Background: Fruits and their by-products are the primary sources of bioactive chemicals in plants. Because of its phytochemical richness, Annona squamosa fruits have gained the alertness of people willing in health-promoting diets. The purpose of this in vitro study was to evaluate the cytocompatibility effect of ethanolic crude extract of Annona squamosa pulp against a human normal cell line as a mouthwash for children. Material and method: The ethanolic extract of Annona squamosa pulp was extracted using the ultrasonic method and then lyophilized to make it powder. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide) test was performed to investigate the cytotoxic activity of the pulp extract on a human normal cell l

Urea formaldehyde resin was prepared by using basic media by yield 95%. The Remaining of ureaplasts resin were prepared in acetic acid media by high yield. Alkyde resins were prepared by condensation polymerization by react Succinic, Maleic, Phthalic anhydrides with Ethylene glycol or Glycerol. Select samples of the prepared alkyde resins were mixed with Azo dyes in special ratio. The mixtures were used as coatings for wood, and compaised with pure dyes. The Coating that some alkyde resins showed better adhesion from using dyes alone. Preparation of wood coating by mixing ureaplast resins and alkyde resins with Azo dyes in special ratios. The coating showed better adhesion, brighter colors and better resistance to heat from Preceding coat

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

The opportunistic multidrug resistance pathogen Pseudomonas aeruginosa has one or several flagella, and the numbers of these sophisticated machines are regulated by the flagellar regulator gene FleN. The flagellar hook gene FlgE is important for its synthesis, motility and tolerance to antibiotics. Bacteriahave resistance to antibiotics, especially to cephalosporin beta-lactam antibiotics. For the current study, 102 clinical specimens were collected and identified using routine laboratory tests and confirmed by Vitek-2 compact system.  A total of 33 isolates of P. aeruginosa were identified. The antibiotic susceptibility test was done by the Vitek 2 Compact system. Flagellar gene detected by conventional PCR revealed that the FleN

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

Copper with different concentrations doped with zinc oxide nanoparticles were prepared from a mixture of zinc acetate and copper acetate with sodium hydroxide in aqueous solution. The structure of the prepared samples was done by X-ray diffraction, atomic force microscopy (AFM) and UV-VIS absorption spectrophotometer. Debye-Scherer formula was used to calculate the size of the prepared samples. The band gap of the nanoparticle ZnO was determined by using UV-VIS optical spectroscopy.