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Testing and characterization of sintered β-tricalcium phosphate coat upon zirconia dental implant using Nd:YAG laser
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This study aims to fabricate and assess the β-tricalcium phosphate (β-TCP) bioactive ceramic coat layer on bioinert ceramic zirconia implants through the direct laser melting technique by applying a long-pulsed Nd:YAG laser of 1064 nm. Surface morphologies, adherence, and structural change in the coatings were evaluated by optical microscopy, field emission scanning electron microscope, hardness, and x-ray diffractometer. The elastic modulus (EM) of the coating was also determined using the nanoindentation test. The quality of the coating was improved when the laser power was 90 W with a decrease in the scan speed to 4 mm s−1. The chemical composition of the coat was maintained after laser processing; also, the Energy Dispersive X-ray maps showed a good distribution of Ca and P particles with some agglomeration on the surface. The crystalline nature of the β-TCPs coat can be concluded from the sharp peaks in the x-ray diffraction patterns. EM was low near the top surface of the coat and increased gradually with the depth. The microhardness value of a coated substrate was lower than the hardness value of a control substrate. Unlike conventional deposition techniques, laser processes can be used to build a coat with optimum bonding and desirable mechanical properties, indicating that processing and coating seem to be attractive for bioinert ceramic zirconia implants.

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Publication Date
Fri Mar 01 2013
Journal Name
Journal Of Engineering
Design and Implementation of a Generalized N-Digit Binary-To-Decimal Converter on an FPGA Seven-Segment Display Using Verilog Hdl Design and Implementation of a Generalized N-Digit Binary-To-Decimal Converter on an FPGA Seven-Segment Display Using Verilog Hdl
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It is often needed to have circuits that can display the decimal representation of a binary number and specifically in this paper on a 7-segment display. In this paper a circuit that can display the decimal equivalent of an n-bit binary number is designed and it’s behavior is described using Verilog Hardware Descriptive Language (HDL). This HDL program is then used to configure an FPGA to implement the designed circuit.

Publication Date
Fri Dec 01 2023
Journal Name
Chemical Methodologies
Investigations on TiO<inf>2</inf>-NiO@In<inf>2</inf>O<inf>3</inf> Nanocomposite Thin Films (NCTFs) for Gas Sensing: Synthesis, Physical Characterization, and Detection of NO<inf>2</inf> and H<inf>2</inf>S Gas Sensors
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Scopus
Publication Date
Tue Dec 09 2025
Journal Name
Journal Of Computer-aided Molecular Design
Synthesis, characterization and density functional theory of a novel dichloro(2-(1-anthracene-9-ylmethyl)-1H-1,2,3-triazole-5-yl) pyridine)Cu(II) and polymeric dichloro(2-(1-anthracene-9-ylmethyl)-1H-1,2,3 -triazole-5-yl)pyridine) Cd(II) complexes
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Publication Date
Sun Feb 10 2019
Journal Name
Iraqi Journal Of Physics
Wear and mechanical prop erties of epoxy/MgO-SiO2 hybrid nanocomposites
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Preparation of epoxy/MgO and epoxy/SiO2 nanocomposites is
studding. The nano composites were processed by different nano
fillers concentrations (0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.07 and
0.1 wt%). Epoxy resin and nanocomposites containing different
shape nano fillers of (MgO:SiO2 composites), are shear mixing with
ratio 1:1,with different nano hybrid fillers concentrations (0.025,
0.05, 0.1, 0.15, 0.2 and 0.25 wt%) to preparation of epoxy/(MgOSiO2)
hybrid nanocomposites. Experimental tests results indicate that
the composite materials have significantly higher modulus of
elasticity than the matrix material but the hybrid nanocomposites
have lower modulus of elasticity. The wear rate was decreased in
nanoc

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Crossref
Publication Date
Tue Dec 27 2022
Journal Name
2022 3rd Information Technology To Enhance E-learning And Other Application (it-ela)
Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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Crossref (5)
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Publication Date
Sat Feb 09 2019
Journal Name
Journal Of The College Of Education For Women
Shadow Removal Using Segmentation Method
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Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
strong criminal capabilities، Using simulation .
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The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

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Crossref
Publication Date
Mon Dec 31 2012
Journal Name
Al-khwarizmi Engineering Journal
Speech Compression Using Multecirculerletet Transform
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Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp

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Publication Date
Sun Jan 01 2023
Journal Name
Ssrn Electronic Journal
Sustainable ENERGY by using AI
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As we live in the era of the fourth technological revolution, it has become necessary to use artificial intelligence to generate electric power through sustainable solar energy, especially in Iraq and what it has gone through in terms of crises and what it suffers from a severe shortage of electric power because of the wars and calamities it went through. During that period of time, its impact is still evident in all aspects of daily life experienced by Iraqis because of the remnants of wars, siege, terrorism, wrong policies ruling before and later, regional interventions and their consequences, such as the destruction of electric power stations and the population increase, which must be followed by an increase in electric power stations,

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Crossref
Publication Date
Sat Mar 10 2012
Journal Name
الدنانير
Cryptography Using Artificial Neural Network
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Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

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