Physica A: Statistical Mechanics and its Applications最新文献

Controlled remote implementation of operation (CRIO) enables to implement operations on a remote state with strong security. We transmit implementations by entangling qubits in photon-cavity-atom system. The photons transferred in fiber and the atoms embedded in optical cavity construct CZ gates. The gates transfer implementations between participants with the permission of controller. We also construct nonadiabatic holonomic controlled gate between alkali metal atoms. Decoherence and dissipation decrease the fidelity of the implementation operators. We apply anti-blockade effect and dynamical scheme to improve the robustness of the gate.

The research on traffic congestion control has seen rapid development in recent years. Investigating the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can offer new methods for alleviating traffic congestion. This paper focuses on studying the bifurcation characteristics and nonlinear control of traffic flow based on the continuous model and the taillight effect. Firstly, the traffic flow model is transformed into a stability model suitable for branching analysis through the use of the traveling wave transform. This transformation facilitates the analysis of stability that reflects unstable traffic characteristics such as congestion. Based on this stability model, the existence condition of Hopf bifurcation is proved and some bifurcation points of the traffic system are identified. Secondly, the congestion and stability mutation behaviors near equilibrium and branching points are studied to understand the formation mechanism of traffic congestion. Finally, control schemes are designed using Chebyshev polynomial approximation and stochastic feedback control to delay or eliminate unstable bifurcation points and relieve traffic congestion. This improved traffic flow model helps explain changes in system stability through bifurcation analysis and identify unstable bifurcation points. It can also effectively manage these points by designing a feedback controller. It is beneficial for controlling sudden changes in traffic system stability behavior and mitigating traffic congestion, with important theoretical significance and practical application value.

Modelling of aggregation processes in space-inhomogeneous systems is extremely numerically challenging since complicated aggregation equations, Smoluchowski equations, are to be solved at each space point along with the computation of particle propagation. Low rank approximation for the aggregation kernels can significantly speed up the solution of Smoluchowski equations, while the particle propagation could be done in parallel. Yet the numerics with many aggregate sizes remains quite resource-demanding. Here, we explore the way to reduce the amount of direct computations by replacing the actual numerical solution of the Smoluchowski equations with the respective density transformations learned with the application of one of machine learning (ML) methods, the conditional normalising flow. We demonstrate that the ML predictions for the space distribution of aggregates and their size distribution require drastically shorter computation time and agree fairly well with the results of direct numerical simulations. Such an opportunity of a quick forecast of space-dependent particle size distribution could be important in practice, especially for the fast (on the timescale of data reading) prediction and visualisation of pollution processes, providing a tool with a reasonable trade off between the prediction accuracy and the computational time.

The wetting transition in the one-dimensional cluster Ising model with opposite boundary fields is studied. Tuning one boundary field while fixing another leads to the occurrence of phase transitions, where the transition points depend on the cluster coupling. Furthermore, the phase diagram is divided into three regions with different phase transition. For weak and strong cluster coupling, the phase transition is continuous and belongs to the same universality of transverse Ising model with boundary fields. For intermediate cluster coupling, the phase transition is first order. In the strong cluster coupling region, the critical region becomes exponentially small and the asymptotic behavior is absent even for lattice size up to $10^4$. A numerical method to solve the energy gap and the correlation length is proposed on an infinite long spin chain. With this method, one can get the critical behavior as close to the critical point as possible provided that the numerical accuracy is high enough. In the light of this method, we clearly show that there is a preasymptotic regime in which the apparent critical exponents depend on the cluster coupling. Moreover, we obtain the accurate energy gap exponent $z\nu$ and the correlation length exponent $\nu$ in the asymptotic critical region.

Mandelbrot’s empirical observation that the coast of Britain is fractal has been confirmed by many authors, but it can be described by the Schramm–Loewner Evolution? Since the self-affine surface of our planet has a positive Hurst exponent, one would not expect a priori any critical behavior. Here, we investigate numerically the roughness and fractal dimension of the isoheight lines of real and artificial landscapes. Using a novel algorithm to take into account overhangs, we find that the roughness exponent of isoheight lines is consistent with unity regardless of the Hurst exponent of the rough surface. Moreover, the effective fractal dimension of the iso-height lines decays linearly with the Hurst exponent of the surface. We perform several tests to verify if the complete and accessible perimeters would follow the Schramm–Loewner Evolution and find that the left passage probability test is clearly violated, implying that coastlines violate SLE.

Marathons are one of the ultimate challenges of human endeavor. In this paper, we apply extreme value statistics to predict fastest marathon times achievable for ten marathons around the world and for both men and women. We concentrate on the theoretical minimum time for each gender at each venue, irrespective of who runs there. Thus, we measure the potential of each marathon, not the actual performance of the runners.

In this study, the Laplace transform approach have been employed to analyze the bound state solutions of two-dimensional Schrödinger equation with isotropic oscillator plus inverse quadratic potential. The wavefunctions and energy equation were derived. Thereafter, the so-called partition function and the thermodynamic properties were evaluated as a function Larmor frequency in the range $0<{\omega }_L<100$ for some values of parameter λ. The findings showed that both the partition function and thermodynamic properties of the molecules were modified significantly by magnetic interactions, which invariably may alter the macroscopic behaviour of the system examined.

This paper investigates the impact of adaptive cruise control(ACC) vehicles on the stochasticity of human driving behavior by constructing a stochastic car-following model of human-driven vehicles (HDVs). Utilizing NGSIM dataset, the relationship between acceleration variance and space headway is analyzed, and a novel stochastic car-following model with headway is proposed to capture the internal stochasticity of drivers. Furthermore, the interaction between HDVs and AVs is explored by discussing stochasticity and stability in mixed traffic flow, using the proposed HDV model. The model parameters are calibrated based on NGSIM dataset and the simulation results indicate that the proposed stochastic car-following model can effectively reproduce the generation and propagation of traffic shocks without lane changes. Additionally, the simulations reveal that as the penetration rate of AVs increases in a lower range (0%–50%), the stochasticity of HDVs and stability in mixed traffic flow is substantially reduced. However, at higher penetration rates, increases in the AV penetration rate have a limited effect on the stochasticity of human driving behavior and the stability of mixed traffic flow. Concurrently, under conditions of low penetration rates, a smaller AV platoon size contributes more effectively to enhancing the stability of traffic flow and suppressing the stochastic behavior of HDVs. This research provides new insights for optimizing traffic flow control with automated vehicles.

We present an agent-based model that examines the microscopic exchange of wealth in a dynamic network to investigate the topological characteristics associated with economic inequality. The model consists of two processes: conservative wealth exchange between connected agents and the rewiring of connections, which depends on the wealth of the agents. The dynamics of wealth and connections are interrelated, as the network structure influences which agents interact with each other. We analyze the time evolution and asymptotic characteristics of the model for different values of a social protection factor ($f$), which favors the poorest agent in each wealth transaction. Our results show that for $f=0$, wealth and connections condense in a single agent, in accordance with mean-field models of wealth exchange. When $f$ is low, agents from the middle and upper classes become favored, leading to the formation of network hubs. However, as $f$ increases, the restriction of the network on exchanges results in an egalitarian society departing from the outcomes observed in the mean-field exchange models.

In this paper we investigate an SIRS-UAU disease-awareness spreading model on multiplex networks that incorporates the influence of mass media. Through the microscopic Markov chain approach, we derive the evolution equations for the probability of an individual being in each possible state and obtain the epidemic threshold, which is shown to be a continuous phase transition point between the disease-free state and the endemic state in the phase diagram. Our results show that increasing the immunity wanning rate will enlarge the epidemic prevalence and reduce the fraction of recovered individuals; however, the temporal immunity has no impact on the epidemic threshold. Moreover, implementing mass media broadcast helps raise the awareness incidence and decrease the fraction of recovered individuals. In addition, for the special case when awareness confers complete self-protection against infection, the mass media broadcast significantly diminishes the epidemic prevalence and increases the epidemic threshold. Nevertheless, when awareness provides only partial self-protective effectiveness, the mass media broadcast plays a limited role in changing the epidemic prevalence and the epidemic threshold. Furthermore, in the special case where the mass media broadcast is not included, we discover the metacritical point, a point above which the epidemic threshold starts to grow nonlinearly with the awareness spreading rate and below which the epidemic threshold is independent of the awareness spreading rate. Conversely, the metacritical point disappears as long as the mass media broadcast is incorporated into the model. Our analytical results are confirmed by extensive Monte Carlo simulations.