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Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks
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Publication Date
Tue Jun 01 2021
Journal Name
Iop Conf. Series: Materials Science And Engineering
Enhancing the mechanical properties of lightweight concrete using mono and hybrid fibers
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Abstract<p>This investigation aims to study some properties of lightweight aggregate concrete reinforced by mono or hybrid fibers of different sizes and types. In this research, the considered lightweight aggregate was Light Expanded Clay Aggregate while the adopted fibers included hooked, straight, polypropylene, and glass. Eleven lightweight concrete mixes were considered, These mixes comprised of; one plain concrete mix (without fibers), two reinforced concrete mixtures of mono fiber (hooked or straight fibers), six reinforced concrete mixtures of double hybrid fibers, and two reinforced concrete mixtures of triple hybrid fibers. Hardened concrete properties were investigated in this study. G</p> ... Show More
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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
Using Fuzzy Games Theory to Determine the optimal Strategy for The Mobile Phone Networks in The Baghdad And Basra governorates
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      The objective of this research is employ the special cases of  function  trapezoid in the composition of fuzzy sets to make decision within the framework of the theory of games traditional to determine the best strategy for the mobile phone networks in the province of  Baghdad and Basra, has been the adoption of different periods of the  functions belonging to see the change happening in the matrix matches and the impact  that the strategies  and decision-making  available to each player and the impact on  societ

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Publication Date
Sat Jun 30 2018
Journal Name
Proceedings Of The International Conference On Intelligent Science And Technology
The nature of intelligent system in buildings
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Publication Date
Fri Dec 30 2022
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review
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Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses

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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Petroleum Research And Studies
Modeling of Oil Viscosity for Southern Iraqi Reservoirs using Neural Network Method
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The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperatu

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Publication Date
Sun Sep 30 2012
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Development of PVT Correlation for Iraqi Crude Oils Using Artificial Neural Network
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Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

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Publication Date
Mon Sep 30 2013
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Optimal Design of Cylinderical Ectrode Using Neural Network Modeling for Electrochemical Finishing
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The finishing operation of the electrochemical finishing technology (ECF) for tube of steel was investigated In this study. Experimental procedures included qualitative
and quantitative analyses for surface roughness and material removal. Qualitative analyses utilized finishing optimization of a specific specimen in various design and operating conditions; value of gap from 0.2 to 10mm, flow rate of electrolytes from 5 to 15liter/min, finishing time from 1 to 4min and the applied voltage from 6 to 12v, to find out the value of surface roughness and material removal at each electrochemical state. From the measured material removal for each process state was used to verify the relationship with finishing time of work piece. Electrochemi

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Publication Date
Sun Jun 15 2025
Journal Name
Iraqi Journal Of Laser
Performance Enhancement of Metasurface Grating Polarizer Using Deep Learning for Quantum Key Distribution Systems
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Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

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Publication Date
Mon Jun 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Theoretical Study and calculation The cold Reaction Rate of Deuteron Fusion In Nickel Metal Using Bose–Einstein Condensate Theory
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In this paper, we focused on the investigated and studied the cold fusion reaction rate for D-D using the theory of Bose-Einstein condensation and depending on the quantum mechanics consideration. The quantum theory was based on the concept of single conventional of deuterons in Nickel-metal due to Bose-Einstein condensation, it has supplied a consistent description and explained of the experimental data. The analysis theory model has capable of explaining the physical behaviour of deuteron induced nuclear reactions in Nickel metals upon the five-star matter, it's the most expected for a quantitative predicted of the physical theory. Based on the Bose-Einstein condensation theorem formulation, we calculation the cold fusion reaction rate fo

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Animal Behaviour And Biometeorology
Optimization of some environmental and nutritional conditions using microtiter plate for Pseudomonas aeruginosa biofilm formation
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One of the most important virulence factors in Pseudomonas aeruginosa is biofilm formation, as it works as a barrier for entering antibiotics into the bacterial cell. Different environmental and nutritional conditions were used to optimize biofilm formation using microtitre plate assay by P. aeruginosa. The low nutrient level of the medium represented by tryptic soy broth (TSB) was better in biofilm formation than the high nutrient level of the medium with Luria Broth (LB). The optimized condition for biofilm production at room temperature (25 °C) is better than at host temperature (37 °C). Moreover, the staining with 0.1% crystal violet and reading the biofilm with wavelength 360 are considered essential factors in

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