Entropy最新文献

{"title":"The Dynamical Evolution Parameter in Manifestly Covariant Quantum Gravity Theory.","authors":"Claudio Cremaschini","doi":"10.3390/e27060604","DOIUrl":"10.3390/e27060604","url":null,"abstract":"<p><p>A remarkable feature of manifestly covariant quantum gravity theory (CQG-theory) is represented by its unconstrained Hamiltonian structure expressed in evolution form. This permits the identification of the corresponding dynamical evolution parameter advancing the quantum-wave equation for the 4-scalar quantum wave function defined on an appropriate Hilbert space. In the framework of CQG-theory, such a temporal parameter is represented by a 4-scalar proper time <i>s</i> identifying a canonical variable with conjugate quantum operator. The observable character of the evolution parameter is also established through its correspondence with the quantum representation of the cosmological constant originating from non-linear Bohm quantum-vacuum interaction, which is shown to admit an intrinsic functional dependence on <i>s</i>. These conclusions overcome the conceptual limitations about the so-called \"problem of time\" mentioned in alternative approaches to quantum gravity available in the literature. Hence, the outcome permits one to promote CQG theory as a viable mathematical setting for the establishment of a theory of quantum gravity consistent with the logical and physical principles of both general relativity and canonical quantum mechanics.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495295","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":"Leveraging Neural ODEs for Population Pharmacokinetics of Dalbavancin in Sparse Clinical Data.","authors":"Tommaso Giacometti, Ettore Rocchi, Pier Giorgio Cojutti, Federico Magnani, Daniel Remondini, Federico Pea, Gastone Castellani","doi":"10.3390/e27060602","DOIUrl":"10.3390/e27060602","url":null,"abstract":"<p><p>This study investigates the use of Neural Ordinary Differential Equations (NODEs) as an alternative to traditional compartmental models and Nonlinear Mixed-Effects (NLME) models for drug concentration prediction in pharmacokinetics. Unlike standard models that rely on strong assumptions and often struggle with high-dimensional covariate relationships, NODEs offer a data-driven approach, learning differential equations directly from data while integrating covariates. To evaluate their performance, NODEs were applied to a real-world Dalbavancin pharmacokinetic dataset comprising 218 patients and compared against a two-compartment model and an NLME within a cross-validation framework, which ensures an evaluation of robustness. Given the challenge of limited data availability, a data augmentation strategy was employed to pre-train NODEs. Their predictive performance was assessed both with and without covariates, while model explainability was analyzed using Shapley additive explanations (SHAP) values. Results show that, in the absence of covariates, NODEs performed comparably to state-of-the-art NLME models. However, when covariates were incorporated, NODEs demonstrated superior predictive accuracy. SHAP analyses further revealed how NODEs leverage covariates in their predictions. These results establish NODEs as a promising alternative for pharmacokinetic modeling, particularly in capturing complex covariate interactions, even when dealing with sparse and small datasets, thus paving the way for improved drug concentration predictions and personalized treatment strategies in precision medicine.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495201","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":"Importance-Aware Resource Allocations for MIMO Semantic Communication.","authors":"Yue Cao, Youlong Wu, Lixiang Lian, Meixia Tao","doi":"10.3390/e27060605","DOIUrl":"10.3390/e27060605","url":null,"abstract":"<p><p>This study proposes a separate source-channel coding (SSCC) framework to address semantic communication challenges in MIMO systems, overcoming the limitations of joint source-channel coding (JSCC) in channel adaptation and model reusability. Traditional systems suffer from bit-level redundancy in 6G, while JSCC struggles with complex channel variations. Our solution decouples semantic processing from channel coding through a three-tier architecture: (1) Variational autoencoder (VAE)-based semantic encoder and decoder for source coding, (2) A communication-informed bottleneck attribution (CIBA) mechanism quantifying feature importance for learning tasks, and (3) An importance-aware resource allocation scheme aligning communication objectives with deep learning tasks. Systematic experiments validate CIBA's effectiveness in deriving importance scores that bridge learning tasks and communication optimization. Comparisons of feature perturbation schemes confirm the necessity of importance-aware resource allocation, with the proposed allocation strategy outperforming conventional methods in task performance metrics. The SSCC design enhances model reusability while maintaining adaptability to diverse MIMO configurations. By integrating interpretable AI with resource management, this work establishes a foundation for SSCC semantic communication systems in resource-constrained environments, prioritizing semantic fidelity and task efficacy over bit-level redundancy. The methodology highlights the critical role of importance awareness in optimizing both communication efficiency and learning task performance.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495280","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":"Research on Task Complexity Measurements in Human-Computer Interaction in Nuclear Power Plant DCS Systems Based on Emergency Operating Procedures.","authors":"Ensheng Pang, Licao Dai","doi":"10.3390/e27060600","DOIUrl":"10.3390/e27060600","url":null,"abstract":"<p><p>Within the scope of digital transformation in nuclear power plants (NPPs), task complexity in human-computer interaction (HCI) has become a critical factor affecting the safe and stable operation of NPPs. This study systematically reviews and analyzes existing complexity sources and assessment methods and suggests that complexity is primarily driven by core factors such as the quantity of, variety of, and relationships between elements. By innovatively introducing Halstead's <i>E</i> measure, this study constructs a quantitative model of dynamic task execution complexity (TEC), addressing the limitations of traditional entropy-based metrics in analyzing interactive processes. By combining entropy metrics and the <i>E</i> measure, a task complexity quantification framework is established, encompassing both the task execution and intrinsic dimensions. Specifically, Halstead's <i>E</i> measure focuses on analyzing operators and operands, defining interaction symbols between humans and interfaces to quantify task execution complexity (TEC). Entropy metrics, on the other hand, measure task logical complexity (TLC), task scale complexity (TSC), and task information complexity (TIC) based on the intrinsic structure and scale of tasks. Finally, the weighted Euclidean norm of these four factors determines the task complexity (TC) of each step. Taking the emergency operating procedures (EOP) for a small-break loss-of-coolant accident (SLOCA) in an NPP as an example, the entropy and <i>E</i> metrics are used to calculate the task complexity of each step, followed by experimental validation using NASA-TLX task load scores and step execution time for regression analysis. The results show that task complexity is significantly positively correlated with NASA-TLX subjective scores and task execution time, with the determination coefficients reaching 0.679 and 0.785, respectively. This indicates that the complexity metrics have high explanatory power, showing that the complexity quantification model is effective and has certain application value in improving human-computer interfaces and emergency procedures.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495350","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":"Sign-Entropy Regularization for Personalized Federated Learning.","authors":"Koffka Khan","doi":"10.3390/e27060601","DOIUrl":"10.3390/e27060601","url":null,"abstract":"<p><p>Personalized Federated Learning (PFL) seeks to train client-specific models across distributed data silos with heterogeneous distributions. We introduce <i>Sign-Entropy Regularization</i> (SER), a novel entropy-based regularization technique that penalizes excessive directional variability in client-local optimization. Motivated by Descartes' Rule of Signs, we hypothesize that frequent sign changes in gradient trajectories reflect complexity in the local loss landscape. By minimizing the entropy of gradient sign patterns during local updates, SER encourages smoother optimization paths, improves convergence stability, and enhances personalization. We formally define a differentiable sign-entropy objective over the gradient sign distribution and integrate it into standard federated optimization frameworks, including FedAvg and FedProx. The regularizer is computed efficiently and applied post hoc per local round. Extensive experiments on three benchmark datasets (FEMNIST, Shakespeare, and CIFAR-10) show that SER improves both average and worst-case client accuracy, reduces variance across clients, accelerates convergence, and smooths the local loss surface as measured by Hessian trace and spectral norm. We also present a sensitivity analysis of the regularization strength ρ and discuss the potential for client-adaptive variants. Comparative evaluations against state-of-the-art methods (e.g., Ditto, pFedMe, momentum-based variants, Entropy-SGD) highlight that SER introduces an orthogonal and scalable mechanism for personalization. Theoretically, we frame SER as an information-theoretic and geometric regularizer that stabilizes learning dynamics without requiring dual-model structures or communication modifications. This work opens avenues for trajectory-based regularization and hybrid entropy-guided optimization in federated and resource-constrained learning settings.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495355","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":"Intrinsic and Measured Information in Separable Quantum Processes.","authors":"David Gier, James P Crutchfield","doi":"10.3390/e27060599","DOIUrl":"10.3390/e27060599","url":null,"abstract":"<p><p>Stationary quantum information sources emit sequences of correlated qudits-that is, structured quantum stochastic processes. If an observer performs identical measurements on a qudit sequence, the outcomes are a realization of a classical stochastic process. We introduce quantum-information-theoretic properties for separable qudit sequences that serve as bounds on the classical information properties of subsequent measured processes. For sources driven by hidden Markov dynamics, we describe how an observer can temporarily or permanently synchronize to the source's internal state using specific positive operator-valued measures or adaptive measurement protocols. We introduce a method for approximating an information source with an independent and identically distributed, Markov, or larger memory model through tomographic reconstruction. We identify broad classes of separable processes based on their quantum information properties and the complexity of measurements required to synchronize to and accurately reconstruct them.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495197","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":"Dynamic-Step-Size Regulation in Pulse-Coupled Neural Networks.","authors":"Jiayi Geng, Fanqing Ji, Shouliang Li, Yulin Shen, Zhen Yang","doi":"10.3390/e27060597","DOIUrl":"10.3390/e27060597","url":null,"abstract":"<p><p>Pulse-coupled neural networks (PCNNs) are capable of segmenting digital images in a multistage unsupervised fashion; however, optimal output selection remains challenging. To address the above problem, this paper emphasizes the role of the step size, which influences the decreasing speed of the membrane potential and the dynamic threshold profoundly. A dynamic-step-size mechanism is proposed, utilizing trigonometric functions to adaptively control segmentation granularity, along with the supervised optimization of a single parameter ϕ via intersection over union (IoU) maximization, reducing tuning complexity. Thus, the number of groups of image segmentation becomes controllable and the model itself becomes more adaptive than ever for various scenarios. Experimental results further demonstrate the enhanced robustness under noise (92.1% Dice at σ=0.2), outperforming SPCNN and PCNN with IoU = 0.8863, Dice = 0.901, and 0.8684 s/image.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495261","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":"Permutation Entropy and Its Niche in Hydrology: A Review.","authors":"Dragutin T Mihailović","doi":"10.3390/e27060598","DOIUrl":"10.3390/e27060598","url":null,"abstract":"<p><p>One effective method for analyzing complexity involves applying information measures to time series derived from observational data. Permutation entropy (PE) is one such measure designed to quantify the degree of disorder or complexity within a time series by examining the order relations among its values. PE is distinguished by its simplicity, robustness, and exceptionally low computational cost, making it a benchmark tool for complexity analysis. This text reviews the advantages and limitations of PE while exploring its diverse applications in hydrology from 2002 to 2025. Specifically, it categorizes the uses of PE across various subfields, including runoff prediction, streamflow analysis, water level forecasting, assessment of hydrological changes, and evaluating the impact of infrastructure on hydrological systems. By leveraging PE's ability to capture the intricate dynamics of hydrological processes, researchers can enhance predictive models and improve our understanding of water-related phenomena.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495326","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":"Phase Coordinate Uncomputation in Quantum Recursive Fourier Sampling.","authors":"Christoffer Hindlycke, Niklas Johansson, Jan-Åke Larsson","doi":"10.3390/e27060596","DOIUrl":"10.3390/e27060596","url":null,"abstract":"<p><p>Recursive Fourier Sampling (RFS) was one of the earliest problems to demonstrate a quantum advantage, and is known to lie outside the Merlin-Arthur complexity class. This work contains a new description of quantum algorithms in phase space terminology, demonstrating its use in RFS, and how and why this gives a better understanding of the quantum advantage in RFS. Most importantly, describing the computational process of quantum computation in phase space terminology gives a much better understanding of why uncomputation is necessary when solving RFS: the advantage is present only when phase coordinate garbage is uncomputed. This is the underlying reason for the limitations of the quantum advantage.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12192199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495342","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":"On the Change of Measure for Brownian Processes.","authors":"Francis J Pinski","doi":"10.3390/e27060594","DOIUrl":"10.3390/e27060594","url":null,"abstract":"<p><p>Sometimes, limits can be singular, implying that they take on different values depending on the order of arithmetic operations. In other words, the limit map lacks commutativity. While all such limits are mathematically valid, only one can be the physical limit. The change of measure for Brownian processes illustrates this phenomenon. A substantial body of elegant mathematics centered around continuous-time Brownian processes has been embraced by the physics community to investigate the nonequilibrium and equilibrium thermodynamics of systems composed of atoms and molecules. In this paper, we derive the continuous-time limit of discrete-time Brownian dynamics, specifically focusing on the change of measure. We demonstrate that this result yields the physical limit that differs from the commonly used expression. Consequently, the concepts of \"the most probable path\", \"minimum thermodynamic action\", and \"the small-noise limit\" are unphysical mathematical artifacts.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 6","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144495319","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}