In this work we run simulation of gas dynamic problems to study the effects of Riemann
problems on the physical properties for this gas.
We studied a normal shock wave travels at a high speed through a medium (shock tube). This
would cause discontinuous change in the characteristics of the medium, such as rapid rise in
velocity, pressure, and density of the flow.
When a shock wave passes through the medium, the total energy is preserved but the energy
which can be extracted as work decreases and entropy increases.
The shock tube is initially divided into a driver and a driven section by a diaphragm. The
shock wave is created by increasing the pressure in the driver section until the diaphragm bursts,
se

This paper presents a novel idea as it investigates the rescue effect of the prey with fluctuation effect for the first time to propose a modified predator-prey model that forms a non-autonomous model. However, the approximation method is utilized to convert the non-autonomous model to an autonomous one by simplifying the mathematical analysis and following the dynamical behaviors. Some theoretical properties of the proposed autonomous model like the boundedness, stability, and Kolmogorov conditions are studied. This paper's analytical results demonstrate that the dynamic behaviors are globally stable and that the rescue effect improves the likelihood of coexistence compared to when there is no rescue impact. Furthermore, numerical simul

In this paper, an ecological model with stage-structure in prey population, fear, anti-predator and harvesting are suggested. Lotka-Volterra and Holling type II functional responses have been assumed to describe the feeding processes . The local and global stability of steady points of this model are established. Finally, the global dynamics are studied numerically to investigate the influence of the parameters on the solutions of the system, especially the effect of fear and anti-predation.

Background: Breast cancer is the most common malignancy affecting the Iraqi population and the leading cause of cancer related mortality among Iraqi women. It has been well documented that prognosis of patients depends largely upon the hormone receptor contents and HER-2 over expression of their neoplasm. Recent studies suggest that Triple Positive (TP) tumors, bearing the three markers, tend to exhibit a relatively favorable clinical behavior in which overtreatment is not recommended. Aim: To document the different frequencies of ER/PR/HER2 breast cancer molecular subtypes focusing on the Triple Positive pattern; correlating those with the corresponding clinico-pathological characteristics among a sample of Iraqi patients diagnosed with th

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Background: Breast cancer still a major cause of disability and mortality among women throughout the world. Lack of awareness and early detection programs in developing countries is a main reason for escalating the mortality.

Objectives: to assess level of awareness about breast cancer among university female students in Baghdad focusing on knowledge of breast cancer risk factors, warning symptoms and signs and knowledge about the screening method specially breast self-examination.

Methods: A cross-sectional study conducted over two months from first of march through April 2015 and included (240) female students in non- medical colleges at Al-Rusafa and A

The research aims to determine the required rate of return according to the Fama and French five-factor model, after strengthening it by adding the indebtedness factor to build the Fama and French six-factor model FF6M-DLE. The effect of the indebtedness factor on the company's profitability and the real value of the ordinary shares calculated according to the (equivalent ascertainment) model and its suitability with the company's situation, and an analysis of the fluctuation between the market value and the real value of the ordinary stocks.

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Background: Breast cancer is the most common
malignancy affecting females worldwide. The association
of Epstein-Barr virus (EBV) with this cancer is a longstanding
interest to this field.
Aim: to investigate the presence of EBV in breast tumor
tissue in relation to age.
Patients and Methods: Paraffin-embedded tissue blocks
from 45 female patients with breast tumors (ranged in age
from 28 to 85 years) were retrieved. The cases were
grouped into two categories: group (A): included 30 cases
with breast carcinoma and group (B): included 15 cases
with benign breast diseases as a control group .The
expression of EBV protein was examined
immunohistochemically.
Results: Twelve (40%) of the 30 breast canc