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Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
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Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with,  an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.

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Publication Date
Mon Sep 30 2024
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
The effect of hydrothermal conditions on surface properties of synthesized nano SBA-15 using sodium silicate
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A successful synthesis of mesoporous Santa Barbara amorphous (SBA-15) with nanoparticle size was attempted using prepared sodium silicate from Iraqi high silica sand. EO20PO70EO20 copolymer was used as a template at highly acidic conditions (pH < 2). The effect of crystallization temperature of (100, 110, 120, and 130 0C), and crystallization time of (24, 48, 72, and 120 h) on surface properties was studied. The experiments were characterized using XRD, FTIR, AFM, BET, and FESEM. The XRD and FTIR tests represent amorphous SBA-15 without any impurities. Decreasing average particle size distribution increased the surface area, and decreased the porosity (pore volume, and pore size) in all experiments. The texture properties were su

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Publication Date
Thu Nov 08 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
The effect of disinfectants on the surface quality of irreversible hydrocolloid impression material and gypsum cast
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Objective: To evaluate the changes in the surface quality of irreversible hydrocolloid impression material
hydrogum following disinfection with 0.525% sodium hypochlorite, 0.2% Chlorehexidine Gluconate, and 4%
Povidone Iodine.
Methodology: Forty specimens of alginate impression materials hydrogum were fabricated according to the
ISO 1563 and were divided into four groups according to the method of solution dipping: group 1: Dip in
0.525% sodium hypochlorite, group 2: Dip in 0.2% chlorhexidine gluconate, Group 3: Dip in 4% Povidone Iodine,
Group 4: No treatment with any solution (control group). Then the specimens were poured in type II stone.
Surface detail was determined using a stainless steel block in accordance w

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Publication Date
Tue Aug 01 2023
Journal Name
Journal Of Ecological Engineering
Optimization of Response Surface Methodology for Removal of Cadmium Ions from Wastewater using Low Cost Materials
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Publication Date
Thu Oct 03 2024
Journal Name
Dentistry 3000
Antimicrobial Efficiency of Hypochlorous Acid Incorporation and its Effect on Surface Properties of Irreversible Hydrocolloid Materials
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Objective: Conventional approaches for disinfection, including spraying and immersion, resulted in only surface disinfection of impressions. As a result, self-disinfecting impression materials incorporated with antimicrobial compounds require more extensive studies. The incorporation of a disinfectant into irreversible hydrocolloid impression materials could eliminate the need for the disinfection step by conventional approaches, including spraying and immersion which only result in surface disinfection of impressions. The study was aimed to investigate the effect of incorporation of hypochlorous acid in irreversible hydrocolloid materials on antimicrobial efficiency, detail reproduction, and dimensional stability. Materials and

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Hydraulic Flow Units for Jeribe Reservoir in Jambour Oil Field Applying Flow Zone Indicator Method
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The Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units.    This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized poros

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Publication Date
Sat Aug 04 2012
Journal Name
University Of Thi-qar Journal
Prediction of Ultimate Soil Bearing Capacity for Shallow Strip Foundation on Sandy Soils by Using (ANN) Techniqu
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Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Hydraulic Flow Units for Jeribe Reservoir in Jambour Oil Field Applying Flow Zone Indicator Method
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The Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units.

   This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized por

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Publication Date
Mon Aug 18 2025
Journal Name
Soil And Sediment Contamination: An International Journal
Remediation Prediction of Contaminated Soil with Crude Oil Using the Optimized Remediation Method for the Iraqi Environment
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Large quantities of petroleum-contaminated soil are generated with increased global energy consumption and crude oil production. This theoretical study evaluates the treatment of 1 ton of petroleum-contaminated soil using seven methods: incineration, physical washing, chemical washing, thermal pyrolysis, Fenton-oxidation-pyrolysis, the biological treatment, and asphaltenes. Data were based on experimental results from the Nahran Bin Omar oil lake in Basra Governorate, Iraq, (2019–2021). The methods were compared by waste generation, treatment cost, and duration. Results indicate that using petroleum-contaminated soil as a raw material for asphalt manufacturing is most beneficial since it is sold as a raw material. Incineration is faster a

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Publication Date
Wed May 01 2024
Journal Name
Baghdad Science Journal
Early Prediction of Nephropathy in Iraqi Patient with Diabetes Type II by Evaluating Some Relevant Biochemical Factors
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داء السكري من النوع 2 (T2DM) هو مرض معقد يصيب العديد من الأعضاء إلى جانب البنكرياس مثل الكلى والكبد والدماغ والعين. بسبب ارتفاع السكر في الدم لفترات طويلة وعدم السيطرة على مرض السكري مع وجود عوامل خطر أخرى ، يمكن أن تحدث مضاعفات مرض السكري. تشمل مضاعفات مرض السكري مضاعفات الأوعية الدموية الدقيقة والأوعية الدموية الكبيرة التي تستهدف الكلى. تهدف الدراسة الحالية إلى التحقق من مستوى الانجوتينسن المحول للانزيم  A

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Publication Date
Thu May 01 2025
Journal Name
2025 3rd International Conference On Business Analytics For Technology And Security (icbats)
Comparison of Deep Neural Network Models (LSTM, Bi-LSTM, GRU and Bi-GRU) for Gold Price Prediction
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This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

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