The Taylor series is defined by the f and g series. The solution to the satellite's equation of motion is expanding to generate Taylor series through the coefficients f and g. In this study, the orbit equation in a perifocal system is solved using the Taylor series, which is based on time changing. A program in matlab is designed to apply the results for a geocentric satellite in low orbit (height from perigee, hp= 622 km). The input parameters were the initial distance from perigee, the initial time, eccentricity, true anomaly, position, and finally the velocity. The output parameters were the final distance from perigee and the final time values. The results of radial distance as opposed to time were plotted for dissimilar times in seconds and their comparison with the exact solution, with the aim of selecting an optimized reference orbit at a height of 622 km. The results indicated that the two series diverged excessively as the time increased from the exact solution, excluding the time of 850 sec. The f and g series had a little shift. Besides, the root mean square error (rmse) is computed for 750 sec. It was about 5 for the two series before diverging at about 180 sec and rapidly growing with time. For 850 sec, the (rmse) is approaching 10 for the two series and increasing quickly over time. So, the (rmse) is directly proportional to time, which means that as time increases, the diverging behavior and the value of the (rmse) will also increase. If more terms (Δt) are used for the two series and more time is included, the two series will deviate from the exact solution. The program's results are compared to other published studies in this field; they demonstrated high convergence.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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This research utilized natural asphalt (NA) deposits from sulfur springs in western Iraq. Laboratory tests were conducted to evaluate the performance of an asphalt mixture incorporating NA and verify its suitability for local pavement applications. To achieve this, a combination of two types of NA, namely soft SNA and hard HNA, was blended to create a binder known as Type HSNA. The resulting HSNA exhibited a penetration grade that adhered to Iraqi specifications. Various percentages of NA (20%, 40%, 60%, and 80%) were added to petroleum asphalt. The findings revealed enhanced physical properties of HSNA, which also satisfied the requirements outlined in the Iraqi specifications for asphalt cement.
Consequently, HS
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Highly Modified Asphalt (HiMA) binders have garnered significant attention due to their superior resistance to rutting, fatigue cracking, and thermal distress under heavy traffic loads and extreme environmental conditions. While elastomeric polymers such as Styrene- Butadiene-Styrene (SBS) have been extensively used in HiMA applications, the potential of plastomeric polymers, including Polyethylene (PE) and Ethylene Vinyl Acetate (EVA), remains largely unexplored. This study aims to evaluate the performance of reference binder (RB) modified with plastomeric HiMA asphalt in comparison to SBS-modified binders and determine the optimal polymer dosage for achieving an optimal balance between rutting resistance and fatigue durability. The experi
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The distribution of the expanded exponentiated power function EEPF with four parameters, was presented by the exponentiated expanded method using the expanded distribution of the power function, This method is characterized by obtaining a new distribution belonging to the exponential family, as we obtained the survival rate and failure rate function for this distribution, Some mathematical properties were found, then we used the developed least squares method to estimate the parameters using the genetic algorithm, and a Monte Carlo simulation study was conducted to evaluate the performance of estimations of possibility using the Genetic algorithm GA.
The research aims to identify intelligence spiritual among a sample of students Baghdad University as well as to identify the differences between students in intelligence spiritual according to variable type (male - female), and variable area of study (Science - a human) and variable (First grade - fourth grade), The research sample consisted of (300) students, were applied scale search - a spiritual Intelligence Scale (prepared by the researcher), has resulted in the search results for: -
The students of the University of Baghdad (sample) enjoyed a high level of spiritual intelligence.
- There are no differences between males and females in the spiritual intelligence.
- There