Entropy最新文献

{"title":"Simulating Public Opinion: Comparing Distributional and Individual-Level Predictions from LLMs and Random Forests.","authors":"Fernando Miranda, Pedro Paulo Balbi","doi":"10.3390/e27090923","DOIUrl":"10.3390/e27090923","url":null,"abstract":"<p><p>Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as data-driven, high-fidelity agents capable of simulating individual opinions under varying informational conditions. Conditioning LLMs on real survey data from the 2020 American National Election Studies (ANES), we investigate their ability to predict individual-level responses across a spectrum of political and social issues in a zero-shot setting, without any training on the survey outcomes. Using Jensen-Shannon distance to quantify divergence in opinion distributions and F1-score to measure predictive accuracy, we compare LLM-generated simulations to those produced by a supervised Random Forest model. While performance at the individual level is comparable, LLMs consistently produce aggregate opinion distributions closer to the empirical ground truth. These findings suggest that LLMs offer a promising new method for simulating complex opinion dynamics and modeling the probabilistic structure of belief systems in computational social science.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174367","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 Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.","authors":"Guo Wei, Yan Liu","doi":"10.3390/e27090921","DOIUrl":"10.3390/e27090921","url":null,"abstract":"<p><p>Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches-primarily reliant on tetranucleotide frequency (TNF) and abundance profiles-often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets-including wastewater and soil microbiomes-demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174161","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-Fused Enhanced Symplectic Geometric Mode Decomposition for Hybrid Power Quality Disturbance Recognition.","authors":"Chencheng He, Wenbo Wang, Xuezhuang E, Hao Yuan, Yuyi Lu","doi":"10.3390/e27090920","DOIUrl":"10.3390/e27090920","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model's recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174414","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 Novel Co-Designed Multi-Domain Entropy and Its Dynamic Synapse Classification Approach for EEG Seizure Detection.","authors":"Guanyuan Feng, Jiawen Li, Yicheng Zhong, Shuang Zhang, Xin Liu, Mang I Vai, Kaihan Lin, Xianxian Zeng, Jun Yuan, Rongjun Chen","doi":"10.3390/e27090919","DOIUrl":"10.3390/e27090919","url":null,"abstract":"<p><p>Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model's ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174108","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 Hungry Daemon: Does an Energy-Harvesting Active Particle Have to Obey the Second Law of Thermodynamics?","authors":"Simon Bienewald, Diego M Fieguth, James R Anglin","doi":"10.3390/e27090918","DOIUrl":"10.3390/e27090918","url":null,"abstract":"<p><p>Thought experiments like Maxwell's Demon or the Smoluchowski-Feynman Ratchet can help in pursuing the microscopic origin of the Second Law of Thermodynamics. Here we present a more sophisticated mechanical system than a ratchet, consisting of a Hamiltonian (non-Brownian) active particle which can harvest energy from an environment which may be in thermal equilibrium at a single temperature. We show that while a phenomenological description would seem to allow the system to operate as a Perpetual Motion Machine of the Second Kind, a full mechanical analysis confirms that this is impossible, and that perpetual energy harvesting within a mechanical system can only occur if the environment has an energetic population inversion similar to a lasing medium.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174296","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":"Modeling the Evolution of Dynamic Triadic Closure Under Superlinear Growth and Node Aging in Citation Networks.","authors":"Li Liang, Hao Liu, Shi-Cai Gong","doi":"10.3390/e27090915","DOIUrl":"10.3390/e27090915","url":null,"abstract":"<p><p>Citation networks are fundamental for analyzing the mechanisms and patterns of knowledge creation and dissemination. While most studies focus on pairwise attachment between papers, they often overlook compound relational structures, such as co-citation. Combining two key empirical features, superlinear node inflow and the temporal decay of node influence, we propose the Triangular Evolutionary Model of Superlinear Growth and Aging (TEM-SGA). The fitting results demonstrate that the TEM-SGA reproduces key structural properties of real citation networks, including degree distributions, generalized degree distributions, and average clustering coefficients. Further structural analyses reveal that the impact of aging varies with structural scale and depends on the interplay between aging and growth, one manifestation of which is that, as growth accelerates, it increasingly offsets aging-related disruptions. This motivates a degenerate model, the Triangular Evolutionary Model of Superlinear Growth (TEM-SG), which excludes aging. A theoretical analysis shows that its degree and generalized degree distributions follow a power law. By modeling interactions among triadic closure, dynamic expansion, and aging, this study offers insights into citation network evolution and strengthens its theoretical foundation.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174390","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":"Constructing Hetero-Microstructures in Additively Manufactured High-Performance High-Entropy Alloys.","authors":"Yuanshu Zhao, Zhibin Wu, Yongkun Mu, Yuefei Jia, Yandong Jia, Gang Wang","doi":"10.3390/e27090917","DOIUrl":"10.3390/e27090917","url":null,"abstract":"<p><p>High-entropy alloys (HEAs) have shown great promise for applications in extreme service environments due to their exceptional mechanical properties and thermal stability. However, traditional alloy design often struggles to balance multiple properties such as strength and ductility. Constructing heterogeneous microstructures has emerged as an effective strategy to overcome this challenge. With the rapid advancement of additive manufacturing (AM) technologies, their unique ability to fabricate complex, spatially controlled, and non-equilibrium microstructures offers unprecedented opportunities for tailoring heterostructures in HEAs with high precision. This review highlights recent progress in utilizing AM to engineer heterogeneous microstructures in high-performance HEAs. It systematically examines the multiscale heterogeneities induced by the thermal cycling effects inherent to AM techniques such as selective laser melting (SLM) and electron beam melting (EBM). The review further discusses the critical role of these heterostructures in enhancing the synergy between strength and ductility, as well as improving work-hardening behavior. AM enables the design-driven fabrication of tailored microstructures, signaling a shift from traditional \"performance-driven\" alloy design paradigms toward a new model centered on \"microstructural control\". In summary, additive manufacturing provides an ideal platform for constructing heterogeneous HEAs and holds significant promise for advancing high-performance alloy systems. Its integration into alloy design represents both a valuable theoretical framework and a practical pathway for developing next-generation structural materials with multiple performance attributes.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174202","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":"Material Removal Rate Enhancement Induced by Electrochemical Discharge Machining for Refractory High-Entropy Alloys Compared with EDM.","authors":"Bolin Dong, Zirui Yao, Chen Qi, Xiaokang Yue, Zufang Zhang, Shunhua Chen","doi":"10.3390/e27090912","DOIUrl":"10.3390/e27090912","url":null,"abstract":"<p><p>Refractory high-entropy alloys (RHEAs) are categorized as difficult-to-machine materials due to their excellent mechanical properties. Electrical discharge machining (EDM) is a special processing method for RHEAs, which faces challenges such as low machining efficiency. In this work, electrochemical discharge machining (ECDM) was proposed for (TiVZrTaW)_99.5N_0.5 and (TiVZrTa)W₅ (at. %, denoted as W20N0.5 and W5, respectively) RHEAs, and their machining performances were investigated and compared with EDM. At a peak current of 25 A, the material removal rate (<i>MRR</i>) using ECDM is more than twice that of EDM for W20N0.5 (reaching to 1.24 mm³/min) and 1.5 times higher than that for W5. Both W20N0.5 and W5 RHEAs exhibited higher <i>MRR</i> in ECDM based on the analyses of the influence of top diameter, bottom diameter, machining depth, and surface roughness (<i>R</i>a). The process and mechanisms of material removal were examined through the microstructural morphology and elemental distribution analyses. This work proposed a more effective route for machining RHEAs by ECDM compared to the conventional EDM.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174454","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":"Uncovering Structure-Rating Associations in Animated Film Character Networks.","authors":"Jue Zeng, Yiwen Tang, Xueming Liu","doi":"10.3390/e27090914","DOIUrl":"10.3390/e27090914","url":null,"abstract":"<p><p>The narrative structure of animated films plays a critical role in shaping audience perception, yet quantitative investigations into how character interaction networks influence film ratings remain limited. To address this gap, we apply complex network theory to analyze 82 animated films, extracting character networks from narrative interactions and examining key topological features-including centrality heterogeneity, protagonist relative centrality, network density, clustering coefficient, average shortest path length, and semantic diversity of relationships. Our findings demonstrate that higher-rated films are characterized by greater disparities in character centrality, lower network density and efficiency, longer average shortest path lengths, and richer semantic diversity. These structural patterns suggest that loosely connected yet hierarchically organized character networks enhance narrative complexity and audience engagement. The proposed framework offers a quantitative, data-driven approach to narrative design and provides a theoretical foundation for analyzing storytelling structures across diverse media, including novels, television series, and comics.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12469051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174306","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 Criterion for Distinguishing Temporally Different Dynamical Systems.","authors":"Evgeny Kagan","doi":"10.3390/e27090916","DOIUrl":"10.3390/e27090916","url":null,"abstract":"<p><p>The paper presents a method for distinguishing dynamical systems with respect to their behavior. The suggested criterion is interpreted as internal time of the ergodic dynamical system, which is a time generated by the system and differs from the external global or reference time. The paper includes a formal definition of the internal time of dynamical system in the form of the entropy ratio, considers its basic properties and gives examples of analysis of dynamical systems.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173594","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}