International Journal of Modern Physics C

Graphene plasmons have advantages over noble metal plasmons such as high tunability and low loss. However, for graphene nanostructures smaller than 10 nm, little is known about their plasmons or whether regular plasmonic behavior exists, despite their potential applications. Here, we present first-principles calculations of plasmon excitations in zigzag graphene nanoribbon segments. Very regular plasmonic behavior is found: Only one plasmon mode exists in the low-energy regime ($<1.5$ eV). The classical electrostatic scaling law still approximately holds when the width ($W$) is larger than $\sim 1.5$ nm but totally fails when $W<1.5$ nm due to quantum effects. The scaling with different doping densities shows that the plasmon is nearly free-electron plasmon instead of Dirac plasmon.

In intracellular transport, the motor proteins may jump between the filaments due to structural deformity where the motion is hindered by presence of bound proteins. This motivates us to study a system of two parallel disordered exclusion processes with Langmuir kinetics under symmetric and asymmetric coupling conditions. Theoretically, continuum mean-field approximation is employed to obtain steady-state equations which are further solved numerically. The steady-state system dynamics are examined for different values of coupling constant $C$, which indicates ratio of lane-changing rates. The effect of binding constant $K$ and defect parameters is also explored. Under the symmetric coupling case, the topological structure of phase diagram turns out to be similar to that in single-lane model, which differs significantly under fully asymmetric coupling. The asymmetric coupling increases magnitude of difference between densities in two lanes. At a very large attachment/detachment rate, the LK dynamics dominate the effect of asymmetric coupling in the lanes. The defect parameters are found to reduce particle flux in the steady state. The numerical results are validated through extensive Monte Carlo simulation.

Predicting the connections or interactions between nodes in networks remains a fundamental task in network analysis. In target systems, nodes with different types are connected with each other through different relations, as in typical heterogeneous networks. Link prediction can help identify implied information in heterogeneous target operational network (HTON) to boost network reconstruction. In view of the complex network structure of target systems and diversified types of edges alongside the low accuracy and poor interpretability issues of previous methods, we proposed an HTON reconstruction framework (HTONRP) incorporating rule-based reasoning and meta-path-based link prediction. To start with, the link rules are extracted based on operational doctrine, military theory, system formulation, etc., and then they are reasoned with initial nodes and relations (input) and the inferred target relations (output) to improve the target systems architecture. Afterwards, the data are input into the meta-path-based link prediction model of HTON, through which the architecture of target systems is further perfected. The experiments are carried out on real data instances of HTON and results suggest that the proposed method can effectively improve the prediction performance and enhance model interpretability compared to traditional baselines.

Pedestrian merging behavior is one of the most frequently observed complex pedestrian behaviors and has a certain limited impact on evacuation efficiency in emergency situations. However, experimental research on the multiple merging behavior of pedestrians is very limited. To explore the macroscopic and microscopic characteristics of multiple-merging under special-angle conditions, this paper studies the pedestrian behavior of the main channel and branches in multiple-merging behavior, and analyzes the microscopic and macroscopic characteristics of pedestrians in the case of multiple merging. The width of the main channel is fixed at 2.4m, the angle between the branches and the main channel is 30, and the change in the width of the merging branches is 0.8, 1.2 and 1.6m. A total of 171 volunteers participated in the experiment, and UWB equipment was used to extract pedestrian trajectories. Obvious lanes were formed during the experiment, and there were obvious ramps. The density of the double branches is significantly higher than that of the single branch, and the maximum saturation value can reach 3.75ped/m². When the width of the branch becomes narrower, the branch entrance and the main channel block each other. When the width of the branch is narrow enough, the influence of the number of branches on the speed of the branch is reduced. When the number of branches increases, to make the distribution of pedestrians more even, the width of the branches also needs to be increased. We also analyzed the headway distance and found that almost all the volunteers in the experiment were under the free system. The results are helpful for correction of the multimerging simulation model and design improvement of the daily multimerging scene.

Three different forcing functions are used with the lattice Boltzmann method (LBM) to simulate the forced isotropic turbulence in periodic boxes at different resolutions ranging from $32^3$ to $256^3$ grid points using the D3Q19 model. The aims of this study are to examine the effect of using different forcing functions on the LBM stability; to track the development of the turbulent fields at several resolutions, to investigate the effect of the weak compressibility due to change of fluid density on the flow simulations, and to identify the effective force type. The injection is performed through adding the force randomly to the collision term. The three forcing methods depend on sine and cosine as functions of the wave numbers and space. The forcing amplitude values of $A=10^{-4}$ and the relaxation time $\tau =0.503$ are fixed in all cases. The single relaxation time model is found stable at such values of the forcing amplitude and the relaxation time. However, the development of the turbulent data at the different resolutions needs about 10000 time-steps to reach the required statistical state including clear visualizations of fine scale vortices. Many simulations have been tested using different values of the relaxation time $\tau$ and the development of the turbulent fields is found faster with fewer time-steps but the stability of the LBM is broken at some resolutions (not necessary the higher resolution). The statistical features of all fields, such as the Taylor and the Kolmogorov micro-scales, the Taylor Reynolds number, the flatness and the skewness, are calculated and compared with the previous efforts. The worm-like vortices are visualized at all cases and it is found that more fine vortices can be extracted as the resolution increases. The energy spectrum has a reasonable Kolmogorov power law at the resolutions of $128^3$ and $256^3$, respectively. Results show that the third forcing method that uses a cosine disturbance function has the best statistical features and the finest visualized vortical structures especially at higher resolutions. Extensive discussions about the density field and its evolution with time at different forcing functions, comparison to Navier–Stokes solutions and the time development of the energy spectra for all cases are also carried out.

This paper proposes a novel optimization approach of the quantum-behaved binary firefly algorithm with a gravitational search algorithm (QBFA-GSA) for discrete feature optimization, which is utilized for the application of human activity recognition. The firefly algorithm (FA) and gravitational search algorithm (GSA) are recently introduced meta-heuristic algorithms that are efficient for optimizing the continuous solution set. The binarized version of the proposed approach enables it to optimize the discrete features and quantum behavior ensures the better diversity of the final optimized features. In the proposed QBFA-GSA approach, the features are optimized by following the combined advantageous attributes of FA and GSA in which the search space is initially explored by firefly agents until the current firefly finds the brighter firefly and further these agents adapt the attributes of GSA to complete the process. These optimized features are passed to deep neural networks (DNN) for the classification of human activities. Here, DNN models of deep convolutional neural networks (DCNN) and DCNN extended with residual blocks (DCNN-RB) are incorporated. The evaluation experiments for human activity recognition are conducted on a benchmark dataset of UCF-101, which is a composition of 101 different activities. The experimental results of the proposed QBFA-GSA approach are superlative to state-of-art techniques, which indicate that the proposed approach is efficient to optimize the features.

It has become a trend for Direct Simulation Monte Carlo (DSMC) to simulate near-continuum gas, which makes the problem of a large amount of computation more prominent in turn. Moreover, heterogeneous architecture is becoming mainstream for modern supercomputers. Speeding DSMC on heterogeneous systems has been a must consideration for DSMC researchers. However, little research on the DSMC program that supports different heterogeneous systems is available. In this paper, we developed a DSMC program supporting heterogeneous systems through OpenMP4.5, and our codes can execute on many accelerators in theory such as Nvidia GPUs, AMD GPUs and Intel Many Integrated Core (MIC) products. The implementation of our heterogeneous DSMC is elaborated first, and then we explain the algorithm proposed to generate random numbers on accelerators. In addition, the optimization methods, including load-balance and granularity refinement strategy, branch elimination, and particles’ removal and entering, are described in detail, respectively. A shock-bubble interaction (SBI) case under Knudsen number of $1.66789\times 10^{-3}$ is utilized to validate the accuracy of our DSMC and do the performance analysis. An Nvidia RTX 2080 serves as a device, and the results show that our program can not only gain accurate flowfield information, but also perform well under different computing loads. More specifically, the whole speedup ratio of $52\sim 63$ can be obtained relative to a single CPU core, and for the subroutine Collision, the ratio can even reach approximately 90.

In this paper, we investigate number-conserving cellular automata with up to five inputs and two states with the goal of comparing their dynamics with diffusion. For this purpose, we introduce the concept of decompression ratio describing expansion of configurations with finite support. We find that a large number of number-conserving rules exhibit abrupt change in the decompression ratio when the density of the initial pattern is increasing, somewhat analogous to the second-order phase transition. The existence of this transition is formally proved for rule 184. Small number of rules exhibit infinite decompression ratio, and such rules may be useful for “engineering” of CA rules which are good models of diffusion, although they will most likely require more than two states.

In this paper, we propose a Structural Hole Routing (SHR) strategy based on the structural hole theory for scale-free networks. The performance of routing strategies on scale-free networks can be measured by two indicators, i.e. the traffic capacity and the average path length. With the SHR strategy, the packets can bypass those center nodes to enhance the traffic capacity of scale-free networks. The simulation results show that our SHR strategy can achieve approximately 6% higher traffic capacity than the efficient routing (ER) strategy. Moreover, the average path length of the SHR strategy is a little higher than that of the ER strategy. Therefore, our SHR routing strategy is preferable to other routing strategies and can be easily implemented through software method.

In this paper, the structural and dynamical properties of the 19-atom double icosahedral silver cluster are studied by using the classical molecular dynamics. It is found that the melting of the 19-atom cluster starts with the migrations of the surface atoms. The surface atoms can interchange positions with each other atoms through three ways. The first and second ways involve with only part of the surface atoms. The third way can involve the surface atoms at all symmetrical positions. With the increase of temperature, the inner atoms can hop onto the surface of the cluster and the position interchange between the surface atoms and the inner atoms can be realized. With further increase of temperature, the isomerization of the cluster becomes frequent and the motion of the atoms of the cluster becomes more and more disordered. When the temperature reaches the value corresponding to the second peak of the heat capacity, the solid cluster turns into the fully melted state.

In this work, we deal with an inverse source problem for the kinetic equation. We obtain numerical solution of the problem by using a hybrid method which is composed of finite difference approximation and Lagrange’s polynomial interpolation. We present necessary conditions for the stability of the finite difference scheme by the Von Neumann method. Finally, we test the proposed method by performing several numerical experiments. We conclude that the relative error in reconstruction of the unknown functions is sufficiently small.

The boundary layer correction method is applied to study asymptotic traveling wave solutions to anisotropic higher-order traffic flow model with viscous coefficient, which clearly indicates that the truncation error between the original and approximate equations is an equivalent infinitesimal of the coefficient. Therefore, the solution is exact in the case when the coefficient vanishes, which corresponds to a certain nonviscous model following the standard hyperbolic and viscous conservation laws. Numerical simulation is implemented to reproduce a typical traveling wave or wide moving jam consisting of a transitional layer (the downstream front) and a shock (the upstream front) profiles, of which the parameters converge not only for refined meshes but for refined viscous coefficient as well. These are highly in agreement with the analytical results.

In this paper, an extended cubic B Spline scheme (CBS) is utilized to solve linear 10th-order boundary value problems (BVPs). Such types of BVPs occur in stability analysis of electrically conducting fluid in a magnetic field. The key concept is that we switched the BVPs to recreate a system that consists of all linear equations. We will alter our problem into such a form that converts the system of 10th-order BVPs and we are struck on a new system of 2nd-order BVPs. The appropriate outcome given by using CBS is contrasted with the accurate root to each problem. For each and every iteration, absolute error is also premeditated. The rule originated here aside from the estimation of the 10th-order BVPs also evaluates derivative from 1st-order to 10th-order of the accurate root. Couple of examples are demonstrated to evidence the adequacy and aptitude of the proffered strategy.

Dynamical degradation is inevitable when chaotic map is implemented on a device with limited precision. To mitigate the insecurity caused by dynamic degradation, an effective method based on linear feedback and parameter perturbation with previous variables is proposed in this paper. We apply this method on the basis of the most widely used one-dimensional logistic map. Through the numerical experiments, this model is proved to have good chaotic characteristics and strong competitiveness. In addition, in order to verify the practicability of this method, we design a simple process pseudo-random bit generator (PRBG), whose security completely depends on the improved logistic map. The results of NIST experiment show that the generated pseudo-random sequence has ideal randomness.