Physica A: Statistical Mechanics and its Applications最新文献

Analysis of investment behavior among Filipinos: Integration of Social exchange theory (SET) and the Theory of planned behavior (TPB) 菲律宾人的投资行为分析：社会交换理论（SET）与计划行为理论（TPB）的融合

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-12 DOI: 10.1016/j.physa.2024.130162

Despite the emergence of more accessible and modern forms of investment, the ever competitive and volatile market remains subject to anomalous irrationalities caused by investors. To this day, predicting their behavior remains difficult with lacking information, and poses a problem for investment platforms to effectively adjust to their predispositions. Therefore, this study aimed to comprehensively analyze the factors that have influenced investors’ behaviors using the integrated construct of the Social Exchange Theory and the Theory of Planned Behavior. With consideration of convenience sampling, a total number of 10,725 data points were collected and analyzed through machine learning algorithms of decision tree and neural network. Specifically, the comparison between long short-term memory (LSTM) and neural network, and random forest classifier and LightGBM were considered. It was found that the investor’s attitude, accessibility to financial services, and perceived economic benefits were the most influential predictors to their behavior, while six other factors also showed varying levels of significance. This study aimed to provide a unique framework which could be utilized by investment platforms to cater to the different behavioral factors expressed by investors. In line with these findings, it is recommended that platforms create flexible solutions that are based on their intentions and preferences, and more user-friendly through the implementation of new technologies. In addition, they are suggested to appeal to novice investors by reducing the burden of costs, promising future benefits, and promoting financial education. The results of this study proved the reliability of the integrated model as a social and behavioral framework, and consequently, LSTM overpowering other tools on accurate forecast made, followed by neural network, and random forest.

尽管出现了更便捷、更现代的投资方式，但竞争激烈、波动剧烈的市场仍然受到投资者非理性行为的影响。时至今日，在信息匮乏的情况下，预测他们的行为仍然十分困难，这也为投资平台有效调整自己的投资倾向带来了难题。因此，本研究旨在利用社会交换理论和计划行为理论的综合建构，全面分析影响投资者行为的因素。在考虑便利抽样的前提下，本研究共收集了 10,725 个数据点，并通过决策树和神经网络的机器学习算法进行分析。具体而言，比较了长短期记忆（LSTM）和神经网络，以及随机森林分类器和 LightGBM。研究发现，投资者的态度、金融服务的可获得性和感知到的经济利益是对其行为影响最大的预测因素，而其他六个因素也表现出不同程度的显著性。本研究旨在提供一个独特的框架，供投资平台利用，以满足投资者所表达的不同行为因素。根据这些研究结果，建议平台根据投资者的意图和偏好制定灵活的解决方案，并通过采用新技术提高用户友好性。此外，还建议平台通过减轻成本负担、承诺未来收益和促进金融教育来吸引新手投资者。本研究的结果证明了作为社会和行为框架的综合模型的可靠性，因此，LSTM 在准确预测方面优于其他工具，其次是神经网络和随机森林。

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引用次数: 0

Ethereum futures and the efficiency of cryptocurrency spot markets 以太坊期货和加密货币现货市场的效率

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-11 DOI: 10.1016/j.physa.2024.130161

This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.

本文研究了以太坊期货合约的推出对主要加密货币（比特币、以太坊、瑞波币、莱特币和狗狗币）现货价格市场效率的影响。利用基于多分形的方法和2017年9月4日至2024年2月16日的每日数据，主要结果显示市场效率略有提高。具体来说，多分叉持续性程度降低，这意味着主要加密货币的市场效率降低。发射后，时间线性相关效应和厚尾效应不那么明显。以太坊期货推出后，广义赫斯特指数的不对称性增加，在加密货币下行趋势下的持续性更高。这种下降趋势在以太坊期货推出后出现，与 COVID-19 大流行的最后阶段相吻合。其他分析表明，肥尾和时间线性相关是多分形的主要来源。结果凸显了在相对较新且波动较大的加密货币领域引入金融衍生品的影响，交易者、套期保值者、投资者和监管者都应关注这一点。

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引用次数: 0

Can Bitcoin trigger speculative pressures on the US Dollar? A novel ARIMA-EGARCH-Wavelet Neural Networks 比特币能否引发美元投机压力？新型 ARIMA-EGARCH 小波神经网络

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-11 DOI: 10.1016/j.physa.2024.130140

In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.

近年来，比特币作为一种数字货币备受关注，引发了越来越多关于其对传统金融市场（尤其是美元）影响的讨论。本研究探讨了比特币与美元之间的关系，尤其是在投机性攻击（投资者试图使货币贬值）和空头挤压（价格快速上涨迫使卖空者迅速回购资产以避免进一步损失）的情况下。该研究采用了一种新颖的混合模型，将自回归移动平均线、广义自回归条件异方差和小波神经网络技术与神经网络方法相结合。研究结果表明，比特币/美元的大量交易活动，尤其是在投机性攻击和空头挤压期间，会对美元/欧元市场产生重大影响，并随着交易者调整策略而增加价格波动。这些调整以及风险管理策略推动了更高的交易量和进一步的波动。我们的研究结果表明，我们的新型混合模型与量子递归神经网络相结合，可以提供最准确的预测，为比特币/美元和美元/欧元市场的交易策略提供有价值的见解。这项研究对政策制定者和市场参与者具有重要意义，强调了理解比特币和美元之间的关系对于金融稳定和有效政策制定的必要性。它还强调了先进建模技术对准确预测加密货币市场行为的必要性。

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引用次数: 0

Impact of surface-roughness and fractality on electrical conductivity of SnS thin films 表面通透度和碎裂度对 SnS 薄膜导电性的影响

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-11 DOI: 10.1016/j.physa.2024.130165

Mono- and multi-fractal geometry have been used to explore the surface characteristics of scanning electron microscopy (SEM) micrographs of the SnS films with thicknesses of 100 nm (SnS1) to 600 nm (SnS4), respectively. For this investigation, the SnS thin films have been grown on fluorine-doped tin oxide (FTO)-coated glass substrate through the thermal evaporation route, and surface morphologies are captured by SEM. Two-dimensional multi-fractal detrended fluctuation analysis (MFDFA) based on the partition function is used to examine whether the surfaces have a multi-fractal nature or not. The partition function is applied to extract the generalized Hurst exponent from the segment size. It has been found that surfaces with higher surface roughness induce substantial nonlinearity and a wider width of the multi-fractal spectrum. The multi-fractal spectrum acquired from the analysis of the geometry and shape of the singularity spectrum is used to quantify the irregularity and complexity of surfaces. Minkowski functionals (MFs) parameters such as volume, boundary, and connectivity were measured for each thin film. Moreover, we tried to correlate the electrical conductivity with the mono- and multi-fractal parameters such as fractal dimension (D_f), singularity strength function (Δα), singularity spectrum Δf(α), and it is observed that the conductivity of a thin film decreases with decreasing fractal dimension. The minimum (maximum) resistivity (conductivity) was observed for the surface having a larger fractal dimension. The present investigation suggests that such SnS surfaces, having minimal resistivity and maximum conductivity on the roughest surface, indicate enhanced light trapping capacity and can be utilized as active layers for advanced optoelectronics devices.

单分形和多分形几何图形被用来探索厚度分别为 100 nm（SnS1）至 600 nm（SnS4）的 SnS 薄膜的扫描电子显微镜（SEM）显微照片的表面特征。在这项研究中，SnS 薄膜是通过热蒸发途径在掺氟氧化锡（FTO）涂层玻璃基底上生长的，并通过扫描电子显微镜捕捉其表面形貌。利用基于分区函数的二维多分形去趋势波动分析（MFDFA）来检验表面是否具有多分形性质。分区函数用于从分段大小中提取广义赫斯特指数。研究发现，表面粗糙度越高的表面，其非线性越大，多分形谱的宽度也越宽。通过分析奇异谱的几何和形状获得的多分形谱可用于量化表面的不规则性和复杂性。我们测量了每种薄膜的闵科夫斯基函数（MFs）参数，如体积、边界和连通性。此外，我们还尝试将电导率与分形维度 (Df)、奇异强度函数 (Δα)、奇异谱 Δf(α)等单分形和多分形参数相关联。分形维数较大的表面的电阻率（电导率）最小（最大）。本研究表明，这种在最粗糙表面上电阻率最小、电导率最大的 SnS 表面显示出更强的光捕获能力，可用作先进光电器件的有源层。

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引用次数: 0

Role of delay in brain dynamics 延迟在大脑动力学中的作用

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-11 DOI: 10.1016/j.physa.2024.130166

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and

M

multiple delays between successive layers, thereby generating a polynomial time-series outputs with

M

. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with

M

outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.

与同步深度学习相比，连接神经元之间延迟的显著变化导致异步大脑动力学不可避免地存在劣势。然而，本研究证明，利用具有单个输出和连续层之间 M 个多重延迟的网络，可以将这一劣势转化为计算优势，从而生成具有 M 个多项式时间序列输出的网络。此外，RoDiB 系统可达到的准确度可与具有 M 个输出的可调单延迟架构相媲美。此外，当输出标签的数量超过完全连接的输入规模时，系统的准确度会显著提高。这些结果主要是通过模拟 CIFAR 数据集上的 VGG-6 获得的，也包括多标签输入。不过，目前只利用了 RoDiB 大量输出中的一小部分，这表明其高级计算能力的潜力还有待发掘。

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引用次数: 0

A network perspective on J.S Bach’s 6 violin sonatas and partitas, BWV 1001 - 1006 从网络角度看巴赫的 6 首小提琴奏鸣曲和 Partitas，BWV 1001 - 1006

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-10 DOI: 10.1016/j.physa.2024.130124

Complex networks and statistical physics have been proposed as powerful frameworks and tools in the analysis of the properties of complex systems and in particular musical pieces. They can reveal variations in musical features such as harmony, melody, rhythm as well as the composer’s style. The empirical study of a wide range of digitized scores of Western classical music and their corresponding networks brought to light quantitative evidence for changes in harmonic complexity. We complement the common topological analysis of these networks with musicological or music-theoretical considerations. We illustrate this by studying J. S. Bach’s sonatas and partitas for solo violin by constructing duration-weighted transition matrices between notes, or melody networks, as well as harmony networks, which are transition matrices between the chords, or equivalently synchronously played notes. We further propose statistical physics measures that were first introduced in the study of socio-economic networks: the partition function and communicability and provide evidence for their significance. Our findings and observations include: the detection of three main communities centered around the tonic, the dominant, and submediant in most of the pieces; the association of the nodes with the highest betweenness centrality, the lowest clustering coefficient and highest in and out degrees respectively with the tonic and the dominant; the high similarity between pieces which share the same key or when the key of one is the dominant of the other; finally, the association of the highest partition function, the shortest average path length, and the highest communicability with the Fugues.

复杂网络和统计物理学被认为是分析复杂系统，特别是音乐作品属性的强大框架和工具。它们可以揭示和声、旋律、节奏以及作曲家风格等音乐特征的变化。通过对大量西方古典音乐数字化乐谱及其相应网络的实证研究，我们发现了和声复杂性变化的量化证据。我们从音乐学或音乐理论的角度对这些网络的常见拓扑分析进行了补充。我们通过研究巴赫（J. S. Bach）的小提琴独奏奏鸣曲和部分奏鸣曲，构建了音符之间的时长加权转换矩阵，即旋律网络，以及和声网络，即和弦之间的转换矩阵，或等同于同步演奏的音符之间的转换矩阵，来说明这一点。我们进一步提出了首次在社会经济网络研究中引入的统计物理测量方法：分区函数和可传播性，并为其重要性提供了证据。我们的发现和观察结果包括：在大多数乐曲中发现了以调性、主音和副主音为中心的三个主要群落；具有最高间度中心性、最低聚类系数和最高进出度的节点分别与调性和主音相关；具有相同调性的乐曲或其中一个调性是另一个调性的乐曲之间具有高度相似性；最后，具有最高分区函数、最短平均路径长度和最高可传播性的乐曲与赋格相关。

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引用次数: 0

Testing the scope of superstatistical time series analysis: Application to the SYM-H geomagnetic index 测试超统计时间序列分析的范围：SYM-H 地磁指数的应用

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-10 DOI: 10.1016/j.physa.2024.130147

An application of time series analysis is proposed from a extended perspective of superstatistics, in which a Boltzmann-like distribution with a power law

E^{m - 1}

density of states has been introduced. This superstatistical scenario is one generated by a superposition of gamma distributions, which has already been hinted in some papers. Two time series for the magnitude of SYM-H geomagnetic index have been taken, covering periods between solar cycles 22 and 24, where data of minimum and maximum solar activity have been extracted. Analyzes were performed from the proposed superstatistical perspective to the corresponding time series, showing concordance that could suggest that each of them is a consequence of a superstatistical-like process.

从超统计的扩展角度提出了时间序列分析的应用，其中引入了具有幂律 Em-1 状态密度的波尔兹曼分布。这种超统计情景是由伽马分布的叠加产生的，这在一些论文中已经有所暗示。我们对 SYM-H 地磁指数的大小提取了两个时间序列，涵盖了太阳周期 22 和 24 之间的时期，并提取了太阳活动最小和最大的数据。从拟议的超统计角度对相应的时间序列进行了分析，结果表明它们之间存在一致性，表明它们都是类似超统计过程的结果。

引用次数: 0

Inequality of creep avalanches can predict imminent breakdown 蠕变雪崩的不平衡性可预测即将发生的故障

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-10 DOI: 10.1016/j.physa.2024.130148

We have numerically studied a mean-field fiber bundle model of fracture at a non-zero temperature and acted upon by a constant external tensile stress. The individual fibers fail due to creep-like dynamics that lead up to a catastrophic breakdown. We quantify the variations in sizes of the resulting avalanches by calculating the Lorenz function and two inequality indices – Gini (

g

) and Kolkata (

k

) indices – derived from the Lorenz function. We show that the two indices cross just prior to the failure point when the dynamics goes through intermittent avalanches. For a continuous failure dynamics (finite numbers of fibers breaking at each time step), the crossing does not happen. However, in that phase, the usual prediction method i.e., linear relation between the time of minimum strain-rate (time at which rate of fiber breaking is the minimum) and failure time, holds. The boundary between continuous and intermittent dynamics is very close to the boundary between crossing and non-crossing of the two indices in the temperature-stress phase space, both drawn from independent analytical calculations and are verified by numerical simulations.

我们对在非零温度和恒定外部拉伸应力作用下断裂的平均场纤维束模型进行了数值研究。单根纤维因蠕变动态而断裂，最终导致灾难性的断裂。我们通过计算洛伦兹函数和两个不平等指数--基尼（g）指数和加尔各答（k）指数--来量化由此产生的雪崩的大小变化。我们发现，当动力学经历间歇性雪崩时，这两个指数会在故障点之前交叉。对于连续破坏动力学（每个时间步长内断裂的纤维数量有限），交叉不会发生。然而，在这一阶段，通常的预测方法，即最小应变率时间（纤维断裂率最小的时间）与失效时间之间的线性关系是成立的。连续动态和间歇动态之间的界限非常接近温度-应力相空间中两个指数交叉和不交叉之间的界限，这两个界限都来自独立的分析计算，并通过数值模拟得到了验证。

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引用次数: 0

Vibration-resonance chimeras in coupled excitable systems with heterogeneous aperiodic excitations 具有异质非周期性激励的耦合可激系统中的振动-共振嵌合体

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-09 DOI: 10.1016/j.physa.2024.130156

In this work, we present a novel chimera-like state known as the vibration-resonance chimera (VRC) state, which is induced by heterogeneous aperiodic (HA) excitations and emerges within the optimal range of amplitude for the HA excitations. We investigate the dynamic behaviors of the FitzHugh-Nagumo model in relation to HA excitations and coupling strength in parameter spaces, identifying specific regions where VRC states exist and analyzing the transition mechanisms between different dynamic behaviors. Using basin stability measures, we find that the mean period of HA excitations affects the multi-stable states of neural systems. Specifically, the model excited by HA excitations with a larger mean period is more likely to exhibit multi-stable states. Additionally, we explore the effects of two levels of heterogeneity in HA excitations, namely amplitude heterogeneity and period heterogeneity, on VRC states. Amplitude heterogeneity can suppress the occurrence of VRC states, whereas period heterogeneity can promote their generation. This work provides valuable insights into complex system dynamics, enhancing our understanding of chimera states in neuronal models and their potential applications in neural networks.

在这项工作中，我们提出了一种被称为振动-共振嵌合（VRC）态的新型嵌合态，它由异质非周期性（HA）激发诱导，并在 HA 激发的最佳振幅范围内出现。我们研究了 FitzHugh-Nagumo 模型在参数空间中与 HA 激发和耦合强度相关的动态行为，确定了 VRC 状态存在的特定区域，并分析了不同动态行为之间的过渡机制。通过盆地稳定性测量，我们发现 HA 激发的平均周期会影响神经系统的多稳态。具体来说，平均周期越大的 HA 激发模型越有可能表现出多稳定状态。此外，我们还探讨了 HA 激发的两个层次的异质性（即振幅异质性和周期异质性）对 VRC 状态的影响。振幅异质性可以抑制 VRC 状态的出现，而周期异质性则可以促进 VRC 状态的产生。这项研究为复杂系统动力学提供了宝贵的见解，加深了我们对神经元模型中嵌合态及其在神经网络中潜在应用的理解。

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引用次数: 0

Study on pedestrian evacuation simulation model considering group behavior 考虑群体行为的行人疏散模拟模型研究

IF 2.8 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications

Pub Date : 2024-10-09 DOI: 10.1016/j.physa.2024.130138

As a crowded place on the university campus, the cafeteria inevitably has many safety hazards, such as fire accidents caused by open flames and large appliances, stampede accidents caused by overcrowding during peak dining hours, etc. Therefore, studying pedestrian evacuation in university campus cafeterias is particularly necessary. Pedestrians on campus mostly travel in groups. Previous studies mainly used continuous models to discuss pedestrian group evacuation behavior. In this study, based on the cellular automaton pedestrian evacuation simulation model, the floor field calculation method was improved. A cellular automaton pedestrian evacuation simulation model considering group behavior was established and applied to the evacuation scenario of students in a university campus cafeteria. The study found that under the different group configurations, the pedestrian ratios, and the pedestrian densities, the pedestrian evacuation efficiency had significant differences. The results showed that when the different group configurations existed in the scenario, the higher the proportion of the pedestrians with three-person front-to-back group configurations, the higher the evacuation efficiency. When only one type of the group configuration existed in the scenario, at low pedestrian density, the evacuation efficiency of the individual pedestrian groups was higher compared to the other six group configurations. While at high pedestrian density, the evacuation efficiency of the three-person front-to-back group configurations was higher. These findings provided important references for pedestrian evacuation in university campus cafeterias and provided insights for the simulation research of group pedestrian evacuation models, contributing to enhancing campus safety management and ensuring the safety of teachers, students, and staff.

食堂作为大学校园中的人员密集场所，不可避免地存在着许多安全隐患，如明火和大型电器引发的火灾事故、就餐高峰期因拥挤引发的踩踏事故等。因此，研究大学校园食堂的行人疏散问题尤为必要。校园内的行人多为集体出行。以往的研究主要采用连续模型来讨论行人群体疏散行为。本研究以蜂窝自动机行人疏散模拟模型为基础，改进了楼层场计算方法。建立了考虑群体行为的蜂窝自动机行人疏散仿真模型，并将其应用于大学校园食堂的学生疏散场景。研究发现，在不同的群体配置、行人比例和行人密度下，行人疏散效率存在显著差异。结果表明，当场景中存在不同的群体配置时，三人前后群体配置的行人比例越高，疏散效率越高。当场景中只存在一种群体配置时，在行人密度较低的情况下，与其他六种群体配置相比，单个行人群体的疏散效率较高。而在行人密度较高的情况下，三人前后分组配置的疏散效率较高。这些研究结果为大学校园食堂的行人疏散提供了重要参考，也为群体行人疏散模型的模拟研究提供了启示，有助于加强校园安全管理，保障师生员工的安全。

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引用次数: 0