In this research velocity of moving airplane from its recorded digital sound is introduced. The data of sound file is sliced into several frames using overlapping partitions. Then the array of each frame is transformed from time domain to frequency domain using Fourier Transform (FT). To determine the characteristic frequency of the sound, a moving window mechanics is used, the size of that window is made linearly proportional with the value of the tracked frequency. This proportionality is due to the existing linear relationship between the frequency and its Doppler shift. An algorithm was introduced to select the characteristic frequencies, this algorithm allocates the frequencies which satisfy the Doppler relation, beside that the tracked frequencies was chosen to be the most occurred frequencies in the power spectrum. Several sizes of the overlap windows have been tested to study the effect of window size on the accuracy of the determined radial velocity of the moving airplane.
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Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.
The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed
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Chronic kidney Failure, a progressive disease, includes both medical and biochemical features that damage kidneys and decrease their abilities to work effectively, this disease is characterized by a chronic disorders to both the innate and adaptive immune systems, generate a complex and not fully understood immune dysfunction. In the present study, (30) men suffering from chronic kidney failure with age in range (40-55) year and (30) healthy men within the same range of age were enrolled in this study. The aim of this study is to highlight the role of immunological aspect (IL-35), hormonal aspects (PTH), some functional proteins and immunological electrolytes in sera of chronic kidney failure (CKF) patients. Biochemical parameters were dete
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Gypsum Plaster is an important building materials, and because of the availabilty of its raw materials. In this research the effect of various additives on the properties of plaster was studied , like Polyvinyl Acetate, Furfural, Fumed Silica at different rate of addition and two types of fibers, Carbon Fiber and Polypropylene Fiber to the plaster at a different volumetric rate. It was found that after analysis of the results the use of Furfural as an additive to plaster by 2.5% is the optimum ratio of addition to that it improved the flexural Strength by 3.18%.
When using Polyvinyl Acetate it was found that the ratio of the additive 2% is the optimum ratio of addition to the plaster, because it improved the value of the flexural stre
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