Cadmium sulfide (CdS) nanocrystalline thin films have been prepared by chemical bath deposition (CBD) technique on commercial glass substrates at 70ºC temperature. Cadmium chloride (CdCl2) as a source of cadmium (Cd), thiourea (CS(NH2)2) as a source of sulfur and ammonia solution (NH4OH) were added to maintain the pH value of the solution at 10. The characterization of thin films was carried out through the structural and optical properties by X-ray diffraction (XRD) and UV-VIS spectroscopy. A UV-VIS optical spectroscopy study was carried out to determine the band gap of the nanocrystalline CdS thin film and it showed a blue shift with respect to the bulk value (from 3.9 - 2.4eV). In present work effects of thickness on the structural and optical properties of CdS nanocrystalline thin films were discussed.
Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mo
... Show MoreThe evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed
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XML is being incorporated into the foundation of E-business data applications. This paper addresses the problem of the freeform information that stored in any organization and how XML with using this new approach will make the operation of the search very efficient and time consuming. This paper introduces new solution and methodology that has been developed to capture and manage such unstructured freeform information (multi information) depending on the use of XML schema technologies, neural network idea and object oriented relational database, in order to provide a practical solution for efficiently management multi freeform information system.
Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different
... Show MoreThis paper develops a fuzzy multi-objective model for solving aggregate production planning problems that contain multiple products and multiple periods in uncertain environments. We seek to minimize total production cost and total labor cost. We adopted a new method that utilizes a Zimmermans approach to determine the tolerance and aspiration levels. The actual performance of an industrial company was used to prove the feasibility of the proposed model. The proposed model shows that the method is useful, generalizable, and can be applied to APP problems with other parameters.
A.C electrical conductivity and dielectric properties for poly
(vinyl alcohol) (PVA) /poly (ethylene oxide) (PEO) blends undoped
and doped with multi-walled carbon nanotube (MWCNTs) with
different concentrations (1, and 3 wt %) in the frequency range
(25x103 - 5x106 Hz) were investigated. Samples of (PVA/PEO)
blends undoped and doped with MWCNTs were prepared using
casting technique. The electrical conductivity measurements showed
that σA.C is frequency dependent and obey the relation σA.C =Aωs for
undoped and doped blends with 1% MWCNTs, while it is frequency
independent with increases of MWCNTs content to 3%. The
exponent s showed proceeding increase with the increase of PEO
ratio (≥50%) for undope
In this search, Ep/SiO2 at (3, 6, 9, 12 %) composites is prepared by hand Lay-up method, to measure the change in the thermal conductivity and Impact Strength of epoxy resin before and after immersion in H2SO4 Solution with a 0.3N for 10 days. The results before immersion decreases with the increase of the weight ratios of the reinforcement material (SiO2), It changed from (82.6×10-2 to 38.7×10-2 W/m.°C) with change weight ratios from (3 to 12) % respectively, but after immersion time in the chemical solution where it was (65.6×10-2 W/m.°C) at the weight ratios (6 %) and became (46.6 × 10-2 W/m.°C) after immersion in sulfuric acid. The results of the Impact strength decreased by increasing the percentage weight ratio, it changed f
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