The effected of the long transmission line (TL) between the actuator and the hydraulic control valve sometimes essentials. The study is concerned with modeling the TL which carries the oil from the electro-hydraulic servovalve to the actuator. The pressure value inside the TL has been controlled by the electro-hydraulic servovalve as a voltage supplied to the servovalve amplifier. The flow rate through the TL has been simulated by using the lumped π element electrical analogy method for laminar flow. The control voltage supplied to servovalve can be achieved by the direct using of the voltage function generator or indirect C⁺⁺ program connected to the DAP-view program built in the DAP-card data acqu

Influence of metal nanoparticles synthesized by microorganisms upon soil-borne microscopic fungus Aspergillus terreus K-8 was studied. It was established that the metal nanoparticles synthesized by microorganisms affect the enzymatic activity of the studied culture. Silver nanoparticles lead to a decrease in cellulase activity and completely suppress the amylase activity of the fungus, while copper nanoparticles completely inhibit the activity of both the cellulase complex and amylase. The obtained results imply that the large-scale use of silver and copper nanoparticles may disrupt biological processes in the soil and cause change in the physiological and biochemical state of soil-borne microorganisms as well.

Natural fibers and particles reinforced composites are being broadly used due to their bio and specific properties such as low density and easy to machine and production with low cost. In this work, water absorption and mechanical properties such as tensile strength, flexural strength and impact strength of recycled jute fibers reinforced epoxy resin were enhanced by treating these fibers with alkaline solution. The recycled jute fibers were treated with different concentration of (NaOH) solution at (25 ⁰C) for a period of (24) hours. From the obtained results, it was found that all these properties are improved when fibers treated with (7.5wt% NaOH) related to untreated fibers. Conversely, the mentioned properties of composit

In this study, Laser Shock Peening (LSP) effect on the polymeric composite materials has been investigated experimentally. Polymeric composite materials are widely used because they are easy to fabricate and have many attractive features. Unsaturated polyester resin as a matrix was selected and Aluminum powder with micro particles as a reinforcement material was used with different volume fraction (2.5%, 5% and 7.5%). Hand lay-up process was used for preparation the composites. Fatigue test with constant amplitude with stress ratio (R =-1) was carried out before and after LSP process with two levels of energy (1Joule and 2Joule). The result showed an increase in the endurance strength of 25.448% at 7.5% volume fraction when peened is 1J

In the present research synthesis and study of biological activity a series of new polymers modified of chitosan with compounds containing azo group. Beginning diazonium salt produced from 3,3'-dimethyl-[1,1'-biphenyl]-4,4'-diamine reacted with concentrated HCl acid and sodium nitrite. The coupling reaction between diazonium salt with substituted aromatic aldehyde to produce Azo derivatives )1-6(. Azo Schiff bases Chitosan )7-12( were synthesized by condensation of Chitosan with Azo derivatives )1-6( in ethanol with some drops of glacial acetic acid. The structural modifications of Chitosan ring (linked to a bioactive azo moiety) were expected to give new derivatives )7-12( with a diverse range of biological functions. These compounds' st

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise