Positive and negative parity states for 114Te have been studied applying the vibration al limit U(5) of Interacting boson model (IBM- 1 ) . The present results have shown their good agreement with experimental data in addition to the determination of the spin/parity of new energy levels are not assigned experimentally as the levels 0+2 and 5+1 and the levels 3"1 and 5-1 . Then back propagation multiLayer neural network used for positive and negative parity states for 114Te and shown their membership to the Vibration limit U(5) the network implemented by MATLAB system.

Breast cancer (BC) is the most prevalent tract cancer in the world, including Iraq. The classified breast tumors to benign, malignant, and radiotherapy. Cancer treatment depends on certain stages such as mastectomy then chemotherapy alone or with radiation therapy or endocrine therapy according to the prognostic features obtained from the pathology report. The present study included 100 females. The women were split into two groups, control group that consisted of 50 apparently healthy females and 50 patients with BC group who undergo the radiotherapy. The current study highlighted on some of the anthropometric measurements, including the oxidative stress index malondialdehyde (MDA), the concentrations of total antioxidant capacity (TAC), s

Breast cancer (BC) is the most prevalent tract cancer in the world, including Iraq. The classified breast tumors to benign, malignant, and radiotherapy. Cancer treatment depends on certain stages such as mastectomy then chemotherapy alone or with radiation therapy or endocrine therapy according to the prognostic features obtained from the pathology report. The present study included 100 females. The women were split into two groups, control group that consisted of 50 apparently healthy females and 50 patients with BC group who undergo the radiotherapy. The current study highlighted on some of the anthropometric measurements, including the oxidative stress index malondialdehyde (MDA), the concentrations of total antioxidant capacity (TAC), s

Background: Breast cancer is the most common cancer in Iraq and the United Kingdom. While the disease is frequently diagnosed among middleaged Iraqi women at advanced stages accounting for the second cause of cancer-related deaths, breast cancer often affects elderly British women yielding the highest survival of all registered malignancies in the UK. Objective: To compare the clinical and pathological profiles of breast cancer among Iraqi and British women; correlating age at diagnosis with the tumor characteristics, receptor-defined biomarkers and phenotype patterns. Methods: This comparative retrospective study included the clinical and pathological characteristics of (1,940) consecutive female patients who were diagnosed with invasive b

The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

In the literature, several correlations have been proposed for bubble size prediction in bubble columns. However these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 % and correlation coefficient of 92.2%. A