In this study, an industrial source) E-Glass fiber) and a natural source (chestnut filler) were combined to improve the properties of polyesters. Hand-layup technique was applied in this work. Polyester (UPE) were reinforced with E-glass fibers, then reinforced with nano chestnut particles. All composites were prepared with (10% wt.) of E-glass for all prepared sheet and this ratio applied for Nano chestnut composite to preparing nano hybrid composites, Nano chestnut particles were used to reinforce E-glass/UPE composites with weight ratio (3%, 6% and 9% wt.). The evaluated mechanical performances for E-glass/NCSP polyester composite were tensile strength, impact strength and hardness. The higher ultimate tensile strength, Y

The wear behavior of alumina particulate reinforced A332 aluminium alloy composites produced by a stir casting process technique were investigated. A pin-on-disc type apparatus was employed for determining the sliding wear rate in composite samples at different grain size (1 µm, 12µm, 50 nm) and different weight percentage (0.05-0.1-0.5-1) wt% of alumina respectively. Mechanical properties characterization which strongly depends on microstructure properties of reinforcement revealed that the presence of ( nano , micro) alumina particulates lead to simultaneous increase in hardness, ultimate tensile stress (UTS), wear resistances. The results revealed that UTS, Hardness, Wear resistances increases with the increase in the percentage of

       Staphylococcus aureus is a common pathogenic agent due to its ability to cause various types of infections, ranging from mild skin infections to sever systemic diseases. One of the most virulence factors of this bacterium is its ability to from biofilms on solid surfaces by anchoring the planktonic cells and by producing a protective layer of extra polymeric substances. Biofilm formation is controlled through many genes. The most important ones are icaA and icaD. Dentures are prosthetic devices that are made of different materials to replace lost teeth. The aim of this study is to examine the ability of different types of denture materials to support the biofilm formation of S. aureus at phenotypic level by detecting ba

       Staphylococcus aureus is a common pathogenic agent due to its ability to cause various types of infections, ranging from mild skin infections to sever systemic diseases. One of the most virulence factors of this bacterium is its ability to from biofilms on solid surfaces by anchoring the planktonic cells and by producing a protective layer of extra polymeric substances. Biofilm formation is controlled through many genes. The most important ones are icaA and icaD. Dentures are prosthetic devices that are made of different materials to replace lost teeth. The aim of this study is to examine the ability of different types of denture materials to support the biofilm formation of S. aureus at p

Due to the deliberate disposal of industrial waste, a great amount of petroleum hydrocarbons pollute the soil and aquatic environments. Bioremediation that depends on the microorganisms in the removal of pollutants is more efficient and cost-effective technology. In this study, five rhizobacteria were isolated from Phragmites australis roots and exposed to real wastewater from Al-Daura refinery with 70 mg/L total petroleum hydrocarbons (TPH) concentration. The five selected rhizobacteria were examined in a biodegradation test for seven days to remove TPH. The results showed that 80% TPH degradation as the maximum value by Sphingomonas Paucimobilis as identified with Vitek® 2 Compact (France).

     This study aimed to explore whether green synthesized copper nanoparticles (CuNPs) can function as an anti-biofilm agent produced by P. mirabilis. The nanoparticles were synthesized from cells free extract of P. mirabilis. Characterization of biosynthesized copper nanoparticles was carried out to determine the chemical and physical properties of the product using atomic-force microscopy (AFM), transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) and UV-visible spectroscopy. The hexagonal structure was confirmed by XRD, size range was marked 13-19nm by TEM. FESEM was used to confirm the surface morphology. AFM analysis was used to reveal the roughne

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

Porosity and permeability are the most difficult properties to determine in subsurface reservoir characterization. The difficulty of estimating them arising from the fact that porosity and permeability may vary significantly over the reservoir volume, and can only be sampled at well location. Secondly, the porosity values are commonly evaluated from the well log data, which are usually available from most wells in the reservoir, but permeability values, which are generally determined from core analysis, are not usually available. The aim of this study is: First, to develop correlations between the core and the well log data which can be used to estimate permeability in uncored wells, these correlations enable to estimate reservoir permeabil