This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

KE Sharquie, AA Noaimi, MS Al-Zoubaidi, Journal of Cosmetics, Dermatological Sciences and Applications, 2015 - Cited by 8

Some maps of the chaotic firefly algorithm were selected to select variables for data on blood diseases and blood vessels obtained from Nasiriyah General Hospital where the data were tested and tracking the distribution of Gamma and it was concluded that a Chebyshevmap method is more efficient than a Sinusoidal map method through mean square error criterion.

Some of metal compounds have been synthesized of record ligand from aldehid interaction of a substance which is salicyladehyde with another material which is urea. During the analysis of the metal component, The prepared complexes were characterized by elemental analysis, IR ,UV-visible , conductivity and magnetic susceptibility measurements. this confirms the ratio[1:1] between the metal and ligand. It is found that theortical values agree with practical values All the studied complexes are suggested as an octahedral stereochemistry.

One of the most important problems in tablet process is to control the flow of the catalyst through the hopper; Controlling the flow can be done either by changing the size of particles or added the different lubricant (stearic acid, starch, graphite) or blending of different lubricants. The study showed that we can control (increase or decrease) on the flow of the catalyst through the hopper by blending different lubricants for the constant percentage. The flow increasing when particles size (0.6 mm) and then decrease with or without lubricants, no effect on flow when particles size lower than (0.2 mm) with use that lubricants, and good flow on (0.4 mm) when use stearic acid and starch.

Cilnidipine is a dihydropyridine calcium channel blocker used to improve the neurological outcome following subarachnoid hemorrhage. It belongs to BCS class II drugs that have a low oral bioavailability of 13%, thus preparation as nanoparticles would be expected to improve bioavailability. The aim of the study is to prepare Cilnidipine as nanoparticles using different carriers and co-carriers, concentrations, and types. Cilnidipine nanoparticles were prepared by a solvent anti-solvent method using different carriers (Soluplus®, Poloxamer 188, PVA cold) with co-stabilizers (PEG200, glycerol) at different ratios. Based on the obtained results, formula N4, which included Soloplus in a 5:5:1.19 weight ratio of drug to

The MTX was converted to MTX  nanoparticles  by the modified method based on changing the pH gradually . For the first time MTX NPs+Meropenem complex were prepared and evaluated as a potential tool to overcome antimicrobial resistance and to improve pharmacokinetics of the drug, the results showed that the antibacterial activity of complex (MTX NPs plus MEM) has increased (from 1( µg/ml) to >0.5( µg/ml) for p1 , from 2( µg/ml) to 1( µg/ml) for p10 and from 8( µg/ml) to 4( µg/ml) for p48).