Crumb rubber modified (CRM) asphalt binder has been affirmed to improve resistance to rutting, moisture susceptibility, low-temperature cracking, and asphalt durability. However, CRM has poor compatibility with asphalt since crumb rubber molecules are vulcanized. The objective of this study was to develop a new method to prepare activated crumb rubber using hydrogen peroxide (H2O2) solution and to explore the rheological properties of H2O2 activated CRM (ACRM) asphalt. Three different percentages of H2O2 solution were used to activate crumb rubber. The surface properties of oxidized rubber were analysed using scanning electron microscopy. Moreover, the pore structure in rubber powder was investigated. The rheological properties of bitumen samples obtained from treated and untreated rubber were characterized by conducting dynamic shear rheometer tests. The test results show that the average pore size of the crumb rubber after activation with H2O2 solution is significantly smaller than that of the inactivated crumb rubber, and the volume and surface area of the crumb rubber pores change with H2O2 solution activation in a certain pattern. With the increase in H2O2 solution content, the contact surface between the particles increases, the floccules and pores of the powder increase, and the interface degree between the crumb rubber powder and the asphalt is strengthened. Solubility of the rubber hydrocarbon and the release ability of the carbon black particles from the crumb rubber in the asphalt binder increase, but the mechanical properties of the crumb rubber, including the strength, elasticity, and wear resistance, decrease. As a result, a reduction is observed in the elasticity, viscosity, high-temperature rutting resistance, and elasticity of the ACRM asphalt.

No abstract

BackgroundGiven a set of t n-length DNA sequences, q satisfying 0 < q ≤ 1, and l and d satisfying 0 ≤ d < l < n, the quorum planted motif search (qPMS) finds l-length strings that occur in at least qt input sequences with up to d mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g., t = 20 and n = 600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more.ResultsWe analyze the effects of t and q on the time performance of qPMS algorithms and find that a large t or a small q causes a longer computation time. Based on this information, we improve the time performance of existing qPMS algorithms by selecting a sample sequence set D’ with a small t and a large q from the large input dataset D and then executing qPMS algorithms on D’. A sample sequence selection algorithm named SamSelect is proposed. The experimental results on both simulated and real data show (1) that SamSelect can select D’ efficiently and (2) that the qPMS algorithms executed on D’ can find implanted or real motifs in a significantly shorter time than when executed on D.ConclusionsWe improve the ability of existing qPMS algorithms to process large DNA datasets from the perspective of selecting high-quality sample sequence sets so that the qPMS algorithms can find motifs in a short time in the selected sample sequence set D’, rather than take an unfeasibly long time to search the original sequence set D. Our motif discovery method is an approximate algorithm.

No abstract