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.

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

    Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network.  The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model  and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Je

In this study NiO - CoO bimetallic catalysts are prepared with two Ni/Co ratios (70:30 and 80: 20) using the precipitation method of nitrate salts. The effects of Ni /Co ratio and preparation methods on the catalyst are analyzed by using different characterization techniques, i.e. atomic absorption (AA) , XRD, surface area and pore volume measurements according to the BET method . The results indicate that the best catalyst is the one containing the percentage of Ni :Co ( 70 : 30 ). Experiments indicate that the optimal conditions to prepare catalyst are stirring for three hours at a temperature of 60oC of the preparation , pH= (8-9) , calcination temperature at 400oC for two hours

The problem of internal sulfate attack in concrete is widespread in Iraq and neighboring countries.This is because of the high sulfate content usually present in sand and gravel used in it. In the present study the total effective sulfate in concrete was used to calculate the optimum SO3 content. Regression models were developed based on linear regression analysis to predict the optimum SO3 content usually referred as (O.G.C) in concrete. The data is separated to 155 for the development of the models and 37 for checking the models. Eight models were built for 28-days age. Then a late age (greater than 28-days) model was developed based on the predicted optimum SO3 content of 28-days and late age. Eight developed models were built for all

According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through

This research discusses application Artificial Neural Network (ANN) and Geographical InformationSystem (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameterswere used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium,Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity.These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala provincewhile it is poor to very polluted at the south of Baghdad City. The selected parameters wer

The removal of turbidity from produced water by chemical coagulation/flocculation method using locally available coagulants was investigated. Aluminum sulfate (alum) is selected as a primary coagulant, while calcium hydroxide (lime) is used as a coagulant aid. The performance of these coagulants was studied through jar test by comparing turbidity removal at different coagulant/ coagulants aid ratio, coagulant dose, water pH, and sedimentation time. In addition, an attempt has been made to examine the relationship between turbidity (NTU) and total suspended solids (mg/L) on the same samples of produced water. The best conditions for turbidity removal can be obtained at 75% alum+25% lime coagulant at coagulant dose of 80 m