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Volume 3, Issue 3 (Fall 2019)
Abstract
In this study, the use of a mixed alumina and aluminum sulfate powder has been studied on thermal conductivity of butyl rubber filled with carbon black used as curing tire bladder composite. The aforementioned filler was added to 1.5 parts by weight in a blend of Bladder. The mixtures were prepared in the internal mixer and the curing characteristics, the mechanical and aging properties as well as the heat conductivity behavior of the composites were measured. To determine the coefficient of thermal diffusion of rubber composite, an immersion sampling method with specific dimensions in the oil bath and heat transfer computer simulation was used using a guessing and error approach. It was observed that the thermal diffusion coefficient of the above mixture rises from an average of 1×10-7 m2/s to an average of 1.3 ×10-7 m2/s without changing the mechanical and aging properties of the mixture. In the following, by choosing a simplified geometry from the tire profile in the near-tire curing conditions, and by simulating heat transfer behavior through the ABAQUS software, the effect of this increase on the thermal diffusivity coefficient was studied on the temperature variations of the inner parts of the tire. It was observed that the temperature of the different points of tire is affected by increasing the thermal conductivity of the tire, Therefore, there is a good potential for reducing the curing time of the tire.
Volume 9, Issue 2 (Spring 2018)
Abstract
Aims: One of the most important areas in medical research is the identification of disease-causing genes, which helps the identification of mechanisms underlying disease and as a result helps the early diagnosis of disease and the better treatment. In recent years, microarray technology has assisted biologists to gain a better understanding of cellular processes. To this end, the application of efficient methods in microarray data analysis is very important. The aim of this study was the introduction of GRAP Gene as Alzheimer’s disease candidate gene using microarray data analysis.
Materials and Methods: In the present bioinformatic study, which was conducted on an Alzheimer's microarray data set containing 12990 genes, 15 patients, and 16 healthy subjects, by combining Fisher, Significance Analysis of Microarray (SAM), and Particle Swarm Optimization (PSO) methods as well as Classification and Regression Tree (CART), a new method was presented for analyzing microarray gene expression data to identify genes involved in Alzheimer's incidence.
Findings: The accuracy level of the proposed method was 90.32% and the interpretation of the results from a biological point of view indicated that the proposed method has worked well; finally, the proposed method introduced 4 genes, of which, until now, 3 genes (75%) have been reported in biological studies as genes that cause Alzheimer’s disease.
Conclusion: In addition to proposing a new feature selection method for the analysis of microarray data, this study has introduced a new gene (GRAP) as a candidate gene related to Alzheimer’s disease.