Aeroelastic flutter in aircraft mechanisms is unavoidable, essentially in the wing and control surface. In this work a three degree-of-freedom aeroelastic wing section with trailing edge flap is modeled numerically and theoretically. FLUENT code based on the steady finite volume is used for the prediction of the steady aerodynamic characteristics (lift, drag, pitching moment, velocity, and pressure distribution) as well as the Duhamel formulation is used to model the aerodynamic loads theoretically. The system response (pitch, flap pitch and plunge) was determined by integration the governing equations using MATLAB with a standard Runge–Kutta algorithm in conjunction with Henon’s method. The results are compared with

Hot mix recycling of asphalt pavements is increasingly being used as one of the major rehabilitation methods by various highway agencies. Besides general savings in costs and energy expended, it also saves our natural resources and environment.  Recycling process presents a sustainable pavement by using the old materials that could be reclaimed from the pavement; these materials could be mixed with recycling agents to produce recycled mixtures. The important expected benefits of recycling process are the conservation of natural resources and reduction of environmental impact. The primary objectives of this work are evaluating the Tensile and Shear Properties of recycled asphalt concrete mixtures, In addition to the

One of the major problems facing the road construction engineer is the collapsible granular soil which may be used for embankment construction. Problems appears when such compacted soil come in touch with water, it exhibits cracking and uncontrolled settlement. Collapsible soils are defined as any unsaturated soil that goes through a radical rearrangement of practice and great loss of volume upon wetting, with or without additional loading. An attempt has been made in this investigation to stabilize the collapsible soil of Nasiriya with asphalt emulsion. Specimens of pure and asphalt emulsion stabilized soil have been prepared using optimum fluid content and tested. The first group of specimens of (60x60x20) cm have been tested for direct s

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

This study explores the challenges in Artificial Intelligence (AI) systems in generating image captions, a task that requires effective integration of computer vision and natural language processing techniques. A comparative analysis between traditional approaches such as retrieval- based methods and linguistic templates) and modern approaches based on deep learning such as encoder-decoder models, attention mechanisms, and transformers). Theoretical results show that modern models perform better for the accuracy and the ability to generate more complex descriptions, while traditional methods outperform speed and simplicity. The paper proposes a hybrid framework that combines the advantages of both approaches, where conventional methods prod