International Journal of Modern Physics C

This paper proposed a pedestrian evacuation model combined with reinforcement learning in order to study how to better guide pedestrians to complete evacuation in specific indoor scenes. This model introduced the way of establishing a scene in cellular automata and formulated reward rules according to the characteristics of the scene. It fitted the psychological activities of pedestrians in the actual evacuation process and trained the strategy of pedestrians at the overall level through the Q-learning algorithm from the reinforcement learning area. A speed control mechanism combined with real statistical data was introduced to simulate the speed attenuation. A simulation platform was built to compare the evacuation conditions under different scenarios and the different total numbers of pedestrians. The research showed that the model could automatically realize the exit selection function of pedestrians and part of conformity behavior. In the same evacuation scenario, this model could show adaptability for the different total numbers of pedestrians.

The propagation of delays is a prevalent issue in high-speed railway (HSR) networks. However, the underlying properties of delay propagation have not been fully explored, hindering the effective management of this problem. In this paper, considering the impact of traffic flow, we model the delay propagation as a traffic-driven susceptible–infected–susceptible (SIS) epidemic spreading process. We introduce a traffic frequency matrix to estimate the traffic rate and further derive the propagation dynamic equations using a continuous-time Markov chain method. We then derive the epidemic threshold of the model, which is proportional to the recovery rate and inversely proportional to the average path length. Finally, through simulation, we study the impacts of various factors including the infection rate, recovery rate, and the degree and number of initial delay stations. Our work provides some insights for the prediction and control of delay propagation in HSR networks.

The spread of homogenized information on social networks can create certain positive promotional effects. However, if it is excessively spread, it may lead to constraining effects and even cause negative impacts. In this paper, a spreading model called C-SIRI is proposed, which conducts an in-depth study on the constraining mechanism in the process of information spreading, as well as the rules and characteristics affecting its impact. Taking the COVID-19 epidemic information spreading on Sina Weibo social network from March 2020 to May 2022 as an example, the reliability of this model is verified. The results show that content homogeneity, participation degree and false proportion have a significant influence on the constraining mechanism of social network information spreading, and the threshold of information spreading constraining is closely related to the spreading times. This research helps to understand the role of constraining mechanism in information spreading and promote the application of social networks in information spreading prediction and control.

It is shown that black holes in a quark gluon plasma (QGP) obeying minimum viscosity bounds exhibit a Schwarzschild radius in close match with the range of interaction of the strong force. For such black holes, an evaporation time of about $10^{16}$ s is estimated, indicating that they would survive by far the quark-gluon plasma era, namely between $10^{-10}$ and $10^{-6}$ s after the big bang. On the assumption that the big-bang generated unequal amounts of quark and antiquarks, this suggests that such unbalance might have survived to this day in the form of excess antiquark nuggets hidden to all but gravitational interactions. A connection with the saturon picture, whereby minimum viscosity regimes would associate with the onset coherent quantum field structures with maximum storage properties, is also established.

Interstitial fluid flow (IFF) and blood flow (BF) are analytically derived using the continuity and momentum equations for a cylindrical tumor. We considered a tumor as a rigid porous media with necrotic core, interstitial fluid and two capillaries. The capillaries have two pressures: arterial input and venous output. To describe BF within the capillaries and IFF inside tumor tissues, Poiseuille’s and Darcy’s laws are used. Here, we have divided the tumor into three parts. Our results show that the center of tumor has the maximum interstitial pressure. The pressure reduces toward the first capillary and between two capillaries. The reduction of pressure continues at the outside of the tumor. The effect of tilted external magnetic field is also studied. The results show that the field has a significant effect on the pressure. The magnetic field reduces the drug delivery at the center and exterior parts of the tumor. Furthermore, we have studied the effect of different parameters, such as interstitial resistance, magnetic field and necrotic core, on the interstitial pressure.

Intracranial aneurysms are localized dilatations of the cerebral arteries that carry a risk of rupture and subsequent subarachnoid hemorrhage, a life-threatening condition. Middle cerebral artery (MCA) aneurysms are a common type of intracranial aneurysm, and endovascular treatment using coils or flow diverters is a common intervention approach. Understanding the hemodynamics, or blood flow patterns, within MCA aneurysms and how they are affected by endovascular treatment is crucial for improving patient outcomes. This numerical study investigates the hemodynamics of blood flow within MCA aneurysms before and after endovascular treatment. Patient-specific geometric models of MCA aneurysms were reconstructed from medical imaging data. Simulations using computational fluid dynamics were conducted to examine flow characteristics, wall shear stress, and additional hemodynamic factors within the aneurysms. The study assessed and contrasted the impact of coil embolization and flow diverter placement on the hemodynamics inside the aneurysms. The findings offer a deeper understanding of the intricate flow dynamics within MCA aneurysms and illustrate the ways in which endovascular treatments can modify these dynamics.

Accurately modeling two-phase flows involving the surfactant is of great significance for understanding the interface evolution dynamics due to the variation of surface tension. In this study, we proposed an Allen–Cahn-based lattice Boltzmann model for two-phase flows with soluble surfactants, in which a new force scheme from the view of continuous surface tension force scheme and the consistent wetting boundary condition are derived. Several numerical tests are conducted, and the results are compared with the model based on the previous potential form (PF) interfacial force scheme. The results indicate that our proposed model can model two-phase flows involving the soluble surfactants, even for high-density ratio situations. The distribution across the interface is accurately predicted. Our model also shows superior performance on two-phase dynamics involving surfactants, i.e. smaller spurious currents and larger range of contact angle. This model can be a favorable alternative for high-density-ratio two-phase flows involving soluble surfactants.

This study investigates the impact of a rotating tube on the flow characteristics within a tube bank consisting of nine cylinders arranged in an in-line configuration, with a pitch-to-diameter ratio of 1.44, using both experimental and numerical approaches. Experiments are performed in a subsonic wind tunnel to measure pressure distributions at various azimuthal angles along the tubes, using a multi-channel differential pressure system. Drag forces are determined via a wire-strain gauge balance. Numerical simulations are conducted using ANSYS Fluent, employing the URANS-based Shear-Stress Transport (SST) k–$\omega$ model, to replicate turbulent cross-flow at two values of Reynolds numbers $\mathrm{\text{Re}}=0.712\times 10^5$ and $\mathrm{\text{Re}}=1.42\times 10^5$. This study explores the effect of a tube rotation within the array on flow separation angles, pressure distributions and vortex shedding in the wake region, focusing on the effects of different positions of the rotating tube on the flow characteristics of the surrounding tubes. Numerical results closely match the experimental data, demonstrating that the rotational motion significantly mitigates flow separation and reduces drag forces on adjacent tubes. Additionally, the position of the rotating tube within the array plays a critical role in optimizing the fluid forces and pressure distribution, offering enhanced control over the flow within tube banks.

Despite extensive efforts to the continuous-time Markov process of the susceptible–infected–susceptible (SIS) model, there remains a dearth of efficient methods for numerically simulating the continuous-time spreading paths in complex networks using discrete-time simulations. In typical discrete-time approaches to the SIS model, time is discretized into small uniform intervals, and synchronous updating schemes are commonly employed to update the states of nodes. However, in this study, we demonstrate that the synchronous updating scheme introduces undesired noise, such as period-doubling and chaotic behaviors, into the spreading paths, diverging from the continuous-time SIS model. Additionally, we observe that the classical synchronous discrete-time scheme underestimates the spreading ability as compared to the corresponding continuous-time SIS model, a discrepancy that is dependent on the length of the time intervals. Leveraging the Taylor formalism, we propose a novel synchronous discrete-time updating method that addresses these issues associated with the classical synchronous discrete-time scheme. Importantly, our proposed method achieves high accuracy regardless of the length of the time intervals, which can be further exploited to enhance simulation speed. Experiments in both artificial and real networks show that our method approximates the continuous-time spreading paths better and costs less computing time than the classical discrete-time synchronous method.

In order to speed up the consensus of the Vicsek model, existing studies assign different coupling strengths according to the inter-particle Euclidean distance, which effectively reduces the convergence step. In this paper, we propose two new coupling functions, resulting in two improved Vicsek models. Improved Model I introduces the exponential Relu coupling function into the original Vicsek model, and Improved Model II introduces the exponential Swish coupling function into the original Vicsek model. Simulations show that both improved Vicsek models have fewer convergence steps than the original improved Vicsek model based on the coupling function. With different system parameters, the number of convergence steps is smaller than the original improved Vicsek model based on the coupling function. In addition, the number of convergence steps further decreases when the exponent increases and then gradually remains constant. The improved Vicsek model II also achieves better results in resisting noise.

This study employs a cellular automata (CA) model to investigate the colonization process of Paraburkholderia phytofirmans (PsJN) in the rhizosphere, a complex ecological environment critical to plant–microbe interactions. The proposed CA model simulates bacterial population dynamics, comparing the behavior of the wild-type strain (PsJN WT) with a mutant strain (PsJN-BpI.1) that exhibits impaired quorum sensing (QS), affecting its motility and communication. The model uses a grid where each cell can either be empty or occupied by bacteria. The spread of bacterial colonies is influenced by the state of neighboring cells, with a circular neighborhood used to simulate colony formation. The transition function incorporates both bacterial motility and population control, two critical factors in rhizospheric colonization. Simulation results show that the wild-type strain demonstrates a higher concentration of colonies near the roots, while the mutant strain exhibits reduced growth in these regions. Comparing the simulations with real rhizosphere colonization images confirms the model’s accuracy and highlights the importance of carefully selecting parameters for reliable outcomes. This CA model successfully captures the colonization behavior of PsJN strains in the rhizosphere, providing valuable insights into bacterial ecology and plant–microbe interactions.

Time series forecasting has practical applications in many fields such as power load forecasting and traffic flow prediction. However, current time series forecasting methods focus more on temporal correlations, with less consideration given to the impact of spatial dimensions on time series predictions. Today, complex spatial–temporal coupling relationships significantly influence time series forecasting. Existing works mostly model time series predictions purely based on temporal correlations, without modeling spatial–temporal correlations, leading to insufficient prediction accuracy in time series forecasting. Inspired by the recent success of Transformers in the graph domain and their successful application in forecasting, we propose a new model for time series prediction called the Sparse Spatial–Temporal Attention Forecasting Network. This network performs adaptive spatial–temporal attention modeling on spatial–temporal graphs and incorporates a sparsity measure function, MLP, and average pooling into the traditional Transformer’s multi-head self-attention mechanism to better weight the attention scores. To improve prediction accuracy, the Sparse Spatial–Temporal Attention Forecasting Network constructs a weighted adjacency matrix based on the topological relationships between spatially adjacent nodes and information such as latitude and longitude. It then builds an adaptive graph using this weighted adjacency matrix to capture hidden spatial–temporal coupling relationships. Simultaneously, it employs a sequence decomposition algorithm based on average pooling to divide the data into two main blocks for separate prediction, thereby enhancing prediction accuracy. The Sparse Spatial–Temporal Attention Forecasting Network’s superior performance is demonstrated through experimental results on the Shanghai Pudong power load dataset and the public dataset PEMS-BAY.

The recent claimed room-temperature superconductivity in Cu-doped lead apatite at ambient pressure is under debate. To identify its physical origin, we conducted a detailed analysis of the crystal structure, band structure, lattice dynamics and magnetic properties of the parent Pb${}_{10}$(PO₄)₆O compound and the two different phases of Pb₉Cu(PO₄)₆O (LK-99) compound. Our results show that the parent Pb${}_{10}$(PO₄)₆O compound is an indirect band gap semiconductor, where Cu doping at the 4f site of Pb leads to a semiconducting to half-metallic transition. Two half-filled flat bands spanning the Fermi energy levels are present in the 4f phase of LK-99, which are mainly formed by hybridization of the d orbitals of Cu with the 2p orbitals of O. In addition, 6h phase of LK-99 always has spin polarization at the bottom of the conduction band and at the top of the valence band, making the material a magnetic semiconductor. Our theoretical research reveals recent experimental reports on the different transport properties of LK-99.

Conventional resource allocation strategies, such as the Simplest Average (SA) resource allocation strategy, often assume identical transmission capacity for all nodes, failing to account for the actual importance of nodes within the network, especially in three-layer networks where inadequate resource allocation to hub nodes can significantly impact performance. Existing methods are primarily designed for single-layer networks and struggle to address the complexity and interdependencies of real-world multi-layer structures, thus limiting the optimization of network traffic capacity. This paper proposes an Effective Logarithm-Degree (EL) resource allocation strategy that allocates node delivery capacity based on the ratio of the logarithmic degrees of nodes across the logical and physical layers. Compared to the Simplest Average resource allocation strategy, the Effective Logarithm-Degree strategy significantly enhances network traffic capacity, increasing it by over 1.4 times and reducing traffic load by 60%, while also optimizing the load balance. Simulation results validate the superiority of the Effective Logarithm-Degree strategy in improving performance and resource utilization efficiency in three-layer network environments.