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.
Semi-parametric regression models have been studied in a variety of applications and scientific fields due to their high flexibility in dealing with data that has problems, as they are characterized by the ease of interpretation of the parameter part while retaining the flexibility of the non-parametric part. The response variable or explanatory variables can have outliers, and the OLS approach have the sensitivity to outliers. To address this issue, robust (resistance) methods were used, which are less sensitive in the presence of outlier values in the data. This study aims to estimate the partial regression model using the robust estimation method with the wavel
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Blood 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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Some mechanical and thermal properties of mullite samples prepared by mixing different phases of alumina and silica powders have been studied according to ASTM methods the cold crushing strength of the sintcred bodies.With different porosity, at room temperature was in the range(18-54)Mpa
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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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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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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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