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Fatigue Characterization for Composite Materials used in Artificial Socket Prostheses with the Adding of Nanoparticles
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Abstract<p>The prostheses sockets use normally composite materials which means that their applications may be related with the human body. Therefore, it was very necessary to improve the mechanical properties of these materials. The prosthetic sockets are subjected to varying stresses in gait cycle scenario which may cause a fatigue damage. Therefore, it is necessary or this work to modify the fatigue behavior of the materials used for manufacturing the prostheses sockets. In this work, different Nano particle materials are used to modify the mechanical properties of the composite materials, and increase the fatigue strength. By using an experimental technique, the effect of using different volume fractions for various types for Nano particle materials on the fatigue behavior for composite materials, and preparing the fatigue samples and tested using the fatigue apparatus. The Nano particles used were (Nano SiO<sub>2</sub> and Nano Al<sub>2</sub>O<sub>3</sub>) materials with volume fraction as (0% to 2%), for each type of Nano material used. The artificial neural network technique was adopted to have a verification for the experimental results and calculating the fatigue life and strength for composite materials, with the addition of nanoparticles and then, a comparison of the results was achieved. The comparison of the results indicate a maximum error between results calculated by two technique did not exceeded about (1%). Then, the results calculated showed that the mechanical properties and fatigue life and strength increase with reinforcement with Nano particle. Also, the results showed that the modified for fatigue limits with materials by (Nano SiO<sub>2</sub>) Nano particle was more than the modified for fatigue limits for materials reinforcement with other materials. Finally, it can be concluded that the modified for fatigue strength, by reinforcement with (Nano SiO<sub>2</sub>), leads to 60% more than fatigue limit without Nano additive.</p>
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Publication Date
Sun Nov 30 2025
Journal Name
بدبي اعمال وقائع المؤتمر الدولي الاول لعلوم المكتبات والمعلومات جامعة الوصل و مكتبة محمد بن راشد
Artificial Intelligence Skills of Information Institutions Workers: A Descriptive Study
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Objectives: This research aims to study the artificial intelligence (AI) skills re-quired by employees in information institutions, specifically university libraries in Iraq, to enhance their services and align with modern technological advancements. It highlights the gap between the current knowledge of employees in Al technologies and their practical applications to improve the services of information institutions. Methodology: The research adopted a descriptive survey method, targeting em- ployees in three prestigious university libraries in Baghdad: the Central Library of the University of Baghdad, the Central Library and House of Books of Al-Mustansiriyah University, and the Central Library of the Iraqi University. A sample of (160)

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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Publication Date
Thu Dec 19 2024
Journal Name
Baghdad Science Journal
Solution of Wave Equation by Linear Regression Artificial Neural Network
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تعتبر المعادلات التفاضلية الموجية من اهم المواضيع التي تمثل على سبيل المثال الحركة الموجية للاهتزازات الأرضية . ومن هنا فان ايجاد  حلول تقريبيه لمثل هذه المعادلات بدقة وسرعه عالية وبشكل اسرع من الحلول التحليلية والمعقدة , اصبح ممكنا من خلال استخدام الذكاء الاصطناعي واساليب  التعلم  الالي. في هذا البحث هناك ثلاثة أهداف الأول هو تحويل مشكلة القيمة الأولية للمعادلة الموجية إلى شكلها القانوني وإيجاد حلها ا

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Publication Date
Sun May 01 2022
Journal Name
International Journal Of Multiphase Flow
Application of artificial neural network to predict slug liquid holdup
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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Optimized Artificial Neural network models to time series
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        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

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Publication Date
Tue Sep 30 2025
Journal Name
المجلة العراقية للعلوم السياسية
Spy vs.AI How Artificial Intelligence Will Remake Espionage
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Publication Date
Sun Jan 01 2023
Journal Name
Materials Today: Proceedings
Synthesis and characterization of some mixed ligands complexes of β-enaminone with some metal ions
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Publication Date
Sun Jan 01 2012
Journal Name
Al-mustansiriya J. College Of Education
Synthesis and characterization of mixed ligand complexes of some metals with ( L- phenylalanine and nicotinamide)
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This paper presents the synthesis and study of some new mixed-liagnd complexes containing nicotinamide(C6H7N2O) symbolized (NA) and phenylalanine (C9H11NO2)symbolized (pheH)] with some metal ions. The resulting products were found to be solid crystalline complexes which have been characterized by :Melting points, Solubility, Molar conductivity. determination the percentage of the metal in the complexes by flame(AAS), magnetic susceptipibility, Spectroscopic Method [FT-IR and UV-Vis]. The proposed structure of the complexes using program , chem office 3D(2006) . The general formula have been given for the prepared complexes :[M(NA)2(phe)]cl M(II): Mn(II) ,Co(II) , Ni(II) , Cu(II) , Zn(II) , Cd(II) & Hg(II) . NA = Nicotinamide= C6H7N2O Phe -

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Publication Date
Tue Aug 02 2011
Journal Name
J. College Of Education / Al-mustansiriya University
Synthesis and characterization of mixed ligand complexes of some metals with ( L- phenylalanine and nicotinamide)
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This paper presents the synthesis and study of some new mixed-ligand complexes containing nicotinamide(C6H7N2O) symbolized (NA) and phenylalanine (C9H11NO2)symbolized (pheH)] with some metal ions. The resulting products were found to be solid crystalline complexes which have been characterized by :Melting points, Solubility, Molar conductivity. determination the percentage of the metal in the complexes by flame(AAS), magnetic susceptipibility, Spectroscopic Method [FT-IR and UV-Vis]. The proposed structure of the complexes using program , chem office 3D(2006) . The general formula have been given for the prepared complexes : [M(NA)2(phe)]cl M(II): Mn(II) ,Co(II) , Ni(II) , Cu(II) , Zn(II) , Cd(II) & Hg(II)). NA = Nicotinamide= C6

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Publication Date
Tue Mar 01 2022
Journal Name
Journal Of Hydrology
Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: Comparative study and insight for federated learning technology
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