Entropy最新文献

{"title":"Entropic Phenomena in Spin-Ice Materials and Magnetic Monopole Excitations.","authors":"Andres Chappa, Ludi Miao","doi":"10.3390/e28091039","DOIUrl":"10.3390/e28091039","url":null,"abstract":"<p><p>Spin ice provides a canonical setting in which geometric frustration, local constraints, and extensive degeneracy generate nontrivial entropy and emergent quasiparticles. In this review, we examine entropic phenomena in spin-ice materials and related pyrochlore magnets, taking entropy as a unifying theme that connects established spin-ice thermodynamics with recent developments in entropy-driven phases, monopole confinement, and interface-engineered monopole matter. We first review the connection between proton disorder in water ice and the ice-rule manifold of pyrochlore spin ice, including the Pauling residual entropy and its calorimetric observation, and then contrast this classical limit with quantum spin ice and lithographically constructed artificial spin ice. We next discuss pyrochlore iridates, where competition between the intrinsic spin-ice interaction and the Ir-induced local exchange field produces two-in-two-out (2I2O), fragmented, and all-in-all-out (AIAO) states with distinct residual entropies, as well as finite-temperature entropy-enhanced phases near competing ground-state boundaries. We next review emergent magnetic monopoles in spin ice, including monopole creation, Dirac strings, magnetic Coulomb interactions, and key experimental signatures of magnetic charge, current, and noise. Building on this framework, we discuss theoretically proposed two-dimensional magnetic monopole gases at spin-ice/pyrochlore iridate interfaces, where boundary conditions stabilize a net magnetic charge and entropy controls the spatial confinement of the monopole gas. Finally, we consider proposed monopole-based devices, including a magnetic field-controlled monopole transistor and position-based monopole traps. By linking classical residual entropy to entropy-driven phase selection, quasiparticle confinement, and device concepts, this review highlights how entropy can evolve from a thermodynamic signature of frustration into a potential design parameter for emergent magnetic matter.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059290","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":"Cross-Frequency Dependence Overlap in the Chinese A-Share Dependence Network.","authors":"Jimao Mo, Jiaqi Zhang, Shijia Chen, Hua Zhang","doi":"10.3390/e28091038","DOIUrl":"10.3390/e28091038","url":null,"abstract":"<p><p>Multiresolution analysis is widely applied to equity markets on the assumption that different frequency bands capture distinct trading behaviors and information flows. Whether those bands reveal structurally distinct equity communities, or mostly re-express a shared dependence backbone, remains unresolved. We address this question in the Chinese A-share market using the maximal overlap discrete wavelet transform (MODWT) and a dual-channel affinity construction that processes positive and negative similarities separately before fusing them into a nonnegative matrix for normalized spectral clustering, over a grid of 15,360 configurations spanning wavelet bases, decomposition levels, and time windows. In the reference configuration (K=25), the adjusted Rand index (ARI) against the Shenwan first-level industry classification is 0.381, comparable to the strongest correlation-based baseline and with no singleton communities. Best-over-grid ARI declines monotonically from 0.381 at decomposition level 2 to 0.139 at level 6, in every time window and across the full configuration ensemble. Band-wise stock-pair similarity vectors show substantial rank agreement (mean Spearman ρ = 0.769), yet cross-band normalized mutual information averages 0.123 over 64 equal-frequency bins, and independently clustered bands give distinct partitions (mean pairwise ARI = 0.328). Rank agreement falls to 0.677 after market removal and to 0.486 after sequential market-and-industry removal. These results document measurable but incomplete cross-frequency dependence overlap, alongside declining industry alignment with increasing decomposition depth within the tested pipeline.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059269","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 Clinical Trial Design for Distributional Outcomes with Noncompliance.","authors":"Xin Guo, Zhi Yang, Siyuan Li, Weining Shen","doi":"10.3390/e28091040","DOIUrl":"10.3390/e28091040","url":null,"abstract":"<p><p>We consider sequential monitoring in early-phase clinical trial designs with patient noncompliance. Unlike most existing methods that focus on modeling a single endpoint, we consider the entire outcome distribution and propose a Bayesian causal effect estimation method based on the Wasserstein distance. Using a principal stratification framework and random forest to classify patient compliance statuses, we quantify a distributional causal-effect magnitude among Compliers by calculating the Wasserstein distance between the barycenters of their potential outcome distributions. The proposed method also adaptively determines early trial termination based on accumulated outcomes. Simulation results show favorable operating characteristics under the scenarios investigated, with the proposed analysis remaining close to the oracle benchmark as noncompliance increases.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059184","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":"Many-Objective Optimisation of Global Quantum Internet Topologies: A Compact Parametric Representation with an Analytical BDCZ Oracle.","authors":"Jesús Gil Ruiz, Diego Rubén Rodríguez Regadera, Rafael Muñoz Gil","doi":"10.3390/e28091041","DOIUrl":"10.3390/e28091041","url":null,"abstract":"<p><p>We address the design of global quantum internet topologies that jointly optimise four physical objectives: average end-to-end Werner-state fidelity, entanglement rate, latency and coverage, evaluated with an analytical BDCZ oracle whose path selection is exact under the stated Werner-composition model and whose satellite links respect a single-satellite visibility limit. The search space is heterogeneous (fibre, satellite, free-space optical), comprising 1002=4950 possible edges over 100 cities; direct per-edge categorical optimisation degenerates. We propose a mixed representation of ten physical design parameters plus one stochastic realisation index (eleven decision variables in total) decoding into a topology via <i>k</i>-nearest-neighbour rules. Seven many-objective algorithms (NSGA-II, NSGA-III, MOEA/D, SMS-EMOA, RVEA, SPEA2, AGE-MOEA-II) are compared at a matched budget over 30 seeds, scored against a shared reference front. NSGA-II attains the highest mean hypervolume (0.353±0.040) but is statistically indistinguishable from SPEA2 and AGE-MOEA-II (0.352, 0.347); after Holm correction across all 63 tests, this top group separates significantly from NSGA-III, SMS-EMOA, RVEA and MOEA/D, though NSGA-III and SMS-EMOA remain indistinguishable in HV/IGD (spacing unresolved). NSGA-II's lead is driven by 6 of its 30 seeds reaching a higher-HV region, a bimodality we report. An eight-variant decoder ablation (NSGA-III) shows fibre is necessary while satellite's contribution is not detectable, and one classical constructor (a Random Geometric Graph) remains non-dominated; none of the 561 front points connects all 100 cities under the satellite model. On four additional real-city instances (five algorithms), MOEA/D is last throughout; SMS-EMOA/RVEA ranks vary by instance, and NSGA-II/NSGA-III reverses, without significance, twice; the fibre transmittance law is cross-checked against SeQUeNCe (Spearman ρ=1.0). The pooled front is released as QI-Bench-Pareto-v1.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149058870","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":"Impact of Recording Conditions and Feature Fusion on Dementia Classification Using Spectral, Entropy, and Complexity EEG Features.","authors":"Kerimay Sari, Samaneh Kouchaki, Daniel Abasolo","doi":"10.3390/e28091034","DOIUrl":"10.3390/e28091034","url":null,"abstract":"<p><p>Resting-state electroencephalography (EEG) is a promising low-cost, non-invasive modality for supporting differential classification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy controls (HCs). However, it remains unclear whether the combination of different EEG feature families consistently improves classification and whether their utility varies with recording condition. We compared spectral, entropy-based, and complexity-based features, both individually and in combination, for AD-HC, AD-MCI, and MCI-HC classification under eyes-closed (EC) and eyes-open (EO) conditions using an age-matched paired subset of the CAUEEG data set. Classification was evaluated using nested leave-one-subject-out cross-validation with two-stage hierarchical LightGBM gain-based feature selection. Under the EC condition, the highest balanced accuracies (BAs) were obtained with spectral features for AD-HC (79.99%), spectral-entropy for AD-MCI (68.33%), and spectral-entropy-complexity for MCI-HC (63.15%). Under the EO condition, spectral features performed best for AD-HC (84.91%) and AD-MCI (66.90%), whereas entropy performed best for MCI-HC (70.14%). Feature fusion therefore showed task-dependent rather than consistent benefits. Although numerical EC-EO differences were observed, none remained significant after paired permutation testing. Overall, the results suggest that the relative usefulness of EEG feature families varies across diagnostic comparisons and recording conditions.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059365","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":"A Dual-Path Attention and Multi-Scale Fusion Network for Crop Disease and Pest Identification.","authors":"Hong Zhang, Fagen Song, Yongqi Yuan, Ge Jin, Qian Zhang","doi":"10.3390/e28091032","DOIUrl":"10.3390/e28091032","url":null,"abstract":"<p><p>Crop diseases and pests severely threaten global food security. While deep learning has shown promise in controlled settings, real-world field conditions-characterized by complex backgrounds, variable lesion scales, and high inter-class similarity-remain challenging. To overcome limitations in feature representation, scale adaptability, and model efficiency, we propose DPMFNet, a lightweight dual-path network integrating Spatial-Channel Dual-Attention (SCDA) and Multi-Scale Depthwise Separable Convolution (MDSC) modules. SCDA enhances critical regions via dynamic channel-spatial weighting with minimal overhead, while MDSC captures multi-scale contextual information through parallel dilated convolutions. Both are embedded into an improved residual block (AttMDSCBlock) to boost representational power while reducing parameters. A cross-attention mechanism fuses local details and global context from dual pathways, and a lightweight pyramid strategy adaptively integrates features across resolutions. Evaluated on PlantVillage and the AI Challenger 2018 dataset, DPMFNet achieves state-of-the-art accuracy among lightweight models, with only 14.24M parameters and 2.55G FLOPs. It demonstrates superior robustness in complex agricultural environments, balancing performance, efficiency, and deployability.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059303","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":"Ideal Heat Engine Cycles at Maximal Efficiency-The Ideal Gas and Beyond.","authors":"Gregory Behrendt, Sebastian Deffner","doi":"10.3390/e28091033","DOIUrl":"10.3390/e28091033","url":null,"abstract":"<p><p>Given a particular heat engine cycle, what is the optimal working medium that results in the highest efficiency? While one might jump to the conclusion that it must surely be the ideal gas, the situation is actually more intricate. Starting with a general Helmholtz potential that depends polynomially on molar volume and temperature, we derive exact expressions for the ideal Stirling, Otto, and Brayton cycles. We find that for the thermodynamic systems described by our ansatz for the Helmholtz potential, the maximal efficiency is achieved if the working medium is described by a fundamental relation linear in temperature. This includes the ideal gas, but also classical harmonic oscillators and phenomenological models of the rubber band.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059397","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":"Entropy-Driven Volatility Prediction for ETF Quantitative Investment in the Chinese Stock Market: A Machine Learning Framework for Barbell Strategy Optimization.","authors":"Keyue Yan, Zihuan Yue, Qiqiao He, Ying Li","doi":"10.3390/e28091035","DOIUrl":"10.3390/e28091035","url":null,"abstract":"<p><p>Volatility is a core determinant of risk management and return optimization in financial investment. We develop an integrated stock-volatility prediction framework that couples multi-dimensional entropy indicators with machine learning models and links the resulting forecasts to a dynamic Barbell Strategy. The strategy controls drawdowns while retaining upside and remains feasible for individual investors. Using data for the Chinese CSI 300, CSI 500, and CSI 1000 index ETFs and a government bond ETF, we construct predictive features and estimate Yang-Zhang Volatility. The framework incorporates four entropy indicators-Shannon Entropy, Fuzzy Entropy, Permutation Entropy, and Dispersion Entropy-and evaluates model performance under 10-day, 15-day, and 20-day prediction and rebalancing frequencies. The empirical results reveal that the volatility forecasting model for the CSI 1000 has the highest R Squared. In practical trading applications, the Random Forest achieves the optimal risk-adjusted returns, and the 15-day and 20-day portfolio frequencies realize a better trade-off between return and risk control.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13606006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059352","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":"Explainable Fuzzy Learner Modelling and Learning Analytics for Human-Centered Teacher Decision Support: A Vocational Education Case Study.","authors":"Eleni Papachristou, Christos Troussas, Akrivi Krouska, Christos Papakostas, Cleo Sgouropoulou","doi":"10.3390/e28091036","DOIUrl":"10.3390/e28091036","url":null,"abstract":"<p><p>Artificial Intelligence (AI)-based educational systems increasingly support personalised learning, adaptive feedback, and learning analytics. However, these capabilities are often addressed separately, with comparatively less attention paid to their integration into interpretable, teacher-facing decision-support frameworks. This study presents the e-Teacher Assistant, a human-centred educational framework that integrates xAPI-style Learning Record Store (LRS) analytics, dynamic learner modelling, Sugeno-type fuzzy inference using interpretable IF-THEN rules, adaptive learning support, and a dual Student Model-Teacher Model architecture. The framework was evaluated during a three-month authentic deployment in a vocational education Computer Networks course involving 117 learners and four educators. Learner evaluation combined a structured questionnaire, open-ended responses, and LRS-based behavioural analytics, while educators evaluated the Teacher Model through a questionnaire and qualitative responses. Learners reported predominantly positive perceptions of the system, including ease of use (92.3%), usefulness of feedback (95.7%), helpful interaction with the AI Assistant (94.9%), support for independent learning (88.0%), and support for educators through learning analytics (87.2-88.0%). Fairness and objectivity were also evaluated positively by 86.3% of learners. All four educators evaluated the Teacher Model positively, and all strongly agreed on its overall usefulness and its support for progress monitoring. Prior familiarity with digital learning tools was significantly associated with evaluations across all seven questionnaire dimensions. LRS analytics further documented learner engagement, repeated assessment activity, and contextual AI use; AI-assisted examination support was recorded in 64.42% of learner-chapter records. Overall, the findings provide initial empirical support for the feasibility of integrating interpretable learner modelling, adaptive AI-assisted support, LRS-based learning analytics, and teacher-facing decision support in an authentic vocational education setting while preserving educator oversight and pedagogical responsibility.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059398","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":"Multi-Scale Residual Attention Network for Chinese Speech Recognition Through Collaborative Design.","authors":"Yuying Li, Hongjie Wan, Akash Sutradhar, Jianing Qin","doi":"10.3390/e28091037","DOIUrl":"10.3390/e28091037","url":null,"abstract":"<p><p>In recent years, the application of multi-scale residuals and attention mechanisms in automatic speech recognition has made some progress. However, existing speech recognition systems still have critical limitations. Many existing systems fail to adequately capture multi-scale speech features, struggling with representation gaps and often relying on inefficient serial attention mechanisms. Additionally, lightweight models, while efficient, often isolate information within grouped architectures, limiting the global feature interaction necessary for speech recognition. To address these persistent challenges, we introduce MSRDA-GSCA, an architecture that synergistically integrates a Multi-Scale Residual Deep Convolutional Attention (MSRDA) network with a Group Shuffled Co-Attention (GSCA) mechanism. MSRDA uses parallel convolutions with varied kernels and dilations, plus adaptive residual connections, to capture diverse temporal dependencies and align heterogeneous features. Its dual-path attention recalibrates channel and spatial information with element-wise max fusion. GSCA employs sequential position-wise channel and spatial attention with intermediate channel shuffling, enabling cross-group interaction and enhancing global feature synergy. Experiments on THCHS-30 and ST-CMDS datasets show that the proposed model outperforms existing methods in Character Error Rate (CER) and Word Error Rate (WER). Ablation studies confirm the complementarity of MSRDA and GSCA. With only 4.65 M parameters, MSRDA-GSCA achieves high recognition accuracy while maintaining low computational complexity for efficient deployment.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"28 9","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13605607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059207","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}