Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

Recently, environmental noise has arisen from various sources, such as those from exhaust mufflers of combustion engines found in cars, trucks, or power generators, which produce significant noise during their operation. Controlling the radiated noise from these mufflers is a major factor in improving acoustic comfort and minimizing the impact on the surrounding communities. Numerous research has been presented for this reason by modification of the internal structure of the exhaust muffler. The main objective of this work is to reduce the noise level emitted from exhaust mufflers. This can be achieved by adjusting structure parameters to attenuate the surrounding environment's radiated noise. Analysis of pressure-wave propagation h

Moisture damage is a primary mode of distress occurring in hot mix asphalt (HMA) pavements in Iraq. Because of the loss of bond, or stripping, caused by the presence of moisture between the asphalt and aggregate, which is a problem in some areas and can be severe in some cases, it is requires to evaluate the design asphalt mixture to moisture susceptibility. Many factors such as aggregate characteristics, asphalt characteristics, environment, traffic, construction practices and drainage can contribute to stripping. Asphalt concrete mixes were prepared at their optimum asphalt content by superpave system and then tested to evaluate their engineering properties, which include tensile strength, resilient modulus, and perman

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

This paper analyzes the effect of scaling-up model and acceleration history on seismic response of closed-ended pipe pile using a finite element modeling approach and the findings of 1 g shaking table tests of a pile embedded in dry and saturated soils. A number of scaling laws were used to create the numerical modeling according to the data obtained from 1 g shake table tests performed in the laboratory. The current study found that the behaviors of the scaled models, in general have similar trends. From numerical modeling on both the dry and saturated sands, the normalized lateral displacement, bending moment, and vertical displacement of piles with scale factors of 2 and 35 are less than those of the pile with a scale factor of 1 and the