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Prediction of Trips Attraction to the Central Business District of Al Nasiriyah City Utilizing an Artificial Neural Network Model
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Estimation of trip attraction and analyzing its main influencing factors are powerful for offering different classifications for business districts and presenting recommendations for improving attractiveness in long term. This is beneficial for designing transportation facilities and infrastructures. The paper presents the prediction of trip attraction using an artificial intelligence technology due to the profits that the technology can possess in shortening time, lowering expenses and saving effort. The new model has utilized six input parameters that have not been considered previously within the area of Nasiriyah city including; age and educational level of the passengers, mode of transport that the passengers use, purpose of the trip, frequency of the weekly visit, and the distance towards the central business district. In this study, the independences - trip attraction data of 224 sets are collected through field observations and home interviews within the area. Neural Network Toolbox in MATLAB is utilized, which is dealt with the six key independences as input whereas with the trip attraction as the output desired to be expected. The model has been generated by adoption of twenty-five artificial neurons in only one single hidden layer. The outcomes have showed a good performance in predicting the trip attraction by utilizing artificial neural network. The coefficient of correlation for training is 0.81445 and for all, including training, testing, and validation, it is 0.73825. The study produces a reliable model as an alternative to complex, high-priced and/or time-consuming models.

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Publication Date
Fri Feb 01 2019
Journal Name
Meta Gene
Cytokine gene variations and their impact on serum levels of IFN-γ, IL-2, IL-4, IL-10 and IL-12 among Iraqi Arabs
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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Kinetic, Isotherm, and Thermodynamic Study of Bismarck Brown Dye Adsorption onto Graphene Oxide and Graphene Oxide-Grafted-Poly (n-butyl methacrylate-co-methacrylic Acid)
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The adsorption behavior of Bismarck brown (BB) dye from aqueous solutions onto graphene oxide GO and graphene oxide-g-poly (n-butyl methacrylate-co-methacrylic acid) GO-g-pBCM as adsorbents was investigated. The prepared GO and GO-g-pBCM were characterized by Fourier transform infrared spectroscopy FTIR, which confirmed the compositions of the prepared adsorbents. Adsorption of BB dye onto GO and GO-g-pBCM was explored in a series of batch experiments under various conditions. The data were examined utilizing Langmuir and Freundlich isotherms. The Langmuir isotherm was seen as increasingly reasonable from the experimental information of dye on formulating adsorbents. Kinetic investigations showed that the experimental data were fitted ve

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Publication Date
Sat Mar 01 2025
Journal Name
Water, Air, & Soil Pollution
Adsorptive Performance of High Surface Area Date Palm Bunch Activated Carbon Towards Methyl Violet and Fuchsin Basic Dyes Removal: Thermochemical Activation And Statistical Optimization
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Herein, date palm (Phoenix dactylifera) bunch (DPB) waste was transformed into activated carbon (DPAC) adsorbent by using microwaveinduced ZnCl2 activation for 15 min at a power of 600 W. Several analytical methods were used to explain the physicochemical parameters of DPBAC including XRD, pHpzc, BET, SEM–EDX, and FTIR. Afterwards, the adsorptive performance of DPBAC was thoroughly investigated for the removal of two structurally different organic dyes namely methyl violet (MV) and fuchsin basic (FB). The key adsorption parameters, including the dose of DPBAC (A: 0.02–0.06 g), the solution pH (B: 4–10), and the contact time (C: 2–20 min) were statistically optimized using the Box-Behnken design with response surface methodology (RSM

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Publication Date
Wed Mar 05 2025
Journal Name
Water, Air, & Soil Pollution
Adsorptive Performance of High Surface Area Date Palm Bunch Activated Carbon Towards Methyl Violet and Fuchsin Basic Dyes Removal: Thermochemical Activation And Statistical Optimization
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Publication Date
Wed Jan 01 2020
Journal Name
Research Journal Of Pharmacy And Technology
Electro-polymerization of poly eugenol on ti and ti alloy dental implant treatment by micro arc oxidation using as anti-corrosion and anti-microbial
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In this work, electrochemical process was presented to polymerized eugenol on Gr.2 and Gr.5 titanium alloys before and after treated by Micro Arc Oxidation (MAO), where Gr.2 is commercial pure titanium and Gr.5 is Ti-6Al-4V dental alloys. The deposited layers were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The adhesion strength of polymeric thin-film was estimation by using pull-off adhesion test and the result was the adhesion strength of PE was (1.23 MPa) on Gr.2 before MAO and increase to (1.98 MPa) on Gr.2 after MAO treatment. The corrosion behavior of Gr.2 and Gr.5 alloy in artificial saliva environment at

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Publication Date
Wed Nov 04 2020
Journal Name
Journal Of Physical Organic Chemistry
Preparation, spectroscopic study of Schiff base derived from dopamine and metal Ni(II), Pd(II), and Pt(IV) complexes, and activity determination as antioxidants
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Abstract<p>A new Schiff base (HL<sub>2</sub>) ligand (4‐{2‐[(2‐hydroxy‐benzylidene)‐amino]‐ethyl}‐benzene‐1,2‐diol) has been synthesized by condensing of 4‐(2‐amino‐ethyl)‐benzene‐1,2‐diol and 2‐hydroxy‐benzaldehyde. In turn, its transition metal complexes were prepared, having the following general formulas: Ni(L<sub>2</sub>)<sub>2</sub>, Pd(L<sub>2</sub>)<sub>2</sub>, and Pt(L<sub>2</sub>)<sub>2</sub>2Cl. The prepared ligand and its metal complexes Ni(II), Pd(II), and Pt(IV) have been characterized by Fourier transform infrared (FTIR) spectra, proton nuclear magnetic resonance (<sup>1</sup>H‐NMR</p> ... Show More
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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Dielectric properties and A.C electrical conductivity analysis of (La<sub>2</sub>O<sub>3</sub>)<sub>1-x</sub>(ZnO)<sub>x</sub>
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Summery<p>The A.C conductivity of three samples of lanthanide oxide : zinc oxide (La<sub>2</sub>O<sub>3</sub>)<sub>1-x</sub>(ZnO)<sub>x</sub> pellets with different zinc oxide content which were sintered 1273 K temperatures were studied using LRC meters in the frequency range of 50–10<sup>6</sup> Hz at temperature of 30 °C. The a.c conductivity, was analyzed depending of the universal power law proposed by Jonsher,. The slope of the relation between logarithm of a.c conductivity and angular frequency represent the s value were in the range (0.44-0.77) which found to increase by increasing of zinc oxide content which coincided with the small po</p> ... Show More
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Publication Date
Thu Oct 03 2013
Journal Name
Heat Transfer-asian Research
CFD Simulation of Heat Transfer Augmentation in a Circular Tube Fitted with Alternative Axis Twisted Tape in Laminar Flow under a Constant Heat Flux
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Publication Date
Sat Jun 01 2024
Journal Name
Results In Materials
Fabrication and characterization of mesoporous calcium silicate and silver-incorporated mesoporous calcium silicate nanoparticles with low cytotoxicity and antibacterial properties as a dental biomaterial
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Publication Date
Sat Aug 09 2025
Journal Name
Scientific Reports
Machine learning models for predicting morphological traits and optimizing genotype and planting date in roselle (Hibiscus Sabdariffa L.)
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Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

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