International Journal of Modern Physics C

The phase synchronization in a network of mean field coupled Hindmarsh–Rose neurons and the control of phase synchrony by an external input has been analyzed in this work. The analysis of interspike interval, with varying coupling strength, reveals the dynamical change induced in each neuron in the network. The bursting phase lines depict that mean field coupling induces phase synchrony in excitatory mode and desynchrony in inhibitory mode. The coefficient of variability, in spatial and temporal domain, signifies the deviations in firing times of neurons, in a collective manner. The Kuramoto order parameter quantifies the intermittent and complete phase synchrony, induced by excitatory mean field coupling. The capability of external input, in the form of spikes, to control the intermittent and complete phase synchrony has been analyzed. The coefficient of variability and Kuramoto order parameter has been studied by varying the amplitude, pulse width and frequency of the input. The studies have shown that high-frequency spike input, with optimum amplitude and pulse width, has high desynchronizing ability, which is substantiated by the parameter space analysis. The control of synchrony in the network of neurons may find application in rectifying neural disorders.

In the original Vicsek model, each particle needs to know its total neighbors’ direction and does not distinguish the coupling strength between them. However, it is very slow to achieve consensus when the density of particles is too high. In this paper, in order to differentiate the coupling strength according to the Euclidean distance between particles, we proposed a new rule and introduced two improved Vicsek models based on this rule. The improved model I puts the exponential symbolic coupling function into the original Vicsek model, and the improved model II puts the exponential Sigmoid coupling function into the original Vicsek model. Simulations show these two kinds of improved Vicsek models all have less convergence steps than the original Vicsek model under different system parameters. Furthermore, when the exponent increases from 1, the convergence steps could be further reduced, then gradually stay unchanged.

With the increasing popularity of online social networking platforms, the amount of social data has grown exponentially. Social data analysis is essential as spamming activities and spammers are escalating over online social networking platforms. This paper focuses on spammer detection on the Twitter social networking platform. Although existing researchers have developed numerous machine learning methods to detect spammers, these methods are inefficient for appropriately detecting spammers on Twitter due to the imbalance of spam and nonspam data distribution, the involvement of diverse features and the applicability of data mechanisms by spammers to avoid their detection. This research work proposes a novel hybrid approach of the gravitational search algorithm and the decision tree (HGSDT) for detecting Twitter spammers. The individual decision tree (DT) algorithm is not able to address the challenges as it is unstable and ineffective for the higher level of favorable data for a particular attribute. The gravitational search algorithm (GSA) constructs the DTs with improved performance as the gravitational forces act as the information-transferring agents through mass agents. Moreover, the GSA is efficient in handling the data of higher dimensional search space. In the HGSDT approach, the construction of the DT and splitting of nodes are performed with the heuristic function and Newton’s laws. The performance of the proposed HGSDT approach is determined for the Social Honeypot dataset and 1KS-10KN dataset by conducting three different experiments to analyze the impact of training data size, features and spammer ratio. The result of the first experiment shows the need of a higher proportion of training data size, the second experiment signifies the more importance of textual content-based features compared to the other feature categories and the third experiment indicates the requirement of balanced data to attain the effective performance of the proposed approach. The overall performance comparison indicates that the proposed HGSDT approach is superior to the incorporated machine learning methods of DT, support vector machine and back propagation neural network for detecting Twitter spammers.

In this paper, our target is to implement and analyze numerical algorithms for the numerical solutions of initial and boundary third-order singular-type equations, and in particular the Emden–Fowler-type equations. Both linear and nonlinear equations are investigated. Two kinds of basis functions in terms of the shifted Chebyshev polynomials of the second kind are employed. Regarding the initial value problems, a new Galerkin operational matrix of derivatives is established and utilized, while in the case of boundary value problems (BVPs), the operational matrix of derivatives of the shifted Chebyshev polynomials of the second kind is employed. Convergence and error estimates of the two proposed expansions are given. The obtained solutions are spectral and they are consequences of the application of tau and collocation methods. These methods serve to convert the problems governed by their initial/boundary conditions into systems of linear or nonlinear algebraic equations which can be solved via suitable numerical solvers. Finally, we support our theoretical study by presenting several examples to ensure the accuracy and efficiency of the proposed algorithms.

In this paper, we design and explore a novel 3D autonomous nonlinear system with logarithmic nonlinearity. It is framed from the Lorenz model by replacing the variable $y$ in the second equation by the nonlinear logarithmic term $ln|y|$. The bi-parametric dynamics of the modeled system is presented. Studies reveal the emergence of shrimp-shaped periodic structures in chaotic regime with period-adding phenomenon, and coexisting periodic-chaotic attractors, which do not occur in the Lorenz model. The stability, bifurcation patterns, and chaotic behaviors of the system are explored with the aid of bifurcation diagrams, phase portraits and Lyapunov exponents. The complexity of the basin sets for the coexisting attractors is also studied.

In this paper, we derive the mathematical model that allows chemotaxis in avascular tumor growth in a two-phase medium. The two phases are the viscous cell phase and the inviscid fluid phase. The conservation of mass–momentum is incorporated in each phase, and appropriate constitutive laws are applied to formulate the governing equations. Further, these equations are simplified into three main variables: cell volume fraction, cell velocity, and nutrient concentration. These variables generate a coupled system of nonlinear partial differential equations. A numerical scheme based on the finite volume method is applied to approximate the solution of cell volume fraction. The finite element method is applied to approximate the solutions of cell velocity and nutrient concentration. In this research paper, we investigate tumor growth when tumor cells move along a fluid containing a diffusible nutrient to which the cells are drawn. We perform some numerical simulations to show the effect of the parameters. The findings of this literature are compatible with the existing literature.

In this paper, a connection between Heider social balance theory and synchronization patterns of a triad of oscillators is investigated. It is shown that the Heider and Kuramoto diagrams can be identified in a way that is consistent with the interpretation of cognitive states in Heider’s theory and with the behavior of coexisting species in a network of ecosystems. While most of this work focuses on networks with predefined fixed couplings, a preliminary investigation of an evolution model for the links of the network is presented, where a universal order parameter allows to formulate a feedback of the status of the oscillators to the coupling links.

For fractional inverse heat conduction problem (FIHCP), this paper introduces a numerical study. For the proposed FIHCP, in addition to the unknown function of temperature, the boundary heat fluxes are also unknown. Related to the two independent variables, the proposed scheme uses a fully spectral collocation treatment. Our technique is determined to be more accurate, efficient and practicable. The obtained results confirmed the exponential convergence of the spectral scheme.

Based on the double distribution function Boltzmann-BGK equations, a cell-centered finite volume lattice Boltzmann method on unstructured grids for high-speed viscid compressible flows is presented. In the equations, the particle distribution function is introduced on the basis of the D2Q17 circular function, and its corresponding total energy distribution function is adopted. In the proposed method, the advective term is evaluated by Roe’s flux-difference splitting scheme, and a limiter is used to prevent the generation of oscillations. The distribution functions on the interface are calculated by piecewise linear reconstruction, in which the gradient is computed by the least-squares approach. In order to do large-scale simulations, a parallel algorithm is illustrated. The present method is validated by a flow around the NACA0012 airfoil and a flow past a circular cylinder at high Mach numbers. The results agree well with the published results, which demonstrate that the present method is an efficient numerical method for high-speed viscid compressible flows. The parallel performance results show that the proposed parallel algorithm achieves 90% parallel efficiency on 4800 cores for a problem with $1.4\times 10^8$ unstructured triangle cells, which shows the potential to perform fast and high-fidelity simulations of large-scale high-speed viscid compressible flows in complicated computational domains.

Connected vehicles can obtain the position and velocity of multiple preceding vehicles. According to the characteristics of connected vehicles, this paper proposes the new control term and improves the original car-following model. The model considers the optimized acceleration and velocity difference information among the preceding vehicles. Meanwhile, linear stability analysis of the proposed model is executed and the stability criterion is given. It shows that the model has better stability compared with the existing car-following models. Three scenarios including car-following evolution, starting process and braking process are simulated, and the response time, space headway and velocity fluctuations are analyzed separately. The numerical simulation results show that the controlling strategy proposed in this paper has a noticeable effect on the stability of the traffic system. In addition, the calibration results based on the measured data show that the proposed model is more consistent with the actual traffic conditions.

Fields exhibit a variety of topological properties, like different topological charges, when field space in the continuum is composed by more than one topological sector. Lattice treatments usually encounter difficulties describing those properties. In this work, we show that by augmenting the usual lattice fields to include extra variables describing local topological information (more precisely, regarding homotopy), the topology of the space of fields in the continuum is faithfully reproduced in the lattice. We apply this extended lattice formulation to some simple models with nontrivial topological charges, and we study their properties both analytically and via Monte Carlo simulations.

The governing equations for fluid flows, i.e. Kadomtsev–Petviashvili–Benjamin–Bona–Mahony (KP-BBM) model equations represent a water wave model. These model equations describe the bidirectional propagating water wave surface. In this paper, an auto-Bäcklund transformation is being generated by utilizing truncated Painlevé expansion method for the considered equation. This paper determines the new bright soliton solutions for $(2+1)$ and $(3+1)$-dimensional nonlinear KP-BBM equations. The simplified version of Hirota’s technique is utilized to infer new bright soliton solutions. The results are plotted graphically to understand the physical behavior of solutions.

In many public spaces (e.g. colleges and shopping malls), people are frequently distributed discretely, and thus, single-source evacuation, which means there’s only one point of origin, is not always a feasible solution. Hence, this paper discusses a multi-source evacuation model and algorithm, which are intended to evacuate all the people that are trapped within the minimum possible time. This study presents a fast flow algorithm to prioritize the most time-consuming source point under the constraint of route and exit capacity to reduce the evacuation time. This fast flow algorithm overcomes the deficiencies in the existing global optimization fast flow algorithm and capacity constrained route planner (CCRP) algorithm. For the fast flow algorithm, the first step is to determine the optimal solution to single-source evacuation and use the evacuation time of the most time-consuming source and exit gate set as the initial solution. The second step is to determine a multi-source evacuation solution by updating the lower limit of the current evacuation time and the exit gate set continually. The final step is to verify the effectiveness and feasibility of the algorithm through comparison.