Calculation of and Key Influencing Factors Analysis on Equivalent Resilient Modulus of a Submerged Subgrade.

To calculate and analyze the equivalent resilient modulus of a submerged subgrade, a constitutive model considering the effect of saturation and matrix suction was introduced using ABAQUS's user-defined material (UMAT)subroutine. The pavement response under falling weight deflectometer (FWD) load was simulated at various water levels based on the derived distribution of the resilient modulus within the subgrade. The equivalent resilient modulus of the subgrade was then calculated using the equivalent iteration and weighted average methods. Based on this, the influence of the material and structural parameters of the subgrade was analyzed. The results indicate that the effect of water level rise on the tensile strain at the bottom of the asphalt layer and the compressive strain at the top of the subgrade is obvious, and its trend is similar to an exponential change. The equivalent resilient modulus of the subgrade basically decreases linearly with the rise in the water level, and there is high consistency between the equivalent iteration and weighted average methods. The saturated permeability coefficient and subgrade height have the most significant effect on the resilient modulus of the subgrade, which should be emphasized in the design of submerged subgrades, and the suggested values of the resilient modulus of the subgrade should be proposed according to the relevant construction conditions.

Integration of hybrid and self-correction method improves the quality of long-read sequencing data.

Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction tools. So far, these two types of tools are separately investigated, and their interplay remains understudied. Here, we integrate hybrid and self-correction methods for high-quality error correction. Our procedure leverages the inter-similarity between long-read data and high-accuracy information from short reads. We compare the performance of our method and state-of-the-art error correction tools on Escherichia coli and Arabidopsis thaliana datasets. The result shows that the integration approach outperformed the existing error correction methods and holds promise for improving the quality of downstream analyses in genomic research.

Machine learning on protein-protein interaction prediction: models, challenges and trends.

Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.

Investigation of Adhesion Properties of Tire-Asphalt Pavement Interface Considering Hydrodynamic Lubrication Action of Water Film on Road Surface.

To obtain the tire−pavement peak adhesion coefficient under different road states, a field measurement and FE simulation were combined to analyze the tire−pavement adhesion characteristics in this study. According to the identified texture information, the power spectral distribution of the road surface was obtained using the MATLAB Program, and a novel tire hydroplaning FE model coupled with a textured pavement model was established in ABAQUS. Experimental results show that here exists an "anti-skid noncontribution area" for the insulation and lubrication of the water film. Driving at the limit speed of 120 km/h, the critical water film thickness for the three typical asphalt pavements during hydroplaning was as follows: AC pavement, 0.56 mm; SMA pavement, 0.76 mm; OGFC pavement, 1.5 mm. The road state could be divided into four parts dry state, wet sate, lubricated state, and ponding state. Under the dry road state, when the slip rate was around 15%, the adhesion coefficient reached the peak value, i.e., around 11.5% for the wet road state. The peak adhesion coefficient for the different asphalt pavements was in the order OGFC > SMA > AC. This study can provide a theoretical reference for explaining the tire−pavement interactions and improving vehicle brake system performance.