Epoxy (EP) – Silica (SiO2) composites are well known composites used in microelectronic industry . So it is important to study their dielectric behavior under different conditions such as
the presence carbon black (UV absorber) and immersion in the water for 30 days .
Dielectric properties were calculated over the frequency range 102 – 106 Hz for epoxy composites with different weight % of micrometer 1.5μm SiO2 particles (60%, 65% and 70wt%) modified with 0.5wt% silane coupling agent to improve adhesion between EP and SiO2 phases .

In this work a hybrid composite materials were prepared containing matrix of polymer (polyethylene PE) reinforced by different reinforcing materials (Alumina powder + Carbon black powder CB + Silica powder). The hybrid composite materials prepared are: • H1 = PE + Al2O3 + CB • H2 = PE + CB + SiO2 • H3 = PE + Al2O3 + CB + SiO2 All samples related to electrical tests were prepared by injection molding process. Mechanical tests include compression with different temperatures and different chemical solutions at different immersion times The mechanical experimentations results were in favour of the samples (H3) with an obvious weakness of the samples (H1) and a decrease of these properties with a rise in temperature and the increasing

The study was carried out in plant tissue culture laboratory, University of Baghdad during the period 2017-2019, as factorial experiment in complete randomized design, to study the effect of PEG at (0, 2, 4, 6 and 8%) on physiological and chemical changes in callus of three sunflower (Ishaqi 1, Aqmar and Al-haga) induced by the cultivation of the young stem in vitro under water stress. The content of callus cells of SOD, POD, CAT and APX enzymes as well as total dissolved carbohydrate were determined as indicators to determine the effect of PEG in callus tissue cells cultivated on medium equipped with the PEG concentrations. The results showed that cultivars were differs significantly, and A-haja variety was superior in increasing SOD to 12

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