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One of the problems for use of rubber in various industries is the surface tension at the surface of rubbers, which results in the crack on the surface, fracture of the rubber and reduces its service life. These tensions are caused by contacting the rubber component with the metal surface and the friction between two surfaces. Roughness of the surface, the composition of the rubber compound, the environmental factors, test conditions and etc. affect the friction between rubbers – metal. Surface roughness plays an important role in sliding between two surfaces and mainly controls friction behavior. On the other hand, roughness effect on the coefficient of friction is controllable using a suitable lubricant. In the present study regarding to the application of JP4 as an aviation fuel, the effect of JP4 fuel as a lubricant was investigated in the reduction of the sliding friction coefficient between the NBR and aluminum surfaces with different roughness. Experimental studies showed, friction coefficient has a good correlation with the mean surface roughness (Ra). By applying JP4 lubricant, the coefficient of friction decreased by about 75%. JP4 fuel as lubricant has changed the trend of varying friction coefficient from decreasing to increasing regard to the surface roughness.

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The aims of this investigation were to compare the accuracy and bias of prediction of Estimated Breeding Values (EBV) for Average Daily Gain (ADG) at 2-4 weeks old by employing pedigree-based BLUP and single-step Genomic BLUP (ssGBLUP) techniques. Additionally, the study aimed to identify the optimal minor allele frequencies (MAF) threshold for pre-selecting SNPs for genetic prediction. The present investigation utilized a total of 488 F2 broiler chickens, which were derived from the crossbreeding of fast-growing Arian chickens and slow-growing native chickens from Urmia, Iran. These chickens were between 2-4 weeks old at the time of the study. Samples were genotyped using the Illumina 60K chicken Beadchip. In order to examine the impact of MAF on prediction accuracy, a total of 48,379 quality-controlled SNPs were categorized into five subgroups based on their MAF values: 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5. The findings substantiated the dominance of ssGBLUP over conventional BLUP techniques. The average accuracy of GP improved by 1.96, 3.87, and 2.12% using ssGBLUP compared to BLUP method for ADG at 2-4 weeks of age, respectively. Using a specific MAF bin and a subset of SNPs based on age group significantly enhanced the accuracy of genomic prediction for ADG traits. Current results highlighted that the pre-selection of SNPs based on allele frequency may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements.

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