A problem of solid waste became in the present day common global problem among all countries, whether developing or developed countries, and can say that no country in the world today is immuning from this dilemma which must find appropriate solutions. The problem has reached a stage that can not ignore or delay, but has became a daily problem occupies the minds of ecologists, economists and politicians took occupies center front in the lists of  priorities for the countries in terms of finding solutions to the rapid scientific and radical them. and that transport costs constitute an important component of total costs borne by the municipal districts in the process of disposal of solid waste, so any improvement in the

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

In this study lattice parameters, band structure, and optical characteristics of pure and V-doped ZnO are examined by employing (USP) and (GGA) with the assistance of First-principles calculation (FPC) derived from (DFT). The measurements are performed in the supercell geometry that were optimized. GGA+U, the geometrical structures of all models, are utilized to compute the amount of energy after optimizing all parameters in the models. The volume of the doped system grows as the content of the dopant V is increased. Pure and V-doped ZnO are investigated for band structure and energy bandgaps using the Monkhorst–Pack scheme's k-point sampling techniques in the Brillouin zone (G-A-H-K-G-M-L-H). In the presence of high V content, the ban

ابن حزم ، أبو محمد بن احمد بن سعيد بن حزم ، ولد في قرطبة (384هـ - 944م) وتوفي ودفن فيها عام (456هـ - 1016م) علم من أعلام الفكر الأندلسي ومن أجّل وأشهر مفكريه أصالة وعبقرية ، ويعد أفضل من مثل الثقافة الأندلسية في الحضارة العربية الإسلامية ، مفكر كبير ، وصاحب ثقافة واسعة غطت مجالات المعرفة كلها ، له اهتمامات متعددة يدل عليها كثرة تصانيفه والحقول التي كتب فيها ، حيث بلغ مجموع تأليفه نحو أربعة مائة مجلد ⁽¹⁾ ، شملت فروع ا

The research shows that the visual image plays an important role when Farzdaq in the issue of aesthetic perception, it enables him to feel a sense of artistic and mental perception to raise astonishment and admiration through his ability to link the optics through the suggestive image to carry us to a new vision imagined full of visual images.

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences