Physica A: Statistical Mechanics and its Applications最新文献

{"title":"Effects of taxes, redistribution actions and fiscal evasion on wealth inequality: An agent-based model approach","authors":"Iago N. Barros ,&nbsp;Marcelo L. Martins","doi":"10.1016/j.physa.2025.130960","DOIUrl":"10.1016/j.physa.2025.130960","url":null,"abstract":"<div><div>In capitalist societies, only one right can be fully exercised without any constraints: the limitless accumulation of wealth. This imperative, or fundamental axiom, is the ultimate cause of the raising waves of inequalities observed today. In this work, we extended the agent-based model proposed by Castro de Oliveira (2017) to study the effects of non-uniform income redistribution policies and tax evasion on the steady-state wealth distribution of economic agents. Our simulational results strongly support that well designed policies of income redistribution are an essential tools for promoting more economically egalitarian and sustainable societies. Furthermore, we show that tax evasion can substantially mitigate the effects of redistribution, pushing the system toward the critical point of absolute wealth condensation and highlighting the importance of strict control over the taxes collected within an economy.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 130960"},"PeriodicalIF":3.1,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160210","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":"Generalized formulation for ideal light-powered systems through energy and entropy flow analysis Part 2: Beyond the first-order evaluation under realistic conditions","authors":"Tetsuo Yabuki","doi":"10.1016/j.physa.2025.130984","DOIUrl":"10.1016/j.physa.2025.130984","url":null,"abstract":"<div><div>This study formulates the ideal efficiency of <em>light-powered systems</em> in the most general form, based on the first principle of energy-entropy flow analysis under the condition of zero entropy generation within the system. A unified formula for the ideal efficiency of <em>light-powered systems</em> is presented in this study. The formula incorporates the absorption ratio <span><math><mrow><mfenced><mrow><mi>ε</mi></mrow></mfenced></mrow></math></span> as an indicator beyond the first-order evaluation based on photon number, for light with a dilution indicator <span><math><mi>d</mi></math></span>, and it is extended to cases where entropy is simultaneously discarded from the system via radiation and heat. Selecting the appropriate <span><math><mi>Y</mi></math></span>-factors and <span><math><mi>p</mi></math></span>-parameters from this study for given conditions allows us to accurately and systematically derive the ideal efficiencies of <em>light-powered systems</em> and correctly classify the multiple ideal efficiencies that were previously confused, such as efficiencies include the Jeter, Spanner, and Landsberg-Petela efficiencies which form the basis of practical efficiency. This study also classified existing <em>light-powered systems</em> into two models: the piston-cylinder radiation model and the flowing radiation model, and demonstrated that the latter model is suitable for micro <em>light-powered systems</em>. Finally, this study clarified two issues with the ideal efficiency proposed by Landsberg and Tonge (often referred to as the Landsberg limit) based on the classical flowing radiation model, and derived a new ideal efficiency using a simple mathematical model based on Einstein's theory of radiation and absorption in a two-level system, which assumes quantum transitions, to resolve those problems. The newly obtained ideal efficiency was found to behave very similarly to the Carnot efficiency.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 130984"},"PeriodicalIF":3.1,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222143","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":"Tumor cells in human brain show multifractality! A multifractal detrended fluctuation analysis from the MRI images","authors":"Somsubhra Saha ,&nbsp;Rajdip Mitra ,&nbsp;Md. Mohi Uddin ,&nbsp;Md. Akhtaruzzaman ,&nbsp;Hamad Al Mohamadi ,&nbsp;Joydeep Chowdhury","doi":"10.1016/j.physa.2025.130983","DOIUrl":"10.1016/j.physa.2025.130983","url":null,"abstract":"<div><div>The multifractal study of tumorous and normal brain MRI images has been reported. The brain MRI images recorded at the axial, colonal, and sagittal planes were used to perform the Multifractal Detrended Fluctuation Analysis (MFDFA) study. The nonlinear graphs of h(q) vs. q and τ(q) vs. q primarily depict that the tumorous MRI images are multifractal, while the normal brain MRI images don't show any sign of multifractal features. The tumorous brain MRI images are multifractal since their singularity spectra exhibit a bell-curve pattern with finite spectral widths (W) in each of the three planes. Interestingly, the MRI images of normal brain does not exhibit such phenomenon. The presence of multifractality in tumor cells as concluded from this manuscript will definitely provide a leeway for other researchers to explore the calculations further in order to identify the benign and malignant tumors, which we believe may exhibit different degrees of multifractalities. For both the tumorous and non-tumorous images, the MF-DFA tool was also used to find their asymmetry indices (B) and correlation coefficients (γ). These investigations lead us to conclude that, multifractality may be used to differentiate between the tumorous and non-tumorous brain MRI images in the Axial, Colonal and Sagittal planes. Furthermore, the findings of this study might encourage other researchers to execute MFDFA analyses at different phases of tumor growth and radiation.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 130983"},"PeriodicalIF":3.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110119","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":"Efficient-ecological-function analyses and multi-objective optimizations for generalized irreversible Carnot heat pumps","authors":"Yiwen Su ,&nbsp;Lingen Chen ,&nbsp;Yanlin Ge ,&nbsp;Shuangshuang Shi ,&nbsp;Huijun Feng","doi":"10.1016/j.physa.2025.130979","DOIUrl":"10.1016/j.physa.2025.130979","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Previous studies proposed exergy-based efficient-ecological-function (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) as a new cycle performance index. In this study, &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; is introduced into a generalized irreversible Carnot heat-pump (CHP) cycle with heat-leak rate (&lt;span&gt;&lt;math&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;), heat-transfer loss and internal-irreversibility-factor (&lt;span&gt;&lt;math&gt;&lt;mi&gt;Φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;). Cycle performances working under the maximum coefficient-of-performance (&lt;span&gt;&lt;math&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;), maximum &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and maximum ecological-function (&lt;span&gt;&lt;math&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;) conditions are compared firstly. Then, single-, dual-, triple-, and quadruple-objective optimizations for the irreversible CHPs are performed with &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, heating load (&lt;span&gt;&lt;math&gt;&lt;mi&gt;π&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;) and &lt;span&gt;&lt;math&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; as well as their different combinations as optimization objectives, and working-fluid temperature-ratio (&lt;span&gt;&lt;math&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;) as optimization variable, by utilizing NSGA-II algorithm. Pareto-frontiers (PFs) under different objective combinations are obtained. Finally, the PF value is selected by using three decision-making-methods (DMMs): TOPSIS, LINMAP, and Shannon entropy. With the same objective function combination, the deviation indexes (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) of three DMMs are compared and the optimal scheme is selected according to the smallest &lt;span&gt;&lt;math&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;. The results for endoreverisble CHP case are also provided. The findings show that &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; places greater emphasis on the trade-off among &lt;span&gt;&lt;math&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mi&gt;π&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, exergy-output-rate, and entropy-generation-rate. Heat-leak-rate transforms curve of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; from parabolic to loop-shaped. The curve of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mi&gt;π&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; is parabolic shape, &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; decreases with increases of &lt;span&gt;&lt;math&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mi&gt;Φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;. In practical heat-pump design, it is necessary to choose a designing point with higher &lt;span&gt;&lt;math&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mi&gt;π&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; in order to improve performance. On the PF, each point represents an optimal equilibrium-state achieved by objective function. For four-obje","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 130979"},"PeriodicalIF":3.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109035","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":"Accurate, Secure and Explainable bitcoin forecasting","authors":"Maryamsadat Bagheri,&nbsp;Paolo Giudici","doi":"10.1016/j.physa.2025.130974","DOIUrl":"10.1016/j.physa.2025.130974","url":null,"abstract":"<div><div>Forecasting the price of bitcoin assets is a difficult task, especially as bitcoins are highly volatile and speculative. In this paper we leverage the non linear capability of deep and machine learning models to enhance bitcoin forecasts. We propose a systematic comparison of different deep learning and machine learning models, based on their Accuracy, Security and Explainability characteristics. The empirical findings reveal that, while CNN–GRU, GRU and LSTM are the most accurate models, for maximum cumulative return and risk adjusted performance GRU and CNN are preferred. Whereas, for transparent and stable decision-making, Random Forest and XGboost are a good choice and, for robustness, CNN and LSTM are the best choice. Ultimately, the choice of a model depends on the objectives of the analysis.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"678 ","pages":"Article 130974"},"PeriodicalIF":3.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106669","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":"Modeling and analyzing multiplatform coupled information propagation dynamics based on real social networks","authors":"Yuewei Wu ,&nbsp;Jinxia Wang ,&nbsp;Qing Yin ,&nbsp;Chang Wu ,&nbsp;Fulian Yin","doi":"10.1016/j.physa.2025.130980","DOIUrl":"10.1016/j.physa.2025.130980","url":null,"abstract":"<div><div>With the booming development of social media, multi-platform information propagation is becoming increasingly prevalent and intricate. Understanding how information spreads rapidly in the multi-platform network has emerged as a pivotal focus in current research. Particularly, information dynamic models are of significance for public opinion governance in the Omnimedia era. Incorporating the comprehensive influence of multiple factors, this paper proposes an emotion-driven Multi-platform Susceptible-Exposed-Propagating-Immune (MP-SEPI) propagation dynamic model based on real social networks. By applying the Monte Carlo (MC) method to implement numerical fitting and comparative analyses in the X-Weibo-TikTok coupled network, we demonstrate the superior performance of the proposed model in simulating multi-platform information diffusion, with values of <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> being over 0.93. Furthermore, our analyses of influencing factors highlight the crucial role of the coupled network and reveal the mechanisms underlying information propagation and emotion evolution across diverse platforms. Therefore, the proposed modeling framework provides efficient strategies for public opinion guidance, regulation, and management, alleviating the burden of relevant agencies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 130980"},"PeriodicalIF":3.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109037","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":"Self-stabilizing lattice model with feedback-feedforward coupling and its nonlinear stability analysis","authors":"Chuan Tian ,&nbsp;Yijun Chen ,&nbsp;Qingxiang Xiao ,&nbsp;Qiongbing Xiong","doi":"10.1016/j.physa.2025.130987","DOIUrl":"10.1016/j.physa.2025.130987","url":null,"abstract":"<div><div>To mitigate traffic congestion, this paper integrates historical traffic flow difference-based feedback and density self-expectation-based feedforward to develop a feedback-feedforward coupled self-stabilizing strategy, and proposes a novel traffic flow lattice model. Using linear stability theory and nonlinear analysis, we investigate the strategy’s mechanism on macroscopic traffic flow stability, deriving the model’s linear stability criterion, and the modified Korteweg-de Vries (mKdV) equation (with its density wave solution) describing congestion propagation near the critical point. Theoretical and simulation results show that, compared with single-information self-stabilizing strategies, the proposed strategy significantly enhances traffic flow stability and robustness, and accelerates disturbance convergence to a steady state. Longer time intervals in the strategy further improve stability and congestion suppression. This research provides new theoretical insights for alleviating traffic congestion.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"678 ","pages":"Article 130987"},"PeriodicalIF":3.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106670","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":"STG-KNet: A Kernel-mapping-based spatial-temporal graph convolution network for pedestrian trajectory prediction","authors":"Yuanzi Xu,&nbsp;Jiafu Yang,&nbsp;Rongjun Cheng","doi":"10.1016/j.physa.2025.130985","DOIUrl":"10.1016/j.physa.2025.130985","url":null,"abstract":"<div><div>Predicting pedestrian trajectories in complex, dynamic, and crowded environments remains a critical challenge for autonomous driving and human-robot interaction. A pervasive challenge among existing methods is their dependence on rigid graph architectures, which hinders their capacity to model the evolving patterns of pedestrian interaction and obscures the potential features of agent-to-agent relationships. Besides, spatial and time-dependent modeling in most model is mixed, and there is a lack of structural decoupling. These issues result in fragmented reasoning and degraded performance in dense pedestrian scenarios. To address these challenges, we propose STG-KNet, a unified spatiotemporal learning framework combining sparse graph convolution with kernel-based structure modeling. STG-KNet features a dual-branch spatiotemporal encoder to decouple and independently model spatial interactions and temporal motion patterns, enhanced by biologically inspired masking strategies. It further introduces a novel Graph Convolutional Kernel Mapping (GCKM) module to convert discrete graph structures into continuous Gaussian similarity matrices, enabling adaptive edge learning and interpretable feature propagation. A Temporal Convolutional Network (TCN) decoder predicts parameters of 2D Gaussian distributions for future positions, supporting multimodal sampling. Comprehensive experiments on the ETH-UCY dataset demonstrate that STG-KNet achieves state-of-the-art accuracy (ADE=0.23, FDE=0.45), outperforming existing models while maintaining structural interpretability and high computational efficiency. In particular, the model shows exceptional generalization in dense and heterogeneous scenes, confirming the effectiveness of sparse kernel-enhanced graph reasoning in trajectory prediction.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"678 ","pages":"Article 130985"},"PeriodicalIF":3.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106673","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":"Enhanced complex network analysis of multi-layer intercity road networks through identification of network-specific dynamics","authors":"Yichen Ye ,&nbsp;Huiying Wen ,&nbsp;Lin Zhang ,&nbsp;Yunxuan Li","doi":"10.1016/j.physa.2025.130986","DOIUrl":"10.1016/j.physa.2025.130986","url":null,"abstract":"<div><div>The multi-layer intercity road network (MIRN), comprising expressways, national highways and provincial highways, displays complex topology and nonlinear responses to perturbations that differ significantly from those of general complex networks. Existing models often simplify MIRNs into single-layer or weakly coupled representations, thereby overlooking network-specific dynamics arising from structural heterogeneity and cross-layer interactions in path selection. To address this gap, an enhanced complex network analysis framework is developed that identifies cross-layer path selection behavior as the key mechanism driving MIRN dynamics. Toll effects on path selection are captured by an enhanced impedance function integrating road attributes, toll costs and congestion effects, which enables unified representation of road hierarchy, spatial demand and user decision-making. Using shortest travel time computed from this impedance, classical network indicators such as network efficiency, betweenness centrality and closeness centrality are redefined to reflect behavior-driven dynamics. Moreover, fine-grained indicators at the path and segment levels are introduced to reflect path preferences and flow reassignment under different toll strategies. These developments establish a causal chain linking toll strategies, cross-layer path selection behavior, and network-specific dynamics, revealing how economic incentives reshape network structure and function. Empirical simulations on the Guangdong–Hong Kong–Macao MIRN demonstrate that expressway tolling shifts large volumes to lower-grade highways, reduces network efficiency, and reconfigures node centrality. Even after toll removal, local bottlenecks and structural changes hinder full recovery, highlighting the irreversibility and path-dependence of MIRN dynamics.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"679 ","pages":"Article 130986"},"PeriodicalIF":3.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109036","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":"Community detection in signed networks: A penalized semidefinite programming framework","authors":"Fengqin Tang ,&nbsp;Han Yang ,&nbsp;Cuixia Li ,&nbsp;Xuejing Zhao","doi":"10.1016/j.physa.2025.130978","DOIUrl":"10.1016/j.physa.2025.130978","url":null,"abstract":"<div><div>Network theory provides a powerful framework for modeling complex systems by representing relationships between entities. While traditional networks encode the presence or absence of interactions, many real-world systems, such as social networks and biological systems, require distinguishing between positive (cooperative) and negative (antagonistic) relationships to capture their underlying dynamics. Signed networks address this need by incorporating edge signs, enabling a more nuanced representation of system structures. In this paper, we study community detection in signed networks under the signed stochastic block model (SSBM). We propose a novel penalty-enhanced semidefinite programming approach, which is derived from a relaxation of maximum likelihood estimation under assumptions of network sparsity. This method explicitly models the asymmetry between positive and negative edges. Our framework is theoretically proven to achieve accurate community recovery, and its practical effectiveness is demonstrated through experiments on both synthetic and real-world datasets.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"678 ","pages":"Article 130978"},"PeriodicalIF":3.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106668","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}