The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The developed ANN mode gave a high correlation coefficient reaching 0.927 for the prediction of TDS from the model and showed high levels of TDS in Al-Hawizeh marsh that pose threats to people using the marsh for drinking and other uses. The dissolved Oxygen concentration has the highest importance of 100% in the model because the water of the marsh is fresh water, while Turbidity had the lowest importance.
In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
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The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be
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Objectives: To assess quality of health care for clients at outpatient consultancy clinics in Al-Hilla City Hospitals, and to find out significant differences between the clients' perspectives toward quality of health care dimensions and their demographic characteristics such as (residence, age, gender, level of education, and occupation ).
Methodology: A descriptive analytic study design was carried out at outpatient consultancy clinics of Al- Hilla city hospital (Al-Hilla and Al-Imam Al-Sadiq general teaching hospital) from April 10 th to June 15 th 2019. Non – probability (purposive) sample of 200 clients who were coming to the Outpatient Consultancy Clinics were selected. Data is collected through used of an assessment tools an
This study aimed to investigate the effect of total suspended solids (TSS) on the performance of a continuously operated dual-chamber microbial fuel cell (MFC) proceeded by primary clarifier to treat actual potato chips processing wastewater. The system was also tested in the absence of the primary clarifier and the results demonstrated a significant effect of TSS on the polarization curve of the MFC which was obtained by operating the graphite anodic electrode against Ag/AgCl reference electrode. The maximum observed power and current densities were decreased form 102.42 mW/m2 and 447.26 mA/m2 to 80.16 mW/m2 and 299.10 mA/m2, respectively due to the adverse effect of TSS. Also
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Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give
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