Pub Date : 2025-02-14DOI: 10.1016/j.physa.2025.130446
Bo-chen Li , Wei Wang , Dan Lv
The dynamic magnetic characteristics and hysteresis behaviors of the double-layered fullerene-like X540@Y540 structure are explored by using the Monte Carlo method. By introducing the non-magnetic layers, the influence of interlayer interaction JRKKY is studied. In the presence of the non-magnetic layer and dynamic magnetic field, the system displays a multitude of magnetic behaviors, and the compensation temperature Tcomp emerges. To further examine the influence of physical parameters on the phase transition temperature TC and compensation temperature Tcomp, we present the curves and 2D color maps of the phase diagrams. Finally, we also investigate the dynamic hysteresis behavior. Due to the existence of relaxation behavior in magnetic systems, when physical parameters change, the position, area, and shape of the dynamic hysteresis loop all change, which is also the unique magnetic behavior exhibited by magnetic systems in dynamic magnetic fields.
{"title":"Dynamic magnetic characteristics and hysteresis behaviors of X540@Y540: A Monte Carlo study","authors":"Bo-chen Li , Wei Wang , Dan Lv","doi":"10.1016/j.physa.2025.130446","DOIUrl":"10.1016/j.physa.2025.130446","url":null,"abstract":"<div><div>The dynamic magnetic characteristics and hysteresis behaviors of the double-layered fullerene-like X<sub>540</sub>@Y<sub>540</sub> structure are explored by using the Monte Carlo method. By introducing the non-magnetic layers, the influence of interlayer interaction <em>J</em><sub><em>RKKY</em></sub> is studied. In the presence of the non-magnetic layer and dynamic magnetic field, the system displays a multitude of magnetic behaviors, and the compensation temperature <em>T</em><sub><em>comp</em></sub> emerges. To further examine the influence of physical parameters on the phase transition temperature <em>T</em><sub><em>C</em></sub> and compensation temperature <em>T</em><sub><em>comp</em></sub>, we present the curves and 2D color maps of the phase diagrams. Finally, we also investigate the dynamic hysteresis behavior. Due to the existence of relaxation behavior in magnetic systems, when physical parameters change, the position, area, and shape of the dynamic hysteresis loop all change, which is also the unique magnetic behavior exhibited by magnetic systems in dynamic magnetic fields.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"662 ","pages":"Article 130446"},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427866","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 : 2025-02-14DOI: 10.1016/j.physa.2025.130433
Asim Ghosh , Soumyajyoti Biswas , Bikas K. Chakrabarti
We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices.
{"title":"Signature of maturity in cryptocurrency volatility","authors":"Asim Ghosh , Soumyajyoti Biswas , Bikas K. Chakrabarti","doi":"10.1016/j.physa.2025.130433","DOIUrl":"10.1016/j.physa.2025.130433","url":null,"abstract":"<div><div>We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the <span><math><mi>Q</mi></math></span> factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"662 ","pages":"Article 130433"},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420276","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 : 2025-02-14DOI: 10.1016/j.physa.2025.130443
Mingyu Shu , Baoliu Liu , Wenpei ouyang , Rengui Sun , Yaoyang Lin
This study investigates the multi-scale dynamic correlations and information spillover effects between climate risks and digital cryptocurrencies using wavelet analysis and the Time-frequency Domain QVAR model. By analyzing non-stationary financial time-series data, we uncover latent patterns and quantify the dynamic interactions between climate risks and cryptocurrency markets across different time scales. The findings reveal significant spillover effects, highlighting how climate risks, particularly through energy-intensive mining and extreme weather disruptions, influence cryptocurrency volatility. The research contributes to the understanding of risk transmission mechanisms in emerging financial markets, offering insights into the broader implications of climate risks on global financial stability. The results underscore the importance of integrating climate risk assessments into cryptocurrency market analyses, providing a foundation for informed policy-making and risk management strategies.
{"title":"Multi-scale dynamic correlation and information spillover effects between climate risks and digital cryptocurrencies: Based on wavelet analysis and time-frequency domain QVAR","authors":"Mingyu Shu , Baoliu Liu , Wenpei ouyang , Rengui Sun , Yaoyang Lin","doi":"10.1016/j.physa.2025.130443","DOIUrl":"10.1016/j.physa.2025.130443","url":null,"abstract":"<div><div>This study investigates the multi-scale dynamic correlations and information spillover effects between climate risks and digital cryptocurrencies using wavelet analysis and the Time-frequency Domain QVAR model. By analyzing non-stationary financial time-series data, we uncover latent patterns and quantify the dynamic interactions between climate risks and cryptocurrency markets across different time scales. The findings reveal significant spillover effects, highlighting how climate risks, particularly through energy-intensive mining and extreme weather disruptions, influence cryptocurrency volatility. The research contributes to the understanding of risk transmission mechanisms in emerging financial markets, offering insights into the broader implications of climate risks on global financial stability. The results underscore the importance of integrating climate risk assessments into cryptocurrency market analyses, providing a foundation for informed policy-making and risk management strategies.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"663 ","pages":"Article 130443"},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438139","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 : 2025-02-14DOI: 10.1016/j.physa.2025.130439
Yuntian Bai , Jie Wang , Jingcheng Su , Qingyi Zhou , Shijian He
Urban rail transit plays a significant role in promoting urban sustainable development. Conducting a comprehensive evaluation of urban rail transit is crucial for the future development of urban rail transit systems. This study focuses on the various factors influencing the development of urban rail transit. Based on the Driving force-Pressure-State-Impact-Response (DPSIR) model, an evaluation framework comprising 19 indicators was established. The logical relationships and directions of influence among these indicators were verified using the Structural Equation model (SEM). Then, the contribution rates of each indicator to the development of urban rail transit were calculated using the Entropy Weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) model. Finally, the obstacles degree model was employed to uncover the facilitators and obstacles in the development of urban rail transit. The results showed that: (1) In China, cities such as Beijing, Shanghai, Guangzhou, and Shenzhen exhibit notably advanced development in urban rail transit compared to other cities. (2) The DPSIR-Entropy-TOPSIS model identifies four distinct modes of rail transit development, each associated with specific influencing factors. (3) Through obstacle degree diagnostics, the analysis reveals the following ranking of obstructive impacts for indicators: social factors > urban rail operational factors > economic factors > infrastructure factors > investment factors > citizen experience factors > other factors. Notably, the obstructive effects of economic, social, and investment factors have shown annual increases. Our findings offer policy recommendations for decision-makers from three key perspectives: improving subsidy and management efficiency, enhancing the quality of urban rail transit for public benefit, and maximizing the economic benefits derived from urban rail transit.
{"title":"Assessment of urban rail transit development using DPSIR-Entropy-TOPSIS and obstacle degree analysis: A case study of 27 Chinese cities","authors":"Yuntian Bai , Jie Wang , Jingcheng Su , Qingyi Zhou , Shijian He","doi":"10.1016/j.physa.2025.130439","DOIUrl":"10.1016/j.physa.2025.130439","url":null,"abstract":"<div><div>Urban rail transit plays a significant role in promoting urban sustainable development. Conducting a comprehensive evaluation of urban rail transit is crucial for the future development of urban rail transit systems. This study focuses on the various factors influencing the development of urban rail transit. Based on the Driving force-Pressure-State-Impact-Response (DPSIR) model, an evaluation framework comprising 19 indicators was established. The logical relationships and directions of influence among these indicators were verified using the Structural Equation model (SEM). Then, the contribution rates of each indicator to the development of urban rail transit were calculated using the Entropy Weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) model. Finally, the obstacles degree model was employed to uncover the facilitators and obstacles in the development of urban rail transit. The results showed that: (1) In China, cities such as Beijing, Shanghai, Guangzhou, and Shenzhen exhibit notably advanced development in urban rail transit compared to other cities. (2) The DPSIR-Entropy-TOPSIS model identifies four distinct modes of rail transit development, each associated with specific influencing factors. (3) Through obstacle degree diagnostics, the analysis reveals the following ranking of obstructive impacts for indicators: social factors > urban rail operational factors > economic factors > infrastructure factors > investment factors > citizen experience factors > other factors. Notably, the obstructive effects of economic, social, and investment factors have shown annual increases. Our findings offer policy recommendations for decision-makers from three key perspectives: improving subsidy and management efficiency, enhancing the quality of urban rail transit for public benefit, and maximizing the economic benefits derived from urban rail transit.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"663 ","pages":"Article 130439"},"PeriodicalIF":2.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445416","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 : 2025-02-13DOI: 10.1016/j.physa.2025.130430
Pei Li , Qisong Xie , Wuyi Chen , Qiang Yang , Shuwei Guo
Nowadays, popular social networks have become important media for many companies to conduct viral marketing, due to their low costs and high efficiencies for information diffusion. However, the fundamental problem of how to calculate the indirect influence probability between users who are not directly connected in social networks has not been well addressed, which is critical for problems like influence maximization and source detection. In this paper, to estimate this indirect influence probability under the independent cascade model, we propose two types of algorithms: the first type originates from Dijkstra’s algorithm, and the second type is based on graph compression. From these algorithms, we provide 4 lower and 2 upper bounds for the indirect influence probability. The performances of these bounds are investigated through computational experiments, from which we observe that the accuracies of some bounds may vary with propagation intensity, and the upper bounds seem to achieve better results than the lower ones. We believe that the findings in this paper can introduce new approaches for the indirect influence probability estimation problem and provide insights in understanding the diffusion dynamics in social networks.
{"title":"Using upper and lower bounds to estimate indirect influence probability in social networks under independent cascade model","authors":"Pei Li , Qisong Xie , Wuyi Chen , Qiang Yang , Shuwei Guo","doi":"10.1016/j.physa.2025.130430","DOIUrl":"10.1016/j.physa.2025.130430","url":null,"abstract":"<div><div>Nowadays, popular social networks have become important media for many companies to conduct viral marketing, due to their low costs and high efficiencies for information diffusion. However, the fundamental problem of how to calculate the indirect influence probability between users who are not directly connected in social networks has not been well addressed, which is critical for problems like influence maximization and source detection. In this paper, to estimate this indirect influence probability under the independent cascade model, we propose two types of algorithms: the first type originates from Dijkstra’s algorithm, and the second type is based on graph compression. From these algorithms, we provide 4 lower and 2 upper bounds for the indirect influence probability. The performances of these bounds are investigated through computational experiments, from which we observe that the accuracies of some bounds may vary with propagation intensity, and the upper bounds seem to achieve better results than the lower ones. We believe that the findings in this paper can introduce new approaches for the indirect influence probability estimation problem and provide insights in understanding the diffusion dynamics in social networks.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"662 ","pages":"Article 130430"},"PeriodicalIF":2.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427865","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 : 2025-02-12DOI: 10.1016/j.physa.2025.130438
Liling Zhu , Ruoxin Li , Jue Wang , Qinxin Xiao , Jing Wen , Junfeng Hao , Da Yang
With the development of Connected Vehicles technology and Cooperative Vehicle Infrastructure System, the “long” platoon has become a promising trend of platooning technology, and long platoons can take the full advantages of platoons in enhancing traffic efficiency and reducing energy consumption. In this paper, we propose a Cellular Automata-based long platoon model in which the platoon is divided into several sub-platoons and virtual leading vehicles are assigned to the sub-platoons dynamically according to the surrounding traffic states. Moreover, to evaluate the proposed model, it is compared with the Lenarska’s model and the traditional Cooperative Adaptive Cruise Control (CACC) model by simulations, and the influences of the long platoon size and traffic perturbations on the platoon are analyzed. The simulations indicate that for the acceleration and deceleration perturbation scenarios, the virtual leaders effectively divide the long platoon into multiple sub-platoons, and its sequence can change dynamically to reduce the influence of the perturbation on the platoon. Compared to the Lenarska’s model and the CACC model, the proposed model reacts to the speed perturbations faster and has smaller speed variations. The proposed model has better stability and safety and is more efficient than the Lenarska’s model and the CACC model.
{"title":"Cellular automata-based long platoon models based on dynamic multi-virtual leading vehicles","authors":"Liling Zhu , Ruoxin Li , Jue Wang , Qinxin Xiao , Jing Wen , Junfeng Hao , Da Yang","doi":"10.1016/j.physa.2025.130438","DOIUrl":"10.1016/j.physa.2025.130438","url":null,"abstract":"<div><div>With the development of Connected Vehicles technology and Cooperative Vehicle Infrastructure System, the “long” platoon has become a promising trend of platooning technology, and long platoons can take the full advantages of platoons in enhancing traffic efficiency and reducing energy consumption. In this paper, we propose a Cellular Automata-based long platoon model in which the platoon is divided into several sub-platoons and virtual leading vehicles are assigned to the sub-platoons dynamically according to the surrounding traffic states. Moreover, to evaluate the proposed model, it is compared with the Lenarska’s model and the traditional Cooperative Adaptive Cruise Control (CACC) model by simulations, and the influences of the long platoon size and traffic perturbations on the platoon are analyzed. The simulations indicate that for the acceleration and deceleration perturbation scenarios, the virtual leaders effectively divide the long platoon into multiple sub-platoons, and its sequence can change dynamically to reduce the influence of the perturbation on the platoon. Compared to the Lenarska’s model and the CACC model, the proposed model reacts to the speed perturbations faster and has smaller speed variations. The proposed model has better stability and safety and is more efficient than the Lenarska’s model and the CACC model.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"662 ","pages":"Article 130438"},"PeriodicalIF":2.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438186","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 : 2025-02-12DOI: 10.1016/j.physa.2025.130435
Kai Chen, Xiaodong Zhao, Yujie Huang, Guoyu Fang
The prediction of pedestrian trajectories plays a crucial role in practical traffic scenarios. However, current methodologies have shortcomings, such as overlooking pedestrians' perception of motion information from neighbor groups, employing simplistic and fixed social state interaction models, and lacking in final position correction. To address these issues, SocialTrans is proposed. It utilizes global observations to model the motion states of pedestrians and their neighbors, constructing separate state tensors to encapsulate social interaction information between them. This design includes a Subject Intention Extraction Module and a Neighbor Perception Intentions Extraction Module, which operate in parallel throughout the observation period to facilitate deep interaction of social states rather than simple end-to-end external fusion. Furthermore, a trajectory prediction optimizer is developed to correct final position predictions and simulate pedestrian motion diversity through trajectory clustering. Experimental validation is conducted on the ETH/UCY and SDD public datasets to evaluate the effectiveness of the proposed approach. The results demonstrate the method's capability to learn historical trajectory information, achieve high-precision predictions, and achieve state-of-the-art performance, particularly outperforming existing SOTA models on the SDD dataset. The algorithm will be made available at https://github.com/XiaodZhao/SocialTrans.
{"title":"SocialTrans: Transformer based social intentions interaction for pedestrian trajectory prediction","authors":"Kai Chen, Xiaodong Zhao, Yujie Huang, Guoyu Fang","doi":"10.1016/j.physa.2025.130435","DOIUrl":"10.1016/j.physa.2025.130435","url":null,"abstract":"<div><div>The prediction of pedestrian trajectories plays a crucial role in practical traffic scenarios. However, current methodologies have shortcomings, such as overlooking pedestrians' perception of motion information from neighbor groups, employing simplistic and fixed social state interaction models, and lacking in final position correction. To address these issues, SocialTrans is proposed. It utilizes global observations to model the motion states of pedestrians and their neighbors, constructing separate state tensors to encapsulate social interaction information between them. This design includes a Subject Intention Extraction Module and a Neighbor Perception Intentions Extraction Module, which operate in parallel throughout the observation period to facilitate deep interaction of social states rather than simple end-to-end external fusion. Furthermore, a trajectory prediction optimizer is developed to correct final position predictions and simulate pedestrian motion diversity through trajectory clustering. Experimental validation is conducted on the ETH/UCY and SDD public datasets to evaluate the effectiveness of the proposed approach. The results demonstrate the method's capability to learn historical trajectory information, achieve high-precision predictions, and achieve state-of-the-art performance, particularly outperforming existing SOTA models on the SDD dataset. The algorithm will be made available at <span><span>https://github.com/XiaodZhao/SocialTrans</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"663 ","pages":"Article 130435"},"PeriodicalIF":2.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445415","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 : 2025-02-11DOI: 10.1016/j.physa.2025.130351
Xiaofeng Cui , Yuling He , Mengyu Li , Weidong Cao , Zhongke Gao
The investigation of oil–water two-phase flow in vertical pipelines holds significant research implications for a multitude of industrial applications, including oil production, chemical processing, and wastewater treatment. This research introduces a complex network-based framework for analyzing multi-node measurement signals from an eight-electrode cyclic excitation conductivity sensor, aimed at recognizing intricate flow patterns in vertical upward oil–water two-phase flow. Initially, experiments on vertical upward oil–water two-phase flow were conducted in a 20 mm diameter pipeline, where flow dynamics were recorded using the aforementioned sensor. During the experiments, flow patterns captured by a high-speed camera included dispersed oil-in-water slug flow (D OS/W), dispersed oil-in-water flow (D O/W), and very fine dispersed oil-in-water flow (VFD O/W). Subsequently, the multivariate pseudo-Wigner–Ville distribution time–frequency representation (PWVD TFR) was employed to characterize the flow behavior from both energy and frequency perspectives. Finally, the sensor’s measurement nodes were treated as nodes in a network, and the mutual information between each time series was calculated to construct a complex network; network metrics were then computed to quantitatively characterize the network topology. The findings indicate that our method can effectively integrate multi-channel measurement signals and reveal the evolution of complex flow behaviors.
{"title":"Complex network-based framework for flow pattern identification in vertical upward oil–water two-phase flow","authors":"Xiaofeng Cui , Yuling He , Mengyu Li , Weidong Cao , Zhongke Gao","doi":"10.1016/j.physa.2025.130351","DOIUrl":"10.1016/j.physa.2025.130351","url":null,"abstract":"<div><div>The investigation of oil–water two-phase flow in vertical pipelines holds significant research implications for a multitude of industrial applications, including oil production, chemical processing, and wastewater treatment. This research introduces a complex network-based framework for analyzing multi-node measurement signals from an eight-electrode cyclic excitation conductivity sensor, aimed at recognizing intricate flow patterns in vertical upward oil–water two-phase flow. Initially, experiments on vertical upward oil–water two-phase flow were conducted in a 20 mm diameter pipeline, where flow dynamics were recorded using the aforementioned sensor. During the experiments, flow patterns captured by a high-speed camera included dispersed oil-in-water slug flow (D OS/W), dispersed oil-in-water flow (D O/W), and very fine dispersed oil-in-water flow (VFD O/W). Subsequently, the multivariate pseudo-Wigner–Ville distribution time–frequency representation (PWVD TFR) was employed to characterize the flow behavior from both energy and frequency perspectives. Finally, the sensor’s measurement nodes were treated as nodes in a network, and the mutual information between each time series was calculated to construct a complex network; network metrics were then computed to quantitatively characterize the network topology. The findings indicate that our method can effectively integrate multi-channel measurement signals and reveal the evolution of complex flow behaviors.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"662 ","pages":"Article 130351"},"PeriodicalIF":2.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420275","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 : 2025-02-11DOI: 10.1016/j.physa.2025.130437
Abhijeet Das , Manas Sehgal , Ashwini Singh , Rishabh Goyal , Mallika Prabhakar , Jeremy Fricke , Isa Mambetsariev , Prakash Kulkarni , Mohit Kumar Jolly , Ravi Salgia
Symmetry and symmetry-breaking in distinct biological cell features or components have been examined in cancer investigations. However, there can be possible limitations in directly interpreting the symmetry-based approach from a physical viewpoint due to the lack of understanding of physical laws governing symmetry in complex systems like cancer. To overcome this, herein, fractal geometry and DNA walk representation were employed to investigate the geometric features i.e., self-similarity and heterogeneity in DNA nucleotide coding sequences of wild-type and mutated oncogenes, tumour-suppressor, and other unclassified genes. The mutation-facilitated self-similar and heterogenous features were quantified by the fractal dimension and lacunarity measures, respectively. Additionally, the geometrical orderedness and disorderedness in the analyzed sequences were interpreted from the combination of the fractal measures. The findings showed distinct fractal features in the case of specific fusion mutations. They also highlight the possible interpretation of the fractal features as geometric analogues concerning explicit observations corresponding to specific cancer types. The two-dimensional multi-fractal analysis highlighted the prominence of mono-fractal scaling in the self-similarity of the analyzed sequences though asymmetric multi-fractal characteristics were vaguely observed. This study highlights the potential of integrating fractal geometry into cancer genomics to bridge the gap between molecular complexity and heterogeneity and translational cancer research.
{"title":"DNA walk of specific fused oncogenes exhibit distinct fractal geometric characteristics in nucleotide patterns","authors":"Abhijeet Das , Manas Sehgal , Ashwini Singh , Rishabh Goyal , Mallika Prabhakar , Jeremy Fricke , Isa Mambetsariev , Prakash Kulkarni , Mohit Kumar Jolly , Ravi Salgia","doi":"10.1016/j.physa.2025.130437","DOIUrl":"10.1016/j.physa.2025.130437","url":null,"abstract":"<div><div>Symmetry and symmetry-breaking in distinct biological cell features or components have been examined in cancer investigations. However, there can be possible limitations in directly interpreting the symmetry-based approach from a physical viewpoint due to the lack of understanding of physical laws governing symmetry in complex systems like cancer. To overcome this, herein, fractal geometry and DNA walk representation were employed to investigate the geometric features i.e., self-similarity and heterogeneity in DNA nucleotide coding sequences of wild-type and mutated oncogenes, tumour-suppressor, and other unclassified genes. The mutation-facilitated self-similar and heterogenous features were quantified by the fractal dimension and lacunarity measures, respectively. Additionally, the geometrical orderedness and disorderedness in the analyzed sequences were interpreted from the combination of the fractal measures. The findings showed distinct fractal features in the case of specific fusion mutations. They also highlight the possible interpretation of the fractal features as geometric analogues concerning explicit observations corresponding to specific cancer types. The two-dimensional multi-fractal analysis highlighted the prominence of mono-fractal scaling in the self-similarity of the analyzed sequences though asymmetric multi-fractal characteristics were vaguely observed. This study highlights the potential of integrating fractal geometry into cancer genomics to bridge the gap between molecular complexity and heterogeneity and translational cancer research.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"662 ","pages":"Article 130437"},"PeriodicalIF":2.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395083","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 : 2025-02-10DOI: 10.1016/j.physa.2025.130426
Zhe Liu , Bing Qiu , Hua Kuang , Xingli Li
The modeling of rescue behavior is an important topic in pedestrian evacuation dynamics. In order to study rescue behavior for the injured pedestrian, a modified social force model is proposed to simulate crowd evacuation through considering the rescue attraction force and different movement mechanisms (e.g., other pedestrians’ avoidance of the rescuers and the injured individuals during the rescue process). The interaction rules between the rescuer and the injured individual are established to describe rescue behavior. A comparison is conducted on the impacts of with or without rescue behavior on evacuation efficiency. The influences of the position distributions and the number of rescuers, the avoidance strength, the rescue time and the distribution of injured pedestrian on evacuation dynamics in a hall are investigated. And the typical spatiotemporal dynamic characteristic during the evacuation process is also discussed. The simulation results show that considering the rescue behavior will reduce the total evacuation time evidently. The evacuation efficiency is the highest when the rescue is located in the center of the hall wall and away from the exit. Furthermore, the shorter the rescue time, the higher the evacuation efficiency, and the avoidance strength plays an important role on evacuation efficiency. In particular, an interesting self-organization phenomenon that the formation of a local rescue channel between the rescuer and the injured individual is discovered. Comparing to one rescuer, multiple rescuers can effectively improve evacuation efficiency. This study can provide a theoretical guidance for fast and safe rescue behavior in emergency situations.
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