Pub Date : 2025-12-31DOI: 10.1016/j.physa.2025.131258
Chulwook Park , Brian D. Fath
Complex systems exhibit strategic interactions in which risk propagation is influenced by networked relationships. This study presents a network–agent model within sports contexts, demonstrating how Nash equilibria emerge from bounded rationality. Using scale-free networks where nodes represent sports agents, we simulate evolutionary game dynamics under varying protection and learning parameters. The model reveals four distinct equilibria: coexistence with sufficient protection, system-wide failure under low protection, partial coexistence with limited protection, and robust protection with minimal failure. The balance between social learning and strategic adaptation crucially determines system behavior; low values produce fragmented strategic clusters, whereas high values drive convergence toward uniform protection strategies. Nash equilibria naturally emerge when competitive outcomes coexist with cooperative protection behaviors, offering practical insights for sports practitioners on prevention strategies, knowledge transfer, and the emergence of complementary specialized roles within teams.
{"title":"Bounded rationality produces Nash equilibria in sports networks: Protection, learning, and strategic adaptation","authors":"Chulwook Park , Brian D. Fath","doi":"10.1016/j.physa.2025.131258","DOIUrl":"10.1016/j.physa.2025.131258","url":null,"abstract":"<div><div>Complex systems exhibit strategic interactions in which risk propagation is influenced by networked relationships. This study presents a network–agent model within sports contexts, demonstrating how Nash equilibria emerge from bounded rationality. Using scale-free networks where nodes represent sports agents, we simulate evolutionary game dynamics under varying protection and learning parameters. The model reveals four distinct equilibria: coexistence with sufficient protection, system-wide failure under low protection, partial coexistence with limited protection, and robust protection with minimal failure. The balance between social learning and strategic adaptation crucially determines system behavior; low values produce fragmented strategic clusters, whereas high values drive convergence toward uniform protection strategies. Nash equilibria naturally emerge when competitive outcomes coexist with cooperative protection behaviors, offering practical insights for sports practitioners on prevention strategies, knowledge transfer, and the emergence of complementary specialized roles within teams.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"685 ","pages":"Article 131258"},"PeriodicalIF":3.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078916","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}
Studying stochastic resonance (SR) and its associated energy consumption is essential for understanding the mechanisms underlying neural information processing and transmission. In this work, the two-compartment model was used to investigate the influence of neuronal morphology and network properties on the detection and transmission of weak signals and the corresponding energy consumption. Transmitting weak signals requires an appropriate noise intensity, and weak signals with higher intensity and lower frequency are more easily detected by neurons. It is shown that neurons with larger dendrites can respond more effectively to weak signals. In small-world networks, neural networks composed of neurons with large dendrites are more sensitive to weak signals. Under specific conditions, the regular connectivity of the networks weakens the response to weak signals. The results of this study may contribute to a better understanding of information processing in the nervous system and the energy regulation involved in this process.
{"title":"Neuronal morphology and network topology modulate weak-signal responses in single neurons and small-world networks","authors":"Jiapei Zeng, Tianyu Li, Qianming Ding, Xueqin Wang, Yong Wu, Ya Jia","doi":"10.1016/j.physa.2025.131259","DOIUrl":"10.1016/j.physa.2025.131259","url":null,"abstract":"<div><div>Studying stochastic resonance (SR) and its associated energy consumption is essential for understanding the mechanisms underlying neural information processing and transmission. In this work, the two-compartment model was used to investigate the influence of neuronal morphology and network properties on the detection and transmission of weak signals and the corresponding energy consumption. Transmitting weak signals requires an appropriate noise intensity, and weak signals with higher intensity and lower frequency are more easily detected by neurons. It is shown that neurons with larger dendrites can respond more effectively to weak signals. In small-world networks, neural networks composed of neurons with large dendrites are more sensitive to weak signals. Under specific conditions, the regular connectivity of the networks weakens the response to weak signals. The results of this study may contribute to a better understanding of information processing in the nervous system and the energy regulation involved in this process.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131259"},"PeriodicalIF":3.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927913","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}
Pub Date : 2025-12-30DOI: 10.1016/j.physa.2025.131241
Manyu Kong, Shunqi Zhang
This study investigates the asymmetric impact of global stock market systemic risk (GSMSR) on the conditional distribution of Bitcoin returns. We develop a robust GSMSR measure by applying the Tail-Event driven Network (TENET) methodology to G20 equity indices. A two-stage semi-parametric framework is then employed to model the dynamic response of Bitcoin’s return distribution across multiple forecasting horizons. Our results show that an increase in GSMSR significantly suppresses both extreme negative and positive Bitcoin returns; however, this effect exhibits mean reversion within a one-month horizon. To quantify Bitcoin’s time-varying vulnerability, we leverage an established framework of tail risk measures, including Downside/Upside Entropy and Expected Shortfall/Longrise, which capture risk dynamics from both relative and absolute perspectives. We find that Bitcoin’s downside risk is not only more volatile but also more sensitive to GSMSR fluctuations than its upside potential, with downside entropy exhibiting pronounced peaks during systemic crises while upside entropy remains relatively stable. Furthermore, over the longer term, these tail risks exhibit a clear “risk trade-off” pattern. Our findings provide new insights into the complex, nonlinear nature of risk transmission between traditional financial markets and cryptocurrencies.
{"title":"Global stock market systemic risk and the asymmetric transmission to Bitcoin’s tail dynamics","authors":"Manyu Kong, Shunqi Zhang","doi":"10.1016/j.physa.2025.131241","DOIUrl":"10.1016/j.physa.2025.131241","url":null,"abstract":"<div><div>This study investigates the asymmetric impact of global stock market systemic risk (GSMSR) on the conditional distribution of Bitcoin returns. We develop a robust GSMSR measure by applying the Tail-Event driven Network (TENET) methodology to G20 equity indices. A two-stage semi-parametric framework is then employed to model the dynamic response of Bitcoin’s return distribution across multiple forecasting horizons. Our results show that an increase in GSMSR significantly suppresses both extreme negative and positive Bitcoin returns; however, this effect exhibits mean reversion within a one-month horizon. To quantify Bitcoin’s time-varying vulnerability, we leverage an established framework of tail risk measures, including Downside/Upside Entropy and Expected Shortfall/Longrise, which capture risk dynamics from both relative and absolute perspectives. We find that Bitcoin’s downside risk is not only more volatile but also more sensitive to GSMSR fluctuations than its upside potential, with downside entropy exhibiting pronounced peaks during systemic crises while upside entropy remains relatively stable. Furthermore, over the longer term, these tail risks exhibit a clear “risk trade-off” pattern. Our findings provide new insights into the complex, nonlinear nature of risk transmission between traditional financial markets and cryptocurrencies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131241"},"PeriodicalIF":3.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886375","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}
Pub Date : 2025-12-29DOI: 10.1016/j.physa.2025.131240
Gustavo L. Kohlrausch , Thiago Dias , Sebastian Gonçalves
Wealth transactions are central to economic activity, and their particularities shape macroeconomic outcomes. We propose an agent-based model to investigate how homophily influences economic inequality. The model simulates wealth exchanges in a dynamic network composed of two groups, and , differentiated by a homophily parameter , which increases intragroup connections within . Economic interactions alternate between conservative wealth exchanges and connection rewiring, both influenced by agents’ wealth and . We examine economic and network dynamics under varying levels of social protection , which favor poorer agents in transactions. At low , results reveal high inequality and link concentration, with impacting only transient dynamics. At high , homophily becomes an economic advantage, as increasing directs wealth flow to group . However, since this flow benefits the wealthiest agents, it simultaneously exacerbates internal inequality within the group. These findings show that homophily is a significant driver of inequality, directing wealth towards the homophilous group and worsening internal disparities.
{"title":"Homophilic effects on economic inequality: A dynamic network agent-based model","authors":"Gustavo L. Kohlrausch , Thiago Dias , Sebastian Gonçalves","doi":"10.1016/j.physa.2025.131240","DOIUrl":"10.1016/j.physa.2025.131240","url":null,"abstract":"<div><div>Wealth transactions are central to economic activity, and their particularities shape macroeconomic outcomes. We propose an agent-based model to investigate how homophily influences economic inequality. The model simulates wealth exchanges in a dynamic network composed of two groups, <span><math><mi>A</mi></math></span> and <span><math><mi>B</mi></math></span>, differentiated by a homophily parameter <span><math><mi>δ</mi></math></span>, which increases intragroup connections within <span><math><mi>A</mi></math></span>. Economic interactions alternate between conservative wealth exchanges and connection rewiring, both influenced by agents’ wealth and <span><math><mi>δ</mi></math></span>. We examine economic and network dynamics under varying levels of social protection <span><math><mi>f</mi></math></span>, which favor poorer agents in transactions. At low <span><math><mi>f</mi></math></span>, results reveal high inequality and link concentration, with <span><math><mi>δ</mi></math></span> impacting only transient dynamics. At high <span><math><mi>f</mi></math></span>, homophily becomes an economic advantage, as increasing <span><math><mi>δ</mi></math></span> directs wealth flow to group <span><math><mi>A</mi></math></span>. However, since this flow benefits the wealthiest agents, it simultaneously exacerbates internal inequality within the group. These findings show that homophily is a significant driver of inequality, directing wealth towards the homophilous group and worsening internal disparities.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131240"},"PeriodicalIF":3.1,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847737","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}
Pub Date : 2025-12-29DOI: 10.1016/j.physa.2025.131249
Shuan Wang, Ning Zhao, Chunhua Zeng
Separation of particles with different masses at the micro- and nanoscales plays a crucial role across various fields, whereby the Ludwig–Soret effect serves as an important mechanism. This effect is typically induced by a stable temperature gradient maintained through a fixed temperature difference. In this paper, using a one-dimensional soft-core bi-component atomic gas model, we demonstrate that complete mass separation due to the Ludwig–Soret effect can occur even under a zero effective average temperature difference. Contrary to the case with a sufficiently large fixed temperature difference, this separation is characterized by the heavy particles accumulating in the low-temperature region, while the light ones accumulating in the high-temperature region. Complete mass separation is facilitated by an increase in the amplitude of the time-varying temperature, and also by a higher finite potential barrier that dictates the interaction between particles. Both a higher potential barrier and a lower average environmental reference temperature require longer integration times to ensure steady-state results. The upper bound of the average environmental reference temperature that enables complete mass separation increases linearly with the potential barrier. When one mass is held fixed while the other is reduced or increased relative to the fixed mass, this upper bound decreases nonlinearly. This separation may stem from probabilistic differences for different masses inherent in the statistical heat bath itself. Our results offer novel insights into controlling mass separation under dynamic nonequilibrium conditions.
{"title":"Mass separation under zero effective average temperature difference","authors":"Shuan Wang, Ning Zhao, Chunhua Zeng","doi":"10.1016/j.physa.2025.131249","DOIUrl":"10.1016/j.physa.2025.131249","url":null,"abstract":"<div><div>Separation of particles with different masses at the micro- and nanoscales plays a crucial role across various fields, whereby the Ludwig–Soret effect serves as an important mechanism. This effect is typically induced by a stable temperature gradient maintained through a fixed temperature difference. In this paper, using a one-dimensional soft-core bi-component atomic gas model, we demonstrate that complete mass separation due to the Ludwig–Soret effect can occur even under a zero effective average temperature difference. Contrary to the case with a sufficiently large fixed temperature difference, this separation is characterized by the heavy particles accumulating in the low-temperature region, while the light ones accumulating in the high-temperature region. Complete mass separation is facilitated by an increase in the amplitude of the time-varying temperature, and also by a higher finite potential barrier that dictates the interaction between particles. Both a higher potential barrier and a lower average environmental reference temperature require longer integration times to ensure steady-state results. The upper bound of the average environmental reference temperature that enables complete mass separation increases linearly with the potential barrier. When one mass is held fixed while the other is reduced or increased relative to the fixed mass, this upper bound decreases nonlinearly. This separation may stem from probabilistic differences for different masses inherent in the statistical heat bath itself. Our results offer novel insights into controlling mass separation under dynamic nonequilibrium conditions.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131249"},"PeriodicalIF":3.1,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886380","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}
Pub Date : 2025-12-27DOI: 10.1016/j.physa.2025.131246
Xiaoyu Tang, Hong Zhang, Guohua Li, Xiangwen Huang, Ting Liu, Zeyu Tu, Xiaoxuan Wang
In recent years, anomalous diffusion dynamics has become a popular research area. Continuous Time Random Walk (CTRW) is a suitable model for describing anomalous diffusion. The statistical inference of anomalous diffusion processes is critical for exposing transport mechanisms in complex media, with precise estimation of essential parameters, such as the diffusion coefficient, posing a significant problem. This paper conducts research based on the proposed coupled CTRW model, which introduces a quadratic dependence relationship between the waiting time and the previous jump length to characterize anomalous diffusion behavior under the energy interaction mechanism. We focus on the Monte Carlo sampling of the trajectories over time, as well as the estimation of energy dependent parameter in this model, which are closely related to the diffusion coefficient. Two situations were examined: one with a flow field and the other without a flow field. Assuming a Gaussian distribution for jump length, we find the analytical solution to the relevant generalized diffusion equation which help us perform parameter estimation. Based on the time-varying migration paths of a large number of particles obtained through random simulation, we have constructed a dual-track research framework that includes model validation and parameter estimation. We employed maximum likelihood estimation (MLE) and two-step generalized method of moments (GMM) for statistical inference of the energy-dependent parameters. We analyzed the impact of several parameter alterations on the estimation results and compared the performance of the two techniques.
{"title":"Estimation of energy-dependent parameter in the coupled continuous time random walk model","authors":"Xiaoyu Tang, Hong Zhang, Guohua Li, Xiangwen Huang, Ting Liu, Zeyu Tu, Xiaoxuan Wang","doi":"10.1016/j.physa.2025.131246","DOIUrl":"10.1016/j.physa.2025.131246","url":null,"abstract":"<div><div>In recent years, anomalous diffusion dynamics has become a popular research area. Continuous Time Random Walk (CTRW) is a suitable model for describing anomalous diffusion. The statistical inference of anomalous diffusion processes is critical for exposing transport mechanisms in complex media, with precise estimation of essential parameters, such as the diffusion coefficient, posing a significant problem. This paper conducts research based on the proposed coupled CTRW model, which introduces a quadratic dependence relationship between the waiting time and the previous jump length to characterize anomalous diffusion behavior under the energy interaction mechanism. We focus on the Monte Carlo sampling of the trajectories over time, as well as the estimation of energy dependent parameter in this model, which are closely related to the diffusion coefficient. Two situations were examined: one with a flow field and the other without a flow field. Assuming a Gaussian distribution for jump length, we find the analytical solution to the relevant generalized diffusion equation which help us perform parameter estimation. Based on the time-varying migration paths of a large number of particles obtained through random simulation, we have constructed a dual-track research framework that includes model validation and parameter estimation. We employed maximum likelihood estimation (MLE) and two-step generalized method of moments (GMM) for statistical inference of the energy-dependent parameters. We analyzed the impact of several parameter alterations on the estimation results and compared the performance of the two techniques.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"683 ","pages":"Article 131246"},"PeriodicalIF":3.1,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841021","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}
Pub Date : 2025-12-27DOI: 10.1016/j.physa.2025.131244
Wilinston da Silva Oliveira , J.A. Plascak , Maria Eugênia Silva Nunes
The spin- quantum disordered Ising ladder, in the presence of a random transverse magnetic field, is studied using the method of recurrence relations in the high temperature limit. The ladder has homogeneous nearest-neighbor (NN) and homogeneous next-nearest-neighbor (NNN) interactions along the two side rails. The two side rails are coupled by disordered rung interactions. Using the recurrence relations method (RRM) in the high-temperature limit, we calculate the first four recurrants exactly and employ a linear extrapolation procedure to generate any necessary number of additional recurrants. In particular, the time autocorrelation function for the component of the dynamic spin variable and its corresponding spectral function were calculated. The dynamics of both quantities depend on the values of the theoretical parameters as well as on the disorder type, and can change from central peak at low fields to collective-mode behavior at higher fields. Notably, the spectral functions exhibited a distinct change in behavior as the NNN coupling strength was varied, suggesting a transition in the dynamical response of the system. An interesting case of rung-transverse-field correlated randomness is also analyzed. In general, NNN interactions play an important role in maintaining temporal coherence, as demonstrated by the slower decay of the spin autocorrelation function and the persistence of the central peak in the spectral function, even under a strong external magnetic field.
{"title":"The role of next-nearest-neighbor interactions in the dynamics of the spin-1/2 disordered Ising ladder in random transverse field","authors":"Wilinston da Silva Oliveira , J.A. Plascak , Maria Eugênia Silva Nunes","doi":"10.1016/j.physa.2025.131244","DOIUrl":"10.1016/j.physa.2025.131244","url":null,"abstract":"<div><div>The spin-<span><math><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></math></span> quantum disordered Ising ladder, in the presence of a random transverse magnetic field, is studied using the method of recurrence relations in the high temperature limit. The ladder has homogeneous nearest-neighbor (NN) and homogeneous next-nearest-neighbor (NNN) interactions along the two side rails. The two side rails are coupled by disordered rung interactions. Using the recurrence relations method (RRM) in the high-temperature limit, we calculate the first four recurrants exactly and employ a linear extrapolation procedure to generate any necessary number of additional recurrants. In particular, the time autocorrelation function for the <span><math><mi>z</mi></math></span> component of the dynamic spin variable and its corresponding spectral function were calculated. The dynamics of both quantities depend on the values of the theoretical parameters as well as on the disorder type, and can change from central peak at low fields to collective-mode behavior at higher fields. Notably, the spectral functions exhibited a distinct change in behavior as the NNN coupling strength was varied, suggesting a transition in the dynamical response of the system. An interesting case of rung-transverse-field correlated randomness is also analyzed. In general, NNN interactions play an important role in maintaining temporal coherence, as demonstrated by the slower decay of the spin autocorrelation function and the persistence of the central peak in the spectral function, even under a strong external magnetic field.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131244"},"PeriodicalIF":3.1,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886374","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}
We investigate minimally nonlinear three-terminal thermoelectric voltage and voltage- temperature probe heat engines with broken time-reversal symmetry, induced by magnetic flux. By extending Onsager relations with a leading-order nonlinear dissipation term, we obtain analytical bounds for both the efficiency at maximum power (EMP) and the efficiency at arbitrary power. Remarkably, both probe configurations exhibit universal EMP bounds that exceed the Curzon–Ahlborn limit, though with distinct dependence on asymmetry and figures of merit. Using a triple-quantum-dot Aharonov–Bohm interferometer as a model system, we demonstrate how magnetic flux and energy anisotropy tune performance: the voltage probe maximizes power, while the voltage–temperature probe achieves higher efficiency. These results establish minimally nonlinear probe heat engines as a generic pathway to surpassing classical efficiency limits in nanoscale thermodynamics.
{"title":"Minimally nonlinear probe-controlled Aharonov–Bohm heat engines with broken time-reversal symmetry: Surpassing the Curzon–Ahlborn limit","authors":"Jayasmita Behera , Salil Bedkihal , Bijay Kumar Agarwalla , Malay Bandyopadhyay","doi":"10.1016/j.physa.2025.131243","DOIUrl":"10.1016/j.physa.2025.131243","url":null,"abstract":"<div><div>We investigate minimally nonlinear three-terminal thermoelectric voltage and voltage- temperature probe heat engines with broken time-reversal symmetry, induced by magnetic flux. By extending Onsager relations with a leading-order nonlinear dissipation term, we obtain analytical bounds for both the efficiency at maximum power (EMP) and the efficiency at arbitrary power. Remarkably, both probe configurations exhibit universal EMP bounds that exceed the Curzon–Ahlborn limit, though with distinct dependence on asymmetry and figures of merit. Using a triple-quantum-dot Aharonov–Bohm interferometer as a model system, we demonstrate how magnetic flux and energy anisotropy tune performance: the voltage probe maximizes power, while the voltage–temperature probe achieves higher efficiency. These results establish minimally nonlinear probe heat engines as a generic pathway to surpassing classical efficiency limits in nanoscale thermodynamics.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131243"},"PeriodicalIF":3.1,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886377","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}
Pub Date : 2025-12-27DOI: 10.1016/j.physa.2025.131248
Yanming Hao , Tianjun Feng , Guozhu Cheng , Jinfeng Li , Feiyan Li , Jiaojiao Liu , Heyao Gao
To better understand the characteristics of heterogeneous traffic flows involving Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), an accurate description of driver behaviour within the model is essential. Since vehicle acceleration decisions are influenced not only by the vehicle’s state at a specific moment, as in traditional Cellular Automaton (CA) models, but also by historical driving data over a period, the Long Short-Term Memory (LSTM) model was introduced. This model is particularly effective at managing long-term sequences, complex nonlinear features, and multivariate dynamic influence data. By training the LSTM model on vehicle driving data from the NGSIM database, it uncovers the underlying driving habits of human drivers and predicts the next acceleration decision. The trained model was then combined with cellular automata to create the Long Short-Term Memory-Cellular Automaton (LSTM-C) model for simulating traffic flow. Comparisons with other models show that the model can replicate the phase transition process in three-phase traffic flow while partly reducing issues caused by sudden vehicle acceleration and abrupt braking. Simulations were performed for heterogeneous traffic flows at different CAV penetration rates, and the results were analysed. The analysis reveals that as CAV penetration increases, the maximum traffic flow also rises. For example, at an 80% penetration rate, the maximum flow increases by 42.5%. Further analysis indicates that higher CAV penetration provides benefits such as faster vehicle speeds, fewer lane changes, and reductions in congestion coefficient and congestion duration.
{"title":"Analysis of heterogeneous traffic flow characteristics based on LSTM","authors":"Yanming Hao , Tianjun Feng , Guozhu Cheng , Jinfeng Li , Feiyan Li , Jiaojiao Liu , Heyao Gao","doi":"10.1016/j.physa.2025.131248","DOIUrl":"10.1016/j.physa.2025.131248","url":null,"abstract":"<div><div>To better understand the characteristics of heterogeneous traffic flows involving Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs), an accurate description of driver behaviour within the model is essential. Since vehicle acceleration decisions are influenced not only by the vehicle’s state at a specific moment, as in traditional Cellular Automaton (CA) models, but also by historical driving data over a period, the Long Short-Term Memory (LSTM) model was introduced. This model is particularly effective at managing long-term sequences, complex nonlinear features, and multivariate dynamic influence data. By training the LSTM model on vehicle driving data from the NGSIM database, it uncovers the underlying driving habits of human drivers and predicts the next acceleration decision. The trained model was then combined with cellular automata to create the Long Short-Term Memory-Cellular Automaton (LSTM-C) model for simulating traffic flow. Comparisons with other models show that the model can replicate the phase transition process in three-phase traffic flow while partly reducing issues caused by sudden vehicle acceleration and abrupt braking. Simulations were performed for heterogeneous traffic flows at different CAV penetration rates, and the results were analysed. The analysis reveals that as CAV penetration increases, the maximum traffic flow also rises. For example, at an 80% penetration rate, the maximum flow increases by 42.5%. Further analysis indicates that higher CAV penetration provides benefits such as faster vehicle speeds, fewer lane changes, and reductions in congestion coefficient and congestion duration.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"683 ","pages":"Article 131248"},"PeriodicalIF":3.1,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841023","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}
Pub Date : 2025-12-26DOI: 10.1016/j.physa.2025.131247
T.R. Kirkpatrick , J.V. Sengers , H. van Beijeren
{"title":"In Memoriam J. Robert “Bob” Dorfman (1937–2025)","authors":"T.R. Kirkpatrick , J.V. Sengers , H. van Beijeren","doi":"10.1016/j.physa.2025.131247","DOIUrl":"10.1016/j.physa.2025.131247","url":null,"abstract":"","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"684 ","pages":"Article 131247"},"PeriodicalIF":3.1,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038730","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}
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