Bacterial contamination of AL-Habania and AL-Tharthar reservoirs were studied during the period from February 2001 to January 2002, samples were collected from four stations in AL-Habania reservoir (AL-Warrar, AL-Theban regulator, middle of the reservoir and the fourth was towards AL-Razzaza reservoir) and from two stations at AL-Tharthar reservoir (Ein AL-Hilwa and the middle of the reservoir). Coliform bacteria, faecal Coliforms, Streptococci, faecal Streptococci and total count of bacteria were used as parameters of bacterial contamination in waters of both reservoirs through calculating the most probable number. Highest count of Coliform bacteria (15000 cell/100ml) was recorded at Ein AL-Hilwa and lowest count at AL-Theban regulator

Amoebas live freely in different climates and parts of the world. Several species of Free Living Amoeba (FLA) are capable of causing serious as well as fatal infections in human beings. The aim of this study was to identify and compare genotypes of water-polluting FLA in major rivers and lakes of Iraq and compare them with FLA isolates from Iran and Turkey. For this purpose, the study included 20 water samples from the Tigris River, Euphrates River , Najaf Sea and Dukan lake in Iraq, 20 water samples from  Marivan, Velasht, and Soleimanshah lakes and Caspian sea in Iran, and 20 water samples from Sabanca, Seyfi , Hazar and Yay lakes in Turkey. The samples were studied by culture methods, invert microscope, and molecular methods.

The aim of current study is estimate the ability of low cost adsorbents, which consist of extracted silica from rice husk ash in treatment of Industrial waste water that contains heavy metals (Cd, Co and Pb) with other pollutants by fixed filters technique with determine the best method for that, and study the effect of a number of variables and parameters. This study involved one waste water samples were collected from. State battery manufacturing company (SBMC) (before treatment unit) at 5th and 22th, of the January 2015. Adsorption tests showed that all tested adsorbent materials had a significant heavy metal removal efficiency. pH values showed a significant impact on adsorption process, but best removal efficiency occurred at pH 4.5

     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

A comparative investigation of gas sensing properties of SnO2 doped with WO3 based on thin film and bulk forms was achieved. Thin films were deposited by thermal evaporation technique on glass substrates. Bulk sensors in the shape of pellets were prepared by pressing SnO2:WO3 powder. The polycrystalline nature of the obtained films with tetragonal structure was confirmed by X-ray diffraction. The calculated crystalline size was 52.43 nm. Thickness of the prepared films was found 134 nm. The optical characteristics of the thin films were studied by using UV-VIS Spectrophotometer in the wavelength range 200 nm to 1100 nm, the energy band gap, extinction coefficient and refractive index of the thin film were 2.5 eV , 0.024 and 2.51, respective

     MWCNTs-OH was used to prepare a flexible gas sensor by deposition as a network on a filter cake using the method of filtration from suspension (FFS). The morphological and structural properties of the MWCNTs network were characterized before and after exposure to Freon gas using FTIR spectra and X-ray diffractometer, which confirmed that the characteristics of the sensor did not change after exposure to the gas. The sensor was exposed to a pure Freon134a gas as well as to a mixture of Freon gas and air with different ratios at room temperature. The experiments showed that the sensor works at room temperature and the sensitivity values increased with increasing operating temperature, to be 58% unt

      An NH₃ gas sensor was prepared from nanocomposite films of indium oxide-copper oxide mixtures with ratios of 0 , 10 , and 20 Vol % of copper oxide. The films were deposited on a glass substrate using chemical spray pyrolysis method (CSP) at 400^oC. The structural properties were studied by using X-ray diffraction (XRD) and atomic force microscopy ( AFM). The structural results showed that the prepared thin films are polycrystalline, with nano grain size. By mixing copper oxide with indium oxide, the grain size of the prepared thin films was decreased and the surface roughness was increased. The UV-Visible spectrometer analysis showed that the prepared thin films have high transmittance.

Photonic Crystal Fiber Fabry–Perot Interferometers (FPI) based on Surface Plasmon Resonance (SPR) was investigated in this paper in order to detect changes in photonic crystal fiber sensitivity with increasing temperature. FPI is composed of a PCF (ESM-12) solid core spliced with a single-mode fiber (SMF) on one side and a 40nm thick gold Nano film on the other. In order to obtain the SPR curve, the end of PCF can be spliced with the side of SMF before covering the gold film on the PCF. SPR results are included in the suggested sensor, based on the conclusions of the investigations. Resolution (R) is 0.0871, Signal-to-Noise Ratio (SNR) is 0.1867, a figure of merit (FOM) is 0.0069, and sensitivity (S) is 1.1481 . This sensor proposed is s

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye