n this study, data or X-ray images Fixable Image Transport System (FITS) of objects were analyzed, where energy was collected from the body by several sensors; each sensor receives energy within a specific range, and when energy was collected from all sensors, the image was formed carrying information about that body. The images can be transferred and stored easily. The images were analyzed using the DS9 program to obtain a spectrum for each object,an energy corresponding to the photons collected per second. This study analyzed images for two types of objects (globular and open clusters). The results showed that the five open star clusters contain roughly the same materials. The clusters are composed of Carbon, Sodium, Magnesium, Aluminum, Silicon, Phosphorus, Sulfur, Nickel, and Germanium, while globular clusters contain the same elements but differ from the elements in the other type in terms of type and abundance. The three stars cluster contains roughly the same material, Carbon, Silicon, Phosphorus, Nickel, and Germanium, except for a small percentage of Neon that appears in the NGC 6121 stars cluster.
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Image recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-wavelet-stationary (sws), stationary-wavelet-wavelet (sww), wavelet-stationary- stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-wavelet-wavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third
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Reactive Powder Concrete (RPC) could be considered as the furthermost significant modern high compressive strength concrete. In this study, an experimental investigation on the impact of micro steel fiber volume fraction ratio and gamma ray irradiation duration influence upon the compressive strength of RPC is presented. Three volume fraction ratios (0.0, 1.0 and 1.5) % was implemented. For each percentage of the adopted fiber ratios, six different irradiation duration was considered; these are (1, 2, 3, 4, 5 and 6) days. Gamma source (Cs-137) of energy (0.662) MeV and activity (6) mci was used. In a case of zero volume fraction ratio, the experimental results showed that gamma ray had a significant influence on the reducing of the
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Reactive Powder Concrete (RPC) could be considered as the furthermost significant modern high compressive strength concrete. In this study, an experimental investigation on the impact of micro steel fiber volume fraction ratio and gamma ray irradiation duration influence upon the compressive strength of RPC is presented. Three volume fraction ratios (0.0, 1.0 and 1.5) % was implemented. For each percentage of the adopted fiber ratios, six different irradiation duration was considered; these are (1, 2, 3, 4, 5 and 6) days. Gamma source (Cs-137) of energy (0.662) MeV and activity (6) mci was used. In a case of zero volume fraction ratio, the experimental results showed that gamma ray had a significant influence on the reducing of the
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The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance p
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The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.
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