Physica A: Statistical Mechanics and its Applications最新文献

Higher-order networks can comprehensively describe interactions among groups, thus emerging as a novel area of exploration in network science. This paper aims to delve into the controllability of higher-order networks, where the network topology is characterized by higher-order interactions and the nodes are higher-dimensional dynamical systems. The collective effects on the network controllability from the dynamics of higher-order interactions, node dynamics, inner interactions, and external control inputs are extensively explored. By applying matrix theory and control theory, some necessary and/or sufficient conditions are developed to determine the controllability of hypergraph networks and simplicial complex networks. Through simulated examples, it becomes evident that the controllability of higher-order networked system is far more complicated than that of traditional networked systems and the higher-order topological structures facilitate the controllability. Remarkably, the integrated network can achieve controllability even when the corresponding traditional network is uncontrollable by external inputs.

Emergencies in public places, particularly confined crowded areas, will disrupt the stability of dense crowds and consequently lead to accidents. To promote public emergency safety, there is a pressing need for efficient modeling methods to investigate the evacuation mechanism in these places and improve the social public safety. This study proposes a Physical Fitness Heterogeneity based Social Force Model (PFH-SFM) that takes into account the heterogeneous desired evacuation velocity caused by the heterogeneity of pedestrian physical fitness, by means of developing the normalized desired velocity ratio. Then, we use PFH-SFM to investigate the relationships between the escape rate and the desired velocity, and between the evacuation duration and the desired velocity in terms of various group sizes with heterogeneous physical fitness, the relationship between the percentage of reduction in evacuation duration and desired velocity when including weak pedestrians, the pedestrian distribution in the evacuation process, the relationship between the total evacuation duration and the desired velocity in terms of various proportions of weak pedestrians and the relationship between the evacuation duration and the desired velocity in terms of various normalized starting and ending velocity ratios by considering various group sizes, respectively. The findings of this study show that the existence of a certain small proportion of pedestrians with weak physical fitness can promote global evacuation dynamics, especially in the case of high crowded density, and can reduce evacuation duration by up to 20% in our experiments. Additionally, when the percentage of pedestrians with weak physical fitness is relatively high, they tend to have a detrimental effect on the evacuation efficiency. Furthermore, there exists a moderate normalized desired starting velocity ratio that maximizes the overall evacuation efficiency; on the other hand, the lower the normalized desired ending velocity ratio is, the more efficient the evacuation is. To the best of the authors’ knowledge, this study is the first time to introduce the concepts of normalized desired starting and ending velocity ratios and innovatively analyzes the impact of the continuously changing desired velocity of pedestrians on the evacuation efficiency in multi-exit scenarios. The results offer valuable insights for relevant stakeholders to formulate effective evacuation plans, so as to enhance urban emergency capacity and minimize social and economic losses.

In the realm of pandemic dynamics, understanding the intricate interplay between disease transmission, interventions, and immunity is pivotal for effective control strategies. Through a rigorous agent-based computer simulation, we embarked on a comprehensive exploration, traversing unmitigated spread, lockdown scenarios, and the transformative potential of vaccination. we unveil that while quarantine unquestionably delays the pandemic peak, it does not act as an impenetrable barrier to halt the progression of infectious diseases. Vaccination factor revealed a potent weapon against outbreaks — higher vaccination percentage not only delayed infection peaks but also substantially curtailed their impact. Our investigation into bond dilution below the percolation threshold presents an additional dimension to pandemic control. We observed that localized isolation through bond dilution offers a more resource-efficient targeted control strategy than blanket lockdowns or large-scale vaccination campaigns.

Non-Fungible Tokens (NFTs) within the metaverse represent a rapidly emerging sector in the digital asset space. This paper provides a comprehensive review of the metaverse’s history and an analysis of the stylized facts of five metaverse NFTs: Axie Infinity, Decentraland, Enjin Coin, Theta Network, and The Sandbox. We examine market efficiency, volatility clustering, leverage effects, and the return-volume relationship. Our key findings show that all NFT returns exhibit kurtosis values significantly exceeding the standard value of three, indicating more peaked and heavier-tailed distributions than a normal distribution. Autocorrelation analysis reveals statistically insignificant results, suggesting minimal influence of past returns on current returns. The Hurst exponent fluctuates between 0.3 and 0.8, indicating relative inefficiency in log returns with varying degrees of trend reinforcement and anti-persistence. The GARCH(1,1) model confirms the presence of volatility clustering, with high persistence of volatility shocks over time, and most NFT returns exhibit a negative leverage effect, where negative returns decrease volatility. These findings provide critical insights for investors, content creators, and policymakers, emphasizing the need for innovative strategies and regulatory considerations in this evolving ecosystem. A comparative analysis using alternative metaverse-related assets from Bloomberg and Yield Guild Games enhances the robustness of our findings, enriching the academic discourse on digital assets and laying the groundwork for future research in metaverse NFTs.

Urban rail transit networks are essential components of urban transportation systems, but they are vulnerable to disruptions that can severely affect passenger mobility and network efficiency. Traditional methods for determining restoration sequences often rely on experiences or importance-based approaches, lacking precision in identifying critical vulnerable station combinations and struggling to find optimal restoration sequences under limited budgets. This paper introduces a three-level model framework aimed at addressing these issues. The middle and lower levels jointly identify the most vulnerable station combinations, while the upper level optimizes the restoration sequence by taking into account budget constraints and changes in resilience metric throughout the restoring period. The effectiveness of the proposed model was validated using four subway lines in Beijing, China. Results demonstrate that the model can effectively identify critical vulnerable station combinations. Additionally, the resilience-based restoration strategy effectively determines the optimal recovery plan for damaged stations under limited budgets, outperforming traditional restoration strategies based on complex networks and offering strong extensibility.