The division partitioning technique has been used to analyze the four electron systems into six-pairs electronic wave functions for ( for the Beryllium atom in its excited state (1s2 2s 3s ) and like ions ( B+1 ,C+2 ) using Hartree-Fock wave functions . The aim of this work is to study atomic scattering form factor f(s) for and nuclear magnetic shielding constant. The results are obtained numerically by using the computer software (Mathcad).

Objective(s): Assess the effectiveness of osteoporosis prevention instruction program on nursing college students’
knowledge at Baghdad University.
Methodology: A quasi-experimental design was used to assess the effectiveness of osteoporosis prevention
instruction program on nursing college students at University of Baghdad from April 2011 to September 2011. A
random sample consisted of (40) females students from first year of Nursing College \ Baghdad University. The data
was collected by using constructed questionnaire, which consists of three parts. Part one: consists of demographic
information and health characteristics .Part two: consists of students’ daily life behaviors which include, dietary
behaviors, an

The transfer of chemical pollutants from bottled water into water due to heat, sunlight and poor storage is one of the most serious threats to human health around the world, the objective of this study was to estimate the pH value and the transport of heavy metals from plastic bottles to water, for this purpose, 30 bottles of water for 10 local brands were collected and divided into three groups, the first was left at room temperature 25°C, The second was placed in a heat oven at 25°C and the third in another oven at 50°C for two weeks. The results showed significant differences at (P<0.05) between water samples, pH value and concentrations of heavy metals (Sb, Pb, Ni, Cu, Cr, Cd and Fe) we

Objectives:  The study aims to identify the effects of an educational program on patients’ knowledge about vascular access care and to find out the relationship between patients knowledge scores and their selected demographic variable.                                                                                  &

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Inelastic magnetic electron scattering M1 at Ex =10.23 MeV form factors in Ca-48 have been investigated. The fp shell model space with four orbits and eight neutrons have been considered and FPD6 has been selected between 32 model space effective interactions to generates the model space vectors for the M1 transition with excitation energy Ex =10.23 MeV and for constructing OBDM. Discarded space (core and higher configuration orbits) has been included through the first order perturbation theory to couple the partice-hole pair of excitation in the calculation of the total M1 form factor and regarding the realistic interaction M3Y as a core polarization interaction with six sets of fitting parameters. Finally the theoretical calculations h

Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T