Physica A: Statistical Mechanics and its Applications最新文献

{"title":"Dynamic role-switching in hypergraphs: Enhancing cooperation via adaptive punishment and reinforcement learning","authors":"Zeyuan Yan ,&nbsp;Hui Zhao ,&nbsp;Li Li","doi":"10.1016/j.physa.2025.130902","DOIUrl":"10.1016/j.physa.2025.130902","url":null,"abstract":"<div><div>Evolutionary game theory, enhanced by reinforcement learning, provides deep insights into cooperation dynamics crucial for collective behaviors in complex systems. As complex network structures, hypergraphs present a robust framework for examining the emergence of cooperation. In this study, we combine evolutionary game theory with an adaptive Q-learning algorithm optimized for hypergraphs structures to explore the effects of a dynamic punishment transition mechanism on collective cooperative behavior. This algorithm allows agents to dynamically adjust roles and engage in introspective learning, moving beyond simple imitation. Extensive Monte Carlo simulations demonstrate that increasing the probability and intensity of punishment significantly promotes cooperation, while moderate punishment costs can catalyze cooperation even under low synergy factors. Moreover, higher discount factors, increased learning rates, and smaller group sizes within hypergraphs further enhance cooperation. This research highlights the critical role of self-adjusting Q-learning and dynamic punishment transition mechanisms in fostering cooperation, providing valuable insights into social dilemma scenarios within complex environments.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130902"},"PeriodicalIF":3.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895945","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}

{"title":"Rules, agents and order","authors":"Amalia Puente ,&nbsp;César A. Terrero-Escalante ,&nbsp;Diego Radillo-Ochoa","doi":"10.1016/j.physa.2025.130915","DOIUrl":"10.1016/j.physa.2025.130915","url":null,"abstract":"<div><div>Complex systems often exhibit highly structured network topologies that reflect functional constraints. In this work, we investigate how, under varying combinations of system-wide selection rules and special agents, different classes of random processes give rise to global order, with a focus restricted to finite-size networks. Using the large-<span><math><mi>N</mi></math></span> Erdős–Rényi model as a null baseline, we contrast purely random link-adding processes with goal-directed dynamics, including variants of the chip-firing model and intracellular network growth, both driven by transport efficiency. Through simulations and structural probes such as <em>k-core</em> decomposition and <em>HITS</em> centrality, we show that purely stochastic processes can spontaneously generate modest functional structures, but that significant departures from random behavior generically require two key conditions: critical topological complexity and dynamic alignment between topology and functionality. Our results suggest that the emergence of functional architectures depends not only on the presence of selection mechanisms or specialized roles, but also on the network’s capacity to support differentiation and feedback. These findings provide insight into how topology–functionality relationships emerge in natural and artificial systems and offer a framework for using random graph baselines to diagnose the rise of global order in evolving finite-size networks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130915"},"PeriodicalIF":3.1,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895946","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}

{"title":"Information capacitance in Ni2MnGa Heusler alloy: A study of the martensitic transformation","authors":"Giulio Arias ,&nbsp;Hans Nowak ,&nbsp;Alejandro A. Heredia-Guevara ,&nbsp;Justiniano Quispe-Marcatoma ,&nbsp;Víctor A. Peña-Rodríguez ,&nbsp;Carlos V. Landauro","doi":"10.1016/j.physa.2025.130913","DOIUrl":"10.1016/j.physa.2025.130913","url":null,"abstract":"<div><div>Information capacitance is a new measure of complexity that is applicable, in principle, to an arbitrary physical system, which makes it interesting for the study of systems near phase transitions. In this context, the Ni<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>MnGa Heusler alloy is of special interest as it exhibits two coupled phase transitions (structural and magnetic). Thus questions arise about how each of them contributes to the complexity of the system; i.e., to the information capacitance. To answer this question, we employ a Hamiltonian model together with a Monte Carlo-Metropolis procedure to calculate the magnetization, the tetragonal (structural) distortion and, mainly, the entropy of the system, since the latter quantity is associated with the information capacitance. The Hamiltonian has three parts: magnetic, elastic (where the Blume–Emery–Griffiths model is used to describe the degree of freedom of the atomic displacements in the lattice), and the magnetoelastic part (which accounts for the interdependence of the magnetic and elastic subsystems). Additionally, entropies (total and partial) were calculated using the interrelation between the thermal entropy, calculated by the specific heat, and the Gibbs definition to study the effects of correlation on the phase transformations that are included in the Monte Carlo calculations. The results show that each phase transition contributes differentially to the correlation, depending on the temperature. This allows us to analyze, for example, the degree of coupling between the magnetic and structural subsystems during different stages of the martensitic phase transitions present in the Ni<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>MnGa system. By extension, this richness of analysis is inherited by the information capacitance. Furthermore, this measure of complexity, along with its marginal form (C<span><math><msub><mrow></mrow><mrow><mi>s</mi><mi>u</mi><mi>m</mi></mrow></msub></math></span>), highlights the distinct phase transitions within the considered magneto-structural model.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130913"},"PeriodicalIF":3.1,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913942","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}

{"title":"Data-driven configurable scenario generation for testing autonomous driving systems in highway environments","authors":"Cheng Wei ,&nbsp;Kenan Mu ,&nbsp;Fei Hui ,&nbsp;Asad Jan Khattak","doi":"10.1016/j.physa.2025.130923","DOIUrl":"10.1016/j.physa.2025.130923","url":null,"abstract":"<div><div>Human-like traffic flow provides a test environment suitable for evaluating the safety of autonomous driving systems. Currently, simulation testing based on data injection faces the problems of small data volumes and high acquisition costs. Although previous studies have been conducted on scenario generation, the following shortcomings remain: the inability to conduct continuous-scenario generation, lack of real-time simulations, and reliance on simulations oriented toward a single-function scenario. To address these shortcomings, this study proposed the concept of behavior incentives as a basis for configurable continuous-scenario generation. First, to better extract the behavioral characteristics of a vehicle, a sampling method was proposed to dimensionally homogenize vehicles’ sequence data. Second, using these processed data, the type of behavior incentive and its numerical format were determined, and a unified behavior incentive framework was developed and populated. Additionally, to complete the lane changing information in the behavior incentive, the vehicle motion and trajectory data were resampled, a velocity-trajectory generation neural network was proposed, and the lane changing trajectory for the behavior incentive framework was generated. After completing all behavior incentive frames, the proposed method was simulated in real time using the Simulation of Urban Mobility traffic-flow simulation software, and the key parameters and functions of the simulation were identified. The simulation results show that the proposed method can not only effectively generate continuous test scenarios, but can also facilitate the addition and modification of parameters to generate configurable test scenarios comprising different states, providing an excellent basis for testing autonomous driving systems.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130923"},"PeriodicalIF":3.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895943","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}

{"title":"Optimum performance characteristics and maximum power output of a low-dissipation quantum heat engine","authors":"Jingyi Chen ,&nbsp;Junyi Wang ,&nbsp;Yuzhuo Pan ,&nbsp;Shanhe Su ,&nbsp;Jincan Chen","doi":"10.1016/j.physa.2025.130916","DOIUrl":"10.1016/j.physa.2025.130916","url":null,"abstract":"<div><div>This study presents a low-dissipation quantum heat engine cycle and analyzes its performance characteristics. Based on the slow driven perturbation theory, the analytic expansion of heat in powers of time is derived. Employing the method of Lagrange multiplier, we establish a constraint relation between the efficiency and the power output. The performances of the low-dissipation heat engine are optimally analyzed by considering the different objective functions. The results underscore the significance of operating the low-dissipation quantum heat engine cycle within the optimal region to attain the large power output and high efficiency simultaneously. These findings contribute to the understanding and optimization of low-dissipation quantum heat engines, providing insights for the development of efficient energy conversion technologies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130916"},"PeriodicalIF":3.1,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913943","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}

{"title":"Solving multi-dimensional fractional Black–Scholes model using deep learning","authors":"Junjia Guo,&nbsp;Hongyan Feng,&nbsp;Yue Kai","doi":"10.1016/j.physa.2025.130908","DOIUrl":"10.1016/j.physa.2025.130908","url":null,"abstract":"<div><div>In this paper, we propose the multi-dimensional fractional Black–Scholes model (MDFBSM) with correlations between different assets for the first time and solve the MDFBSM employing deep learning method by using the reformulation of the backward stochastic differential equations (BSDEs). The fractional Black–Scholes model (FBSM) is an extension of the traditional Black–Scholes model, which adopts the fractional Brownian motion (FBM) to describe the dynamic changes of asset prices, so as to capture the long-term memory, thick tail, autocorrelation and hidden dynamic changes in the financial market. Its complexity and “curse of dimensionality” makes the MDFBSM very difficult to solve. Thus, this paper uses BSDEs to reformulate the partial differential equations (PDEs). Combining the TensorFlow framework with the gradient of the solution as the policy function and the error between the solution of the BSDE and the prescribed terminal condition as the loss function, we approximate the policy function of the model by minimizing the residuals of the PDEs through a neural network approach, thus overcoming the “curse of dimensionality” problem. To verify the validity of this paper, the historical data of 67 futures contracts in China are used for empirical analysis. And then, we find that our results can truly reflect the dynamics of asset prices in the real market.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130908"},"PeriodicalIF":3.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863982","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}

{"title":"Lane-changing strategies of autonomous vehicles and social dilemmas in mixed traffic: A simulation study","authors":"Nikita V. Bykov,&nbsp;Maksim A. Kostrov","doi":"10.1016/j.physa.2025.130909","DOIUrl":"10.1016/j.physa.2025.130909","url":null,"abstract":"<div><div>This study investigates the impact of different lane-changing strategies of autonomous vehicles (AVs) on traffic dynamics and social efficiency in mixed traffic conditions. We introduce a multi-agent traffic model based on a cellular automaton framework, incorporating human-driven vehicles (HDVs) and three types of AVs: non-lane-changing (AV), cooperative (AV-C), and permissive (AV-D). Each AV type follows distinct longitudinal and lateral rules under Adaptive Cruise Control (ACC) or Cooperative ACC (CACC). The simulation results reveal that non-lane-changing AVs maximize traffic flow but struggle with obstacle avoidance. AV-C agents maintain platoon integrity, while AV-D agents improve maneuverability at the cost of platoon stability. We analyze the emergence of social dilemmas using the Social Efficiency Deficit (SED) metric and identify conditions under which individual rationality conflicts with global traffic performance. The findings highlight the need for hybrid control strategies and external incentives to support early-stage AV deployment and ensure cooperative equilibria.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130909"},"PeriodicalIF":3.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864024","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}

{"title":"Multifractal and informational analysis of air traffic flow: A case in mainland China","authors":"Zongbei Shi ,&nbsp;Mo Zhao ,&nbsp;Min He ,&nbsp;Yichen Xue ,&nbsp;Jiajie Yan ,&nbsp;Xinglong Wang","doi":"10.1016/j.physa.2025.130918","DOIUrl":"10.1016/j.physa.2025.130918","url":null,"abstract":"<div><div>Air traffic flow is a crucial indicator of aviation operational performance, and plays a pivotal role in ensuring the smooth and orderly management of airspaces. Analyzing the multifractal characteristics and informational properties of air traffic flow helps reveal complex fluctuations and identify evolutionary trends. In this study, we analyze the air traffic flow data from five months in 2019, covering 138 sectors distributed across seven regions in mainland China. We utilize multifractal detrended fluctuation analysis (MFDFA) and Fisher–Shannon analysis to characterize the dynamic behaviors of these air traffic flow series. The results indicate that air traffic flows in all studied regions exhibit clear multifractal properties, characterized by right-skewed multifractal spectra. These multifractal characteristics are primarily driven by long-range correlations present within the air traffic flows. Particularly notable multifractal features are observed in the southwest, north, and east regions of mainland China. Moreover, multifractal properties are prevalent across high-altitude sectors, demonstrating heterogeneous spatial distribution patterns. Among these regions, Xinjiang exhibits the highest level of organization and orderliness in air traffic flow, and correspondingly, the weakest multifractal strength. The relationship between the Fisher information and Shannon entropy of air traffic sectors in China displays bi-segmental scaling, following a power-law distribution. This finding further underscores the presence of distinct variations in operational patterns among different air traffic sectors.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130918"},"PeriodicalIF":3.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887286","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}

{"title":"Morphological and fractal properties of meningioma growth","authors":"Jorge Roblero ,&nbsp;Miguel Martín-Landrove","doi":"10.1016/j.physa.2025.130899","DOIUrl":"10.1016/j.physa.2025.130899","url":null,"abstract":"<div><div>Morphological and fractal properties of the tumor interface serve as key descriptors of tumor growth dynamics. For meningiomas, previous studies have highlighted growth patterns that diverge from the proliferative-invasive model typically associated with malignant brain tumors, such as gliomas. In this work, two distinct approaches are employed to investigate the scale-dependent properties of meningioma growth. Initially, longitudinal volume data sets for meningiomas are analyzed, employing an allometric growth model to distinctly differentiate between asymptomatic and anaplastic (petroclival) meningiomas. Secondly, a scaling analysis of tumor interfaces extracted from contrast-enhanced MRI established a clear distinction among tumor grades. This analysis not only confirmed previous findings regarding tumor growth dynamics but also revealed significant morphological and scaling properties essential for characterizing tumor grades.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130899"},"PeriodicalIF":3.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864022","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}

{"title":"Prediction of lncRNA-disease association based on heterogeneous graph contrastive learning","authors":"Silin Sun,&nbsp;Yuhan Xiu,&nbsp;Bingwei Zhou,&nbsp;Zixuan Qi,&nbsp;Bo Liu,&nbsp;Haixia Long","doi":"10.1016/j.physa.2025.130883","DOIUrl":"10.1016/j.physa.2025.130883","url":null,"abstract":"<div><div>Long non-coding RNAs (lncRNAs) play pivotal roles in human physiology and disease pathogenesis; however, accurately predicting their associations with diseases remains a significant challenge due to the limited predictive accuracy of existing methodologies. To address this issue, we introduce HGCL-LDA, an innovative framework that utilizes self-supervised graph contrastive learning to achieve high-precision predictions. Our methodology consists of three key steps: First, we construct a heterogeneous graph (HLD) by integrating diverse similarity metrics and association data. Second, we generate multi-view graphs through advanced data augmentation and perturbation techniques, followed by the extraction of node embeddings using a GCN encoder. Finally, we perform contrastive learning by constructing positive and negative sample pairs and employ XGBoost to predict potential lncRNA-disease associations. Extensive experiments on four datasets demonstrate HGCL-LDA’s superior performance over state-of-the-art models.The model was applied to lung, gastric, and liver cancers, predicting the top 15 lncRNAs potentially associated with each cancer type. Among these predictions, 14 lncRNAs for lung cancer, 13 for gastric cancer, and 12 for liver cancer were experimentally validated, respectively, highlighting its biomedical relevance. These results not only demonstrate the robustness and reliability of our framework but also highlight its potential to uncover novel lncRNA-disease associations, thereby advancing our understanding of disease mechanisms and contributing to the development of targeted therapeutic strategies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"677 ","pages":"Article 130883"},"PeriodicalIF":3.1,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863983","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}