Matrix asphalt material has poor road performance, which is improved by adding polymer modified agents. The original asphalt material experiment research has a certain limitation in that it cannot truly expose the mechanism and influence of modifiers on asphalt materials. To explore the gain effect of the modified agent on the asphalt material's road performance, the molecular dynamic simulation technology is used to explain its mechanism in the micro dimension. The three parameters of the free volume score, radial distribution function and average directional shift diffusion coefficient are analysed to improve the performance of the modifier on asphalt material, an exploration of the microdimension shows that the amount of SBS modifiers is 6% and the amount of rubber modified asphalt material is 22%. The improvement of materials is better than that of rubber modifiers.

We report a new fabrication method for a fully stretchable supercapacitor based on single wall carbon nanotube (SWCNT)-coated electrospun rubber nanofibers as stretchable supercapacitor electrodes. The deposition conditions of SWCNT on hydrophobic rubber nanofibers are experimentally optimized to induce a uniform coating of SWCNT. For surfactant-assisted coating of SWCNT, both water contact angle and sheet resistance were the lowest compared to the cases with other surface treatment methods, indicating the most effective coating approach. The excellent electromechanical properties of this electrode under the stretching condition is demonstrated by the measurement of Young’s modulus and normalized sheet resistance. This superb tolerance of the electrode with respect to stretching results from i) high aspect ratios of both nanofiber templates and the SWCNT conductors, ii) highly elastic nature of rubbery nanofiber, and iii) strong adherence of SWCNT-coated nanofiber on the elastic ecoflex substrate. Electrochemical and electromechanical measurements on the stretchable supercapacitor devices reveal that the volumetric capacitance (15.2 F cm-3 at 0.021 A cm-3) of the unstretched state is maintained for strains of up to 40 %. At this level of strain, the capacitance after 1,000 charge/discharge cycles is not significantly reduced. The high stability of our stretchable device suggests potential future applications in various types of wearable energy storage devices.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.