It has been shown recently that there is a need to design smart structures, such as smart houses, in order to be controlled in different ways. That will be in high demand due to its usefulness for some people who are incapable of reaching some control units that require direct interaction with human beings. In this paper, we propose and develop a new enhanced electroencephalography (EEG)-based smart structure setup that can be utilized to assist people, with or without disorders, to control devices in an easy and comfortable way. Ten people of a wide range of ages (20–65) and both genders actively participated in this research. Consequently, eight EEG channels are employed in this study to cover most of the brain’s regions, and the prot

 The general approach of this research is to assume that the small nonlinearity can be separated from the linear part of the equation of motion. The effect of the dynamic fluid force on the pump structure system is considered vibrates at its natural frequency but the amplitude is determined by the initial conditions. If the motion of the system tends to increase the energy of the pump structure system, the vibration amplitude will increase and the pump structure system is considered to be unstable. A suitable MATLAB program was used to predict the stability conditions of the pump structure vibration. The present research focuses on fluid pump problems, namely, the role played by damping coefficient C, damping factor

In this paper, the static analysis for finding the best location of boxes inside the composite wing-box structure has been performed. A software ANSYS (ver.11) was used to analyses the Aluminum wing to find the maximum stresses reached in. These results are used as a base for the composite wingbox to find the numbers of layers and location of the box beam and its dimensions so that the composite wingbox may carry the same loading conditions in the Aluminum wing. Analysis showed that a composite wingbox having two boxes is better than the single or triple boxes wing based on stress to weight ratio. Mass saving of (40%) had been achieved when composite wing-box is used instead of Aluminum wing.

Photodetector based on Rutile and Anatase TiO₂ nanostructures/n-Si Heterojunction

Theoretical spectroscopic study of  Beryllium Oxide has been carried out, Boltzmann distribution of P, Q and R branches in the range of (0<J<13) at temperature 4200K for (0-0) band for electronic transitions B¹Σ⁺-A¹Π and B¹Σ-X¹Σ. The Boltzmann distribution of these branches has a maximum values at equal J approximately while the values of relative population are different. For the B¹Σ⁺- X¹Σ⁺ transition the branch's lines extend towards lower wavenumber. This is because (B_v'-B_v") value is negative, i.e  B_v'< B_v" For B¹Σ⁺-A¹Π

Iron oxide (Fe3O4) nanoparticles were synthesized via an eco-friendly green approach by adding Phoenix dactylifera extract to the aqueous solution of ferric chloride. The effect of annealing temperature (Ta) (100-150) °C on particle size was studied. X-ray diffraction (XRD), UV-visible spectroscopy, atomic force microscopy (AFM), and field emission scanning electron microscopy (FESEM) were used to evaluate the produced nanoparticles. According to XRD spectra, the crystallite size of the samples was determined using the Scherrer formula. AFM and FE-SEM were used to determine surface morphology. A UV-Vis optical spectroscopic examination was carried out to determine the band gap energy of the iron oxide nanoparticles. It was found th

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet