Conjugate heat transfer has significant implications on heat transfer characteristics, particularly in thick wall applications and small diameter pipes. In this study, a three-dimensional numerical investigation was carried out using commercial CFD software “ANSYS FLUENT” to study the influence of conjugate heat transfer of laminar flow in mini channels at constant heat flux wall conditions. Two parameters were studied and analyzed: the wall thickness and thermal conductivity and their effect on heat transfer characteristics such as temperature profile and Nusselt number. Thermal conductivity of (0.25, 10, 202, and 387) W/m²C and wall thickness of (1, 5, and 50) mm were used for a channel of (1*2) mm cross

Briefly the term of cyber security is a bunch of operations and procedures working on insurance and protecting the network, computer devices, the programs and data from attack and from damaging penetration, also from breaking, abstraction and disturbing in spite of the fact that the concept of cyber conflict is got widening. So, the needs arise in the state to secure cyberspace and protect it by several methods to confront the electronic intrusions and threats which is known as cyber security. Countries seek to preserve its national security in particular the United States of America after the events of September 11 ,2001. In addition, the United States follow all ways to take over cyber threats.

        The performance of asphalt concrete pavement has affected by many factors, the temperature is the most important environmental one which has a large effect on the structural behavior of flexible pavement materials. The main cause of premature failure of pavement is the rutting, Due to the viscoelastic nature of the asphalt cement, rutting is more pronounced in hot climate areas because the viscosity of the asphalt binder which is
inversely related to rutting is significantly reduced with the increase in temperature resulting in a more rut susceptible paving mixtures. The objective of this study is to determine the effect of temperatures variations on the permanent deformation parameters (perm

Internal curing is a method that has been advised to decrease the primary age cracking, mainly of concrete mixes using low (water to cementitious materials - w/cm) ratios corresponding to the self-compacting concrete-(SCC). This research aims to study the effect of the internal curing using saturated lightweight aggregate- (LWA) on the steel reinforcing corrosion in SCC. In this research, crushed bricks or thermostone were partially replaced by (20%) by the weight of sand and volumetrically measured. The results showed that the steel reinforcement of internally cured concrete showed a slight increase in corrosion up to 300 days of exposure to the saline solution (containing 3.5% NaCl). The ability of using the crushed bricks or thermostone

Internal curing is a method that has been advised to decrease the primary age cracking, mainly of concrete mixes using low (water to cementitious materials - w/cm) ratios corresponding to the self-compacting concrete-(SCC). This research aims to study the effect of the internal curing using saturated lightweight aggregate- (LWA) on the steel reinforcing corrosion in SCC. In this research, crushed bricks or thermostone were partially replaced by (20%) by the weight of sand and volumetrically measured. The results showed that the steel reinforcement of internally cured concrete showed a slight increase in corrosion up to 300 days of exposure to the saline solution (containing 3.5% NaCl). The ability of using the crushed bricks or thermostone

     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