EntropyPub Date : 2025-04-19DOI: 10.3390/e27040443
Yi Wang, Feng Li, Mengge Lv, Tianzhen Wang, Xiaohang Wang
{"title":"A Multi-Index Fusion Adaptive Cavitation Feature Extraction for Hydraulic Turbine Cavitation Detection.","authors":"Yi Wang, Feng Li, Mengge Lv, Tianzhen Wang, Xiaohang Wang","doi":"10.3390/e27040443","DOIUrl":"https://doi.org/10.3390/e27040443","url":null,"abstract":"<p><p>Under cavitation conditions, hydraulic turbines can suffer from mechanical damage, which will shorten their useful life and reduce power generation efficiency. Timely detection of cavitation phenomena in hydraulic turbines is critical for ensuring operational reliability and maintaining energy conversion efficiency. However, extracting cavitation features is challenging due to strong environmental noise interference and the inherent non-linearity and non-stationarity of a cavitation hydroacoustic signal. A multi-index fusion adaptive cavitation feature extraction and cavitation detection method is proposed to solve the above problems. The number of decomposition layers in the multi-index fusion variational mode decomposition (VMD) algorithm is adaptively determined by fusing multiple indicators related to cavitation characteristics, thus retaining more cavitation information and improving the quality of cavitation feature extraction. Then, the cavitation features are selected based on the frequency characteristics of different degrees of cavitation. In this way, the detection of incipient cavitation and the secondary detection of supercavitation are realized. Finally, the cavitation detection effect was verified using the hydro-acoustic signal collected from a mixed-flow hydro turbine model test stand. The detection accuracy rate and false alarm rate were used as evaluation indicators, and the comparison results showed that the proposed method has high detection accuracy and a low false alarm rate.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12027118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964219","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}
{"title":"Retina-Inspired Models Enhance Visual Saliency Prediction.","authors":"Gang Shen, Wenjun Ma, Wen Zhai, Xuefei Lv, Guangyao Chen, Yonghong Tian","doi":"10.3390/e27040436","DOIUrl":"https://doi.org/10.3390/e27040436","url":null,"abstract":"<p><p>Biologically inspired retinal preprocessing improves visual perception by efficiently encoding and reducing entropy in images. In this study, we introduce a new saliency prediction framework that combines a retinal model with deep neural networks (DNNs) using information theory ideas. By mimicking the human retina, our method creates clearer saliency maps with lower entropy and supports efficient computation with DNNs by optimizing information flow and reducing redundancy. We treat saliency prediction as an information maximization problem, where important regions have high information and low local entropy. Tests on several benchmark datasets show that adding the retinal model boosts the performance of various bottom-up saliency prediction methods by better managing information and reducing uncertainty. We use metrics like mutual information and entropy to measure improvements in accuracy and efficiency. Our framework outperforms state-of-the-art models, producing saliency maps that closely match where people actually look. By combining neurobiological insights with information theory-using measures like Kullback-Leibler divergence and information gain-our method not only improves prediction accuracy but also offers a clear, quantitative understanding of saliency. This approach shows promise for future research that brings together neuroscience, entropy, and deep learning to enhance visual saliency prediction.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959380","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}
EntropyPub Date : 2025-04-18DOI: 10.3390/e27040440
Francesco Tosti Guerra, Andrea Napoletano, Andrea Zaccaria
{"title":"The Intrinsic Dimension of Neural Network Ensembles.","authors":"Francesco Tosti Guerra, Andrea Napoletano, Andrea Zaccaria","doi":"10.3390/e27040440","DOIUrl":"https://doi.org/10.3390/e27040440","url":null,"abstract":"<p><p>In this work, we propose to study the collective behavior of different ensembles of neural networks. These sets define and live on complex manifolds that evolve through training. Each manifold is characterized by its intrinsic dimension, a measure of the variability of the ensemble and, as such, a measure of the impact of the different training strategies. Indeed, higher intrinsic dimension values imply higher variability among the networks and a larger parameter space coverage. Here, we quantify how much the training choices allow the exploration of the parameter space, finding that a random initialization of the parameters is a stronger source of variability than, progressively, data distortion, dropout, and batch shuffle. We then investigate the combinations of these strategies, the parameters involved, and the impact on the accuracy of the predictions, shedding light on the often-underestimated consequences of these training choices.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983568","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}
EntropyPub Date : 2025-04-18DOI: 10.3390/e27040437
Edward Bormashenko
{"title":"Landauer's Principle: Past, Present and Future.","authors":"Edward Bormashenko","doi":"10.3390/e27040437","DOIUrl":"https://doi.org/10.3390/e27040437","url":null,"abstract":"<p><p>\"Thermodynamics is only physical theory of universal content, which I am convinced will never be overthrown, within the framework of applicability of its basic concepts [...].</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143975169","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}
EntropyPub Date : 2025-04-18DOI: 10.3390/e27040438
Xinyu Gu, Xianghao Hou, Boxuan Zhang, Yixin Yang, Shuanping Du
{"title":"Robust Smoothing Cardinalized Probability Hypothesis Density Filter-Based Underwater Multi-Target Direction-of-Arrival Tracking with Uncertain Measurement Noise.","authors":"Xinyu Gu, Xianghao Hou, Boxuan Zhang, Yixin Yang, Shuanping Du","doi":"10.3390/e27040438","DOIUrl":"https://doi.org/10.3390/e27040438","url":null,"abstract":"<p><p>In view of the typical multi-target scenarios of underwater direction-of-arrival (DOA) tracking complicated by uncertain measurement noise in unknown underwater environments, a robust underwater multi-target DOA tracking method is proposed by incorporating Saga-Husa (SH) noise estimation and a backward smoothing technique within the framework of the cardinalized probability hypothesis density (CPHD) filter. First, the kinematic model of underwater targets and the measurement model based on the received signals of a hydrophone array are established, from which the CPHD-based multi-target DOA tracking algorithm is derived. To mitigate the adverse impact of uncertain measurement noise, the Saga-Husa approach is deployed for dynamic noise estimation, thereby reducing noise-induced performance degradation. Subsequently, a backward smoothing technique is applied to the forward filtering results to further enhance tracking robustness and precision. Finally, extensive simulations and experimental evaluations demonstrate that the proposed method outperforms existing DOA estimation and tracking techniques in terms of robustness and accuracy under uncertain measurement noise conditions.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964348","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}
EntropyPub Date : 2025-04-18DOI: 10.3390/e27040439
Zhenning Chen, Xinyu Zhang, Siyang Wang, Youren Wang
{"title":"MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT.","authors":"Zhenning Chen, Xinyu Zhang, Siyang Wang, Youren Wang","doi":"10.3390/e27040439","DOIUrl":"https://doi.org/10.3390/e27040439","url":null,"abstract":"<p><p>Different from conventional federated learning (FL), which relies on a central server for model aggregation, decentralized FL (DFL) exchanges models among edge servers, thus improving the robustness and scalability. When deploying DFL into the Internet of Things (IoT), limited wireless resources cannot provide simultaneous access to massive devices. One must perform client scheduling to balance the convergence rate and model accuracy. However, the heterogeneity of computing and communication resources across client devices, combined with the time-varying nature of wireless channels, makes it challenging to estimate accurately the delay associated with client participation during the scheduling process. To address this issue, we investigate the client scheduling and resource optimization problem in DFL without prior client information. Specifically, the considered problem is reformulated as a multi-armed bandit (MAB) program, and an online learning algorithm that utilizes contextual multi-arm slot machines for client delay estimation and scheduling is proposed. Through theoretical analysis, this algorithm can achieve asymptotic optimal performance in theory. The experimental results show that the algorithm can make asymptotic optimal client selection decisions, and this method is superior to existing algorithms in reducing the cumulative delay of the system.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987424","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}
EntropyPub Date : 2025-04-17DOI: 10.3390/e27040434
Daniel Algom, Daniel Fitousi
{"title":"Information Theory in Perception of Form: From Gestalt to Algorithmic Complexity.","authors":"Daniel Algom, Daniel Fitousi","doi":"10.3390/e27040434","DOIUrl":"https://doi.org/10.3390/e27040434","url":null,"abstract":"<p><p>In 1948, Claude Shannon published a revolutionary paper on communication and information in engineering, one that made its way into the psychology of perception and changed it for good. However, the path to truly successful applications to psychology has been slow and bumpy. In this article, we present a readable account of that path, explaining the early difficulties as well as the creative solutions offered. The latter include Garner's theory of sets and redundancy as well as mathematical group theory. These solutions, in turn, enabled rigorous objective definitions to the hitherto subjective Gestalt concepts of figural goodness, order, randomness, and predictability. More recent developments enabled the definition of, in an exact mathematical sense, the key notion of complexity. In this article, we demonstrate, for the first time, the presence of the association between people's subjective impression of figural goodness and the pattern's objective complexity. The more attractive the pattern appears to perception, the less complex it is and the smaller the set of subjectively similar patterns.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958250","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}
EntropyPub Date : 2025-04-17DOI: 10.3390/e27040435
Jun-Ichi Maskawa
{"title":"Empirical Study on Fluctuation Theorem for Volatility Cascade Processes in Stock Markets.","authors":"Jun-Ichi Maskawa","doi":"10.3390/e27040435","DOIUrl":"https://doi.org/10.3390/e27040435","url":null,"abstract":"<p><p>This study investigates the properties of financial markets that arise from the multi-scale structure of volatility, particularly intermittency, by employing robust theoretical tools from nonequilibrium thermodynamics. Intermittency in velocity fields along spatial and temporal axes is a well-known phenomenon in developed turbulence, with extensive research dedicated to its structures and underlying mechanisms. In turbulence, such intermittency is explained through energy cascades, where energy injected at macroscopic scales is transferred to microscopic scales. Similarly, analogous cascade processes have been proposed to explain the intermittency observed in financial time series. In this work, we model volatility cascade processes in the stock market by applying the framework of stochastic thermodynamics to a Langevin system that describes the dynamics. We introduce thermodynamic concepts such as temperature, heat, work, and entropy into the analysis of financial markets. This framework allows for a detailed investigation of individual trajectories of volatility cascades across longer to shorter time scales. Further, we conduct an empirical study primarily using the normalized average of intraday logarithmic stock prices of the constituent stocks in the FTSE 100 Index listed on the London Stock Exchange (LSE), along with two additional data sets from the Tokyo Stock Exchange (TSE). Our Langevin-based model successfully reproduces the empirical distribution of volatility-defined as the absolute value of the wavelet coefficients across time scales-and the cascade trajectories satisfy the Integral Fluctuation Theorem associated with entropy production. A detailed analysis of the cascade trajectories reveals that, for the LSE data set, volatility cascades from larger to smaller time scales occur in a causal manner along the temporal axis, consistent with known stylized facts of financial time series. In contrast, for the two data sets from the TSE, while similar behavior is observed at smaller time scales, anti-causal behavior emerges at longer time scales.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003980","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}
EntropyPub Date : 2025-04-16DOI: 10.3390/e27040431
Shasha Liang, Ziqi Wang, Xinyue Wang
{"title":"A Study Based on b-Value and Information Entropy in the 2008 Wenchuan 8.0 Earthquake.","authors":"Shasha Liang, Ziqi Wang, Xinyue Wang","doi":"10.3390/e27040431","DOIUrl":"https://doi.org/10.3390/e27040431","url":null,"abstract":"<p><p>Earthquakes, as serious natural disasters, have greatly harmed human beings. In recent years, the combination of acoustic emission technology and information entropy has shown good prospects in earthquake prediction. In this paper, we study the application of acoustic emission b-values and information entropy in earthquake prediction in China and analyze their changing characteristics and roles. The acoustic emission b-value is based on the Gutenberg-Richter law, which quantifies the relationship between magnitude and occurrence frequency. Lower b-values are usually associated with higher earthquake risks. Meanwhile, information entropy is used to quantify the uncertainty of the system, which can reflect the distribution characteristics of seismic events and their dynamic changes. In this study, acoustic emission data from several stations around the 2008 Wenchuan 8.0 earthquake are selected for analysis. By calculating the acoustic emission b-value and information entropy, the following is found: (1) Both the b-value and information entropy show obvious changes before the main earthquake: during the seismic phase, the acoustic emission b-value decreases significantly, and the information entropy also shows obvious decreasing entropy changes. The b-values of stations AXI and DFU continue to decrease in the 40 days before the earthquake, while the b-values of stations JYA and JMG begin to decrease significantly in the 17 days or so before the earthquake. The information entropy changes in the JJS and YZP stations are relatively obvious, especially for the YZP station, which shows stronger aggregation characteristics of seismic activity. This phenomenon indicates that the regional underground structure is in an extremely unstable state. (2) The stress evolution process of the rock mass is divided into three stages: in the first stage, the rock mass enters a sub-stabilized state about 40 days before the main earthquake; in the second stage, the rupture of the cracks changes from a disordered state to an ordered state, which occurs about 10 days before the earthquake; and in the third stage, the impending destabilization of the entire subsurface structure is predicted, which occurs in a short period before the earthquake. In summary, the combined analysis of the acoustic emission b-value and information entropy provides a novel dual-parameter synergy framework for earthquake monitoring and early warning, enhancing precursor recognition through the coupling of stress evolution and system disorder dynamics.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968460","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}
EntropyPub Date : 2025-04-16DOI: 10.3390/e27040432
Nan Yang, Ting Yu
{"title":"Quantum Synchronization via Active-Passive Decomposition Configuration: An Open Quantum-System Study.","authors":"Nan Yang, Ting Yu","doi":"10.3390/e27040432","DOIUrl":"https://doi.org/10.3390/e27040432","url":null,"abstract":"<p><p>In this paper, we study the synchronization of dissipative quantum harmonic oscillators in the framework of a quantum open system via the active-passive decomposition (APD) configuration. We show that two or more quantum systems may be synchronized when the quantum systems of interest are embedded in dissipative environments and influenced by a common classical system. Such a classical system is typically termed a controller, which (1) can drive quantum systems to cross different regimes (e.g., from periodic to chaotic motions) and (2) constructs the so-called active-passive decomposition configuration, such that all the quantum objects under consideration may be synchronized. The main finding of this paper is that we demonstrate that the complete synchronizations measured using the standard quantum deviation may be achieved for both stable regimes (quantum limit circles) and unstable regimes (quantum chaotic motions). As an example, we numerically show in an optomechanical setup that complete synchronization can be realized in quantum mechanical resonators.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12026420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958799","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}