Abstract

      Oil is the most important natural resources in Iraq and represents the goal to others as well as Iraqi people. It is gift from God to all Iraqi people now and future. So we must maintain it and invest its revenue that achieve development in country and ensure the next generations' rights in it without external costs or negative externalities from extracted and invested it.

    The most problems that we attempt to solve by this research are the exhausted, environmental degradation and theft from next generation that produced with oil contracts between Iraq and foreign companies. From here was th

This paper is focused on orthogonal function approximation technique FAT-based adaptive backstepping control of a geared DC motor coupled with a rotational mechanical component. It is assumed that all parameters of the actuator are unknown including the torque-current constant (i.e., unknown input coefficient) and hence a control system with three motor control modes is proposed: 1) motor torque control mode, 2) motor current control mode, and 3) motor voltage control mode. The proposed control algorithm is a powerful tool to control a dynamic system with an unknown input coefficient. Each uncertain parameter/term is represented by a linear combination of weighting and orthogonal basis function vectors. Chebyshev polynomial is used

The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Back ground: Zygote produce from once a sperm fertilizes an egg cell. Then, the zygote (unicellular) will begin chain of cellular cleavages to produce multicellular mass, its embryo, the differentiated to different tissues and organism. The development of the embryo is called embryogenesis. Coenzyme Q10, is an antioxidant produced in the body. It boosts cellular energy and may enhance the immune system. CoQ10 is present and measurable in seminal fluid, the concentration of CoQ10 directly correlates with both sperm count and motility. It is beneficial in the prevention and treatment a wide range of health problems. Objectives: The present study was aimed to investigate the possibility of using coenzyme Q10 to improve in vitro fertilization (

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s