Entropy

11 pages, 703 KB

Open AccessArticle

Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm

by Salim Lahmiri, Chakib Tadj and Christian Gargour

Entropy 2025, 27(11), 1109; https://doi.org/10.3390/e27111109 (registering DOI) - 27 Oct 2025

Abstract

Voice analysis and classification for biomedical diagnosis purpose is receiving a growing attention to assist physicians in the decision-making process in clinical milieu. In this study, we develop and test deep feedforward neural networks (DFFNN) to distinguish between healthy and unhealthy newborns. The [...] Read more.

Voice analysis and classification for biomedical diagnosis purpose is receiving a growing attention to assist physicians in the decision-making process in clinical milieu. In this study, we develop and test deep feedforward neural networks (DFFNN) to distinguish between healthy and unhealthy newborns. The DFFNN are trained with acoustic features measured from newborn cries, including auditory-inspired amplitude modulation (AAM), Mel Frequency Cepstral Coefficients (MFCC), and prosody. The configuration of the DFFNN is optimized by using Bayesian optimization (BO) and random search (RS) algorithm. Under both optimization techniques, the experimental results show that the DFFNN yielded to the highest classification rate when trained with all acoustic features. Specifically, the DFFNN-BO and DFFNN-RS achieved 87.80% ± 0.23 and 86.12% ± 0.33 accuracy, respectively, under ten-fold cross-validation protocol. Both DFFNN-BO and DFFNN-RS outperformed existing approaches tested on the same database. Full article

(This article belongs to the Section Signal and Data Analysis)

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38 pages, 1328 KB

Open AccessArticle

A Systematic Approach to Exergy Efficiency of Steady-Flow Systems

by Yunus A. Çengel and Mehmet Kanoğlu

Entropy 2025, 27(11), 1108; https://doi.org/10.3390/e27111108 (registering DOI) - 26 Oct 2025

Abstract

Exergy efficiency is a measure of thermodynamic perfection. A device that operates reversibly has an exergy efficiency of 100 percent and is said to be thermodynamically perfect. A reversible process involves zero entropy generation and thus zero exergy destruction since X_destroyed = [...] Read more.

Exergy efficiency is a measure of thermodynamic perfection. A device that operates reversibly has an exergy efficiency of 100 percent and is said to be thermodynamically perfect. A reversible process involves zero entropy generation and thus zero exergy destruction since X_destroyed = T₀S_gen. Exergy efficiency is generally defined as the ratio of exergy output to exergy input η_ex = X_output/X_input = 1 − (X_destroyed+X_loss)/X_input or the ratio of exergy recovered to exergy expended η_ex = X_recovered/X_expended = 1 − X_destroyed/X_expended. In this paper, exergy efficiency relations are obtained first for a general steady-flow system using both approaches. Then, explicit general relations are obtained for common steady-flow devices, such as turbines, compressors, pumps, nozzles, diffusers, valves and heat exchangers, as well as heat engines, refrigerators, and heat pumps. For power and refrigeration cycles, five different forms of exergy efficiency relations are developed, and their equivalence is demonstrated. With the unified approach presented here and the insights provided, the controversy and confusion associated with different exergy efficiency definitions are largely alleviated. Full article

(This article belongs to the Section Thermodynamics)

16 pages, 334 KB

Open AccessArticle

An Efficient and Secure Semi-Quantum Secret Sharing Scheme Based on W State Sharing of Specific Bits

by Kai Xing, Rongbo Lu, Sihai Liu and Lu Lan

Entropy 2025, 27(11), 1107; https://doi.org/10.3390/e27111107 (registering DOI) - 26 Oct 2025

Abstract

This paper presents a semi-quantum secret sharing (SQSS) protocol based on three-particle W states, designed for efficient and secure secret sharing in quantum-resource-constrained scenarios. In the protocol, a fully quantum-capable sender encodes binary secrets using W, while receivers with limited quantum capabilities [...] Read more.

This paper presents a semi-quantum secret sharing (SQSS) protocol based on three-particle W states, designed for efficient and secure secret sharing in quantum-resource-constrained scenarios. In the protocol, a fully quantum-capable sender encodes binary secrets using W, while receivers with limited quantum capabilities reconstruct the secret through collaborative Z basis measurements and classical communication, ensuring no single participant can obtain the complete information independently. The protocol employs a four-state decoy photon technique (

{| 0 ⟩, | 1 ⟩, | + ⟩, | - ⟩}

) and position randomization, combined with photon number splitting (PNS) and wavelength filtering (WF) technologies, to resist intercept–resend, entanglement–measurement, and double controlled-NOT(CNOT) attacks. Theoretical analysis shows that the detection probability of intercept–resend attacks increases exponentially with the number of decoy photons (approaching 1). For entanglement–measurement attacks, any illegal operation by an attacker introduces detectable quantum state disturbances. Double CNOT attacks are rendered ineffective by the untraceability of particle positions and mixed-basis strategies. Leveraging the robust entanglement of W states, the protocol proves that the mutual information between secret bits and single-participant measurement results is strictly zero, ensuring lossless reconstruction only through authorized collaboration. Full article

(This article belongs to the Special Issue Quantum Information Security)

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15 pages, 1252 KB

Open AccessArticle

Information–Entropy Analysis of Stellar Evolutionary Stages with Application to FS CMa Objects

by Zeinulla Zhanabaev, Aigerim Akniyazova and Yeskendyr Ashimov

Entropy 2025, 27(11), 1106; https://doi.org/10.3390/e27111106 (registering DOI) - 26 Oct 2025

Abstract

Theoretical foundations are presented for the application of information–entropy methods from statistical physics to the determination of stellar evolutionary stages. A balance equation involving normalized conditional information and entropy is proposed. The conditional information is defined as the difference between the entropy of [...] Read more.

Theoretical foundations are presented for the application of information–entropy methods from statistical physics to the determination of stellar evolutionary stages. A balance equation involving normalized conditional information and entropy is proposed. The conditional information is defined as the difference between the entropy of the phase space and the conditional probability entropy. A correspondence is demonstrated between theoretical predictions and observational data from stellar emission spectra with respect to their evolutionary classification. The proposed methodology is further applied to the analysis of complex FS CMa-type objects, which exhibit dusty and gaseous structures with components at different evolutionary stages. In this context, the conditional information derived from asymmetric spectral lines is shown to be consistent with the theoretical criteria for the evolutionary status of single, binary, and unclassified stars. Full article

(This article belongs to the Section Astrophysics, Cosmology, and Black Holes)

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22 pages, 30853 KB

Open AccessArticle

Morphology, Polarization Patterns, Compression, and Entropy Production in Phase-Separating Active Dumbbell Systems

by Lucio Mauro Carenza, Claudio Basilio Caporusso, Pasquale Digregorio, Antonio Suma, Giuseppe Gonnella and Massimiliano Semeraro

Entropy 2025, 27(11), 1105; https://doi.org/10.3390/e27111105 (registering DOI) - 25 Oct 2025

Abstract

Polar patterns and topological defects are ubiquitous in active matter. In this paper, we study a paradigmatic polar active dumbbell system through numerical simulations, to clarify how polar patterns and defects emerge and shape evolution. We focus on the interplay between these patterns [...] Read more.

Polar patterns and topological defects are ubiquitous in active matter. In this paper, we study a paradigmatic polar active dumbbell system through numerical simulations, to clarify how polar patterns and defects emerge and shape evolution. We focus on the interplay between these patterns and morphology, domain growth, irreversibility, and compressibility, tuned by dumbbell rigidity and interaction strength. Our results show that, when separated through MIPS, dumbbells with softer interactions can slide one relative to each other and compress more easily, producing blurred hexatic patterns, polarization patterns extended across entire hexatically varied domains, and stronger compression effects. Analysis of isolated domains reveals the consistent presence of inward-pointing topological defects that drive cluster compression and generate non-trivial density profiles, whose magnitude and extension are ruled by the rigidity of the pairwise potential. Investigation of entropy production reveals instead that clusters hosting an aster/spiral defect are characterized by a flat/increasing entropy profile mirroring the underlying polarization structure, thus suggesting an alternative avenue to distinguish topological defects on thermodynamical grounds. Overall, our study highlights how interaction strength and defect–compression interplay affect cluster evolution in particle-based active models, and also provides connections with recent studies of continuum polar active field models. Full article

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19 pages, 321 KB

Open AccessArticle

Entropy Production and Irreversibility in the Linearized Stochastic Amari Neural Model

by Dario Lucente, Giacomo Gradenigo and Luca Salasnich

Entropy 2025, 27(11), 1104; https://doi.org/10.3390/e27111104 (registering DOI) - 25 Oct 2025

Abstract

One among the most intriguing results coming from the application of statistical mechanics to the study of the brain is the understanding that it, as a dynamical system, is inherently out of equilibrium. In the realm of non-equilibrium statistical mechanics and stochastic processes, [...] Read more.

One among the most intriguing results coming from the application of statistical mechanics to the study of the brain is the understanding that it, as a dynamical system, is inherently out of equilibrium. In the realm of non-equilibrium statistical mechanics and stochastic processes, the standard observable computed to determine whether a system is at equilibrium or not is the entropy produced along the dynamics. For this reason, we present here a detailed calculation of the entropy production in the Amari model, a coarse-grained model of the brain neural network, consisting of an integro-differential equation for the neural activity field, when stochasticity is added to the original dynamics. Since the way to add stochasticity is always to some extent arbitrary, particularly for coarse-grained models, there is no general prescription to do so. We precisely investigate the interplay between noise properties and the original model features, discussing in which cases the stationary state is in thermal equilibrium and which cases it is out of equilibrium, providing explicit and simple formulae. Following the derivation for the particular case considered, we also show how the entropy production rate is related to the variation in time of the Shannon entropy of the system. Full article

(This article belongs to the Section Non-equilibrium Phenomena)

39 pages, 1789 KB

Open AccessArticle

Higher-Order Correlations Between Thermodynamic Fluctuations in Compressible Aerodynamic Turbulence

by Georges A. Gerolymos and Isabelle Vallet

Entropy 2025, 27(11), 1103; https://doi.org/10.3390/e27111103 (registering DOI) - 25 Oct 2025

Abstract

This paper studies the exact and approximate relations between fluctuations in thermodynamic variables (pressure, density and temperature) that are imposed by the dilute-gas (

Z = 1

) equation-of-state (0.80EoS), which is a satisfactory approximation of air thermodynamics for a wide range of [...] Read more.

This paper studies the exact and approximate relations between fluctuations in thermodynamic variables (pressure, density and temperature) that are imposed by the dilute-gas (

Z = 1

) equation-of-state (0.80EoS), which is a satisfactory approximation of air thermodynamics for a wide range of pressures and temperatures. It focuses on triple- and higher-order correlations, extending previous studies that concentrated on second-order moments, with emphasis on the mathematical relations, which are generally valid independently of the particular flow configuration. Exact equations are developed both involving only single-variable moments and relating the correlations between variables. These contain nonlinear terms generated by the density-temperature fluctuation product in the fluctuating 0.80EoS. The importance of the nonlinear terms in the 6 exact equations between the 10 third-order moments is assessed using 0.80DNS (direct numerical simulation) data for compressible turbulent plane channel (0.80TPC) flows and analyzed using general statistical inequalities involving third-order and fourth-order moments. The corresponding linearized system between third-order moments is studied to determine approximate relations and 4-tuples of linearly independent moments. These mathematical tools are then used to analyze 0.80TPC flow 0.80DNS data on the triple correlations between the thermodynamic variables. Full article

(This article belongs to the Section Thermodynamics)

12 pages, 2253 KB

Open AccessArticle

Enhancing Migraine Trigger Surprisal Predictions: A Bayesian Approach to Establishing Prospective Expectations

by Dana P. Turner, Emily Caplis, Twinkle Patel and Timothy T. Houle

Entropy 2025, 27(11), 1102; https://doi.org/10.3390/e27111102 (registering DOI) - 25 Oct 2025

Abstract

Prior work has demonstrated that higher surprisal, a measure quantifying the unexpectedness of a trigger exposure, predicts headache onset over 12 to 24 h. However, these analyses relied on retrospective expectations of trigger exposure formed after extended data collection. To operationalize surprisal prospectively, [...] Read more.

Prior work has demonstrated that higher surprisal, a measure quantifying the unexpectedness of a trigger exposure, predicts headache onset over 12 to 24 h. However, these analyses relied on retrospective expectations of trigger exposure formed after extended data collection. To operationalize surprisal prospectively, Bayesian methods could update expectations dynamically over time. The objective of this study was to extend the application of surprisal theory for predicting migraine attack risk by developing methods to estimate trigger variable likelihood in real time, under conditions of limited personal observation. In a prospective daily diary study of individuals with migraine (N = 104), data were collected over 28 days, including stress, sleep, and exercise exposures. Bayesian models were applied to estimate daily expectations for each variable under uninformative and empirical priors derived from the sample. Stress was modeled using a hurdle-Gamma distribution, sleep using discrete outcomes from a Normal distribution, and exercise using a Bernoulli distribution. Surprisal was calculated based on the predictive distribution at each time point and compared to static empirical surprisal values obtained after full data collection. Dynamic Bayesian surprisal values systematically differed from retrospective empirical estimates, particularly early in the observation period. Divergence was larger and more variable under uninformative priors but attenuated over time. Empirically informed priors produced more stable, lower-bias surprisal trajectories. Substantial individual variability was observed across exposure types, especially for exercise behavior. Prospective surprisal modeling is feasible but highly sensitive to prior specification, especially in sparse data contexts (e.g., a binary exposure). Incorporating empirical or individually informed priors may improve early model calibration, though individual learning remains essential. These methods offer a foundation for real-time headache forecasting and dynamic modeling of brain–environment interactions. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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23 pages, 815 KB

Open AccessArticle

A New Lower Bound for Noisy Permutation Channels via Divergence Packing

by Lugaoze Feng, Guocheng Lv, Xunan Li and Ye Jin

Entropy 2025, 27(11), 1101; https://doi.org/10.3390/e27111101 (registering DOI) - 25 Oct 2025

Abstract

Noisy permutation channels are applied in modeling biological storage systems and communication networks. For noisy permutation channels with strictly positive and full-rank square matrices, new achievability bounds are given in this paper, which are tighter than existing bounds. To derive this bound, we [...] Read more.

Noisy permutation channels are applied in modeling biological storage systems and communication networks. For noisy permutation channels with strictly positive and full-rank square matrices, new achievability bounds are given in this paper, which are tighter than existing bounds. To derive this bound, we use the

ϵ

-packing with Kullback–Leibler divergence as a distance and introduce a novel way to illustrate the overlapping relationship of error events. This new bound shows analytically that for such a matrix W, the logarithm of the achievable code size with a given block n and error probability

ϵ

is closely approximated by

ℓ log (\frac{\sqrt{n}}{- Φ^{- 1} (ϵ / G)}) + log V (W)

, where

ℓ = rank (W) - 1

,

G = 2 (\binom{ℓ + 1}{2})

, and

V (W)

is a characteristic of the channel referred to as channel volume ratio. Our numerical results show that the new achievability bound significantly improves the lower bound of channel coding. Additionally, the Gaussian approximation can replace the complex computations of the new achievability bound over a wide range of relevant parameters. Full article

(This article belongs to the Special Issue Next-Generation Channel Coding: Theory and Applications)

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19 pages, 487 KB

Open AccessArticle

Trust-Aware Causal Consistency Routing for Quantum Key Distribution Networks Against Malicious Nodes

by Yi Luo and Qiong Li

Entropy 2025, 27(11), 1100; https://doi.org/10.3390/e27111100 (registering DOI) - 24 Oct 2025

Abstract

Quantum key distribution (QKD) networks promise information-theoretic security for multiple nodes by leveraging the fundamental laws of quantum mechanics. In practice, QKD networks require dedicated routing protocols to coordinate secure key distribution among distributed nodes. However, most existing routing protocols operate under the [...] Read more.

Quantum key distribution (QKD) networks promise information-theoretic security for multiple nodes by leveraging the fundamental laws of quantum mechanics. In practice, QKD networks require dedicated routing protocols to coordinate secure key distribution among distributed nodes. However, most existing routing protocols operate under the assumption that all relay nodes are honest and fully trustworthy, an assumption that may not hold in realistic scenarios. Malicious nodes may tamper with routing updates, causing inconsistent key-state views or divergent routing plans across the network. Such inconsistencies increase routing failure rates and lead to severe wastage of valuable secret keys. To address these challenges, we propose a distributed routing framework that combines two key components: (i) Causal Consistency Key-State Update, which prevents malicious nodes from propagating inconsistent key states and routing plans; and (ii) Trust-Aware Multi-path Flow Optimization, which incorporates trust metrics derived from discrepancies in reported states into the path-selection objective, penalizing suspicious links and filtering fabricated demands. Across 50-node topologies with up to 30% malicious relays and under all three attack modes, our protocol sustains a high demand completion ratio (DCR) (mean

0.90

, range

0.81

–

0.98

) while keeping key utilization low (

16.6

keys per demand), decisively outperforming the baselines—Multi-Path Planned (DCR

0.48

,

30.8

keys per demand) and OSPF (DCR

\leq 0.12

, 296 keys per demand; max 1601). These results highlight that our framework balances reliability and efficiency, providing a practical and resilient foundation for secure QKD networking in adversarial environments. Full article

(This article belongs to the Section Quantum Information)

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23 pages, 2406 KB

Open AccessArticle

Dynamic Hyperbolic Tangent PSO-Optimized VMD for Pressure Signal Denoising and Prediction in Water Supply Networks

by Yujie Shang and Zheng Zhang

Entropy 2025, 27(11), 1099; https://doi.org/10.3390/e27111099 (registering DOI) - 24 Oct 2025

Abstract

Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on empirical parameter selection or optimize only a subset of parameters, lacking [...] Read more.

Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on empirical parameter selection or optimize only a subset of parameters, lacking a robust mechanism for identifying noise-dominant components post-decomposition. To address these issues, this paper proposed a novel denoising framework termed Dynamic Hyperbolic Tangent PSO-optimized VMD (DHTPSO-VMD). The DHTPSO algorithm adaptively adjusts inertia weights and cognitive/social learning factors during iteration, mitigating the local optima convergence typical of traditional PSO and enabling automated VMD parameter selection. Furthermore, a dual-criteria screening strategy based on Variance Contribution Rate (VCR) and Correlation Coefficient Metric (CCM) is employed to accurately identify and eliminate noise-related Intrinsic Mode Functions (IMFs). Validation using pressure data from District A in Zhejiang Province, China, demonstrated that the proposed DHTPSO-VMD method significantly outperforms benchmark approaches (PSO-VMD, EMD, SABO-VMD, GWO-VMD) in terms of Signal-to-Noise Ratio (SNR), Mean Absolute Error (MAE), and Mean Square Error (MSE). Subsequent forecasting experiments using an Informer model showed that signals preprocessed with DHTPSO-VMD achieved superior prediction accuracy (R² = 0.948924), underscoring its practical utility for smart water supply management. Full article

(This article belongs to the Section Signal and Data Analysis)

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9 pages, 816 KB

Open AccessTechnical Note

Euclidean-Lorentzian Dichotomy and Algebraic Causality in Finite Ring Continuum

by Yosef Akhtman

Entropy 2025, 27(11), 1098; https://doi.org/10.3390/e27111098 - 24 Oct 2025

Abstract

We present a concise and self-contained extension of the Finite Ring Continuum (FRC) program, showing that symmetry-complete prime shells

F_{p}

with

p = 4 t + 1

exhibit a fundamental Euclidean-Lorentzian dichotomy. A genuine Lorentzian quadratic form cannot be realized within a [...] Read more.

We present a concise and self-contained extension of the Finite Ring Continuum (FRC) program, showing that symmetry-complete prime shells

F_{p}

with

p = 4 t + 1

exhibit a fundamental Euclidean-Lorentzian dichotomy. A genuine Lorentzian quadratic form cannot be realized within a single space-like prime shell

F_{p}

, since to split time from space one requires a time coefficient

c^{2}

in the nonsquare class of

F_{p}^{\times}

, but then

c \notin F_{p}

. An explicit finite-field Lorentz transformation is subsequently derived that preserves the Minkowski form and generates a finite orthogonal group

O (Q_{ν}, F_{p^{2}})

of split type (Witt index 1). These results demonstrate that the essential algebraic features of special relativity—the invariant interval and Lorentz symmetry—emerge naturally within finite-field arithmetic, thereby establishing an intrinsic relativistic algebra within FRC. Finally, this dichotomy implies the algebraic origin of causality: Euclidean invariants reside within a space-like shell

F_{p}

, while Lorentzian structure and causal separation arise in its quadratic spacetime extension

F_{p^{2}}

. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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1 pages, 126 KB

Open AccessCorrection

Correction: Zou et al. Geometrical Bounds on Irreversibility in Squeezed Thermal Bath. Entropy 2023, 25, 128

by Chen-Juan Zou, Yue Li, Jia-Kun Xu, Jia-Bin You, Ching Eng Png and Wan-Li Yang

Entropy 2025, 27(11), 1097; https://doi.org/10.3390/e27111097 - 24 Oct 2025

Abstract

In the published article [...] Full article

(This article belongs to the Special Issue Quantum Thermodynamics: Fundamentals and Applications)

16 pages, 280 KB

Open AccessArticle

On Quasi-Cyclic Codes of Index 3

by Kanat Abdukhalikov and Rasha M. Shat

Entropy 2025, 27(11), 1096; https://doi.org/10.3390/e27111096 - 23 Oct 2025

Abstract

Quasi-cyclic codes of index 3 over finite fields are studied. We give a classification of such codes. Their duals with respect to the Euclidean and Hermitian inner products are investigated. We give a characterization of self-orthogonal and dual-containing codes. A quasi-cyclic code of [...] Read more.

Quasi-cyclic codes of index 3 over finite fields are studied. We give a classification of such codes. Their duals with respect to the Euclidean and Hermitian inner products are investigated. We give a characterization of self-orthogonal and dual-containing codes. A quasi-cyclic code of index 3 is generated by at most three elements. We describe conditions when such a code (or its dual) is generated by one element. Full article

(This article belongs to the Special Issue Discrete Math in Coding Theory, 2nd Edition)

18 pages, 908 KB

Open AccessArticle

Bayesian Estimation of Multicomponent Stress–Strength Model Using Progressively Censored Data from the Inverse Rayleigh Distribution

by Asuman Yılmaz

Entropy 2025, 27(11), 1095; https://doi.org/10.3390/e27111095 - 23 Oct 2025

Abstract

This paper presents a comprehensive study on the estimation of multicomponent stress–strength reliability under progressively censored data, assuming the inverse Rayleigh distribution. Both maximum likelihood estimation and Bayesian estimation methods are considered. The loss function and prior distribution play crucial roles in Bayesian [...] Read more.

This paper presents a comprehensive study on the estimation of multicomponent stress–strength reliability under progressively censored data, assuming the inverse Rayleigh distribution. Both maximum likelihood estimation and Bayesian estimation methods are considered. The loss function and prior distribution play crucial roles in Bayesian inference. Therefore, Bayes estimators of the unknown model parameters are obtained under symmetric (squared error loss function) and asymmetric (linear exponential and general entropy) loss functions using gamma priors. Lindley and MCMC approximation methods are used for Bayesian calculations. Additionally, asymptotic confidence intervals based on maximum likelihood estimators and Bayesian credible intervals constructed via Markov Chain Monte Carlo methods are presented. An extensive Monte Carlo simulation study compares the efficiencies of classical and Bayesian estimators, revealing that Bayesian estimators outperform classical ones. Finally, a real-life data example is provided to illustrate the practical applicability of the proposed methods. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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24 pages, 4420 KB

Open AccessArticle

AttSCNs: A Bayesian-Optimized Hybrid Model with Attention-Guided Stochastic Configuration Networks for Robust GPS Trajectory Prediction

by Xue-Bo Jin, Ye-Qing Wang, Jian-Lei Kong, Yu-Ting Bai and Ting-Li Su

Entropy 2025, 27(11), 1094; https://doi.org/10.3390/e27111094 - 23 Oct 2025

Abstract

Trajectory prediction in the Internet of Vehicles (IoV) is crucial for enhancing road safety and traffic efficiency; however, existing methods often fail to address the challenges of colored noise in GPS data and long-term dependency modeling. To overcome these limitations, this paper proposes [...] Read more.

Trajectory prediction in the Internet of Vehicles (IoV) is crucial for enhancing road safety and traffic efficiency; however, existing methods often fail to address the challenges of colored noise in GPS data and long-term dependency modeling. To overcome these limitations, this paper proposes AttSCNs, a probabilistic hybrid framework integrating stochastic configuration networks (SCNs) with an attention-based encoder to model trajectories while quantifying prediction uncertainty. The model leverages SCNs’ stochastic neurons for adaptive noise filtering, attention mechanisms for dependency learning, and Bayesian hyperparameter optimization to infer robust configurations as a posterior distribution. Experimental results on real-world GPS datasets (10,000+ urban/highway trajectories) demonstrate that AttSCNs significantly outperform conventional approaches, reducing RMSE by 36.51% compared to traditional SCNs and lowering MAE by 97.8% compared to Kalman filter baselines. Moreover, compared to the LSTM model, AttSCNs achieve a 52.5% reduction in RMSE and a 68.5% reduction in MAE, with real-time inference speed. These advancements position AttSCNs as a robust, noise-resistant solution for IoV applications, offering superior performance in autonomous driving and smart city systems. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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10 pages, 1153 KB

Open AccessArticle

Entanglement Islands in 1D and 2D Lattices with Defects

by Ivan P. Christov

Entropy 2025, 27(11), 1093; https://doi.org/10.3390/e27111093 - 23 Oct 2025

Abstract

We investigate the spatial structure of quantum entanglement in one- and two-dimensional lattice systems containing structural defects, using the Time-Dependent Quantum Monte Carlo (TDQMC) method. By constructing reduced density matrices from ensembles of guide waves, we resolve spatial variations in both Coulomb-mediated entanglement [...] Read more.

We investigate the spatial structure of quantum entanglement in one- and two-dimensional lattice systems containing structural defects, using the Time-Dependent Quantum Monte Carlo (TDQMC) method. By constructing reduced density matrices from ensembles of guide waves, we resolve spatial variations in both Coulomb-mediated entanglement and coherence without requiring full many-body wavefunctions. This approach reveals localized regions, entanglement islands, where quantum correlations are enhanced or suppressed due to the presence of vacancies or interaction inhomogeneities. In 1D systems, entanglement tends to concentrate near defects, while in 2D systems, we observe bridge-like and radially symmetric domains. Our results demonstrate that TDQMC offers a scalable and physically transparent framework for real-space quantum information analysis, with implications for information transfer in atomic-size structures, quantum materials, entanglement-based sensing, and coherent state engineering. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series on Quantum Entanglement)

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4 pages, 146 KB

Open AccessEditorial

Semantic Information Theory and Applications

by Meixia Tao, Kai Niu and Youlong Wu

Entropy 2025, 27(11), 1092; https://doi.org/10.3390/e27111092 - 23 Oct 2025

Abstract

Traditional information theory provides a rigorous foundation for information compression and reliable symbol transmission [...] Full article

(This article belongs to the Special Issue Semantic Information Theory)

17 pages, 1775 KB

Open AccessArticle

Self-Diffusion in Two-Dimensional Colloidal Systems: A Computer Simulation Study

by Piotr Polanowski and Andrzej Sikorski

Entropy 2025, 27(11), 1091; https://doi.org/10.3390/e27111091 - 22 Oct 2025

Abstract

The dynamics of dense colloidal systems are not fully understood. In the study of these types of systems, computer simulations based on the so-called hard sphere model play a significant role. In the presented work, we consider a system of hard spheres of [...] Read more.

The dynamics of dense colloidal systems are not fully understood. In the study of these types of systems, computer simulations based on the so-called hard sphere model play a significant role. In the presented work, we consider a system of hard spheres of the same size but different mobilities (molecules with high mobility correspond to solvent molecules, while molecules with reduced mobility are colloid particles) at varying concentrations. For this purpose, a two-dimensional lattice and an thermal model of such systems was designed. In order to determine the properties of such systems, a Monte Carlo computer simulation was used, employing the Dynamic Lattice Liquid (DLL) algorithm. Our main aim was to determine how the dynamic behavior of the system in the short time affects the long-time behavior. For this purpose, we investigated the cross-ratios of the diffusion coefficients in the short and long time of the considered system elements. It was found that the reduction in the solvent mobility with increasing concentration of colloidal particles in a short time leads to a very similar reduction in the mobility of the colloid particles in a long time, but we do not observe such behavior in the case of the solvent, i.e., there is a decrease in the value of the solvent diffusion coefficient in the long time with the change in the concentration of colloid particles, but it is difficult to connect it in a simple way with the decrease in the diffusion coefficient in the short time. Full article

(This article belongs to the Special Issue Statistical Mechanics of Lattice Gases)

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