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Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects
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Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was (26.24%), and (5.5%), and AA was (74%), and (94.5%), for cost and time model, respectively. The researcher concluded that the ANN model has a strong correlation and high accuracy, indicating that these models are characterized by high efficiency and good performance in predicting cost and time.

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Publication Date
Thu Jun 08 2023
Journal Name
Iraqi Journal Of Laser
PDF Lab-On-a-Chip an integrated microfluidic device sensitive low-Cost, and Rapid with a syringe pump for Analysis of Ibuprofen
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Abstract: Microfluidic devices present unique advantages for the development of efficient drug assay and screening. The microfluidic platforms might offer a more rapid and cost-effective alternative. Fluids are confined in devices that have a significant dimension on the micrometer scale. Due to this extreme confinement, the volumes used for drug assays are tiny (milliliters to femtoliters).

 In this research, a microfluidic chip consists of micro-channels carved on substrate materials built by using Acrylic (Polymethyl Methacrylate, PMMA) chip was designed using a Carbon Dioxide (CO2) laser machine. The CO2 parameters have influence on the width, depth, roughness of the chip. In order to have regular

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Publication Date
Wed Dec 01 2021
Journal Name
Computers & Electrical Engineering
Utilizing different types of deep learning models for classification of series arc in photovoltaics systems
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Publication Date
Fri Jul 19 2019
Journal Name
Communications Chemistry
Positive functional synergy of structurally integrated artificial protein dimers assembled by Click chemistry
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Abstract<p>Construction of artificial higher order protein complexes allows sampling of structural architectures and functional features not accessible by classical monomeric proteins. Here, we combine in silico modelling with expanded genetic code facilitated strain promoted azide-alkyne cycloaddition to construct artificial complexes that are structurally integrated protein dimers and demonstrate functional synergy. Using fluorescent proteins sfGFP and Venus as models, homodimers and heterodimers are constructed that switched ON once assembled and display enhanced spectral properties. Symmetrical crosslinks are found to be important for functional enhancement. The determined molecular structure of one artific</p> ... Show More
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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Taibah University Medical Sciences
Preparing polycaprolactone scaffolds using electrospinning technique for construction of artificial periodontal ligament tissue
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Objectives The strategies of tissue-engineering led to the development of living cell-based therapies to repair lost or damaged tissues, including periodontal ligament and to construct biohybrid implant. This work aimed to isolate human periodontal ligament stem cells (hPDLSCs) and implant them on fabricated polycaprolactone (PCL) for the regeneration of natural periodontal ligament (PDL) tissues. Methods hPDLSCs were harvested from extracted human premolars, cultured, and expanded to obtain PDL cells. A PDL-specific marker (periostin) was detected using an immunofluorescent assay. Electrospinning was applied to fabricate PCL at three concentrations (13%, 16%, and 20% weight/volume) in two forms, which were examined through field emission

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Publication Date
Tue Aug 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Estimate Raw Water Salinity for the Tigris River for a Long Time Using a Mathematical Model
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Abstract<p>The measurement data of the raw water quality of Tigris River were statistically analyzed to measure the salinity value in relation to the selected raw water quality parameters. The analyzed data were collected from five water treatment plants (WTPs) assembled alongside of the Tigris River in Baghdad: Al-Karkh, Al-Karama, Al-Qadisiya, Al-Dora, and Al-Wihda for the period from 2015 to 2021. The selected parameters are total dissolved solid (TDS), electrical conductivity (EC), pH and temperature. The main objective of this research is to predicate a mathematical model using SPSS software to calculate the value of salinity along the river, in addition, the effect of electrical conductivi</p> ... Show More
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Publication Date
Thu Jun 27 2019
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
RECORD OF GYRODACTYLUS BYCHOWSKIANUS BOGOLEPOVA, 1950 (MONOGENEA, GYRODACTYLIDAE) FOR THE FIRST TIME IN IRAQ FROM GILLS OF THE CYPRINID FISH ARABIBARBUS GRYBUS
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The monogenean Gyrodactylus bychowskianus Bogolepova, 1950 is recorded in the present study for the first time in Iraq from the gills of the cyprinid fish Arabibarbus grypus (Heckel, 1843); which was collected from the Tigris River at Al-Taji Beach north of Baghdad Province during the period from July until November 2018.

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Publication Date
Tue Dec 01 2015
Journal Name
The Journal Of The Acoustical Society Of America
Underdetermined reverberant acoustic source separation using weighted full-rank nonnegative tensor models
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In this paper, a fusion of K models of full-rank weighted nonnegative tensor factor two-dimensional deconvolution (K-wNTF2D) is proposed to separate the acoustic sources that have been mixed in an underdetermined reverberant environment. The model is adapted in an unsupervised manner under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. The derivation of the algorithm and the development of proposed full-rank K-wNTF2D will be shown. The algorithm also encodes a set of variable sparsity parameters derived from Gibbs distribution into the K-wNTF2D model. This optimizes each sub-model in K-wNTF2D with the required sparsity to model the time-varying variances of the sources in the s

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
PDF Comparison based on Various FSO Channel Models under Different Atmospheric Turbulence
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Recently, wireless communication environments with high speeds and low complexity have become increasingly essential. Free-space optics (FSO) has emerged as a promising solution for providing direct connections between devices in such high-spectrum wireless setups. However, FSO communications are susceptible to weather-induced signal fluctuations, leading to fading and signal weakness at the receiver. To mitigate the effects of these challenges, several mathematical models have been proposed to describe the transition from weak to strong atmospheric turbulence, including Rayleigh, lognormal, Málaga, Nakagami-m, K-distribution, Weibull, Negative-Exponential, Inverse-Gaussian, G-G, and Fisher-Snedecor F distributions. This paper extensive

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Engineering
Water Movement through Soil under Drip Irrigation using Different Hydraulic Soil Models
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Drip irrigation is one of the conservative irrigation techniques since it implies supplying water directly on the soil through the emitter; it can supply water and fertilizer directly into the root zone. An equation to estimate the wetted area in unsaturated soil is taking into calculating the water absorption by roots is simulated numerically using HYDRUS (2D/3D) software. In this paper, HYDRUS comprises analytical types of the estimate of different soil hydraulic properties. Used one soil type, sandy loam, with three types of crops; (corn, tomato, and sweet sorghum), different drip discharge, different initial soil moisture content was assumed, and different time durations. The relative error for the different hydrauli

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