For structural concrete members that may expose to serious earthquake, overload or accident impact, the design of ductility must be given the same importance as the flexural strength. The aim of this investigation is to study the change in ductility of structural concrete flexural members during their exposure to limited cycles of repeated loading. Twenty full-scale beam specimens have been fabricated in to two identical groups; each group consisted of ten specimens. The first group was tested under monotonic static loading to failure and regarded as control beams, while the specimens of the second group were subjected to ten cycles of repeated loading with constant load interval, which ranged between 40% and 60% of ultimate load. S

Increased downscaling of CMOS circuits with respect to feature size and threshold voltage has a result of dramatically increasing in leakage current. So, leakage power reduction is an important design issue for active and standby modes as long as the technology scaling increased. In this paper, a simultaneous active and standby energy optimization methodology is proposed for 22 nm sub-threshold CMOS circuits. In the first phase, we investigate the dual threshold voltage design for active energy per cycle minimization. A slack based genetic algorithm is proposed to find the optimal reverse body bias assignment to set of noncritical paths gates to ensure low active energy per cycle with the maximum allowable frequency at the optimal supply vo

   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