand, little is known about how the fatty acids (saturated and unsaturated) terpenes and steroids found in Iraqi Apium graveolens affect the human cancer cells. The purpose of this study was to examine the effects of Iraqi Apium graveolens petroleum ether extract on the human prostate cancer cell line (PC3). Subsidiary extraction and phytochemical analysis by GC/MS were performed.The dry and fresh aerial parts (leaves and stem) of Apium graveolens were extracted using a Soxhlet device with 70 % ethanol, then fractionated with petroleum ether. Then Gas Chromatography System was used to identify the bioactive

Mishrif Formation was deposited during The Cenomanian-Early Turonian, which has been studied in selected Tuba and Zubair OilFields, these wells (TU-5, TU-24, TU-40, ZB-41, ZB-42, and ZB-46) are located within Mesopotamian basin at southern Iraq and considered as a major carbonate reservoir in Iraq and the Arabian Gulf. The palaeontological investigations mainly depending on benthonic foraminifera of the studied wells of Tuba and Zubair Oilfields in Mishrif Formation, twenty-four species belonging to fourteen genera are recognized of benthonic foraminifera, which has been recognized through this study, especially benthonic foraminiferal, indicating four zones as follows:

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi