Physica A: Statistical Mechanics and its Applications最新文献_第9页

Calibrating a self-propelled particle model for area-based heterogeneous traffic using quadratic optimisation 基于二次优化的区域异构交通自推进粒子模型标定

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.physa.2025.131224

Yawar Ali , K. Ramachandra Rao , Ashish Bhaskar , Niladri Chatterjee

This study introduces a traffic modelling framework that treats vehicles as self-propelled particles (SPPs) interacting in a two-dimensional, heterogeneous, and disordered environment. Unlike conventional lane-based models, which assume homogeneity and linear interactions, this approach captures the complexity of real-world traffic where diverse vehicles operate without strict lane discipline. The model employs a synchronous update structure in which each vehicle-agent adjusts its speed and direction based on local interactions, balancing acceleration, alignment, and repulsion forces. A core contribution of this work is a quadratic optimisation-based calibration procedure that fits the self-propelled particle model to large-scale trajectory data while preserving behavioural interpretability. Instead of relying on heuristic tuning, the model parameters are estimated using a constrained quadratic programming (QP) formulation, solved with the Gurobi optimiser. This enables precise, context-sensitive calibration of behavioural parameters, such as speed responsiveness, alignment sensitivity, and directional repulsion, across various unique traffic interaction scenarios defined by vehicle type (6 ×6), relative position (4), and density levels (3), totalling 432 scenarios. The result is a high-fidelity representation of driving behaviour that can adapt to varied traffic compositions and density levels. Beyond motion prediction, the calibrated model offers interpretability and insight into traffic dynamics. It reveals how different vehicle types interact under varied density levels, how risk perception varies across spatial zones, and how local coordination influences flow stability. Importantly, the framework claims to develop a real-time digital twin for proactive safety analysis and efficient operation. By observing variations in behavioural parameters over time, the model can flag early signs of instability or unsafe interactions, enabling timely interventions. The proposed model, formulated as a system of discrete-time equations, presents a scalable solution for modelling disordered traffic systems. It bridges theoretical insights from swarm dynamics with the practical needs of traffic engineering, offering a path forward for simulating, understanding, and improving complex urban mobility, particularly in area-based and heterogeneous traffic environments.

本研究介绍了一种交通建模框架，该框架将车辆视为在二维、异构和无序环境中相互作用的自推进粒子（SPPs）。与传统的基于车道的模型（假设同质性和线性相互作用）不同，这种方法捕捉到了现实世界交通的复杂性，即各种车辆在没有严格车道规则的情况下运行。该模型采用同步更新结构，其中每个车辆代理根据局部相互作用调整其速度和方向，平衡加速度、对准力和排斥力。这项工作的核心贡献是基于二次优化的校准程序，该程序使自推进粒子模型适合大规模轨迹数据，同时保持行为可解释性。而不是依赖于启发式调整，模型参数估计使用约束二次规划（QP）公式，解决与Gurobi优化器。这使得精确的、上下文敏感的行为参数校准成为可能，如速度响应、对齐灵敏度和方向排斥，跨越各种独特的交通交互场景，由车辆类型（6 ×6）、相对位置(4)和密度水平(3)定义，总共432种场景。结果是一个高保真的驾驶行为表示，可以适应不同的交通组成和密度水平。除了运动预测，校准模型提供了可解释性和洞察交通动态。揭示了不同车辆类型在不同密度水平下的相互作用，风险感知在不同空间区域的变化，以及局部协调对流量稳定性的影响。重要的是，该框架声称开发了一个实时数字孪生体，用于主动安全分析和高效运行。通过观察行为参数随时间的变化，该模型可以标记出不稳定或不安全互动的早期迹象，从而能够及时干预。该模型是一个离散时间方程系统，为无序交通系统的建模提供了一个可扩展的解决方案。它将群体动力学的理论见解与交通工程的实际需求联系起来，为模拟、理解和改善复杂的城市交通提供了一条前进的道路，特别是在基于区域和异构的交通环境中。

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引用次数: 0

A model for quantum game on networks 网络上的量子博弈模型

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.physa.2025.131217

Xu Zhang , Guanrong Chen

A model of quantum game on networks is introduced, describing how the topology of a network impacts the quantum game. This framework includes the quantum multi-oligopoly Cournot model on networks. Furthermore, the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) equation on networks is introduced, which represents the interaction change of the quantum state on a network. Based on the GKSL equation, a model for the quantum replicator dynamics on networks is proposed, providing a framework for the dynamic quantum evolutionary game on networks.

介绍了网络上的量子博弈模型，描述了网络拓扑结构对量子博弈的影响。该框架包括网络上的量子多寡头古诺模型。在此基础上，引入了表征网络中量子态相互作用变化的GKSL方程。基于GKSL方程，提出了网络上量子复制子动力学模型，为网络上的量子动态进化博弈提供了一个框架。

引用次数: 0

Enhancing causal discovery in financial networks with piecewise quantile regression 用分段分位数回归增强金融网络的因果发现

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.physa.2025.131185

Cameron Cornell, Lewis Mitchell, Matthew Roughan

Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, do not fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme — allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer — a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets.

金融网络可以利用投机资产价格序列中的统计依赖关系来构建。在用于推断这些网络的各种方法中，普遍依赖于预测建模来捕捉相互关联效应。这些方法通常模拟平均响应信息的流动，或市场中波动性和风险的传播。这种技术虽然很有见地，但并没有完全捕捉到投机市场中可能存在的更广泛的分布层面的因果关系。本文介绍了一种新颖的方法，将分位数回归与分段线性嵌入方案相结合，使我们能够构建因果关系网络，以识别金融市场固有的复杂尾部相互作用。将该方法应用于260个加密货币回报序列，我们发现了显著的尾尾因果效应和实质性的因果不对称。我们确定硬币的倾向是自我影响的，具有相对稀疏的交叉变量效应。综合评估所有链接类型，比特币作为主要影响者脱颖而出——这是传统线性平均响应分析中遗漏的细微差别。我们的研究结果为分布因果关系建模引入了一个全面的框架，为金融市场因果关系的更全面表征铺平了道路。

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引用次数: 0

First-passage times in a pure-birth coalescence model with size-dependent rates 具有大小依赖率的纯生聚结模型的首次传代时间

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.physa.2025.131221

Santosh Kudtarkar

We study a simple spatially homogeneous pure-birth growth model in which the size of a single “collector” cluster increases in unit steps with rate

λ_{n} = k n^{γ}

where

n

is the cluster size, and analyse the first-passage time (FPT) to reach a threshold size

N

. For arbitrary growth exponent

γ > 0

and initial size

n_{0}

, we solve the discrete master equation and obtain an exact finite-

N

first passage time distribution (FPTD) in hypoexponential (phase-type) form via Laplace transforms. From this closed form, we derive explicit small-time and large-time asymptotics for fixed

N

, showing how the need to complete

M = N - n_{0}

sequential stages suppresses very early passages and how the slowest stage controls the far tail. We then analyse the large-

N

behaviour: for

0 < γ \leq 1

the mean FPT diverges (as

N^{1 - γ}

or

log N

), while for

γ > 1

the FPT converges to a non-degenerate limit with finite Hurwitz-zeta moments and an infinite-product Laplace transform. In this strongly accelerating regime we obtain a small-time saddle-point asymptotic of essential-singularity type with an explicit power-law prefactor that makes the dependence on

n_{0}

and

γ

transparent. Together, these results clarify how

N

,

γ

, and

n_{0}

jointly shape the FPT distribution and provide a mathematically controlled benchmark linking exact stochastic growth to large-deviation onset criteria in applications such as warm-rain initiation and other aggregation-driven processes.

本文研究了一个简单的空间齐次纯生生长模型，其中单个“收集器”簇的大小以λn=knγ的速率以单位步长增长，其中n为簇大小，并分析了首次通过时间（FPT）达到阈值大小n。对于任意生长指数γ>；0和初始大小n0，我们通过拉普拉斯变换求解离散主方程，得到了精确的有限n次首次通过时间分布（FPTD）。从这个封闭的形式，我们得到明确的小时间和大时间渐近的固定N，说明需要完成M=N−n0顺序阶段如何抑制非常早期的通道和最慢的阶段如何控制远尾。然后我们分析了大n的行为：对于0<；γ≤1，平均FPT发散（作为N1−γ或logN），而对于γ>；1， FPT收敛到具有有限Hurwitz-zeta矩和无限积拉普拉斯变换的非退化极限。在这种强加速状态下，我们得到了一个具有显式幂律前因子的小时间鞍点渐近本质奇点型，使得对n0和γ的依赖透明。总之，这些结果阐明了N、γ和N是如何共同塑造FPT分布的，并提供了一个数学控制的基准，将精确的随机增长与暖雨起始和其他聚集驱动过程等应用中的大偏差开始标准联系起来。

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引用次数: 0

Digitization can stall swarm transport: Commensurability locking in quantized-sensing chains 数字化会阻碍群体传输：量化感知链中的通约性锁定

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-18 DOI: 10.1016/j.physa.2025.131225

Caroline N. Cappetto , Penelope Messinger , Kaitlyn S. Yasumura , Miro Rothman , Tuan K. Do , Gao Wang , Liyu Liu , Robert H. Austin , Shengkai Li , Trung V. Phan

We present a minimal model for autonomous robotic swarms in both one-dimensional and higher-dimensional spaces, where identical, field-driven agents interact pairwise to self-organize spacing and independently follow local gradients sensed through quantized digital sensors. We show that the collective response of a multi-agent train amplifies sensitivity to weak gradients beyond what is achievable by a single agent. We discover a fractional transport phenomenon in which, under a uniform gradient, collective motion freezes abruptly whenever the ratio of intra-agent sensor separation to inter-agent spacing satisfies a number-theoretic commensurability condition. This commensurability locking persists even as the number of agents tends to infinity. We find that this condition is exactly solvable on the rationals – a dense subset of real numbers – providing analytic, testable predictions for when transport stalls. Our findings establish a surprising bridge between number theory and emergent transport in swarm robotics, informing design principles with implications for collective migration, analog computation, and even the exploration of number-theoretic structure via physical experimentation.

我们提出了一个在一维和高维空间中自主机器人群体的最小模型，其中相同的场驱动代理两两相互作用以自组织间距并独立地遵循通过量化数字传感器感知的局部梯度。我们表明，多智能体训练的集体响应放大了对弱梯度的敏感性，超出了单个智能体所能达到的水平。我们发现了一种分数输运现象，在均匀梯度下，当智能体内部传感器间距与智能体间间距的比值满足数论通约性条件时，集体运动突然冻结。即使代理的数量趋于无穷大，这种可通约性锁定仍然存在。我们发现这个条件在有理数上是完全可解的——实数的密集子集——为运输何时停止提供了分析的、可测试的预测。我们的研究结果在群体机器人的数论和紧急运输之间建立了一座令人惊讶的桥梁，为集体迁移、模拟计算甚至通过物理实验探索数论结构提供了设计原则。

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引用次数: 0

Electric conductivity in open one-dimensional constriction 开放一维收缩中的电导率

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-16 DOI: 10.1016/j.physa.2025.131220

E.Kh. Alpomishev , G.G. Adamian , N.V. Antonenko

The non-Markovian two-dimensional dynamics of a charged particle, that is linearly coupled to a neutral bosonic heat bath, confined in a harmonic oscillator in one direction, and free in the other, is investigated in an external uniform magnetic field and two perpendicular time-dependent electric fields. Analytical expressions are derived for the time-dependent diagonal and non-diagonal electric conductivities. The roles of dissipative and non-Markovian effects are studied in the transport of charged quantum particles. The step-like structure of the diagonal electric conductivity versus magnetic field during the transient process is discussed.

研究了一个带电粒子的非马尔可夫二维动力学，该带电粒子与中性玻色子热浴线性耦合，在一个方向上被限制在谐振子中，在另一个方向上自由，在一个均匀的外部磁场和两个垂直的时间相关电场中。导出了随时间变化的对角电导率和非对角电导率的解析表达式。研究了耗散效应和非马尔可夫效应在带电量子粒子输运中的作用。讨论了瞬态过程中对角线电导率随磁场的阶梯结构。

引用次数: 0

Quantum phase transition and spectral statistics in Bose–Hubbard model 玻色-哈伯德模型中的量子相变和谱统计

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-16 DOI: 10.1016/j.physa.2025.131193

Ziba Saleki , Amir Jalili , Yan-An Luo , Feng Pan , Ai-Xi Chen

This study focuses on the quantum phase transition and the analysis of energy spectra with the objective of identifying chaotic and regular regimes within the Bose–Hubbard model, utilizing an algebraic theoretical framework. Additionally, we examine the variation of entanglement in the vicinity of quantum phase transitions induced by adjusting the pairing strength. To achieve this objective, we categorize the energy spectra to determine the chaoticity parameter (

q

) by analyzing the distribution of level spacing ratios. Our findings reveal that regions proximate to Rabi and Fock symmetries exhibit a Poisson-like distribution, whereas the vicinity of the shape-phase transition (Josephson regime) tends to display an intermediate distribution between regular and chaotic. Furthermore, within the scope of this study, we employ the Cramér–Rao lower bound to identify the minimum variance of the estimators.

本研究着重于量子相变和能谱分析，目的是利用代数理论框架识别Bose-Hubbard模型中的混沌和规则制度。此外，我们还研究了通过调整配对强度引起的量子相变附近纠缠的变化。为了实现这一目标，我们对能量谱进行分类，通过分析能级间距比的分布来确定混沌参数(q)。我们的研究结果表明，接近Rabi和Fock对称的区域呈现出类泊松分布，而形状-相转变（Josephson政权）附近则倾向于显示出介于规则和混沌之间的中间分布。此外，在本研究的范围内，我们采用cramsamr - rao下界来识别估计量的最小方差。

引用次数: 0

Coupled dynamics of competitive information and epidemic propagation in multiplex networks via evolutionary game theory 基于进化博弈论的多路网络中竞争信息与流行病传播的耦合动力学

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-16 DOI: 10.1016/j.physa.2025.131215

Xifen Wu , Haibo Bao

This paper introduces a two-layer multiplex network model to explore the interplay between information diffusion, behavioral adaptation, and epidemic propagation. The upper layer captures the competitive spreading, forgetting, and switching of positive and negative information, while the lower layer represents epidemic dynamics with infection, vaccination, and recovery processes. The two layers are interconnected through infection-induced dissemination of positive information and awareness-driven behavioral modification. Evolutionary game theory with Fermi updating is employed to describe adaptive vaccination strategies, where payoff functions integrate awareness, infection risk, and protection costs. Analytical results derived using the microscopic Markov chain approach (MMCA) show that the epidemic threshold is determined by the largest eigenvalue of a weighted structural matrix incorporating topology, information diffusion, and behavioral feedback. Numerical simulations confirm the theoretical predictions, indicating that positive information and adaptive vaccination increase the epidemic threshold, whereas misinformation and high vaccination costs reduce it. These findings highlight the crucial influence of information credibility, adaptive behavior, and network structure on epidemic outcomes, providing insights for designing integrated public health intervention strategies.

本文引入了一个双层多路网络模型来探讨信息扩散、行为适应和流行病传播之间的相互作用。上层捕获了竞争性传播、遗忘以及正面和负面信息的转换，而下层则代表了感染、接种疫苗和恢复过程的流行病动态。这两个层面通过感染引起的积极信息传播和意识驱动的行为改变相互联系。采用费米更新的进化博弈论来描述适应性疫苗接种策略，其中收益函数集成了意识、感染风险和保护成本。利用微观马尔可夫链方法（MMCA）得出的分析结果表明，流行病阈值由结合拓扑、信息扩散和行为反馈的加权结构矩阵的最大特征值决定。数值模拟证实了理论预测，表明积极信息和适应性疫苗接种提高了流行阈值，而错误信息和高接种成本降低了流行阈值。这些发现突出了信息可信度、适应性行为和网络结构对流行病结果的重要影响，为设计综合公共卫生干预策略提供了见解。

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引用次数: 0

Yet another exponential Hopfield model 又是一个指数Hopfield模型

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-16 DOI: 10.1016/j.physa.2025.131223

Linda Albanese , Andrea Alessandrelli , Adriano Barra , Peter Sollich

We propose and analyze a new variation of the so-called exponential Hopfield model, a recently introduced family of associative neural networks with unprecedented storage capacity. Our construction is based on a cost function defined through exponentials of standard Mean Squared Error (MSE) loss function per pattern, which naturally favors configurations corresponding to perfect recall. Despite not being a mean-field system, the model admits a tractable mathematical analysis of its dynamics and retrieval properties that agree with those for the original exponential model introduced by Ramsauer and coworkers. By means of a signal-to-noise approach, we demonstrate that stored patterns remain stable fixed points of the zero-temperature dynamics up to an exponentially large number of patterns in the system size. We further quantify the basins of attraction of the retrieved memories, showing that while enlarging their radius reduces the overall load, the storage capacity nonetheless retains its exponential scaling. An independent derivation in the perfect recall regime confirms these results and provides an estimate of the relevant prefactors. We also compare typical case (as standard in statistical mechanics) vs worst case (as standard in machine learning) recall criteria, finding an exponential storage capacity even for the latter case. Our findings thus complement and extend previous studies on exponential Hopfield networks, establishing that even under robustness constraints these models preserve their exceptional storage capabilities. Beyond their theoretical interest, such networks point towards principled mechanisms for massively scalable associative memory, potentially offering a theoretical way out of the storage-bottleneck problem caused by the current trend of digital data production doubling roughly every couple of years. As an illustration, we show that in order to store 150 zettabytes, i.e. approximately all digital data stored worldwide at present, an exponential Hopfield model of the proposed type with less than a hundred neurons would suffice.

我们提出并分析了所谓的指数Hopfield模型的新变体，这是最近引入的具有前所未有存储容量的联想神经网络家族。我们的构建基于通过每个模式的标准均方误差（MSE）损失函数的指数定义的成本函数，这自然有利于与完美召回相对应的配置。尽管不是平均场系统，但该模型允许对其动力学和检索特性进行易于处理的数学分析，这些特性与Ramsauer及其同事引入的原始指数模型一致。通过信噪方法，我们证明了存储模式在系统大小的指数数量级上保持零温度动力学的稳定不动点。我们进一步量化了检索记忆的吸引力盆地，表明虽然扩大其半径降低了总体负载，但存储容量仍然保持其指数尺度。在完美召回制度中的独立推导证实了这些结果，并提供了相关前因子的估计。我们还比较了典型情况（作为统计力学的标准）和最坏情况（作为机器学习的标准）的召回标准，发现即使在后一种情况下也有指数级的存储容量。因此，我们的发现补充并扩展了之前对指数Hopfield网络的研究，证明即使在鲁棒性约束下，这些模型也能保持其卓越的存储能力。除了他们的理论兴趣之外，这种网络指向了大规模可扩展联想记忆的原则机制，潜在地提供了一种理论上的方法来解决当前数字数据产量大约每隔几年翻一番的趋势所导致的存储瓶颈问题。作为一个例子，我们表明，为了存储150 zb，即目前全世界存储的大约所有数字数据，所提出的类型的指数Hopfield模型需要少于100个神经元就足够了。

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引用次数: 0

Universality, criticality and complexity of flight delay propagation 飞行延迟传播的普遍性、临界性和复杂性

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2025-12-16 DOI: 10.1016/j.physa.2025.131209

Ying Wu, Jieyi Wu, Yuhan Zhang, Yisheng Cui, Qi Zhang, Longlong Sun

Passenger flight delay propagation of air transportation systems causes significant disruption to people’s daily lives, and many studies have therefore been conducted to understand the underlying mechanisms. However, whether the behaviors work near the criticality macroscopically and which specific delay propagation dynamics dominate microscopically still remain unclear. In this work, we investigate the statistical laws of bursty activity of delay propagation based on the historical records of the United States from 2004 to 2023. Our empirical results provide support for the process of delay propagation in real systems’ operating near a critical state. Furthermore, we firstly identify that the behaviors of Full Service Carriers (FSCs) and Low Cost Carriers (LCCs) may fall into the universality classes of complex and simple contagion, respectively. By analyzing the individual time series of airports microscopically together with the topological properties of delay propagation networks, we confirm that the propagation dynamics of flight delays are determined by the characteristics of network structures of airlines. The point-to-point (P2P) patterns of LCCs make sudden delays unable to be processed timely and thus easily propagate, which meets the scenario of simple contagion. While for FSCs, we find that the hub-and-spoke (HS) structures result in the simple contagion dynamics for hub airports and complex contagion for spoke airports, the complexity of which is empirically shown to originate from their ability of absorbing delays. Consequently, we identify an anti-reinforcement mechanism for the delay propagation behaviors of FSCs from empirical data. We believe that the general ideas presented here will stimulate further research on modeling flight delay propagation dynamics and contribute to solving practical problems in other fields.

航空运输系统的旅客航班延误传播对人们的日常生活造成了重大影响，因此人们开展了许多研究来了解其潜在机制。然而，这些行为在宏观上是否在临界附近工作，以及具体的延迟传播动力学在微观上占主导地位，目前还不清楚。本文基于美国2004 - 2023年的历史记录，研究了延迟传播的突发活动的统计规律。我们的实证结果为实际系统在接近临界状态时的延迟传播过程提供了支持。此外，我们首先确定了全服务运营商（FSCs）和低成本运营商（lcc）的行为可能分别属于复杂传染和简单传染的普遍性类别。通过对机场个体时间序列的微观分析，结合延误传播网络的拓扑特性，证实航班延误的传播动力学是由航空公司网络结构特征决定的。lcc的点对点（P2P）模式使得突发延迟无法得到及时处理，容易传播，满足简单传染的情形。而对于fsc，我们发现轮辐结构导致枢纽机场的简单传染动力学和轮辐机场的复杂传染动力学，经验表明其复杂性源于它们吸收延误的能力。因此，我们从经验数据中确定了FSCs延迟传播行为的反强化机制。我们相信，本文提出的一般思想将促进对飞行延迟传播动力学建模的进一步研究，并有助于解决其他领域的实际问题。

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引用次数: 0