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

Synthesis and study liquid crystalline properties of new compounds with terminal groups of amides ([III]a-c,[IV]a-c and [VI]n), alkoxy series[V]n or ester with azo linkage ([IX]a-c and[X]a-c) containing thaizole ring. These series were synthesized by many steps starting from 4- hydroxyacetophenone or 4-aminoacetophenone. The synthesized compounds were characterized using melting points, FTIR, C.H.N.S analysis and for some of them 1H NMR spectroscopy. The liquid crystalline properties were studied by hot stage polarizing microscopy and differential scanning calorimetry DSC. All compounds of series [III]a-c,[IV]a-c and compounds [V]n showed enantiotropic liquid crystal. While the series [VI]n showed nematic mesomorphism except [VI]8 did not s

In this research, experimental and numerical studies were carried out to investigate the performance of encased glass-fiber-reinforced polymer (GFRP) beams under fire. The test specimens were divided into two peer groups to be tested under the effect of ambient and elevated temperatures. The first group was statically tested to investigate the monotonic behavior of the specimens. The second group was exposed to fire loading first and then statically tested to explore the residual behavior of the burned specimens. Adding shear connectors and web stiffeners to the GFRP beam was the main parameter in this investigation. Moreover, service loads were applied to the tested beams during the fire. Utilizing shear connectors, web stiffeners,

In this research, experimental and numerical studies were carried out to investigate the performance of encased glass-fiber-reinforced polymer (GFRP) beams under fire. The test specimens were divided into two peer groups to be tested under the effect of ambient and elevated temperatures. The first group was statically tested to investigate the monotonic behavior of the specimens. The second group was exposed to fire loading first and then statically tested to explore the residual behavior of the burned specimens. Adding shear connectors and web stiffeners to the GFRP beam was the main parameter in this investigation. Moreover, service loads were applied to the tested beams during the fire. Utilizing shear connectors, web stiffeners,

The ejector refrigeration system is a desirable choice to reduce energy consumption. A Computational Fluid Dynamics CFD simulation using the ANSYS package was performed to investigate the flow inside the ejector and determine the performance of a small-scale steam ejector. The experimental results showed that at the nozzle throat diameter of 2.6 mm and the evaporator temperature of 10^oC, increasing boiler temperature from 110^oC to 140^oC decreases the entrainment ratio by 66.25%. At the boiler temperature of 120^oC, increasing the evaporator temperature from 7.5 to 15 ^oC increases the entrainment ratio by 65.57%. While at the boiler temperature of 120^oC and

The new compounds synthesized by sequence reactions starting from a reaction of 4-hydroxybenzaldehyde with 1,5-dibromo pentane to produce dialdehyde)I( .Then compound )I( reacted with different aromatic amines to give schiff bases )IIIV(,thereafter added acetyl chloride to schiff bases to yield N-acyl derivatives)VVII(.While1,3-diazetine derivatives)VIII-X( were synthesized from the reaction of N-acyl derivatives with sodium azide.The reaction of thiourea with N-acyl compounds led to formation of thiourea derivatives (XI-XIII).Finally, the pyrimidine compounds )XIV-XVI( were synthesized by ring closure reaction of compounds(XIXIII) with diethyl malonate.The synthesized compounds were characterized by measurements of melting points,FTIR,1H-N

As an important resource, entanglement light source has been used in developing quantum information technologies, such as quantum key distribution(QKD). There are few experiments implementing entanglement-based deterministic QKD protocols since the security of existing protocols may be compromised in lossy channels. In this work, we report on a loss-tolerant deterministic QKD experiment which follows a modified “Ping-Pong”(PP) protocol. The experiment results demonstrate for the first time that a secure deterministic QKD session can be fulfilled in a channel with an optical loss of 9 dB, based on a telecom-band entangled photon source. This exhibits a conceivable prospect of ultilizing entanglement light source in real-life fiber-based

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa