Research aims to develop a novel technique for segmental beam fabrication using plain concrete blocks and externally bonded Carbon Fiber Reinforced Polymers Laminates (CFRP) as a main flexural reinforcement. Six beams designed an experimentally tested under two-point loadings. Several parameters included in the fabrication of segmental beam studied such as; bonding length of carbon fiber reinforced polymers, the surface-to-surface condition of concrete segments, interface condition of the bonding surface, and thickness of epoxy resin layers. Test results of the segmental beams specimens compared with that gained from testing reinforced concrete beam have similar dimensions for validations. The results show the effectiven

Liquid-liquid membrane extraction technique, pertraction, using three types of solvents (methyl isobutyl ketone, n-butyl acetate, and n-amyl acetate) was used for recovery of penicillin V from simulated fermentation broth under various operating conditions of pH value (4-6) for feed and (6-8) for receiver phase, time (0-40 min), and agitation speed (300-500 rpm) in a batch laboratory unit system. The optimum conditions for extraction were at pH of 4 for feed, and 8 for receiver phase, rotation speed of 500 rpm, time of 40 min, and solvent of MIBK as membrane, where more than 98% of penicillin was extracted. 

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

In this paper, Bayes estimators for the shape and scale parameters of Weibull distribution have been obtained using the generalized weighted loss function, based on Exponential priors. Lindley’s approximation has been used effectively in Bayesian estimation. Based on theMonte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s).

The following dilution 5×10-1, 10-1, 10?2 , 10-3 gm/L for the indigenous isolate of Bacillus thuringiensis bacteria and the commercially isalate were used for experiments against the different stages of fig moth of E.cautella which exposed by filter paper method. The results showed that mortality of larval stages was increased with the increasing concentration of the biocide, in addition to increase in the mortality of the larval stages reached to the highest percentage in the third days of treatment of the larval stage in comparison with the first and second days of exposure. The results also showed that the sensitivity of larval stages was increased in first and second instars while reduced in the last instars .The high percentage

     The ferric oxide nanoparticles (Fe₂O₃) which are deposited at interface which is related to hole collecting buffer layer [poly(3,4-ethyl-enedioxythiophene): poly(styrene-sulfonate) (PEDOT: PSS)] as well as regioregular poly(3-hexyl-thiophene): Zinc oxide nanoparticles (P3HT): (ZnO) active layer have been considerable increasing the performance of solar cell. Also, the solar cell devices have been fabricated with a weight ratio of 1:0.7, 1:0.8, 1:0.9 and 1:1 of P3HT and ZnO, respectively. In addition, photo physical characteristics regarding such devices with different value of the weight ratio were examined. This work is indicating that the absorption spectrum related to blend will be broad

     Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN