Anti-Skid Characteristics of Asphalt Pavement Based on Partial Tire Aquaplane Conditions

This study presented a finite element model of radial tire-asphalt pavement interaction using ABAQUS 6.14 software to investigate the skid resistance properties of asphalt pavement under partial tire aquaplane conditions. Firstly, the pavement profile datum acquired by laser scanning were imported to Finite Element Analysis (FEA) software to conduct the pavement modeling. Secondly, a steady state rolling analysis of a tire on three types of asphalt pavements under drying conditions was carried out. Variation laws of the friction coefficient of the radial tire on different pavements with different pavement textures, tire pressures, and loads on the tire were examined. Subsequently, calculation results of the steady state rolling analysis were transmitted to dynamic explicit analysis, and an aquaplane model of a radial tire on asphalt pavements was built by inputting the flow Euler grids. The tire-pavement adhesive characteristics under partial aquaplane conditions are discussed regarding the aquaplane model. Influences of the thickness of water film, the texture of asphalt pavement, and the rolling speed of the tire on the vertical pavement-tire contact force are analyzed. It is found that the vertical contact force between open graded friction course (OGFC) pavement and tire is the highest, followed by stone mastic asphalt (SMA) pavement and dense graded asphalt concrete (AC) pavement surface. The vertical contact force between tire and pavement will be greatly reduced, even with increasing speed or water film thickness. As tire speed increases from 70 km/h to 130 km/h, the tire-pavement contact force is reduced by about 25%. Moreover, when the thickness of water film increases from 0 (dry condition) to 4 mm and then to 12 mm, the vertical contact force reduced 50% and 15%, respectively, compared with under the dry contact condition. This study provided a key theoretical reference for safe driving on wet pavements

Comparative exploration on bifurcation behavior for integer-order and fractional-order delayed BAM neural networks

In the present study, we deal with the stability and the onset of Hopf bifurcation of two type delayed BAM neural networks (integer-order case and fractional-order case). By virtue of the characteristic equation of the integer-order delayed BAM neural networks and regarding time delay as critical parameter, a novel delay-independent condition ensuring the stability and the onset of Hopf bifurcation for the involved integer-order delayed BAM neural networks is built. Taking advantage of Laplace transform, stability theory and Hopf bifurcation knowledge of fractional-order differential equations, a novel delay-independent criterion to maintain the stability and the appearance of Hopf bifurcation for the addressed fractional-order BAM neural networks is established. The investigation indicates the important role of time delay in controlling the stability and Hopf bifurcation of the both type delayed BAM neural networks. By adjusting the value of time delay, we can effectively amplify the stability region and postpone the time of onset of Hopf bifurcation for the fractional-order BAM neural networks. Matlab simulation results are clearly presented to sustain the correctness of analytical results. The derived fruits of this study provide an important theoretical basis in regulating networks

A study on a feasible no-root approach on Android

National Research Foundation (NRF) Singapor

Unlocking the mystery of the hard-to-sequence phage genome: PaP1 methylome and bacterial immunity

BACKGROUND: Whole-genome sequencing is an important method to understand the genetic information, gene function, biological characteristics and survival mechanisms of organisms. Sequencing large genomes is very simple at present. However, we encountered a hard-to-sequence genome of Pseudomonas aeruginosa phage PaP1. Shotgun sequencing method failed to complete the sequence of this genome. RESULTS: After persevering for 10 years and going over three generations of sequencing techniques, we successfully completed the sequence of the PaP1 genome with a length of 91,715 bp. Single-molecule real-time sequencing results revealed that this genome contains 51 N-6-methyladenines and 152 N-4-methylcytosines. Three significant modified sequence motifs were predicted, but not all of the sites found in the genome were methylated in these motifs. Further investigations revealed a novel immune mechanism of bacteria, in which host bacteria can recognise and repel modified bases containing inserts in a large scale. This mechanism could be accounted for the failure of the shotgun method in PaP1 genome sequencing. This problem was resolved using the nfi(-) mutant of Escherichia coli DH5α as a host bacterium to construct a shotgun library. CONCLUSIONS: This work provided insights into the hard-to-sequence phage PaP1 genome and discovered a new mechanism of bacterial immunity. The methylome of phage PaP1 is responsible for the failure of shotgun sequencing and for bacterial immunity mediated by enzyme Endo V activity; this methylome also provides a valuable resource for future studies on PaP1 genome replication and modification, as well as on gene regulation and host interaction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-803) contains supplementary material, which is available to authorized users

CT-Based Risk Factors for Mortality of Patients With COVID-19 Pneumonia in Wuhan, China: A Retrospective Study

Purpose: Computed tomography (CT) characteristics associated with critical outcomes of patients with coronavirus disease 2019 (COVID-19) have been reported. However, CT risk factors for mortality have not been directly reported. We aim to determine the CT-based quantitative predictors for COVID-19 mortality.Methods: In this retrospective study, laboratory-confirmed COVID-19 patients at Wuhan Central Hospital between December 9, 2019, and March 19, 2020, were included. A novel prognostic biomarker, V-HU score, depicting the volume (V) of total pneumonia infection and the average Hounsfield unit (HU) of consolidation areas was automatically quantified from CT by an artificial intelligence (AI) system. Cox proportional hazards models were used to investigate risk factors for mortality.Results: The study included 238 patients (women 136/238, 57%; median age, 65 years, IQR 51–74 years), 126 of whom were survivors. The V-HU score was an independent predictor (hazard ratio [HR] 2.78, 95% confidence interval [CI] 1.50–5.17; p = 0.001) after adjusting for several COVID-19 prognostic indicators significant in univariable analysis. The prognostic performance of the model containing clinical and outpatient laboratory factors was improved by integrating the V-HU score (c-index: 0.695 vs. 0.728; p < 0.001). Older patients (age ≥ 65 years; HR 3.56, 95% CI 1.64–7.71; p < 0.001) and younger patients (age < 65 years; HR 4.60, 95% CI 1.92–10.99; p < 0.001) could be further risk-stratified by the V-HU score.Conclusions: A combination of an increased volume of total pneumonia infection and high HU value of consolidation areas showed a strong correlation to COVID-19 mortality, as determined by AI quantified CT