In this research work, a simulator with time-domain visualizers and configurable parameters using a continuous time simulation approach with Matlab R2019a is presented for modeling and investigating the performance of optical fiber and free-space quantum channels as a part of a generic quantum key distribution system simulator. The modeled optical fiber quantum channel is characterized with a maximum allowable distance of 150 km with 0.2 dB/km at =1550nm. While, at =900nm and =830nm the attenuation values are 2 dB/km and 3 dB/km respectively. The modeled free space quantum channel is characterized at 0.1 dB/km at =860 nm with maximum allowable distance of 150 km also. The simulator was investigated in terms of the execution of the BB84 p

<p>The demand for internet applications has increased rapidly.  Providing quality of service (QoS) requirements for varied internet application is a challenging task. One important factor that is significantly affected on the QoS service is the transport layer. The transport layer provides end-to-end data transmission across a network. Currently, the most common transport protocols used by internet application are TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). Also, there are recent transport protocols such as DCCP (data congestion control protocol), SCTP (stream congestion transmission protocol), and TFRC (TCP-friendly rate control), which are in the standardization process of Internet Engineering Task

Abstract

 A surface fitting model is developed based on calorimeter data for two famous brands of household compressors. Correlation equations of ten coefficient polynomials were found as a function of refrigerant saturating and evaporating temperatures in range of (-35℃ to -10℃) using Matlab software for cooling capacity, power consumption, and refrigerant mass flow rate.

Additional correlations equations for these variables as a quick choice selection for a proper compressor use at ASHRAE standard that cover a range of swept volume range (2.24-11.15) cm³.

The result indicated that these surface fitting models are accurate with in ± 15% for 72 compressors model of cooling cap

There is an interesting potential for the use of GFRP-pultruded profiles in hybrid GFRP-concrete structural elements, either for new constructions or for the rehabilitation of existing structures. This paper provides experimental and numerical investigations on the flexural performance of reinforced concrete (RC) specimens composite with encased pultruded GFRP I-sections. Five simply supported composite beams were tested in this experimental program to investigate the static flexural behavior of encased GFRP beams with high-strength concrete. Besides, the effect of using shear studs to improve the composite interaction between the GFRP beam and concrete as well as the effect of web stiffeners of GFRP were explored. Encasing the GFRP

Advancing the multi-scale performance of asphalt pavements requires innovative binder modifications that address limitations in rutting resistance, fatigue resistance, and durability across the binder, mixture, and structural levels. This study evaluates the performance of asphalt cement, mixtures, and pavement systems modified with a combination of polyethylene (PE) and carbon nanotubes (CNTs). The binder was modified using 4% PE and varying CNT contents (0.5%, 1%, 1.5%, and 2% by weight of the modified binder). Binder performance was assessed through conventional and rheological tests, including penetration, softening point, viscosity, performance grade (PG) evaluation, and master curve analysis. Mixture-level performance was eval

Background: Developing Goalkeeper’s performance is imperative to defence the goal. As the Football match’s nature needs implementning the skill with strength, and speed. so, the Goalkeeper demands special physical abilities that enabling him the ball blocking skill masterly. Objetive: The study aimed to identify the effect of developing some special physical abilities on the goalkeeper’s performance level while blocking executed balls from the penalty mark in football using plyometric training. Methodology: The experimental approach (continuous training that divided into two phases: General preparation, and Special preparation stage) was adopted for one experimental group by pre/post-measurements. The research sample was delib

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

This study investigates the characterization and mechanical performance of Stone Mastic Asphalt (SMA) mixtures modified with two types of polymers: styrene–butadiene–styrene (SBS) and high-molecular-weight polyethylene (PE). Neat asphalt cement PG 64-16 was modified using a higher content of SBS and PE at concentrations of 6%, 7%, and 8% by weight of asphalt through the dry blending method to produce Highly Modified Asphalts (HiMA). The physical and rheological properties of the modified binders were evaluated using penetration, softening point, rotational viscosity, and dynamic shear rheometer (DSR) tests. Also, their phase compatibility and morphological changes were evaluated using the storage stability testing and scanning electron