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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models
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Publication Date
Sun May 26 2019
Journal Name
Iraqi Journal Of Science
Environmental Geochemical Assessment of Heavy Metals in Soil and Sediment of (Shatt-Al-Hilla) Babil Governorate, Central Iraq
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Heavy metals concentration in the soils and sediments has increased worldwide during the last century as a result of the rapid increase in population which combined by an increase in human activity as agriculture,  industrial and many other activities. Ten soil and three river sediment samples were collected from 10 main sampling stations at Shatt Al-Hilla River from Sada area to Dora Bridge in Babylon province. The chemical analysis of the sedimentation sample in the laboratory included pH calculation, electrical conductivity (EC) (Cation Exchange Capacity (CEC), organic matter (OM), and heavy metals as (Mn, Ni, Cr, Zn, Cu, Co, Pb, Cd, As and Fe). Indirect geochemical background (IGB) of heavy metal was calculated by the iterative

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Publication Date
Fri Nov 29 2019
Journal Name
Iraqi Journal Of Physics
Synthesis and Characterization of P3HT Metal Nanoparticles Hybrid Junction
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In this work the fabrication and characterization of poly(3-hexylthiophene) P3HT-metallic nanoparticles (Ag, Al). Pulsed Laser Ablation (PLA) technique was used to synthesis the nanoparticles in liquid. The Fourier Transformer Infrared (FTIR) for all samples indicate the chemical interaction between the polymer and the nanoparticles. Scanning Electron Microscopic (SEM) analysis showed the particle size for P3HT-AgNps samples between 44.50 nanometers as well the spherical structure. While for P3HT-AlNps samples was flakes shape. Energy Dispersive X-ray (EDX) spectra show the existing of amount of metallic nanoparticles.

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Publication Date
Sun Jan 01 2023
Journal Name
Computers, Materials & Continua
Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems
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Publication Date
Sun Dec 01 2019
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment And Total Dissolved Solids Prediction For Tigris River In Baghdad City Using Mathematical Models
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Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

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Publication Date
Sun Aug 06 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Effects of Rustamiyah treatment plant effluent on concentration of some heavy metals in water and sediment of Diyala river
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      Concentrations  of  four  heavy  metals, Zinc , Copper , Lead  and  Cadimium  were  determed  in  water  and  sediment  samples   collected bi-weekly  from  six  sampling sites  on  the  lower  part  of  River  Diyala    during  low  flow  period  (August  to  October)  and   high  flow  period  (April  to  June). A  reference  point site (1)  was  situated   upstream the  effects  of  the  effluent. Present  work  describes  the  e

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Publication Date
Mon Jan 13 2020
Journal Name
International Journal Of Productivity And Performance Management
An examination of talent management processes in Australian higher education
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Purpose

The purpose of this paper is to understand the best processes that are currently used in managing talent in Australian higher education (HE) and to examine the policies in terms of talent management processes (TMPs) that are derived from objective one. Pragmatic benefits for academic institutions focused on enhancing talent.

Design/methodology/approach

This study selects the mixed method as its research design. In the qualitative study, there were three methods: brainstorming, focus group and individual interviews, followed by the quantitative questionnaire

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Publication Date
Thu Oct 13 2022
Journal Name
Computation
A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq
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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Engineering
Water Quality Assessment and Total Dissolved Solids Prediction using Artificial Neural Network in Al-Hawizeh Marsh South of Iraq
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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 develope

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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Engineering/
Water quality assessment and total dissolved solids prediction using artificial neural network in Al-Hawizeh marsh south of Iraq
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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

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Publication Date
Sun Dec 17 2017
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
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In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as  a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid

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