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Abundant real-time vehicle trajectory information provides an important guarantee for the driving safety and drivers' decision-making in the intelligent transportation system (ITS), which improves the transport efficiency of traffic flow at the micro level. In this paper, we propose an adaptive car-following model to study the influence of the preceding vehicles' information on the motion state of the target and the kinetic characteristics of traffic flow in the ITS, where the space headway, the velocity difference and the acceleration of preceding vehicles are considered simultaneously. According to the generalized diagram structure of road traffic, we mainly consider the two vehicles ahead and introduce adaptive parameters to control the proportion of different influencing factors. The rationality of the extended model is verified via real-world vehicle trajectory fitting experiments with the collected empirical data. Theoretical derivations are then carried out to describe the evolution of traffic flow under different scenarios, where the linear stability conditions and nonlinear analysis on small disturbance are included. Numerical tests are made to simulate the driving conditions and verify the accuracy of theoretical results. Computing the engine power of vehicles, we summarize the impact of traffic congestions and stop-and-go behaviors on the energy consumption. Diverse micro motion states and traffic flow dynamics can be revealed based on our car-following model, which will serve as both theoretical basis and experimental examples for the real-world traffic. [ABSTRACT FROM AUTHOR]

We focus on the localized characteristics of lump and interaction solutions to two extended Jimbo–Miwa equations. Based on the Hirota bilinear method and the test function method, we construct the exact solutions to the extended equations including lump solutions, lump–kink solutions, and two other types of interaction solutions, by solving the under-determined nonlinear system of algebraic equations for associated parameters. Finally, analysis and graphical simulation are presented to show the dynamical characteristics of our solutions and the interaction behaviors are revealed. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a (3+1)-dimensional nonlinear evolution equation and its reduction is studied by use of the Hirota bilinear method and the test function method. With symbolic computation, diversity of exact solutions is obtained by solving the under-determined nonlinear system of algebraic equations for the associated parameters. Finally, analysis and graphical simulation are given to reveal the propagation and dynamical behavior of the solutions. [ABSTRACT FROM AUTHOR]

Accurate prediction of DNA-protein binding (DPB) is of great biological significance for studying the regulatory mechanism of gene expression. In recent years, with the rapid development of deep learning techniques, advanced deep neural networks have been introduced into the field and shown to significantly improve the prediction performance of DPB. However, these methods are primarily based on the DNA sequences measured by the ChIP-seq technology, failing to consider the possible partial variations of the motif sequences and errors of the sequencing technology itself. To address this, we propose a novel computational method, termed MSDenseNet, which combines a new fault-tolerant coding (FTC) scheme with the dense connectional deep neural networks. Three important factors can be attributed to the success of MSDenseNet: First, MSDenseNet utilizes a powerful feature representation approach, which transforms the raw DNA sequence into fusion coding using the fault-tolerant feature sequence; Second, in terms of network structure, MSDenseNet uses a multi-scale convolution within the dense layer and the multi-scale convolution preceding the dense block. This is shown to be able to significantly improve the network performance and accelerate the network convergence speed, and third, building upon the advanced deep neural network, MSDenseNet is capable of effectively mining the hidden complex relationship between the internal attributes of fusion sequence features to enhance the prediction of DPB. Benchmarking experiments on 690 ChIP-seq datasets show that MSDenseNet achieves an average AUC of 0.933 and outperforms the state-of-the-art method. The source code of MSDenseNet is available at https://github.com/csbio-njust-edu/msdensenet. The results show that MSDenseNet can effectively predict DPB. We anticipate that MSDenseNet will be exploited as a powerful tool to facilitate a more exhaustive understanding of DNA-binding proteins and help toward their functional characterization. [Display omitted] • This study proposes a new hybrid encoding method to improve the predictive performance of DPB from DNA sequences. • Adding the multi-scale convolution within the dense layer can effectively extract informative features of different scales. • By adding the multi-scale convolution before the dense block, the convergence speed of the model can be accelerated. • Benchmarking experiments demonstrate that MSDenseNet can achieve a superior performance on the 690 ChIP-seq datasets. • An online webserver of MSDenseNet has been made publicly available at http://csbio.njust.edu.cn/bioinf/msdensenet. [ABSTRACT FROM AUTHOR]

Rail-guided vehicle is a logistics management device widely used to perform various material handling operations instead of manual labor. In processing scenarios, the dimensions of the material transfer path of a rail-guided vehicle are typically very large, which makes the optimization of the material transfer path very difficult. The transdifferentiation behavior of lower organisms was introduced into the evolutionary algorithm, and a large-scale differential evolution algorithm based on the transdifferentiation strategy was proposed, for achieving high-efficiency processing. This strategy makes it possible for some individuals with poor fitness to reach maturity again and be selected for the next iteration after losing some information and returning to their juvenile stage, which helps maintain the diversity of the population. Simulation results show that the proposed algorithm not only achieves an average 25.68% higher output rate than the comparison algorithms on the test cases but also has an excellent and stable effect distribution level on the extended problem space, which shows that the superiority of the proposed algorithm is not affected by the processing parameters. This research is expected to provide technical guidance for the processing of key components in the ship and aviation manufacturing industries. The code with a 31-page manual is available on our project homepage https://github.com/MLNST-JUST/DE-TS. • Rail-guided vehicle helps to speed up material production. • Transdifferentiation strategy helps to maintain the diversity of the population. • The rail-guided vehicle can control computer number controllers in cooperation. • The optimization of the distribution of CNCs increases the upper limit of output. • The difficulty of optimization increases with the increase of logistics demand. [ABSTRACT FROM AUTHOR]

Thin-film composite (TFC) nanofiltration membranes were fabricated via interfacial polymerization using 2,2′-oxybis-ethylamine (2,2′-OEL) as an aqueous monomer. The chemical composition and morphology of the membrane surface were confirmed by X-ray photoelectron spectroscopy (XPS), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and scanning electronic microscopy (SEM). The results showed that interfacial polymerization successfully occurred and pore size of the resultant membranes was in the range of nanofiltration. Hydrophilicity of the membranes was investigated through water contact angle measurement. Water flux and salt rejection measurement showed that the prepared TFC membranes had a high flux at 35.6 L/m 2 h despite the salt rejection slightly decreased. The prepared TFC membranes showed good antifouling performance (best flux recovery ratio was 98.5%) and fouling resistant capacity increased with the concentration of 2,2′-OEL. Compared with the PIP-TMC membranes, low concentration of 2,2′-OEL could maintain as high Na 2 SO 4 rejection of membrane as PIP-TMC membranes and show better fouling resistance performance. [ABSTRACT FROM AUTHOR]

In order to understand the densification kinetics of a Ti(C 0.7 N 0.3)-WC-Mo 2 C–NbC-(Co, Ni) compact, the linear shrinkages were measured using a horizontal dilatometer at different heating rates (2, 4, and 6 °C/min) in an argon atmosphere. Densification is more efficient when it is sintered at a lower heating rate. A master sintering curve was constructed over a range from the relative density of approximately 0.658–0.816; the corresponding sintering activation energy was 630 kJ/mol. With the aid of an Arrhenius curve, the sintering behaviour of cermet was analysed at different temperatures. The sintering process changed from solid-phase to liquid-phase sintering at approximately 1385 °C. The sintering activation energy turned negative when the temperature exceeded 1385 °C at a heating rate of 2 °C/min due to the formation of a liquid phase, and the exothermic process of dissolution-precipitation. • Compact densification is more efficient when sintering at lower heating rates. • A master sintering curve can be constructed for a combination of powders. • Arrhenius curve shows that the sintering of cermets is a complex process. • Sintering process changes from solid-to liquid-phase at ∼1385 °C. • Activation energy drops above 1385 °C due to liquid phase/dissolution-precipitation. [ABSTRACT FROM AUTHOR]