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FINITE ELEMENT ANALYSIS OF HUMAN AND ARTIFICIAL ARTICULAR CARTILAGE
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Joint diseases, such as osteoarthritis, induce pain and loss of mobility to millions of people around the world. Current clinical methods for the diagnosis of osteoarthritis include X-ray, magnetic resonance imaging, and arthroscopy. These methods may be insensitive to the earliest signs of osteoarthritis. This study investigates a new procedure that was developed and validated numerically for use in the evaluation of cartilage quality. This finite element model of the human articular cartilage could be helpful in providing insight into mechanisms of injury, effects of treatment, and the role of mechanical factors in degenerative
conditions, this three-dimensional finite element model is a useful tool for understanding of the stress distributions within articular cartilage in response to external loads and investigating both the prevention of injury and the pathological degeneration of the joints.
In this study, 21 models were analysed by using ANSYS workbench v12.1: four normal articular cartilage models (distal femur, patella, medial and lateral tibia). A redesign to the distal femur model was done to get osteoarthritis articular cartilage (simple and deep) seven models by making partial cut without affecting the subchondral bone, and full cut with part of the subchondral bone in different diameters. Finally a treatment done by replacing the defective parts with artificial articular cartilages with different types of treatment. The finite element analysis studied depending on a Von Mises criteria and total deformation in different activities. The results shows that Autologous Chondrocyte Implementation is the best treatment way and it is close by 87.50% to normal cartilage. This procedure can be used as a diagnostic procedure for osteoarthritic patients and to choose the best treatment options.

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Publication Date
Fri Jan 31 2025
Journal Name
Aip Conference Proceedings
Classification of oral cavity cancer using linear discriminant analysis (LDA) and principal component analysis (PCA)
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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
Thu Mar 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
An Artificial Intelligence Algorithm to Optimize the Classification of the Hepatitis Type
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Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the

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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Thu Mar 21 2019
Journal Name
J. Eng. Appl. Sci
Developing an Arabic handwritten recognition system by means of artificial neural network
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The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single l

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Publication Date
Sun Oct 19 2025
Journal Name
Studies In Systems, Decision And Control
The Role of Artificial Intelligence in Achieving Tax Compliance: Evidence from Iraq
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This study focuses on how tax administrations in Iraq use Artificial Intelligence (AI) techniques to monitor tax evasion for individuals and companies to achieve Tax Compliance (TC). AI was measured through four dimensions: Advanced Data Analytics Techniques (ADAT), Explainable AI (EAI), Machine learning (ML), and Robotic Process Automation (RPA). At the same time, TC was measured through registration, accounting, and tax payment stages. We relied on the questionnaire form to measure the variables. A sample of employees in the General Tax Authority in Iraq was selected, and a questionnaire was distributed to 132 people. The results indicated that the dimensions of AI affect achieving TC at all stages. This study provides evidence of using A

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Publication Date
Tue Jun 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
The Impact of Merger strategy on human resources management practices
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The current research aims to test the impact of the strategy of merger (as an explanatory variable) in human resources management practices (as a response variable), and the importance of the subject being an important topic that mimics the Iraqi environment, where has seen many mergers that have not been addressed by former researchers in the field. In addition, the future prospects carry many mergers, and the problem of research was the lack of understanding among departments in how to manage the integration and deal with it, on the basis of scientific which reflected negatively on the practices of human resources management, and the research was based on two main hypotheses Six sub-hypotheses emerge to explore the correlation

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Publication Date
Sun Jun 01 2025
Journal Name
Medical Journal Of Babylon
Detection of Human Parvovirus (B19) in Beta Thalassemia Major Patients
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Abstract<sec> <title>Background:

Beta-thalassemia major (β-TM) is inheritable condition with many complications especially in children. The blood-borne viral infection was proposed as a risk factor due to recurrent blood transfusion regimen (hemotherapy).

Objectives:

This study aimed to investigate Human parvovirus B19 (PVB19) prevalence in β-TM patients by serological and molecular means.

Materials and Methods:

This is a cross-section

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Publication Date
Fri Jan 01 2021
Journal Name
Review Of International Geographical Education Online
Role of ERP Systems in Improving Human Resources Management Processes
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This study investigates the role of Enterprise Resources Planning (ERP) systems in improving human resources management (HRM) processes. The rapid environmental changes led to increased demand on the ERP systems, which have changed the manual effort to technology-based processes, providing solutions focusing on the integration of all departments to achieve goals for the entire organization. HRM processes are mainly made up of two classes: strategic and operational HRM. An ERP system works to integrate both of them, making HRM processes more efficient, effective and feasible to provide support to the organization as a whole (inside and outside). In this article, a modest framework is proposed to describe HRM process integrity in relation to

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Publication Date
Mon Jan 01 2024
Journal Name
Iraqi Journal Of Hematology
Detection of active human cytomegalovirus in patients with multiple myeloma
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Abstract:<sec><title>BACKGROUND:

Human cytomegalovirus (HCMV) infection is ubiquitous and successfully reactivated in patients with immune dysfunction as in patient with multiple myeloma (MM), causing a wide range of life-threatening diseases. Early detection of HCMV and significant advances in MM management has amended patient outcomes and prolonged survival rates.

OBJECTIVES:

The aim of the study was to estimate the frequency of active HCMV in MM patients.

MATERIALS AND METHODS:

This is a case–control study involved 50 MM patients attending Hematology Center, Bag

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