Pub Date : 2024-10-10DOI: 10.1016/j.physa.2024.130147
Ewin Sánchez
An application of time series analysis is proposed from a extended perspective of superstatistics, in which a Boltzmann-like distribution with a power law density of states has been introduced. This superstatistical scenario is one generated by a superposition of gamma distributions, which has already been hinted in some papers. Two time series for the magnitude of SYM-H geomagnetic index have been taken, covering periods between solar cycles 22 and 24, where data of minimum and maximum solar activity have been extracted. Analyzes were performed from the proposed superstatistical perspective to the corresponding time series, showing concordance that could suggest that each of them is a consequence of a superstatistical-like process.
{"title":"Testing the scope of superstatistical time series analysis: Application to the SYM-H geomagnetic index","authors":"Ewin Sánchez","doi":"10.1016/j.physa.2024.130147","DOIUrl":"10.1016/j.physa.2024.130147","url":null,"abstract":"<div><div>An application of time series analysis is proposed from a extended perspective of superstatistics, in which a Boltzmann-like distribution with a power law <span><math><msup><mrow><mi>E</mi></mrow><mrow><mi>m</mi><mo>−</mo><mn>1</mn></mrow></msup></math></span> density of states has been introduced. This superstatistical scenario is one generated by a superposition of gamma distributions, which has already been hinted in some papers. Two time series for the magnitude of SYM-H geomagnetic index have been taken, covering periods between solar cycles 22 and 24, where data of minimum and maximum solar activity have been extracted. Analyzes were performed from the proposed superstatistical perspective to the corresponding time series, showing concordance that could suggest that each of them is a consequence of a superstatistical-like process.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130147"},"PeriodicalIF":2.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432118","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 : 2024-10-10DOI: 10.1016/j.physa.2024.130148
Tarun Ram Kanuri , Subhadeep Roy , Soumyajyoti Biswas
We have numerically studied a mean-field fiber bundle model of fracture at a non-zero temperature and acted upon by a constant external tensile stress. The individual fibers fail due to creep-like dynamics that lead up to a catastrophic breakdown. We quantify the variations in sizes of the resulting avalanches by calculating the Lorenz function and two inequality indices – Gini () and Kolkata () indices – derived from the Lorenz function. We show that the two indices cross just prior to the failure point when the dynamics goes through intermittent avalanches. For a continuous failure dynamics (finite numbers of fibers breaking at each time step), the crossing does not happen. However, in that phase, the usual prediction method i.e., linear relation between the time of minimum strain-rate (time at which rate of fiber breaking is the minimum) and failure time, holds. The boundary between continuous and intermittent dynamics is very close to the boundary between crossing and non-crossing of the two indices in the temperature-stress phase space, both drawn from independent analytical calculations and are verified by numerical simulations.
{"title":"Inequality of creep avalanches can predict imminent breakdown","authors":"Tarun Ram Kanuri , Subhadeep Roy , Soumyajyoti Biswas","doi":"10.1016/j.physa.2024.130148","DOIUrl":"10.1016/j.physa.2024.130148","url":null,"abstract":"<div><div>We have numerically studied a mean-field fiber bundle model of fracture at a non-zero temperature and acted upon by a constant external tensile stress. The individual fibers fail due to creep-like dynamics that lead up to a catastrophic breakdown. We quantify the variations in sizes of the resulting avalanches by calculating the Lorenz function and two inequality indices – Gini (<span><math><mi>g</mi></math></span>) and Kolkata (<span><math><mi>k</mi></math></span>) indices – derived from the Lorenz function. We show that the two indices cross just prior to the failure point when the dynamics goes through intermittent avalanches. For a continuous failure dynamics (finite numbers of fibers breaking at each time step), the crossing does not happen. However, in that phase, the usual prediction method i.e., linear relation between the time of minimum strain-rate (time at which rate of fiber breaking is the minimum) and failure time, holds. The boundary between continuous and intermittent dynamics is very close to the boundary between crossing and non-crossing of the two indices in the temperature-stress phase space, both drawn from independent analytical calculations and are verified by numerical simulations.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130148"},"PeriodicalIF":2.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445652","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 : 2024-10-09DOI: 10.1016/j.physa.2024.130138
Sheng-Hui Qin, Na Li
As a crowded place on the university campus, the cafeteria inevitably has many safety hazards, such as fire accidents caused by open flames and large appliances, stampede accidents caused by overcrowding during peak dining hours, etc. Therefore, studying pedestrian evacuation in university campus cafeterias is particularly necessary. Pedestrians on campus mostly travel in groups. Previous studies mainly used continuous models to discuss pedestrian group evacuation behavior. In this study, based on the cellular automaton pedestrian evacuation simulation model, the floor field calculation method was improved. A cellular automaton pedestrian evacuation simulation model considering group behavior was established and applied to the evacuation scenario of students in a university campus cafeteria. The study found that under the different group configurations, the pedestrian ratios, and the pedestrian densities, the pedestrian evacuation efficiency had significant differences. The results showed that when the different group configurations existed in the scenario, the higher the proportion of the pedestrians with three-person front-to-back group configurations, the higher the evacuation efficiency. When only one type of the group configuration existed in the scenario, at low pedestrian density, the evacuation efficiency of the individual pedestrian groups was higher compared to the other six group configurations. While at high pedestrian density, the evacuation efficiency of the three-person front-to-back group configurations was higher. These findings provided important references for pedestrian evacuation in university campus cafeterias and provided insights for the simulation research of group pedestrian evacuation models, contributing to enhancing campus safety management and ensuring the safety of teachers, students, and staff.
{"title":"Study on pedestrian evacuation simulation model considering group behavior","authors":"Sheng-Hui Qin, Na Li","doi":"10.1016/j.physa.2024.130138","DOIUrl":"10.1016/j.physa.2024.130138","url":null,"abstract":"<div><div>As a crowded place on the university campus, the cafeteria inevitably has many safety hazards, such as fire accidents caused by open flames and large appliances, stampede accidents caused by overcrowding during peak dining hours, etc. Therefore, studying pedestrian evacuation in university campus cafeterias is particularly necessary. Pedestrians on campus mostly travel in groups. Previous studies mainly used continuous models to discuss pedestrian group evacuation behavior. In this study, based on the cellular automaton pedestrian evacuation simulation model, the floor field calculation method was improved. A cellular automaton pedestrian evacuation simulation model considering group behavior was established and applied to the evacuation scenario of students in a university campus cafeteria. The study found that under the different group configurations, the pedestrian ratios, and the pedestrian densities, the pedestrian evacuation efficiency had significant differences. The results showed that when the different group configurations existed in the scenario, the higher the proportion of the pedestrians with three-person front-to-back group configurations, the higher the evacuation efficiency. When only one type of the group configuration existed in the scenario, at low pedestrian density, the evacuation efficiency of the individual pedestrian groups was higher compared to the other six group configurations. While at high pedestrian density, the evacuation efficiency of the three-person front-to-back group configurations was higher. These findings provided important references for pedestrian evacuation in university campus cafeterias and provided insights for the simulation research of group pedestrian evacuation models, contributing to enhancing campus safety management and ensuring the safety of teachers, students, and staff.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130138"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445649","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 : 2024-10-09DOI: 10.1016/j.physa.2024.130156
Qian Li , Youming Lei
In this work, we present a novel chimera-like state known as the vibration-resonance chimera (VRC) state, which is induced by heterogeneous aperiodic (HA) excitations and emerges within the optimal range of amplitude for the HA excitations. We investigate the dynamic behaviors of the FitzHugh-Nagumo model in relation to HA excitations and coupling strength in parameter spaces, identifying specific regions where VRC states exist and analyzing the transition mechanisms between different dynamic behaviors. Using basin stability measures, we find that the mean period of HA excitations affects the multi-stable states of neural systems. Specifically, the model excited by HA excitations with a larger mean period is more likely to exhibit multi-stable states. Additionally, we explore the effects of two levels of heterogeneity in HA excitations, namely amplitude heterogeneity and period heterogeneity, on VRC states. Amplitude heterogeneity can suppress the occurrence of VRC states, whereas period heterogeneity can promote their generation. This work provides valuable insights into complex system dynamics, enhancing our understanding of chimera states in neuronal models and their potential applications in neural networks.
在这项工作中，我们提出了一种被称为振动-共振嵌合（VRC）态的新型嵌合态，它由异质非周期性（HA）激发诱导，并在 HA 激发的最佳振幅范围内出现。我们研究了 FitzHugh-Nagumo 模型在参数空间中与 HA 激发和耦合强度相关的动态行为，确定了 VRC 状态存在的特定区域，并分析了不同动态行为之间的过渡机制。通过盆地稳定性测量，我们发现 HA 激发的平均周期会影响神经系统的多稳态。具体来说，平均周期越大的 HA 激发模型越有可能表现出多稳定状态。此外，我们还探讨了 HA 激发的两个层次的异质性（即振幅异质性和周期异质性）对 VRC 状态的影响。振幅异质性可以抑制 VRC 状态的出现，而周期异质性则可以促进 VRC 状态的产生。这项研究为复杂系统动力学提供了宝贵的见解，加深了我们对神经元模型中嵌合态及其在神经网络中潜在应用的理解。
{"title":"Vibration-resonance chimeras in coupled excitable systems with heterogeneous aperiodic excitations","authors":"Qian Li , Youming Lei","doi":"10.1016/j.physa.2024.130156","DOIUrl":"10.1016/j.physa.2024.130156","url":null,"abstract":"<div><div>In this work, we present a novel chimera-like state known as the vibration-resonance chimera (VRC) state, which is induced by heterogeneous aperiodic (HA) excitations and emerges within the optimal range of amplitude for the HA excitations. We investigate the dynamic behaviors of the FitzHugh-Nagumo model in relation to HA excitations and coupling strength in parameter spaces, identifying specific regions where VRC states exist and analyzing the transition mechanisms between different dynamic behaviors. Using basin stability measures, we find that the mean period of HA excitations affects the multi-stable states of neural systems. Specifically, the model excited by HA excitations with a larger mean period is more likely to exhibit multi-stable states. Additionally, we explore the effects of two levels of heterogeneity in HA excitations, namely amplitude heterogeneity and period heterogeneity, on VRC states. Amplitude heterogeneity can suppress the occurrence of VRC states, whereas period heterogeneity can promote their generation. This work provides valuable insights into complex system dynamics, enhancing our understanding of chimera states in neuronal models and their potential applications in neural networks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130156"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432119","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 : 2024-10-09DOI: 10.1016/j.physa.2024.130136
Kexin Wang , Xiang Wang , Wenjuan E , Mingdi Fan , Jiaxin Tong
Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.
{"title":"Multi-objective optimization for connected and automated truck platoon control with improved CACC model","authors":"Kexin Wang , Xiang Wang , Wenjuan E , Mingdi Fan , Jiaxin Tong","doi":"10.1016/j.physa.2024.130136","DOIUrl":"10.1016/j.physa.2024.130136","url":null,"abstract":"<div><div>Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130136"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528806","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 : 2024-10-09DOI: 10.1016/j.physa.2024.130141
Anusuya Pal , Miho Yanagisawa
Out-of-equilibrium processes, such as sessile droplet drying, often result in distinctive macroscopic residual patterns in systems containing molecules, proteins, and colloids. Protein–glucose mixtures are particularly effective models for studying the behavior of complex fluids containing biomolecules. This study investigates the drying patterns of lysozyme droplets with varying initial glucose concentrations. Without glucose, the crack patterns are chaotic and dispersed throughout the droplet. Interestingly, cracks predominantly form around the droplet edges at intermediate glucose concentrations, while the deposits become uniform and crack-free at high glucose concentrations. To understand and classify the unique patterns related to the initial compositional changes, we developed an automated pattern recognition pipeline. We used two methods for analyzing images captured throughout the drying process. The first method involved extracting statistical textural parameters from the images as quantitative features for machine learning classifiers. The second method utilized a neural network-based classifier to directly classify the images, achieving an accuracy of 97%. The results demonstrate the effectiveness of using images from the entire drying process, not just the final images, for pattern classification. This approach may be useful in gaining a fundamental understanding of unique crack pattern that emerge when glucose is added to a protein solution.
{"title":"Pattern recognition of drying lysozyme–glucose droplets using machine learning classifiers","authors":"Anusuya Pal , Miho Yanagisawa","doi":"10.1016/j.physa.2024.130141","DOIUrl":"10.1016/j.physa.2024.130141","url":null,"abstract":"<div><div>Out-of-equilibrium processes, such as sessile droplet drying, often result in distinctive macroscopic residual patterns in systems containing molecules, proteins, and colloids. Protein–glucose mixtures are particularly effective models for studying the behavior of complex fluids containing biomolecules. This study investigates the drying patterns of lysozyme droplets with varying initial glucose concentrations. Without glucose, the crack patterns are chaotic and dispersed throughout the droplet. Interestingly, cracks predominantly form around the droplet edges at intermediate glucose concentrations, while the deposits become uniform and crack-free at high glucose concentrations. To understand and classify the unique patterns related to the initial compositional changes, we developed an automated pattern recognition pipeline. We used two methods for analyzing images captured throughout the drying process. The first method involved extracting statistical textural parameters from the images as quantitative features for machine learning classifiers. The second method utilized a neural network-based classifier to directly classify the images, achieving an accuracy of 97%. The results demonstrate the effectiveness of using images from the entire drying process, not just the final images, for pattern classification. This approach may be useful in gaining a fundamental understanding of unique crack pattern that emerge when glucose is added to a protein solution.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130141"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.physa.2024.130154
Guizhen Chen , Tom Van Woensel , Jinhua Xu , Yikai Luo , Yan Li
Accurately assessing the resilience of the road network is crucial for responding to emergencies and enhancing public safety. Signal control plays a significant role in managing traffic flow. However, its impact is often overlooked in resilience assessments, where traffic flow and signal control are usually considered separately. A Movement-Specific Resilience (MSR) assessment model is proposed to integrate signal timing into resilience analysis. To accurately represent traffic flow paths under phase control, a dual graph is used to depict the topological network, allowing the assessment of relationships among all movements at an intersection. Based on this, a cascading failure model is developed to analyze the impact of signal control on traffic flow reassignment, reflecting how signal timing influences traffic flow propagation after failures. The method is validated using data collected from a sub-road network in Xi’an city. Results reveal the cumulative resilience of single lanes is not equivalent to the resilience of road segments. The MSR is higher when the network’s failure degree is low and decreases as the failure level increases. Furthermore, road saturation is inversely related to MSR, while MSR is proportional to capacity. MSR remains unaffected by failures and oversaturation when capacity exceeds a certain threshold. These insights could be a theoretical foundation for bolstering resilience via signal control adjustments.
{"title":"Assessing movement-specific resilience of a signalized road network under lane-level cascading failure","authors":"Guizhen Chen , Tom Van Woensel , Jinhua Xu , Yikai Luo , Yan Li","doi":"10.1016/j.physa.2024.130154","DOIUrl":"10.1016/j.physa.2024.130154","url":null,"abstract":"<div><div>Accurately assessing the resilience of the road network is crucial for responding to emergencies and enhancing public safety. Signal control plays a significant role in managing traffic flow. However, its impact is often overlooked in resilience assessments, where traffic flow and signal control are usually considered separately. A Movement-Specific Resilience (MSR) assessment model is proposed to integrate signal timing into resilience analysis. To accurately represent traffic flow paths under phase control, a dual graph is used to depict the topological network, allowing the assessment of relationships among all movements at an intersection. Based on this, a cascading failure model is developed to analyze the impact of signal control on traffic flow reassignment, reflecting how signal timing influences traffic flow propagation after failures. The method is validated using data collected from a sub-road network in Xi’an city. Results reveal the cumulative resilience of single lanes is not equivalent to the resilience of road segments. The MSR is higher when the network’s failure degree is low and decreases as the failure level increases. Furthermore, road saturation is inversely related to MSR, while MSR is proportional to capacity. MSR remains unaffected by failures and oversaturation when capacity exceeds a certain threshold. These insights could be a theoretical foundation for bolstering resilience via signal control adjustments.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130154"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432117","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 : 2024-10-09DOI: 10.1016/j.physa.2024.130158
Changchang Che, Shici Luo, Wangyang Zong, Yuli Zhang, Helong Wang
To enhance the perception of vehicle trajectory information and lane-changing decision-making capabilities in intelligent connected vehicles during multivehicle interaction scenarios, we propose a novel method based on a Multimodal Adversarial Informer (MAI) for highway multivehicle lane-changingrajectory prediction. This method achieves spatiotemporal features of target and surrounding vehicles through graph learning of temporal features and spatial adjacency matrices. Considering the heading angle and vehicle local X-axis displacement, the vehicle trajectory samples are categorized for training and validation of the multimodal Informer. A multi-criterion discriminator is utilized to judge whether the generated trajectory fits the requirements of accuracy and rationality. After adversarial learning, the optimal vehicle lane-changing trajectory prediction is obtained using the proposed MAI. Experiments conducted with the NGSIM dataset demonstrate the comparative performance of baseline models on three different noise-added testing datasets using MAE, RMSE, and R² metrics. The MAI model consistently outperforms the others, achieving the lowest MAE and RMSE and the highest R² values across all datasets, indicating superior predictive accuracy and fit. Furthermore, the results show that the proposed MAI framework maintains a relatively low prediction error over both short-term and long-term horizons compared to baseline models.
为了增强智能网联汽车在多车交互场景中对车辆轨迹信息的感知和变道决策能力，我们提出了一种基于多模态对抗信息器（MAI）的高速公路多车变道轨迹预测新方法。该方法通过对时间特征和空间邻接矩阵的图学习，获得目标车辆和周围车辆的时空特征。考虑到航向角和车辆局部 X 轴位移，对车辆轨迹样本进行分类，以便对多模态信息器进行训练和验证。利用多标准判别器来判断生成的轨迹是否符合准确性和合理性的要求。经过对抗学习后，利用所提出的 MAI 获得最佳车辆变道轨迹预测。使用 NGSIM 数据集进行的实验表明，在三个不同的噪声添加测试数据集上，基线模型使用 MAE、RMSE 和 R² 指标进行了性能比较。MAI 模型的性能始终优于其他模型，在所有数据集上都获得了最低的 MAE 和 RMSE 值以及最高的 R² 值，表明其预测准确性和拟合度都非常出色。此外，结果表明，与基线模型相比，拟议的 MAI 框架在短期和长期范围内都能保持相对较低的预测误差。
{"title":"Multimodal adversarial Informer for highway vehicle lane-changing trajectory prediction","authors":"Changchang Che, Shici Luo, Wangyang Zong, Yuli Zhang, Helong Wang","doi":"10.1016/j.physa.2024.130158","DOIUrl":"10.1016/j.physa.2024.130158","url":null,"abstract":"<div><div>To enhance the perception of vehicle trajectory information and lane-changing decision-making capabilities in intelligent connected vehicles during multivehicle interaction scenarios, we propose a novel method based on a Multimodal Adversarial Informer (MAI) for highway multivehicle lane-changingrajectory prediction. This method achieves spatiotemporal features of target and surrounding vehicles through graph learning of temporal features and spatial adjacency matrices. Considering the heading angle and vehicle local X-axis displacement, the vehicle trajectory samples are categorized for training and validation of the multimodal Informer. A multi-criterion discriminator is utilized to judge whether the generated trajectory fits the requirements of accuracy and rationality. After adversarial learning, the optimal vehicle lane-changing trajectory prediction is obtained using the proposed MAI. Experiments conducted with the NGSIM dataset demonstrate the comparative performance of baseline models on three different noise-added testing datasets using MAE, RMSE, and R² metrics. The MAI model consistently outperforms the others, achieving the lowest MAE and RMSE and the highest R² values across all datasets, indicating superior predictive accuracy and fit. Furthermore, the results show that the proposed MAI framework maintains a relatively low prediction error over both short-term and long-term horizons compared to baseline models.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130158"},"PeriodicalIF":2.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427657","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}
The Ethereum blockchain and its ERC20 token standard have revolutionized the landscape of digital assets and decentralized applications. ERC20 tokens are programmable and interoperable tokens, enabling various applications and token economies. Transaction graphs, representing the flow of the value between wallets within the Ethereum network, have played a crucial role in understanding the system’s dynamics, such as token transfers and the behavior of traders. Here, we explore the evolution of daily transaction graphs of ERC20 token transactions, which sheds light on the trader’s behavior during the Black Swan Events – 2018 crypto crash and the COVID-19 pandemic. By using the tools from network science and differential geometry, we analyze 0.98 billion of ERC20 token transaction data from November 2015 to January 2023. Our analysis reveals an increase in diverse interaction among the traders and a greater adoption of ERC20 tokens in a maturing Ethereum ERC20 financial ecosystem after the Crypto Crash 2018 and the COVID-19 pandemic. Before the crash and the COVID-19 pandemic, most traders interacted with other traders in an isolated or restricted manner, with each trader focusing solely on either buying or selling activities. However, after the crash and during the pandemic, most traders diversely interacted among themselves by participating in both buying and selling activities. In addition, we observe no significant negative impact of the COVID-19 pandemic on user behavior in the financial ecosystem.
{"title":"Impact of Black Swan Events on Ethereum blockchain ERC20 token transaction networks","authors":"Moturi Pradeep , Uday Kumar Reddy Dyapa , Sarika Jalan , Priodyuti Pradhan","doi":"10.1016/j.physa.2024.130129","DOIUrl":"10.1016/j.physa.2024.130129","url":null,"abstract":"<div><div>The Ethereum blockchain and its ERC20 token standard have revolutionized the landscape of digital assets and decentralized applications. ERC20 tokens are programmable and interoperable tokens, enabling various applications and token economies. Transaction graphs, representing the flow of the value between wallets within the Ethereum network, have played a crucial role in understanding the system’s dynamics, such as token transfers and the behavior of traders. Here, we explore the evolution of daily transaction graphs of ERC20 token transactions, which sheds light on the trader’s behavior during the Black Swan Events – 2018 crypto crash and the COVID-19 pandemic. By using the tools from network science and differential geometry, we analyze 0.98 billion of ERC20 token transaction data from November 2015 to January 2023. Our analysis reveals an increase in diverse interaction among the traders and a greater adoption of ERC20 tokens in a maturing Ethereum ERC20 financial ecosystem after the Crypto Crash 2018 and the COVID-19 pandemic. Before the crash and the COVID-19 pandemic, most traders interacted with other traders in an isolated or restricted manner, with each trader focusing solely on either buying or selling activities. However, after the crash and during the pandemic, most traders diversely interacted among themselves by participating in both buying and selling activities. In addition, we observe no significant negative impact of the COVID-19 pandemic on user behavior in the financial ecosystem.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130129"},"PeriodicalIF":2.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432120","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 : 2024-10-05DOI: 10.1016/j.physa.2024.130131
Ronghua Shang , Ruolin Li , Chi Wang , Weitong Zhang , Songhua Xu , Dongzhu Feng
Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings. To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA). Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings. Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes. After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes. Secondly, a multi-fusion method based on smoothed neighborhood information is constructed. Each node in the original graph is smoothed together with its neighbor nodes. The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy. This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data. AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets. Experimental results show that AFEF_LAA can achieve higher node classification accuracy.
{"title":"Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment","authors":"Ronghua Shang , Ruolin Li , Chi Wang , Weitong Zhang , Songhua Xu , Dongzhu Feng","doi":"10.1016/j.physa.2024.130131","DOIUrl":"10.1016/j.physa.2024.130131","url":null,"abstract":"<div><div>Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings.<!--> <!-->To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA).<!--> <!-->Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings.<!--> <!-->Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes.<!--> <!-->After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes.<!--> <!-->Secondly, a multi-fusion method based on smoothed neighborhood information is constructed.<!--> <!-->Each node in the original graph is smoothed together with its neighbor nodes.<!--> <!-->The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy.<!--> <!-->This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data.<!--> <!-->AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets.<!--> <!-->Experimental results show that AFEF_LAA can achieve higher node classification accuracy.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"654 ","pages":"Article 130131"},"PeriodicalIF":2.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441619","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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