IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-13 DOI: 10.3390/e27090951

Shaohu Ding, Guangsheng Zhou, Xinyu Wang, Weibin Li

{"title":"Fault Diagnosis of Wind Turbine Rotating Bearing Based on Multi-Mode Signal Enhancement and Fusion.","authors":"Shaohu Ding, Guangsheng Zhou, Xinyu Wang, Weibin Li","doi":"10.3390/e27090951","DOIUrl":"10.3390/e27090951","url":null,"abstract":"Wind turbines operate under harsh conditions, heightening the risk of rotating bearing failures. While fault diagnosis using acoustic or vibration signals is feasible, single-modal methods are highly vulnerable to environmental noise and system uncertainty, reducing diagnostic accuracy. Existing multi-modal approaches also struggle with noise interference and lack causal feature exploration, limiting fusion performance and generalization. To address these issues, this paper proposes CAVF-Net-a novel framework integrating bidirectional cross-attention (BCA) and causal inference (CI). It enhances Mel-Frequency Cepstral Coefficients (MFCCs) of acoustic and short-time Fourier transform (STFT) features of vibration via BCA and employs CI to derive adaptive fusion weights, effectively preserving causal relationships and achieving robust cross-modal integration. The fused features are classified for fault diagnosis under real-world conditions. Experiments show that CAVF-Net attains 99.2% accuracy with few iterations on clean data and maintains 95.42% accuracy in high-entropy multi-noise environments-outperforming single-model acoustic and vibration by 16.32% and 8.86%, respectively, while significantly reducing information uncertainty in downstream classification.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174453","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

Quantum Computing for Transport Network Optimization. 量子计算用于传输网络优化。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-13 DOI: 10.3390/e27090953

Jiangwei Ju, Zhihang Liu, Yuelin Bai, Yong Wang, Qi Gao, Yin Ma, Chao Zheng, Kai Wen

{"title":"Quantum Computing for Transport Network Optimization.","authors":"Jiangwei Ju, Zhihang Liu, Yuelin Bai, Yong Wang, Qi Gao, Yin Ma, Chao Zheng, Kai Wen","doi":"10.3390/e27090953","DOIUrl":"10.3390/e27090953","url":null,"abstract":"Public transport systems play a crucial role in the development of large cities. Bus network design to optimize passenger flow coverage in a global metropolis is a challenging task. As an essential part of bus travel planning, considering the bus transfer factor in the existing extremely complex and extensive public bus network usually leads to a optimization problem characterized by high-dimensionality and non-linearity. While classical computers struggle to deal with this kind of problems, quantum computers shed new light into this field. The coherent Ising machine (CIM), a specialized optical quantum computer using a photonic dissipative architecture, has shown its remarkable computational power in combinatorial optimization problems. We construct the classical model and the quadratic unconstrained binary optimization (QUBO) model of the bus route optimization problem, and solve it using a classical computer and CIM, respectively. Our experimental results demonstrate the significant acceleration capability of CIM over classical computers in finding the optimal or near-optimal solutions, albeit subject to the hardware limitations of the 100-qubit CIM.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174309","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

Advances of Quantum Key Distribution and Network Nonlocality. 量子密钥分配与网络非局域性研究进展。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-13 DOI: 10.3390/e27090950

Minming Geng

引用次数: 0

Identifying Network Propagation Sources Using Advanced Centrality Measures. 使用高级中心性方法识别网络传播源。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-12 DOI: 10.3390/e27090948

Damian Frąszczak

{"title":"Identifying Network Propagation Sources Using Advanced Centrality Measures.","authors":"Damian Frąszczak","doi":"10.3390/e27090948","DOIUrl":"10.3390/e27090948","url":null,"abstract":"We live in a time dominated by interconnected networks surrounding us on all fronts. The emergence of social media platforms has driven the expansion of social networks, facilitating fast communication worldwide. Responses to content shared on these platforms can be seen as a propagation process, where information spreads through social networks. Analyzing propagation graphs presents a significant challenge in identifying sources, which is crucial in various fields. This includes detecting the origins of disinformation, identifying patient zero in an epidemic, and tracing the initial sources of viral trends or malware. Numerous studies have attempted to identify these sources using methods similar to centrality measures which assign a value indicating the likelihood of being a source. While centrality measures are a popular topic, with many new measures introduced each year, only a few have been explored in the context of source identification. This article explores a wide range of centrality measures in the context of source identification. The results help identify the most effective measures and pave the way for the development of more efficient detection techniques. Additionally, an analysis was conducted considering multiple hops in the propagation network, providing deeper insights into the impact of extended neighborhood structures on detection performance.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12469922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174389","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

The Realization of One-to-Two-Port Beam Division in a Five-Channel Acoustic System. 五声道声学系统中一对二端口波束分割的实现。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-12 DOI: 10.3390/e27090949

Rui Wang, Zhicheng Xu, Shuai Tang, Wencong Zhang, Jiabin Hou, Haipeng Cui, Yang Liu

引用次数: 0

Two Types of Geometric Jensen-Shannon Divergences. 两类几何Jensen-Shannon散度。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-11 DOI: 10.3390/e27090947

Frank Nielsen

{"title":"Two Types of Geometric Jensen-Shannon Divergences.","authors":"Frank Nielsen","doi":"10.3390/e27090947","DOIUrl":"10.3390/e27090947","url":null,"abstract":"The geometric Jensen-Shannon divergence (G-JSD) has gained popularity in machine learning and information sciences thanks to its closed-form expression between Gaussian distributions. In this work, we introduce an alternative definition of the geometric Jensen-Shannon divergence tailored to positive densities which does not normalize geometric mixtures. This novel divergence is termed the extended G-JSD, as it applies to the more general case of positive measures. We explicitly report the gap between the extended G-JSD and the G-JSD when considering probability densities, and show how to express the G-JSD and extended G-JSD using the Jeffreys divergence and the Bhattacharyya distance or Bhattacharyya coefficient. The extended G-JSD is proven to be an f-divergence, which is a separable divergence satisfying information monotonicity and invariance in information geometry. We derive a corresponding closed-form formula for the two types of G-JSDs when considering the case of multivariate Gaussian distributions that is often met in applications. We consider Monte Carlo stochastic estimations and approximations of the two types of G-JSD using the projective γ-divergences. Although the square root of the JSD yields a metric distance, we show that this is no longer the case for the two types of G-JSD. Finally, we explain how these two types of geometric JSDs can be interpreted as regularizations of the ordinary JSD.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174339","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

Analytic Solutions and Entropy Production of the Double-Diffusive Equation System. 双扩散方程组的解析解和熵的产生。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-10 DOI: 10.3390/e27090946

Imre Ferenc Barna, László Mátyás

引用次数: 0

Five Fristonian Formulae. 五个弗里斯顿公式。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-10 DOI: 10.3390/e27090944

Thomas Parr, Giovanni Pezzulo, Rosalyn Moran, Maxwell Ramstead, Axel Constant, Anjali Bhat

引用次数: 0

Exergy Efficiency of Closed and Unsteady-Flow Systems. 闭式和非定流系统的火用效率。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-10 DOI: 10.3390/e27090943

Yunus A Çengel, Mehmet Kanoğlu

{"title":"Exergy Efficiency of Closed and Unsteady-Flow Systems.","authors":"Yunus A Çengel, Mehmet Kanoğlu","doi":"10.3390/e27090943","DOIUrl":"10.3390/e27090943","url":null,"abstract":"Exergy efficiency is viewed as the degree of approaching reversible operation, with a value of 100 percent for a reversible process characterized by zero entropy generation or equivalently zero exergy destruction since Xdestroyed = T0Sgen. As such, exergy efficiency becomes a measure of thermodynamic perfection. There are different conceptual definitions of exergy efficiency, the most common ones being (1) the ratio of exergy output to exergy input ηex = Xoutput/Xinput = 1 - (Xdestroyed + Xloss)/Xinput, (2) the ratio of the product exergy to fuel exergy ηex = Xproduct/Xfuel = 1 - (Xdestroyed + Xloss)/Xfuel, and (3) the ratio of exergy recovered to exergy expended ηex = Xrecovered/Xexpended = 1 - Xdestroyed/Xexpended. Most exergy efficiency definitions are formulated with steady-flow systems in mind, and they are generally applied to systems in steady operation such as power plants and refrigeration systems whose exergy content remains constant. If these definitions are to be used for closed and unsteady-flow systems, the terms need to be interpreted broadly to account for the exergy change of the systems as exergy input or output, as appropriate. In this paper, general exergy efficiency relations are developed for closed and unsteady-flow systems and their use is demonstrated with applications. Also, the practicality of the use of the term exergy loss Xloss is questioned, and limitations on the definition ηex = Wact,out/Wrev,out are discussed.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174464","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

On the Entropy-Based Localization of Inequality in Probability Distributions. 基于熵的概率分布中不等式的局部化。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-09-10 DOI: 10.3390/e27090945

Rajeev Rajaram, Nathan Ritchey, Brian Castellani

{"title":"On the Entropy-Based Localization of Inequality in Probability Distributions.","authors":"Rajeev Rajaram, Nathan Ritchey, Brian Castellani","doi":"10.3390/e27090945","DOIUrl":"10.3390/e27090945","url":null,"abstract":"We present a novel method for localizing inequality within probability distributions by applying a recursive Hahn decomposition to the degree of uniformity-a measure derived from the exponential of Shannon entropy. This approach partitions the probability space into disjoint regions exhibiting progressively sharper deviations from uniformity, enabling structural insights into how and where inequality is concentrated. To demonstrate its broad applicability, we apply the method to both standard and contextualized systems: the discrete binomial and continuous exponential distributions serve as canonical cases, while two hypothetical examples illustrate domain-specific applications. In the first, we analyze localized risk concentrations in disease contraction data, revealing targeted zones of epidemiological disparity. In the second, we uncover stress localization in a non-uniformly loaded beam, demonstrating the method's relevance to physical systems with spatial heterogeneity. This decomposition reveals aspects of structural disparity that are often obscured by scalar summaries. The resulting recursive tree offers a multi-scale representation of informational non-uniformity, capturing the emergence and localization of inequality across the distribution. The framework may have implications for understanding entropy localization, transitions in informational structure, and the dynamics of heterogeneous systems.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174266","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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