IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040405

Hao Yang, Dunbo Cai, Ling Qian, Runqing Zhang, Songfeng Lu, Chengfu Sun

引用次数: 0

Signatures of Extreme Events in Cumulative Entropic Spectrum. 累积熵谱中极端事件的特征。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040410

Ewa A Drzazga-Szczȩśniak, Adam Z Kaczmarek, Marta Kielak, Shivam Gupta, Jakub T Gnyp, Katarzyna Pluta, Zygmunt Ba K, Piotr Szczepanik, Dominik Szczȩśniak

引用次数: 0

Measurement-Induced Symmetry Restoration and Quantum Mpemba Effect. 测量诱导对称恢复和量子姆潘巴效应。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040407

Giuseppe Di Giulio, Xhek Turkeshi, Sara Murciano

引用次数: 0

Identifying Influential Nodes Based on Evidence Theory in Complex Network. 基于证据理论的复杂网络影响节点识别。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040406

Fu Tan, Xiaolong Chen, Rui Chen, Ruijie Wang, Chi Huang, Shimin Cai

{"title":"Identifying Influential Nodes Based on Evidence Theory in Complex Network.","authors":"Fu Tan, Xiaolong Chen, Rui Chen, Ruijie Wang, Chi Huang, Shimin Cai","doi":"10.3390/e27040406","DOIUrl":"https://doi.org/10.3390/e27040406","url":null,"abstract":"Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster-Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster-Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster-Shafer evidence theory processes conflicting evidence using Dempster's rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster-Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990742","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}

引用次数: 0

A New Discrete Analogue of the Continuous Muth Distribution for Over-Dispersed Data: Properties, Estimation Techniques, and Application. 过分散数据连续Muth分布的一种新的离散模拟：性质、估计技术和应用。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040409

Howaida Elsayed, Mohamed Hussein

引用次数: 0

Limit Laws for Sums of Logarithms of k-Spacings. k-间隔的对数和的极限律。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040411

Paul Deheuvels

引用次数: 0

Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model. 基于局部有效距离积分重力模型的重要节点识别。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-10 DOI: 10.3390/e27040408

Sheng Zhang, Fuhao Liu, Yuyuan Huang, Ziqiang Luo, Ka Sun, Hongmei Mao

引用次数: 0

Explainable Fault Classification and Severity Diagnosis in Rotating Machinery Using Kolmogorov-Arnold Networks. 基于Kolmogorov-Arnold网络的旋转机械可解释故障分类与严重程度诊断。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-09 DOI: 10.3390/e27040403

Spyros Rigas, Michalis Papachristou, Ioannis Sotiropoulos, Georgios Alexandridis

{"title":"Explainable Fault Classification and Severity Diagnosis in Rotating Machinery Using Kolmogorov-Arnold Networks.","authors":"Spyros Rigas, Michalis Papachristou, Ioannis Sotiropoulos, Georgios Alexandridis","doi":"10.3390/e27040403","DOIUrl":"https://doi.org/10.3390/e27040403","url":null,"abstract":"Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme cases, catastrophic damage. This study presents a methodology that utilizes Kolmogorov-Arnold Networks-a recent deep learning alternative to Multilayer Perceptrons. The proposed method automatically selects the most relevant features from sensor data and searches for optimal hyper-parameters within a single unified approach. By using shallow network architectures and fewer features, the resulting models are lightweight, easily interpretable, and practical for real-time applications. Validated on two widely recognized datasets for bearing fault diagnosis, the framework achieved perfect F1-Scores for fault detection and high performance in fault and severity classification tasks, including 100% F1-Scores in most cases. Notably, it demonstrated adaptability by handling diverse fault types, such as imbalance and misalignment, within the same dataset. The availability of symbolic representations provided model interpretability, while feature attribution offered insights into the optimal feature types or signals for each studied task. These results highlight the framework's potential for practical applications, such as real-time machinery monitoring, and for scientific research requiring efficient and explainable models.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990738","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}

引用次数: 0

Instability of Financial Time Series Revealed by Irreversibility Analysis. 不可逆性分析揭示的金融时间序列不稳定性。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-09 DOI: 10.3390/e27040402

Youping Fan, Yutong Yang, Zhen Wang, Meng Gao

{"title":"Instability of Financial Time Series Revealed by Irreversibility Analysis.","authors":"Youping Fan, Yutong Yang, Zhen Wang, Meng Gao","doi":"10.3390/e27040402","DOIUrl":"https://doi.org/10.3390/e27040402","url":null,"abstract":"Since the 2008 global economic crisis, the detection of financial instabilities has garnered extensive research attention, particularly through the application of time-series analysis. In this study, a novel time-series analysis method, integrating the Kullback-Leibler Divergence (KLD) metric with a sliding window technique, is proposed to detect instabilities in time-series data, especially in financial markets. Global financial time series from 2004 to 2022 were analyzed. The raw time series were preprocessed into return rate series and transformed into complex networks using the directed horizontal visibility graph (DHVG) algorithm, effectively preserving temporal variabilities in network topologies. The KLD method was evaluated through both retrospective analysis and real-time monitoring. It successfully identified idiosyncratic incidents in the financial market, correlating them with specific economic events. Compared to traditional metrics (e.g., moments) and econometric methods, KLD demonstrated superior performance in capturing sequence information and detecting anomalies without requiring linear regression models. Although initially designed for financial data, the KLD method is versatile and can be applied to other types of time series as well.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959454","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}

引用次数: 0

Secure Wireless Communication for Correlated Legitimate User and Eavesdropper Channels via Movable-Antenna Enhanced Frequency Diverse Array. 基于可动天线增强型分频阵列的合法用户与窃听者相关信道安全无线通信。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-04-09 DOI: 10.3390/e27040401

Xuehan Wu, Huaizong Shao, Jingran Lin, Ye Pan, Weijie Xiong

{"title":"Secure Wireless Communication for Correlated Legitimate User and Eavesdropper Channels via Movable-Antenna Enhanced Frequency Diverse Array.","authors":"Xuehan Wu, Huaizong Shao, Jingran Lin, Ye Pan, Weijie Xiong","doi":"10.3390/e27040401","DOIUrl":"https://doi.org/10.3390/e27040401","url":null,"abstract":"Physical-layer (PHY) security is widely used as an effective method for ensuring secure wireless communications. However, when the legitimate user (LU) and the eavesdropper (Eve) are in close proximity, the channel coupling can significantly degrade the secure performance of PHY. Frequency diverse array (FDA) technique addresses channel coupling issues by introducing frequency offsets among array elements. However, FDA's ability to secure communication relies mainly on frequency domain characteristics, lacking the spatial degrees of freedom. The recently proposed movable antenna (MA) technology serves as an effective approach to overcome this limitation. It offers the flexibility to adjust antenna positions dynamically, thereby further decoupling the channels between LU and Eve. In this paper, we propose a novel MA-FDA approach, which offers a comprehensive solution for enhancing PHY security. We aim to maximize the achievable secrecy rate through the joint optimization of all antenna positions at the base station (BS), FDA frequency offsets, and beamformer, subject to the predefined regions for antenna positions, frequency offsets range, and energy constraints. To solve this non-convex optimization problem, which involves highly coupled variables, the alternating optimization (AO) method is employed to cyclically update the parameters, with the projected gradient ascent (PGA) method and block successive upper-bound minimization (BSUM) method being employed to tackle the challenging subproblems. Simulation results demonstrate that the MA-FDA approach can achieve a higher secrecy rate compared to the conventional phased array (PA) or fixed-position antenna (FPA) schemes.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992293","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}

引用次数: 0

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