The purpose of the present work is to calculate the expectation value of potential energy for different spin states (??? ? ???,??? ? ???) and compared it with spin states (??? , ??? ) for lithium excited state (1s2s3s) and Li- like ions (Be+,B+2) using Hartree-Fock wave function by partitioning techanique .The result of inter particle expectation value shows linear behaviour with atomic number and for each atom and ion the shows the trend ??? < ??? < ??? < ???

World statistics proved that the most of work dangerous accidents, which causes death, are occurred in the construction works. These accidents related to many causes such as loss of workers experience and ignoring rules of safety requirements, especially young workers. Due to the risk of accidents that may occur in the site of work, the idea of this study crystallized to show the relationship between the age of worker and number of injuries and accidents, to identify the causes of these injuries, and to put the appropriate solutions to avoid or reduce the risk of work injuries. Also, the research shows the main principles of safety requirements to forming a clear picture about the subject of the study. A questioner form was prepared to c

   The region of Kirkuk and its surrounding areas, including (Baba, Jambour, Qara Chuq, Qaiyarah, Demir Dagh, Bai Hassan, Taq Taq, Makhul, Gilabat as well as southern Mosul and the cities of Erbil and Sulymania, are known as one of the oldest discovered oil fields in northern Iraq. This area presents a significant opportunity for further organic geochemical analysis to describe maturation zones and estimate economically generated hydrocarbons with particular reference to the Sargelu formation, to enhance hydrocarbons productivity. To assess the potential of these oil fields, it is essential to perform correlation, comparisons, and geochemical analyses of the data collected from exploration wells in the surrounding area. This appro

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

BACKGROUND: Many genetic factors are known to be related to osteoporosis, and currently the role of the glucagon-like peptide-1 receptor (GLP-1R) gene in bone health has been studied intensively. Some variation of this gene, such as rs1042044 and rs6458093, are known to be linked to metabolic diseases and lower bone mineral density, however their specific contribution to osteoporosis remains largely unexplored. Therefore, this study was conducted to investigate the combined genotypic effect of rs1042044 and rs6458093 as a genetic risk factor for osteoporosis in postmenopausal Iraqi women.METHODS: Blood samples from 75 osteoporosis patients and 75 healthy controls, aged 45-85, were collected. DNA was extracted, and a region of GLP-1R

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Schizophrenia (ZP) is a common devastating cognitive disorder; however, the etiology of its incidence remains largely unclear yet. Although some evidence blames both environ ment and genetic factor as main drivers of ZP, but the clear mechanism of their effect elusive till now. Of interest, severe anemia conditions can lead to hypoxia in CNS which disturbs the normal structures and functions of some area in the brain resulting in abnormalities in the neurons communications and decrease synaptic plasticity. Additionally, modulation of cytoskeleton constituents is thought to participate to ZP development. Recently, it has been revealed that iron deficiency, which is the main cause of anemia, has a significant impact on the cytoskeleton