International Journal of Modern Physics C

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency.

This paper discusses a numerical study of a category of fractional generalized Cattaneo models. Non-Newtonian fluids have been widely used in engineering and industry throughout the last decades. The above model is treated using two autonomous consecutive spectral collocation strategies. For the current model, our technique has proven to be more accurate, efficient, and workable. The analysis indicates that the spectral method is exponentially convergent.

To gain a deeper understanding of the high-temperature superconductivity in doped cuprates, the constrained-path quantum Monte Carlo method was used to explore the superconducting pairing correlation function and effective pairing interaction in the three-band Hubbard model. Our main observation was that in the low doping region, compared to other symmetries, $d$-wave superconducting pairing dominates. The Coulomb interaction $U$ suppresses $d$-wave pairing correlation, while the effective pairing interaction is both enhanced by the increasing $U$ and the nearest attraction. These results are used to understand the superconducting state in copper oxide high-temperature superconductors.

Nowadays, an increasing number of people use social networks to receive up-to-date information and express their personal opinions, and popular social networks have become important platforms to conduct viral-marketing for many companies. However, due to the existences of negative opinions and hostile relationships, some spreading behaviors will receive much more undesired responses. To study this process of competitive diffusion, we consider heterogeneous opinions (positive and negative ones) and heterogeneous relationships (friendly and hostile ones), and assume the reaction of a user after receiving a message is determined by the received message type, his/her own opinion and the type of relationship between him/her and the neighbor who sends this message. We then modify the duplicate forwarding model to characterize the diffusion dynamics in competitive diffusion, and define the term positive (negative) user influence which is the mean number of positive (negative) messages received by users after a user generates a message. These user influences and the corresponding diffusion threshold can be analyzed theoretically, which are verified by simulations. We then study the impacts of different factors on user influences on some real networks, and observe that messages of some type are easier to be forwarded and received in a given network if the message spreading intensity approaches the diffusion threshold and users of this type have a larger average homophily factor. These findings can help to explain why a large number of boycotts may be attracted if a user or company publishes a post or advertisement in a social network, and we believe this analysis framework will be of use for advertisers to conduct viral-marketing.

With the incredible advancement of information technology, the communication between vehicles (V2V), vehicle and infrastructure (V2I) improves the traffic efficiency and safety. In this work, we present a modified coupled map car-following model with the control terms of the velocity difference and headway difference under V2X (vehicle to everything) environment. Then we study the impact of the control term on the stability and dynamic performance, respectively. The stability of the modified model is analyzed and the conditions for the stability of the traffic flow are presented. Besides, the simulations are carried out from the micro and macro perspectives. The numerical simulation results are consistent with the theory, and the validity of the model is verified. Moreover, in order to study the macro properties of traffic, through sumo numerical experiments, the evolution of the macro quantity of the traffic system has also been studied.

An effective traffic flow prediction can serve as a foundation for control decisions on intelligent transportation. However, in view of the nonstationarity and complexity of traffic flow sequences, it is impossible to fully extract the dynamic change laws of time-series based on traditional forecasting models. Traffic flow data are often disturbed by noise during the collection. The existence of noise data may affect the features of the sequence itself or cover the real change trend of the series, resulting in the decline of prediction reliability. A hybrid prediction model based on variational mode decomposition–convolutional neural network–gated recurrent unit (VMD–CNN–GRU) is presented to increase the predictability of traffic flow, which is combined by VMD, CNN and GRU. First, the original time-series is decomposed into K components by VMD, and the noise part is eliminated to improve the modeling accuracy. Next, the time characteristics of traffic flow are mined by constructing the CNN–GRU network in Keras, a deep learning framework. Each sub-sequence is trained and predicted separately as an input vector. The total expected value of traffic flow is then calculated by superimposing the predicted value of each subsequence. The model performance is verified by the open-source dataset of actual England highways. The results show that compared with other models, the hybrid model established in this paper significantly raises the precision of traffic flow forecasting. The results could offer some useful insights for predicting traffic flow.

In finite populations, the stability of three-strategy cyclic dominance dynamics is investigated. A model for three-strategy cyclic dominance games is proposed. The stability of dynamical system corresponding to the model is compared with that of replicator dynamics, it verifies that the results of finite populations are quite different from that of replicator dynamics.

In the process of traffic information transmission, traffic flux delay often occurs. To solve traffic jams resulting from traffic flux delay, we put forward a novel feedback control mode to establish a lattice model accounting for the difference between the optimal estimation and the delayed flux (called for OEDF model). The stability condition, which is closely related to the OEDF model, is derived according to the linear stability analysis. Moreover, the important positive contribution of the OEDF model is demonstrated via numerical simulation from two aspects including density evolution and hysteresis loop, respectively.

The rapid development of social networks makes the rumour, other false news disseminate to the people in a short period. Online users in social networks are dynamically changing the connectivity over time. The effect of dynamic connections results in stochastic variation which is termed as noise. In this paper, a nonlinear rumour propagation model is formulated, the basic regeneration number $R_0$ of the proposed model is computed and the stability for the model is discussed. Further, we extend the model to stochastic rumour propagation for online social networks incorporating noise. The existence and uniqueness of the stochastic rumour propagation for the homogeneous network are investigated. Optimal control strategy of stochastic rumour spreading model in online social network is investigated to control the parameters. A comparison between deterministic and stochastic rumour spreading model in online social network is numerically illustrated.

The Angular Spectrum Representation (ASR) is applied to describe semi-analytically the optical force densities acting on linear dielectric media when a quasi-monochromatic tightly focused Gaussian beam is applied. This method is seen to be inherently faster than conventional finite-difference schemes. Numerical simulations of the optical force densities were also performed and found to be in agreement with the literature, providing a complementary tool for the study of opto-mechanical effects in matter.

Introduction: The expansion of pulmonary tumors and their alterations take place in a dynamic manner, so that vigorous segmentation of the obtained images is accurately required.

Methods: In this research, an extended algorithm in region growing was executed on CT lung tumors to investigate precise tumor region and edges. First, a new threshold via definition of greater target region around the initial tumor was implemented in MATLAB software. Second, nearby points were settled in an array and then these points were updated established upon the tumor growth to delineate the fresh tumor edges. Here, farthest distance from the center of color intensity point of the initial tumor was selected to grow the region in the algorithm. Third, fresh tumor boundary was determined via an interpolation between these fresh points by sketching lines from the tumor midpoint. Then, the edge correction was implemented and the fresh region was attached to the principal region to attain a segmented tumor exterior.

Results: The proposed technique enhanced the tumor recognition by 96% and 91% maximum and minimum accuracy, respectively, in comparison with basilar method. In inclusive algorithm, the percentage of conformity had a positive effect on realization of the threshold value and renewal of the relative amount by 13% enhancement over accuracy assessment. Also when compared to basilar algorithm, it was found that at least 12% of the percentage differences in conformity segment the tumor area in lung CT images. The proposed algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions. The algorithm also guarantees the independence of the results from the starting point.

Conclusion: According to the definition of the center of mass of the tumor color intensity, the proposed extended algorithm may be generalized to the 3D images regardless of the matrix size and the image thickness. The combination of techniques such as machine learning is expected to improve segmentation accuracy for different types of nodule and tumor CT images.

Implications for practice: Proposed extended algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions.

The analysis of Boundary Layer Flows (BLFs) is of huge significance due to its large-scale applications most likely in aerodynamics, manufacturing of vehicles front faces and many other advanced research areas. Therefore, this study is organized by exercising significant important engineering parameters like surface convection, radiative flux, MHD and thermal and mass diffusion. The developed model embedded the concrete effects of velocity slip, diffusion convection, thermal radiations, magnetic field, chemical reaction and thermo-mass gradients. Later, the numerical treatment is made via RK scheme and the model efficaciously is tackled with desired accuracy. Then the results are decorated over the region (wedge and sheet) and deeply examined. It is observed that slippery surface of wedge and sheet $(S_1=0.1,0.3,0.5,0.7,0.9)$ enlarges the fluid movement rapidly. The directed magnetic field ($M=0.0,0.1,0.2,0.3,0.4)$ controlled the fluid motion which can be beneficial from industrial view point. Further, temperature and concentration distributions upsurge by strengthening convective heat ($B_i=0.1,0.2,0.3,0.4,0.5)$ and diffusion convection processes. Moreover, skin friction and Nusselt number improved against the parameters under consideration.

This investigation discusses the problems of fixed-/predefined-time stabilization and synchronization of memristor chaotic circuits (MCCs). Specially, all of the proposed control schemes are differentiable, namely smooth, which are superior to the previous finite-/fixed-time control techniques, because the discontinuous signum and absolute functions are not contained anymore. Comparing with the traditional fast convergence of chaotic systems, the upper-bound estimation of convergence time in this investigation is not only irrelevant to the initial values of MCCs, but also concise and explicit. Moreover, according to the Lyapunov stability theory, the sufficient criteria are established successively for ensuring the fixed-/predefined-time stabilization and synchronization of MCCs. Finally, the numerical simulations are placed to validate the effectiveness and feasibility of obtained results, in which the comparison is made and the effect of controlling parameters on the convergence speed is further explored.

Studying the transport characteristics of carriers in quantum dot (QD) film provides theory support for the structure design and performance improvement of QD film device. However, time of flight experiment can only test the global optoelectric current signal brought by the carrier transport, and cannot analyze the carrier transport in the transport layer. Here, the hopping transport model of photogenerated carriers in QD films was established to study the expansion and drift movement of carriers in the PDE module of COMSOL. According to the material properties of the actual QD films, the carrier transport in single-size QD films was studied.