International Journal of Modern Physics C

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users’ interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks.

BisGMA, a crucial component of dental resin widely employed in oral care, undergoes an in-depth computational investigation in this study. Employing advanced computational tools such as Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and General Utility Lattice Program (GULP), we explore a wide range of physical parameters. These encompass the stress–strain variations, Young’s and linear moduli, pair distribution functions, RMSD trajectory analysis, enthalpy, entropy, elastic constants, self-energy, zero-point energy and optical and dielectric constants. This multifaceted analysis enhances our understanding on intricate correlation of this material, offering insights that are frequently challenging to obtain.

The paper aims to perform numerical simulations of flapping flight using Discrete Vortex Method (DVM) scheme. The scheme is suitable for moderately low Reynolds number $(<\sim 10^5)$ and computationally less expensive as the flow domain doesn’t need to be discretized at each time step. Flapping of a one-dimensional (1D) flexible filament in a two-dimensional (2D) inviscid flow is simulated. The effect of bending by varying the wing shape along the spanwise length on aerodynamic performance was studied. It is observed that compared to rigid wings, bending wings are found to be better in generating lift. The effect of various bending wing configurations ($F1$, $F2$, $F3$ and $F4$) and different methods of imposing the bending ($W1$, $W2$ and $W3$) is studied. It was demonstrated that applying wing bending only during the downstroke phase ($W2$) is more effective than imposing bending throughout the flapping cycle ($W1$). Moreover, an effort is made to replicate the bending configuration observed in manta ray fish ($W3$) to investigate its impact on flow domain characteristics, lift, and thrust forces. Furthermore, the inclusion of a winglet is found to significantly enhance lift generation. In addition to the study of bending effects on aerodynamic performance, the study also seeks to emulate a unique aspect of bat flight kinematics, specifically the dynamic variation in wing span length during flapping. In a comparative analysis of two span length variation strategies, it is discerned that exclusively varying span length during the upstroke phase is the optimal approach for achieving increased lift generation. The study highlights the crucial role of wing bending and span length modulation in achieving elevated lift forces while simultaneously reducing drag. These findings are seen as holding significant promise for the design and optimization of Micro Air Vehicles (MAVs) utilizing flapping-based lift generation mechanisms, contributing substantially to the identification of optimal parameters for enhancing MAVs’ aerodynamic performance and operational efficiency.

This study aims to solve the problem of small key space in image cryptosystems based on logistic mapping. First, a new one-dimensional (1D) chaotic system, with a wide continuous chaotic interval, a large Lyapunov exponent and obvious chaotic characteristics are presented. Subsequently, a novel image encryption algorithm based on the new 1D chaotic system and dynamic DNA encoding is designed. Compared with other DNA coding methods, the proposed image encryption algorithm encodes chaotic sequences and ensures that the sequence elements at different positions correspond to different DNA coding schemes. This will help to overcome the fixity of DNA coding and make the proposed dynamic DNA coding easy to operate and implement. Finally, the cipher image is obtained by scrambling and bit XOR operation based on the chaotic sequences. The fixed DNA coding method is compared with other chaotic image encryption schemes, and the experimental results indicate that the image encryption algorithm has higher security and can resist common attacks.

The modeling of blind people’s behavior is an important topic in pedestrian evacuation dynamics. In order to study movement characteristics of blind people in evacuation process and the effect of indoor obstacles on the evacuation efficiency of blind people, an extended cellular automata model is proposed to simulate blind people evacuation by considering the influence of obstacles. The control variable method is adopted to study the effect of indoor obstacles on the blind evacuation efficiency rule. The numerical simulation is applied to analyze the evacuation process of blind people under different setting (e.g. the location, size and other factors) of obstacles and different evacuation scenarios. The shunt effect of obstacles is discussed and the layout strategy of obstacles is optimized. The results show that the longitudinal obstacles in front of the exit can effectively play the diversion role of obstacles when the obstacle is at a certain distance from the exit, which will improve the evacuation efficiency of the blind. Furthermore, a horizontal obstacle of appropriate length can constrain the trajectory of the blind people and improve the efficiency of evacuation, but when the horizontal obstacle is too long, it will induce extra jam on both sides of the obstacle and hinder the evacuation of the blind. The finding can provide the theoretical basis for the blind people’s organization, management and the optimization of evacuation strategies during an emergency.

This research aims to analyze and implement two numerical spectral schemes for solving linear and nonlinear time fractional Cable equations (TFCEs). Two modified sets of shifted Gegenbauer polynomials (SGPs) are used as basis functions. The approximation of the solution is written as a product of the two chosen basis function sets. For these methods, the principle idea is to convert the problem governed by the underlying conditions into a set of linear and nonlinear algebraic equations that can be solved using appropriate numerical techniques. The upper estimate of the truncation error for the proposed expansion is investigated. In the end, four examples are presented to illustrate the accuracy of the suggested schemes.

This study presents a new intelligent computing method based on the Levenberg–Marquardt back-propagation-based supervised neural network (LMB-SNN) for the flow of Oldroyd 4-constant fluid in the forward roll coating process (OFC-FRCP). The mathematical formulations for OFC-FRCP are converted into ordinary differential equations using lubrication approximation theory (LAT) and suitable dimensionless parameters. The analytic expressions for velocity distribution, pressure and pressure gradients have been obtained using Adomian’s Decomposition Method (ADM) for several situations. The fluid’s separation point and coating thickness are then calculated using integration. The reference dataset for LMB-SNN has been obtained by ADM for multiple scenarios by adjusting various physical variables. The neural network’s training, testing and validation are carried out in parallel to minimize the mean-squared error function (MSEF). The proposed LMB-SNN’s effectiveness was confirmed by analyzing the MSEF, error histograms and regression plots. The precision of the method is verified by the close agreement between numerical outputs and dataset values. The intelligent numerical results computed using ADM and LMB-SNN are presented in various graphs and tables. The method’s accuracy is demonstrated by the numerical outputs closest to the built and dataset values at levels between $10^{-9}$ and $10^{-12}$.

This paper explores the impact of viscosity ratio and surface wettability on immiscible viscous fingering instability within a rectangular channel. Numerical investigations are conducted across a range of viscosity ratios (VR) from 0.0009 to 0.5 and wall wettability ($\theta$) from 15° to 150°. The volume of fluid (VOF) model is employed to track the development of finger-shaped instability at the fluid interface. Our results indicate that higher viscosity ratios lead to increased displacement efficiency. Additionally, we find the formation of necking at low VR, which diminishes at higher VR values. The finger-shaped pattern splits into two parts at a wettability of 15°; beyond this threshold, no such splitting occurs. Furthermore, a transition from hydrophilic to superhydrophobic wettability abolishes necking, resulting in enhanced displacement efficiency. Notably, as wettability shifts from hydrophilic to super hydrophobic, instability shifts toward the left side. These findings hold relevance for applications in drug delivery, clinical processes and oil recovery.

Robustness is one of the most critical characteristics of complex networks, determining their stability and resilience in the face of various disturbances. With the research on complex networks, people have observed that higher-order networks can also affect the structure and function of the network. Therefore, when studying network robustness, we also need to consider the interaction between higher-order networks and low-order networks. To delve deeper into the investigation and comprehension of robustness in complex networks, this paper introduces a model of higher-low order networks with adjustable coupling strength. In this model, network nodes are categorized into autonomous and nonautonomous nodes, with coupling strength adjusted using three methods: random coupling, degree-related coupling, and local coupling. Validation is performed on ER networks, BA networks, SW networks, as well as several real-world networks. The results indicate that as coupling strength increases, network robustness correspondingly diminishes, and the network exhibits intricate percolation behavior.

Tensile mechanical tests of GH4169 superalloy were performed for smooth and notch specimens at room temperature. Moreover, a finite element method (FEM) was employed by an Abaqus simulation software to provide the tensile test processes during elastic and plastic deformation. Comparing the experimental measurements with the simulation and analysis results, it was found that the $K_{\text{et}}$ value decreased with increasing $K_t$ value, and the $K_{\text{et}}$ value of smooth and notched specimens were close to the $\sqrt{K_{\epsilon }\cdot K_{\sigma }}$ value of both elastic deformation and plastic deformation. Reasonable explanations were also provided for the stress concentration factor $K_{\sigma }$ and strain concentration factor $K_{\epsilon }$.

This study explored whether a policy with maximum velocity limitation prior to each jam point in a traffic flow system can mitigate jam situations in a highway. To quantify this question, a new cellular automata traffic model based on the Revised S-NFS model was established. We perused two specific scenarios: a jam naturally brought by a high traffic density, and one brought by a lane-closed section placed as an explicit bottleneck. The result of multi-agent simulation (MAS) reveals that such maximum velocity limitation policy can certainly mitigate jam situations when each jam is naturally bought up not by a bottleneck, as proved by several statistics, and cannot improve the traffic flux.

In order to unravel the physical and mathematical mystery of synchronized-flow mechanism and to reveal the fundamental mechanism and origin of synchronized flow produced by nonlinear stochastic processes, we have produced simple stochastic traffic flow models (the gradual-NaSch, Phoenix and mPhoenix models) with nonlinear safe speeds. In the mNaSch model and our gradual-NaSch model, the $i$th vehicle’s speed is the same as or different from the $j$th vehicle’s speed because they are discrete values. In the mNaSch and gradual-NaSch models, the same discrete values of the $i$th and $j$th states make it easy to identify synchronized flow with speed-synchronized phase of the $i$th and $j$th states. On the other hand, when we deal with synchronized flow in continuous traffic flow models, we face a problem. Continuous values cause the difficulty in identification of synchronization. In order to definitely clarify whether or not synchronized flow occurs in continuous models, we have established a novel idea of Exchange approach to recognizing synchronized flow as speed-synchronized phase. Our idea is generated from the analogical image that the whole system of synchronized metronomes does not change even if the $i$th and $j$th synchronized metronomes are exchanged. The Exchange approach (exchanging the $i$th vehicle’s state for any $j$th vehicle’s state in the whole system of traffic flow), which causes distortion such as a collision if non-synchronized vehicle’s states are exchanged and makes it possible to definitely clarify whether or not synchronized flow occurs in continuous models, is applied to our simple stochastic continuous model (the mPhoenix model). On the basis of the Exchange approach, we can recognize that the mPhoenix model surely reproduces synchronized flow. In addition, we have proposed mathematical approach to deriving nonlinear safe speeds which guarantee collision free driving and reproduce synchronized flow.

The gravity model is a widely recognized tool for estimating the movement of people and goods. In this study, we introduce two gravitational variables, population size and regional GDP per capita (RGDPPC), to explain the characteristics of population movement between and within cities in South Korea. A log-linearized gravity model is employed to run regression analyses at three spatial levels: the national level (encompassing the entirety of South Korea), the metropolitan level (focusing on the Seoul and Busan Metropolitan Transportation Areas) and the city level (specifically in Seoul and Busan). The study incorporates data on various modes of transportation from 246 of the 250 municipalities in South Korea. Predictive performance of the model is better when utilizing national-level data. However, as spatial area decreases and population density increases, the models explanatory power decreases significantly when relying solely on data related to either population size or RGDPPC. The findings suggest that incorporation of both population size and RGDPPC into the gravity model best captures the dynamics of traffic flow within economically integrated regions. This relationship is analogous to gravitational fields generated by two distinct types of mass. Including both population size and RGDPPC, the gravity model can be leveraged effectively to estimate traffic patterns, particularly within regions characterized by high economic integration.

The authors of this research paper have introduced a new method for solving Hamilton–Jacobi (HJ) equations called third-order weighted essentially nonoscillatory (WENO) method. This method uses exponential polynomials to construct numerical fluxes and smoothness indicators, which helps distinguish between singular and smooth regions more efficiently. The smoothness indicators are created using a finite difference operator that eliminates exponential polynomials. The numerical flux is constructed using an interpolation method based on exponential polynomials, which results in better outcomes around steep gradients. The new method maintains a high level of accuracy (i.e. three) in smooth regions, even near critical points. The authors have presented some numerical results to demonstrate the effectiveness of the new method and compared it with other WENO methods.