This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s

        In this work, analytical study for simulating a Fabry-Perot bistable etalon (F-P cavity) filled with a dispersive optimized nonlinear optical material (Kerr type) such as semiconductors Indium Antimonide (InSb). Because of a trade off between the etalon finesse values and driving terms, an optimization procedures have been done on the InSb etalon/CO laser parameters, using critical switching irradiance (I_c) via simulation systems of optimization procedures of optical cavity. in order to achieve the  minimum switching power and faster switching time, the optimization parameters of the finesse values and driving terms on optical bistability and switching dynamics must be studied.

This paper presents an improved technique on Ant Colony Optimization (ACO) algorithm. The procedure is applied on Single Machine with Infinite Bus (SMIB) system with power system stabilizer (PSS) at three different loading regimes. The simulations are made by using MATLAB software. The results show that by using Improved Ant Colony Optimization (IACO) the system will give better performance with less number of iterations as it compared with a previous modification on ACO. In addition, the probability of selecting the arc depends on the best ant performance and the evaporation rate.

We investigated at the optical properties, structural makeup, and morphology of thin films of cadmium telluride (CdTe) with a thickness of 150 nm produced by thermal evaporation over glass. The X-ray diffraction study showed that the films had a crystalline composition, a cubic structure, and a preference for grain formation along the (111) crystallographic direction. The outcomes of the inquiry were used to determine these traits. With the use of thin films of CdTe that were doped with Ag at a concentration of 0.5%, the crystallization orientations of pure CdTe (23.58, 39.02, and 46.22) and CdTe:Ag were both determined by X-ray diffraction. orientations (23.72, 39.21, 46.40) For samples that were pure and those that were doped with

A spectrophotometric determination of azithromycin was optimized using the simplex model. The approach has been proven to be accurate and sensitive. The analyte has been reacted with bromothymol blue (BTB) to form a colored ion pair which has been extracted in chloroform in a buffer medium of pH=4 of potassium phthalate. The extracted colored product was assayed at 415 nm and exhibited a linear quantification range over (1 - 20) g/ml. The excipients did not exhibit any interferences with the proposed approach for assaying azithromycin in pharmaceutical formulations.

The study of surface hardness, wear resistance, adhesion strength, electrochemical corrosion resistance and thermal conductivity of coatings composed from sodium silicate was prepared using graphite micro-size particles and carbon nano particles as fillers respectively of concentration of (1-5%), for the purpose of covering and protecting the oil distillation towers. The results showed that the sodium silicate coating reinforced with carbon nano-powder has higher resistance to stitches, mechanical wear, adhesive and thermal conductivity than graphite/sodium silicate composite especially when the ratio 5% and 1%, the electrochemical corrosion test confirmed that the coating process of stainless steel 304 lead to increasin

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g