In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). The results showed that the method performed well in all simulation scenarios with respect to different sample sizes.
TiO2 thin films were deposited by reactive d.c magnetron sputtering method on a glass substrate with various ratio of gas flow (Oxygen /Argon) (50/50, 100/50 and 150/50) at substrate temperature 573K. It can be observe that the optical energy gap of TiO2 thin films dependent on the ratio of gas flow (oxygen/argon), it varies between (3.45eV-3.57eV) also it is seen that the optical constants (α, n, K, εr and εi ) has been varied with the change of the ratio of gas flow (Oxygen /Argon).
Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp
... Show MoreVarious nutritional solutions given to patients contain amino acids. Possible interference of this supplementation with selected aminoglycoside antiboiotics, namely gentamycin and streptomycin was evaluated in vitro. In minimal medium, E.coli was inhibited by gentamycin and by streptomycin. Circumvention of this inhibition was obtained with a mixture of 20 amino acids in the medium. Deletion of amino acids revealved that circumvention. specific amino acids were required for such Deletion of the aromatic amino acids or cysteine abolished the protection against gentamycin and streptomycin, while the deletion of the branched chain amino acids abolished the protection against streptomycin only. Thereonine, on the other hand, appears to be essen
... Show MoreIn this paper, CdS/Si hetrojunction solar cell has been made by
Chemical Bath Deposition (CBD) of CdS thin film on to
monocrystalline silicon substrate. XRD measurements approved that
CdS film is changing the structure of CdS films from mixed
hexagonal and cubic phase to the hexagonal phase with [101]
predominant orientation. I-V characterization of the hetrojunction
shows good rectification, with high spectral responsivity of 0.41
A/W, quantum efficiency 90%,and specific detectivity 2.9*1014
cmHz1/2W -1 .
Nanoparticles generation by laser ablation of a solid target in a liquid environment is an easy method. Cadmium Telluride (CdTe) colloidal nanoparticles have been synthesized by laser ablation Nd:YAG with wavelengths of 1064nm and double frequency at 532 nm, number of pulses 50 pulses, with pulse energy= 620mJ, 700mJ of a solid target CdTe is immersed in double distilled deionized water (DDIW) and in methanol liquid. Influences of the laser energy and different solutions on the formation and optical characterization of the CdTe nanoparticles have been studied using atomic force microscope (AFM) and the UV-Vis absorption. As a results, it leads to the absorbance in UV-Vis spectra of samples prepared in water at laser wavelength of 532nm i
... Show MoreNano-crystalline iron oxide nanoparticles (magnetite) was synthesized by open vessel ageing process. The iron chloride solution was prepared by mixing deionized water and iron chloride tetrahydrate. The product was characterized by X-Ray, Surface area and pore volume by Brunauer-Emmet-Teller, Atomic Force Microscope (AFM) and Fourier Transform Infrared Spectroscopy(FTIR) . The results showed that the XRD in compatibility of the prepared iron oxide (magnetite) with the general structure of standard iron oxide, and in Fourier Transform Infrared Spectroscopy, it is strong crests in 586 bands, because of the expansion vibration manner related to the metal oxygen absorption band (Fe–O bonds in the crystals of iron ox
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