Physica A: Statistical Mechanics and its Applications最新文献_第4页

Risk spillover between cryptocurrencies and traditional currencies: An analysis based on neural network quantile regression

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-31 DOI: 10.1016/j.physa.2025.130560

Shunqi Zhang , Qiuhua Xu , Xuerou Ding , Kefei Han

{"title":"Risk spillover between cryptocurrencies and traditional currencies: An analysis based on neural network quantile regression","authors":"Shunqi Zhang , Qiuhua Xu , Xuerou Ding , Kefei Han","doi":"10.1016/j.physa.2025.130560","DOIUrl":"10.1016/j.physa.2025.130560","url":null,"abstract":"<div><div>The burgeoning prominence of cryptocurrencies within the global financial landscape necessitates a reevaluation of their interplay with conventional currencies. This paper employs a neural network quantile regression (NNQR) framework to delineate a risk spillover network encompassing nine cryptocurrencies and eleven traditional currencies. Our findings suggest that cryptocurrencies are less affected by traditional currencies during systemic crises such as the COVID-19 pandemic, despite the escalation of system-wide risk. Cryptocurrency exposures also come mainly within their markets during special times, which exhibits a significant degree of autonomy. This autonomy positions them as potential short-term hedges against policy-induced risks. Furthermore, our study also finds that cryptocurrencies have less betweenness centrality compared to traditional currencies, but their closeness centrality is not much different from traditional currencies. Our research identifies the Canadian dollar and the Indian rupee as being notably vulnerable to risk spillovers emanating from the cryptocurrency sector. However, there are significant differences in the traditional currencies that have a considerable impact on different cryptocurrencies. This study offers novel perspectives for investors considering the utilization of cryptocurrencies for out-of-market risk hedging strategies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130560"},"PeriodicalIF":2.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traceability model of malicious messages against data sparsity in time-varying networks

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-29 DOI: 10.1016/j.physa.2025.130551

Tun Li, Yuhao Li, Zhou Li, Weidong Ma, Rong Wang, Yinxue Yi, Yunpeng Xiao

{"title":"Traceability model of malicious messages against data sparsity in time-varying networks","authors":"Tun Li, Yuhao Li, Zhou Li, Weidong Ma, Rong Wang, Yinxue Yi, Yunpeng Xiao","doi":"10.1016/j.physa.2025.130551","DOIUrl":"10.1016/j.physa.2025.130551","url":null,"abstract":"<div><div>This paper proposes an innovative traceability model to address the issue of tracing malicious information in time-varying social networks, overcoming the limitations of traditional methods in dealing with dynamic structures and sparse historical data. This study introduces a time-window mechanism, simplifying the dynamic topology into static snapshots, thereby capturing the time-varying characteristics of network topology more accurately. To tackle the challenge of sparse historical data on malicious information propagation, this paper creatively combines the independent cascade model, maximum likelihood estimation, and probabilistic graphs, significantly improving the accuracy of calculating the likelihood of a node being the source of propagation. Furthermore, a new propagation similarity metric is proposed, and a global expectation function within an attention mechanism is introduced to assign weights to the propagation source based on network stability, significantly reducing the complexity of traditional traceability algorithms. Experimental results demonstrate that the proposed model exhibits excellent performance in practical applications, accurately and timely identifying malicious information sources even with limited public dataset availability. This study not only introduces a novel model and methodology but also provides new insights and directions for future research on malicious information traceability in dynamic social networks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130551"},"PeriodicalIF":2.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Static conductivity of liquid metallic hydrogen

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-29 DOI: 10.1016/j.physa.2025.130557

S.A. Maslov, P.R. Levashov, S.A. Trigger

引用次数: 0

Joint lane management and signal optimization for mixed-autonomy intersections: An analytical approach

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-28 DOI: 10.1016/j.physa.2025.130556

Qingquan Liu , Yaming Guo , Yunlong An , Meng Li

{"title":"Joint lane management and signal optimization for mixed-autonomy intersections: An analytical approach","authors":"Qingquan Liu , Yaming Guo , Yunlong An , Meng Li","doi":"10.1016/j.physa.2025.130556","DOIUrl":"10.1016/j.physa.2025.130556","url":null,"abstract":"<div><div>Lane management approaches have been extensively studied as effective strategies for managing mixed-autonomy traffic, where autonomous vehicles (AVs) and human-driven vehicles (HDVs) coexist. While much of the existing research on lane management focuses on highway scenarios, the complexities of managing mixed-autonomy traffic at intersections, where both lane configuration and signal timing play crucial roles, remain underexplored. This study integrates the management of dedicated AV lanes and signal optimization at isolated intersections using an analytical approach. First, we estimate the saturation flow rate in mixed lanes across varying AV penetration rates, based on the expected headway of the mixed traffic flow. Then, we analyze vehicle delay at the intersection, with lane configuration plans and signal timing as key variables, while also accounting for the assignment of AV flow between mixed and dedicated AV lanes. Building on this analytical model, we formulate a joint optimization problem for lane configuration and signal timing as a mixed-integer nonlinear programming (MINLP) model. To address the non-convex nature of the model, we decompose it into sub-problems, each informed by theoretical insights, thereby reducing solution complexity. A heuristic algorithm is then developed to solve the joint optimization problem effectively. Numerical experiments validate the superiority of the proposed joint optimization approach. Sensitivity analysis is conducted to assess the impact of various parameters, including traffic state variables and hyper-parameters for the heuristic algorithm. Furthermore, we explore scenarios in which dedicated AV lanes provide positive effects. Theoretical and numerical results offer valuable insights for improving traffic management at intersections in mixed-autonomy environments.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"668 ","pages":"Article 130556"},"PeriodicalIF":2.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling stochastic Langevin dynamics in fractal dimensions

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-28 DOI: 10.1016/j.physa.2025.130570

Rami Ahmad El-Nabulsi , Waranont Anukool

{"title":"Modeling stochastic Langevin dynamics in fractal dimensions","authors":"Rami Ahmad El-Nabulsi , Waranont Anukool","doi":"10.1016/j.physa.2025.130570","DOIUrl":"10.1016/j.physa.2025.130570","url":null,"abstract":"<div><div>The Langevin equation is a Newtonian equation describing the evolution of a dynamical system when subjected to a combination of deterministic and fluctuating or random forces. It is one of best-known stochastic differential equations in statistical physics and kinetic theory describing the motion of a complex dynamical system of particles perturbed by some white noise. This equation is usually used based on the assumption that the location of the particle at a moment depends only on its preceding location and not on that of long time before. Its solution is of Markov property that expresses a loss-memory evolution of the system. In this study, a fractal Langevin equation is proposed to study the random walks of particles exhibiting strange displacements driven by Gaussian white noise and memory kernel. Two different models have been introduced: local and nonlocal kernels. The first model is suitable to describe subdiffusion, whereas the second model, the dynamics exhibit random oscillations that show considerable fluctuations in frequency and amplitude. Our models show that the stochastic oscillation arises from a fractal random walk process, and prove the relevance of fractals in stochastic anomalous random walk processes. Additional features have been discussed.</div></div><div><h3>Pacs classification</h3><div>05.40.Fb;</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130570"},"PeriodicalIF":2.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GHENet: Attention-based Hurst exponents for the forecasting of stock market indexes

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-27 DOI: 10.1016/j.physa.2025.130540

Joao B. Florindo, Reneé Rodrigues Lima, Francisco Alves dos Santos, Jerson Leite Alves

{"title":"GHENet: Attention-based Hurst exponents for the forecasting of stock market indexes","authors":"Joao B. Florindo, Reneé Rodrigues Lima, Francisco Alves dos Santos, Jerson Leite Alves","doi":"10.1016/j.physa.2025.130540","DOIUrl":"10.1016/j.physa.2025.130540","url":null,"abstract":"<div><div>Financial forecasting is a challenging and important task, with several different approaches being explored, including deep learning methods. However, most existing deep learning approaches focus on price data and traditional technical indicators. The highly complex nature of financial time series suggests potential benefits from non-linear dynamics tools. Based on that, here we propose GHENet, a model that injects non-linear dynamics information, via generalized Hurst exponents, into a deep learning predictor. To leverage the power of the Hurst features, we process them by a self-attention module, which allows the model to attend the most relevant features. The performance of our method is investigated in the forecasting of several world-wide stock market indexes and in a trading simulation. GHENet outperforms other state-of-the-art approaches, including complex deep learning models and methods that inject exogenous variables into the data. Our proposal also demonstrates to be tolerant to hyperparameter tuning, which facilitates its use “out-of-the-box”.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130540"},"PeriodicalIF":2.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A unified traffic flow prediction model considering node differences, spatio-temporal features, and local-global dynamics

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-27 DOI: 10.1016/j.physa.2025.130554

Qian Shang , Qingyong Zhang , Chao Ju , Quan Zhou , Zhihui Yang

{"title":"A unified traffic flow prediction model considering node differences, spatio-temporal features, and local-global dynamics","authors":"Qian Shang , Qingyong Zhang , Chao Ju , Quan Zhou , Zhihui Yang","doi":"10.1016/j.physa.2025.130554","DOIUrl":"10.1016/j.physa.2025.130554","url":null,"abstract":"<div><div>Traffic flow prediction is one of the core technologies in Intelligent Transportation Systems (ITS) and has extensive application value. The primary challenge lies in efficiently modeling the complex spatio-temporal dependencies within traffic data. Although spatio-temporal graph neural network models are regarded as effective solutions, their performance is limited by incomplete graph connectivity and the use of identical modeling approaches for all nodes, which not only hinders the learning of dynamic traffic patterns but also overlooks the heterogeneity between nodes. To address these limitations, a novel traffic flow prediction model based on dynamic spatio-temporal modeling with node differences is proposed. Specifically, an exogenous node selection module is designed to identify nodes highly correlated with the endogenous node (i.e., the node to be predicted) to assist in prediction. Subsequently, differentiated modeling approaches are employed: the endogenous node is represented using local–global embedding to capture its local–global features. In contrast, exogenous nodes are modeled using global embedding to obtain their global representations, thereby achieving comprehensive feature characterization. Finally, a spatio-temporal attention network is utilized to capture the spatio-temporal interactions among nodes. Extensive experiments on three real-world traffic datasets demonstrate that the proposed model achieves significant performance improvements over state-of-the-art baseline methods. The experimental results reveal that the proposed framework not only achieves superior predictive accuracy but also maintains highly competitive computational efficiency.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130554"},"PeriodicalIF":2.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-enhanced dynamic path decisions for emergency stewards in emergency evacuations

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-27 DOI: 10.1016/j.physa.2025.130561

Peng Yang, Bozheng Zhang, Kai Shi, Yi Hui

{"title":"Machine learning-enhanced dynamic path decisions for emergency stewards in emergency evacuations","authors":"Peng Yang, Bozheng Zhang, Kai Shi, Yi Hui","doi":"10.1016/j.physa.2025.130561","DOIUrl":"10.1016/j.physa.2025.130561","url":null,"abstract":"<div><div>In emergency situations such as indoor fires, emergency stewards can significantly influence the evacuation behavior of trapped individuals, and the rationality of their own path decisions is crucial to the overall evacuation effectiveness. This paper introduces an improved social force model to reflect the impact of stewards on the behavior of those in distress, and designs a decision-making framework based on simulation models and Deep Reinforcement Learning (DRL) technology to optimize the path decisions of stewards in dynamic scenarios. The simulation model is used to simulate various scenarios to obtain sufficient sample data; the role of DRL is to interact with the environment and dynamically guide individuals towards optimal paths using learned effective strategies. Within this framework, the decision training for emergency stewards employs a Modified Priority Experience Deep Q-Network (MPE-DQN), avoiding areas with high personnel density to optimize evacuation path decisions. The safety metric during the evacuation process is measured by personnel density per unit area, and evacuation time is chosen as the efficiency metric. Simulation experiments conducted in AnyLogic show that compared to the standard DQN algorithm, our framework, using the MPE-DQN algorithm, increased safety by 58.77 % and improved efficiency by 14.2 %.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130561"},"PeriodicalIF":2.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regulation of photon–photon interaction on quantum phase transitions of the two-mode three-level quantum Rabi model

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-26 DOI: 10.1016/j.physa.2025.130549

Yan Zhang , Wen-Bo Li , Bin-Bin Mao , Maoxin Liu

引用次数: 0

Hopf bifurcation analysis and control of two-lane car-following model for connected vehicles with driver assistance systems

IF 2.8 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-03-25 DOI: 10.1016/j.physa.2025.130559

Xueyi Guan , Jin Qin , Rongjun Cheng , Ting Wang , Wei Hao

{"title":"Hopf bifurcation analysis and control of two-lane car-following model for connected vehicles with driver assistance systems","authors":"Xueyi Guan , Jin Qin , Rongjun Cheng , Ting Wang , Wei Hao","doi":"10.1016/j.physa.2025.130559","DOIUrl":"10.1016/j.physa.2025.130559","url":null,"abstract":"<div><div>Connected vehicles (CVs) serve as an important connection from human-driven vehicles (HDVs) to pure connected autonomous vehicles, whose main function is to improve driver’s operation combined with driver assistance systems (DAS). By utilizing information shared on CVs, DAS is capable of automatically calculating and analyzing safe and efficient driving behaviors for drivers. However, drivers may operate by combining their personal thoughts with shared recommendation of DAS, which will usually result in a response time-delay. Therefore, the paper presents an innovative feedback control strategy that considers the driver’s visual angle response time-delay for the two-dimensional two-lane car-following model (TDTLCFM) combined with compliance rate of CVs drivers to DAS to optimize and improve DAS. Through the linear analysis and Hopf bifurcation analysis to obtain the stability conditions and equilibrium points of the traffic flow. And then designing the feedback controller to suppress traffic instability so as to suppress traffic bifurcation caused by driver’s response time-delay. Finally, numerical simulation was conducted on 100 vehicles, and the simulation results show that the controller can effectively suppress traffic congestion without changing the equilibrium point, significantly improving traffic efficiency and stability. At the same time, numerical simulations of heterogeneous traffic flow composed of HDVs and CVs indicate that the stability of mixed flows varies with the market penetration rate of CVs, the higher the market penetration rate, the stronger the anti-interference ability of the mixed flow.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"667 ","pages":"Article 130559"},"PeriodicalIF":2.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0