Cloth simulation and animation has been the topic of research since the mid-80's in the field of computer graphics. Enforcing incompressible is very important in real time simulation. Although, there are great achievements in this regard, it still suffers from unnecessary time consumption in certain steps that is common in real time applications.   This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing. This research achieves success in cloth simulation on the VHC through enhancing the position-based dynamics (PBD) framework by computing a series of positional constraints which implement constant densities. Also, the self-collision and collision wit

أثر استراتيجية التدريس البصري باستخدام الانـفوجرافيك في التحصيل والتفكير البصري لدى طلاب المرحلة المتوسطة فـي مادة الرياضيات

تلخصت مشکلة هذا البحث في التعرف على الدوافع التي تقف وراء تعرض الطلبة الدارسين في أقسام اللُّغة الإنکليزية للقنوات الفضائية الناطقة باللُّغة الإنکليزية والإشباعات المتحققة عن هذا التعرض في هذا المجتمع الخاص الذي يشکل بيئة اجتماعية علمية محددة، عن طريق تطبيق نظرية الإستخدامات والإشباعات في إطار المجتمعات الخاصة، وسعى هذا البحث إلى تحقيق ثلاثة أهداف هي، قياس استخدامات لطلبة الدارسين في أقسام اللُّغة الإنک

بسمة الأوقاتي/ أكاديمية وباحثة في العلاقات الدولية تولي الرئيس الأمريكي دونالد ترامب منصبه لولاية ثانية في العشرين من كانون الثاني / يناير من هذا العام (2025) وهو أكثر تمسكاً بعقيدته السياسية التي كان عنوانها في ولايته الأولى (أمريكا أولا / America First) وحاملا لتوجهات جيوسياسية جديدة ومثيرة للجدل

Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed.  A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification