Background: Dimensional changes of acrylic denture bases after polymerization results in need for further adjustments or even ends with technical failure of the finished dentures. The purpose of this study was to estimate the linear dimensional changes for different palatal depths when using multiple investment materials and polymerization techniques. Materials and methods: Ninety upper complete denture bases were constructed for this study. They were divided into two main groups according to the polymerization methods: conventional water bath and experimental autoclave (short and long cycles). Each main group was further subdivided into three subgroups according to the palatal depth (shallow, medium and deep). Furthermore, for each palatal depth; complete denture bases were invested either with dental stone or laboratory silicone. For each upper complete denture, measurements of linear dimensional changes were done by fixation of metallic screws on the tissue surface of the denture base. The distances were measured by using travelling microscope with an accuracy of 0.001 %. The data were statistically analyzed using three way analysis of variance (ANOVA) for three variables, which were (palatal depth, investments and polymerization techniques), LSD test and student T test for comparisons between groups. Results: There were significant improvements in the dimensional accuracy of denture bases cured with autoclave compared with water bath. Also, silicone investments were a successful alternative to stone, study data shows that short autoclave processing with silicone reduces the magnitude of the linear dimensional changes. On the other hand, long autoclave processing and stone investments were better than silicone in reducing dimensional changes. Conclusion: The findings of this study indicates that the use of autoclave processing in acrylic curing is a promising alternative to the conventional water bath and leads to better dimensional stability for the finished dentures in all oral configurations and palatal depths. Also, Silicone is more preferable than stone, although it's not as strong investing materials when compared with stone.
The first thing that comes to mind is the highly important question of whether there were some effects of human behavior and its fluctuations on the theories of the efficient market and the contemporary investment portfolio. According to what has been said by the proponents of these two theories; when the optimal return is realized, the efficiency of the market is achieved in terms of perfect information on prices and risk that supposed to be predetermined in a rational way.
he other question that imposed here is “at what time people should be rational in their investments in the security markets ?”. This means that investors are rational for their efforts devoted to utility maximization, which are p
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Transforming the common normal distribution through the generated Kummer Beta model to the Kummer Beta Generalized Normal Distribution (KBGND) had been achieved. Then, estimating the distribution parameters and hazard function using the MLE method, and improving these estimations by employing the genetic algorithm. Simulation is used by assuming a number of models and different sample sizes. The main finding was that the common maximum likelihood (MLE) method is the best in estimating the parameters of the Kummer Beta Generalized Normal Distribution (KBGND) compared to the common maximum likelihood according to Mean Squares Error (MSE) and Mean squares Error Integral (IMSE) criteria in estimating the hazard function. While the pr
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In this investigation, metal matrix composites (MMCs) were manufactured by using powder technology. Aluminum 6061 is reinforced with two different ceramics particles (SiC and B4C) with different volume fractions as (3, 6, 9 and 12 wt. %). The most important applications of particulate reinforcement of aluminum matrix are: Pistons, Connecting rods etc. The specimens were prepared by using aluminum powder with 150 µm in particle size and SiC, B4C powder with 200 µm in particle size. The chosen powders were mixed by using planetary mixing setup at 250 rpm for 4hr.with zinc stearate as an activator material in steel ball milling. After mixing process the powders were compacted by hydraulic
... Show MoreBlood lipids are important mediators of host defense during the acute phase of innate immunity. Parasites may induce significant changes in lipid parameters, as has been shown in vitro study where substitution of serum by lipid/cholesterol in medium and in experimental models (in vivo). Thus changes in lipid profile occur in patients that having active infections with most of the parasites. Toxoplasma cannot synthesize cholesterol and depends upon acquisition of low density lipoprotein (LDL)-derived from the host cell, via endocytosis mediated by the LDL receptor or the LDL receptor-related protein.The present study is conducted to evaluate the changes in lipid profile in T. gondii infected women.A total of patients included 87 aborted wom
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This paper is concerned with the numerical solutions of the vorticity transport equation (VTE) in two-dimensional space with homogenous Dirichlet boundary conditions. Namely, for this problem, the Crank-Nicolson finite difference equation is derived. In addition, the consistency and stability of the Crank-Nicolson method are studied. Moreover, a numerical experiment is considered to study the convergence of the Crank-Nicolson scheme and to visualize the discrete graphs for the vorticity and stream functions. The analytical result shows that the proposed scheme is consistent, whereas the numerical results show that the solutions are stable with small space-steps and at any time levels.
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Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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