Entropy最新文献

{"title":"Spreading Dynamics from Social-Network Models to Complex Agricultural Systems: A Mini-Review.","authors":"Jiao Wu, Xiuwen Feng, Lihui Zhu, Yingpeng Liu, Muhua Zheng","doi":"10.3390/e28101080","DOIUrl":"10.3390/e28101080","url":null,"abstract":"<p><p>Spreading dynamics have been extensively studied in social, communication, and epidemiological networks, leading to substantial advances in understanding spreading thresholds, outbreak patterns, temporal dynamics, and phase transitions. However, their extension to other complex systems, particularly agricultural systems, remains less systematically explored. In this mini-review, we summarize representative spreading models and theoretical approaches developed for complex networks. We first review spreading dynamics on single-layer networks and then discuss their extensions to multilayer, temporal, and higher-order networks, highlighting how increasingly complex interaction structures affect spreading thresholds, outbreak patterns, and phase transitions. We subsequently examine how these models and theoretical approaches can be transferred from social networks to complex agricultural systems. Representative applications include crop diseases and pests, livestock diseases, agricultural information and technology diffusion, and coupled disease-information processes. Finally, we discuss emerging directions for extending spreading theories to increasingly complex agricultural systems. This review may help clarify the generality and transferability of spreading theories across different networked systems and stimulate their further development in complex agricultural contexts.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264611","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":"Condorcet Cycles, Contextuality, and the Emergence of Quantum-like Collective States.","authors":"Leopoldo Trieste, Giuseppe Turchetti","doi":"10.3390/e28101076","DOIUrl":"10.3390/e28101076","url":null,"abstract":"<p><p>The Condorcet paradox is traditionally viewed as a macro-level phenomenon of collective decision-making, showing that individually rational agents with coherent and transitive preferences may nevertheless generate socially intransitive majority outcomes. This interpretation assumes that cyclicity emerges exclusively through aggregation. In this study, we distinguish between micro-Condorcet dynamics, arising from contextual interactions within individual decision processes, and macro-Condorcet dynamics, emerging through majority aggregation. To investigate the relationship between these two levels, we introduce a minimal contextual model in which pairwise decisions are generated sequentially and linked through a contextual coupling mechanism. Although individual agents remain locally coherent and entirely classical at the microscopic level, each decision modifies the context of subsequent comparisons. Simulation results show that increasing contextuality progressively increases the probability of both micro- and macro-level Condorcet cycles. The transition between the two levels is strongly influenced by heterogeneity in decision thresholds, particularly those governing intermediate and closing comparisons. Rather than being driven primarily by initial preference relations, collective cycles emerge from heterogeneous evaluations of intermediate alternatives, which amplify contextual effects and facilitate cycle closure. A critical contextuality threshold is observed beyond which classical representability breaks down and no probability distribution over transitive preference rankings can reproduce the observed pairwise preference probabilities. These findings suggest that quantum-like probabilistic structures may emerge naturally as higher-level representations of contextual collective decision systems.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264397","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":"MBQG-Net: A Multi-Scale Bidirectional Query-Guided Gated Network for Aero-Engine Remaining Useful Life Prediction.","authors":"Xiao Hu, Hao Qi, Jing Yu, Chengwu Lu, Lingli Zhang","doi":"10.3390/e28101082","DOIUrl":"10.3390/e28101082","url":null,"abstract":"<p><p>Remaining Useful Life (RUL) prediction for aircraft engines is a critical task in prognostics and health management, aiming to extract degradation information from multi-sensor operational data to support maintenance scheduling and operational risk management. Existing hybrid temporal models have explored various combinations of convolutional networks, temporal convolutions, recurrent networks, and attention mechanisms; however, there remains room for further coordination regarding the positional roles of different modules within fixed windows, bidirectional context organization, and training objectives. To address this, we propose a Multi-scale Bidirectional Query-Guided Gated Network, MBQG-Net. MBQG-Net employs one-dimensional convolution and parallel bidirectional dilated convolutions to extract local and multi-range temporal features, and utilizes multi-head Query-Value temporal attention to generate window-level context-enhanced representations prior to recurrent state aggregation. The sequence enhanced by Query-based temporal weighting and Value aggregation is then fed into a stacked bidirectional GRU for subsequent bidirectional gated state aggregation. During training, an asymmetric weighted mean squared error is adopted, assigning higher weights to RUL overestimation errors. MBQG-Net was evaluated on all four official test sets (FD001-FD004) of the NASA C-MAPSS dataset under a unified experimental protocol. It achieved the strongest overall regression performance on FD001 and FD003 and maintained competitive overall conventional regression performance on the multi-condition FD002 and FD004 subsets. The model obtained the lowest NASA Score among the compared methods on all four subsets, although the difference on FD002 was marginal. On FD004, MBQG-Net achieved a NASA Score of 930.9088, representing a 28.27% reduction relative to the second-best result. Experimental results demonstrate that MBQG-Net maintains a favorable balance between conventional regression accuracy and direction-sensitive error control across different operating-condition and fault-mode settings, offering a structurally clear hybrid temporal modeling solution for multi-sensor aircraft engine RUL prediction.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264504","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":"The Distribution of Lexical Information Within Dependency Spans: Cross-Linguistic Evidence from 22 Languages.","authors":"Xiangyu Kang, Shuiyuan Yu","doi":"10.3390/e28101083","DOIUrl":"10.3390/e28101083","url":null,"abstract":"<p><p>Dependency distance measures the linear separation between syntactically related words but does not describe how information is distributed across their interval. We analyzed summed XGLM-2.9B word surprisal in 1,346,122 dependency spans from 22 Universal Dependencies languages at distances 4-10. Clustering 154 position-aligned profiles yielded a broadly descending type and a type with a terminal rise; 20 languages retained their assignment across distances. Leave-one-language-out comparison did not support a common three-segment structure: the one-standard-error rule selected two segments for the descending type and three for the rising type. The descending type's initial decline disappeared after separate adjustments for sentence position and preceding context. Standardizing word-length and model subword-count distributions reduced the rising type's mean final-word increase from +1.996 to +0.408 bits (95% interval [-0.024, +0.898]). In held-out sentences, real dependencies in eight languages independently classified by a training-set terminal decrease by 0.928 bits more than nearby nonedge intervals sharing the final part-of-speech tag (95% interval [-1.498, -0.217]). A shared part-of-speech component was negatively associated with terminal surprisal in the original descending type. Dependency-span alignment reveals recurring information profiles and locates a conditional terminal contrast for further tests of syntactic prediction.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264399","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":"Hybrid Chaos via Entropy-Greedy Map Switching: A Statistical and Predictability Analysis.","authors":"Alexandru Dinu, Corina Nitu","doi":"10.3390/e28101071","DOIUrl":"10.3390/e28101071","url":null,"abstract":"<p><p>We propose and analyse a hybrid chaotic pseudorandom generator that combines the logistic, tent, and Hénon maps under a deterministic entropy-greedy selector. At each step every map proposes a candidate; the selector keeps the one that maximises the plug-in Shannon entropy of a sliding output window, emits it, and advances only the winning map's state. We benchmark all four generators plus a Mersenne Twister (MT19937) reference on uniformity (Kolmogorov-Smirnov statistic, KS), lag-correlation, entropy, Lyapunov exponent, and sensitivity. The hybrid reduces the logistic KS deviation by 26.9% and the maximum short-lag Pearson correlation by 6.5%. An explicit predictability analysis shows that the logistic and tent states are exposed, giving an adversary 92% one-step accuracy; only the hidden Hénon coordinate blocks free-running cloning, which diverges within about eight steps. Rank-uniformisation drops KS to 2.5×10-5 and passes a NIST SP 800-22 subset, while the lag-1 rank dependence is unchanged. Structural modifications-state masking for all component maps and continuous-time replacements with hidden coordinates-are discussed as routes to reducing predictability.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264151","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":"Robust Disturbance Estimation-Based Control: Case Study on an Airship Attitude Tracking Problem.","authors":"Adrian-Mihail Stoica, Valentin Pană, Irina Beatrice Ştefănescu","doi":"10.3390/e28101069","DOIUrl":"10.3390/e28101069","url":null,"abstract":"<p><p>This paper presents a design methodology for the automatic control system of an airship, accounting for atmospheric disturbances and parametric modeling uncertainties. The derived design model is a linear stochastic system with multiplicative noise incorporating both the airship's uncertain dynamics and the wind model. First, an H∞ state feedback control law is derived for the stochastic system to ensure robust stability and trajectory tracking performance. Subsequently, a robust Kalman filter is designed to estimate the turbulence model states based on available measurements. It is demonstrated that the optimal gain of this robust filter depends on the solution to a coupled system of specific Riccati and Lyapunov equations. Numerical results indicate improved heading-tracking performance for the considered turbulent simulation when the estimated wind-gust states are incorporated into the feedback law. Under parametric uncertainty, the robust Kalman-type filter also exhibits lower sensitivity of the heading-estimation error than the classical Kalman filter.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264490","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":"Bayesian-Guided Cooperative RL Beamforming for Wireless Adversarial User Detection.","authors":"Parmida Geranmayeh, Onur Günlü","doi":"10.3390/e28101070","DOIUrl":"10.3390/e28101070","url":null,"abstract":"<p><p>In next-generation wireless networks, communication systems are expected to go beyond simple data transmission and simultaneously provide high data rates, efficiency, and security. This requirement has motivated the extensive adoption of machine learning methods to develop intelligent and real-time network management frameworks, enabling the system to continuously monitor and react to channel variations and user behavior while maintaining efficient information delivery. In this context, the integration of machine learning with beamforming enables adaptive and data-driven beam direction selection, improving both the efficiency and security of wireless links. In this work, a 3GPP-based system model is first implemented under a no-attacker scenario, and an exhaustive search is employed as a reference to identify the best beamforming configurations. The proposed framework is then evaluated in the presence of an attacker and under different network scalability conditions. We demonstrate that the reinforcement learning-based approaches, namely Q-learning and State-Action-Reward-State-Action (SARSA), consistently outperform random selection in terms of total channel capacity, attacker-detection accuracy, and performance stability. Among the evaluated reinforcement learning methods, Q-learning achieves the best overall trade-off between detection accuracy and computational efficiency. Our results indicate that the proposed framework provides a stable, scalable, and effective solution for joint beamforming and security-aware decision-making in dynamic and adversarial wireless environments.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264403","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":"Temporal Dynamics of Groundwater Levels: A Complex Networks-Based Approach.","authors":"Bhadran Deepthi, Bellie Sivakumar","doi":"10.3390/e28101068","DOIUrl":"10.3390/e28101068","url":null,"abstract":"<p><p>This study explores the application of complex network theory to examine the temporal dynamics of groundwater levels in a region. It presents the first ever application of a coupled chaos theory-complex network framework to investigate the temporal dynamics and connectivity in groundwater levels in a region. For implementation, daily groundwater-level data observed over the period 2010-2014 from each of 71 wells across the United States are considered. Each well is considered as a network. In order to construct the temporal network from groundwater time series, basic concepts of chaos theory are coupled with those of complex network theory. First, the phase-space reconstruction technique is used to reconstruct the single-variable groundwater time series in a multi-dimensional phase space. Next, the optimum dimension required for the phase-space reconstruction is determined using the false nearest neighbour (FNN) algorithm. Each reconstructed vector in the phase space is considered as a node in the network, and the connections between the nodes (i.e., links) are identified based on the distance threshold between the reconstructed vectors. Four complex networks-based measures-degree centrality, betweenness centrality, closeness centrality, and clustering coefficient-are used to examine the properties of the groundwater-level network. The optimal embedding dimensions from the FNN method for the 71 groundwater level time series are found to range from 4 to 18, suggesting a wide range of complexity in the 71 groundwater level time series. However, a large majority (i.e., 62) of the time series have dimensions in the range of 4-10, suggesting low-to-medium-level complexity of the groundwater level dynamics. The results for the four network measures are degree centrality in the range 0.12-0.55, betweenness centrality in the range 604-4750, closeness centrality in the range 0.00009-0.00034, and clustering coefficient in the range 0.709-0.863. No region-specific patterns are observed in the four network measures, although, depending upon the measure, networks of wells in some states have higher/lower values and networks in some other states have a good mix of high and low values. Further, Spearman rank correlation analysis shows that the four network measures do not have significant relationships with key statistical characteristics (mean, standard deviation, and coefficient of variation) of the groundwater levels. This indicates that the differences in the network measures cannot be explained only by the basic statistical properties of the groundwater level records.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264582","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":"Correlations of Symmetry and Frequency in Alphabets and in Natural Systems.","authors":"Edward Bormashenko, Shraga Shoval, Mark Frenkel, Michael Nosonovsky","doi":"10.3390/e28101066","DOIUrl":"10.3390/e28101066","url":null,"abstract":"<p><p>We investigate the relationship between the graphical symmetry of symbols and their frequency of occurrence in culturally evolved symbolic systems and compare the observed tendencies with those found in physical structures. Capital letters of the English and Russian alphabets were classified according to their invariance under vertical reflection, horizontal reflection, and rotation through 180°. The graphical symmetry of numerical figures was analyzed across four numeral systems. Symmetry was examined for 1000 Japanese Kanji arranged into frequency-ranked groups. Spearman's and Kendall's rank-correlation tests revealed positive symmetry-frequency tendencies in both alphabets, although the associations did not reach conventional statistical significance. A strong and statistically significant positive association was obtained for the average symmetry of the numerical figures and their leading-digit frequencies predicted by Benford's law. The Japanese Kanji produced the strongest statistical result. Despite their different cultural origins and graphical organization, the investigated alphabets and numeral systems exhibited surprisingly similar symmetry-class entropies, suggesting a possible common balance between graphical regularity and symbol distinguishability. For comparison, seven-bond and eight-bond colloidal clusters demonstrated the opposite tendency, with asymmetric configurations occurring more frequently and dominating their symmetry-class distributions.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264435","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":"Elastic Ranking-Driven Time Series Feature Optimization for Aeroengine Remaining Useful Life Prediction.","authors":"Ruihao Xin, Sudan Bai, Jiankang Fan, Cong Gao, Xin Feng","doi":"10.3390/e28101067","DOIUrl":"10.3390/e28101067","url":null,"abstract":"<p><p>This paper proposes a hierarchical recurrent neural network framework that leverages a context-gated mechanism and the elastic sorting of time series indicators, addressing the strong dynamic coupling characteristics of aeroengine operational time series data. This framework addresses the coupling of multi-physical field signals and the interleaved cross-scale degradation characteristics of engine time series data. The framework achieves high-precision predictions of residual service life under complex operating conditions by deeply integrating multi-modal feature engineering with hierarchical time series modeling. An elastic ranking algorithm for time series indexes is designed to adaptively fuse multiple sensors' time domains, frequency domains, and nonlinear characteristics, thereby constructing a dynamic feature space sensitive to load conditions. This approach addresses the limitations of traditional fixed feature combinations in characterizing variable load conditions. Additionally, a multi-scale stack dilation convolution module is introduced to exponentially enlarge the receptive field, allowing for the capture of long-range dependencies in weakly degraded signals. A bidirectional time-frequency converter decomposes the signal into low-frequency trend components and high-frequency anomaly components, enabling coupling characterization through cross-scale feature interactive gating. Furthermore, context-gated cyclic units based on hierarchical cyclic networks are constructed to hierarchically integrate multi-modal features while dynamically adjusting time-dependent weights, thereby enhancing the model's causal reasoning ability for long sequence degradation patterns. In four typical experiments conducted on the C-MAPSS dataset, the ERCG-HRNN demonstrates significant improvements in RMSE and Score compared to existing methods.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 10","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13703398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149264457","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}