Journal Description
Entropy
Entropy
is an international and interdisciplinary peer-reviewed open access journal of entropy and information studies, published monthly online by MDPI. The International Society for the Study of Information (IS4SI) and Spanish Society of Biomedical Engineering (SEIB) are affiliated with Entropy and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, PubMed, PMC, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Physics, Multidisciplinary) / CiteScore - Q1 (Mathematical Physics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Entropy.
- Companion journals for Entropy include: Foundations, Thermo and MAKE.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.6 (2022)
Latest Articles
High-Throughput Polar Code Decoders with Information Bottleneck Quantization
Entropy 2024, 26(6), 462; https://doi.org/10.3390/e26060462 - 28 May 2024
Abstract
In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization
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In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization has a significant impact on the decoder area, power consumption and throughput. Thus, lower bit widths are preferred for efficient implementations but degrade the error correction capability. To address this issue, a non-uniform quantization based on the Information Bottleneck (IB) method is proposed that enables a low bit width while maintaining the essential information. Many investigations on the use of the IB method for Low-density parity-check code) LDPC decoders exist and have shown its advantages from an implementation perspective. However, for polar code decoder implementations, there exists only one publication that is not based on the state-of-the-art Fast Simplified Successive-Cancellation (Fast-SSC) decoding algorithm, and only synthesis implementation results without energy estimation are shown. In contrast, our paper presents several optimized Fast-SSC polar code decoder implementations using IB-based quantization with placement and routing results using advanced 12 nm FinFET technology. Gains of up to 16% in area and 13% in energy efficiency are achieved with IB-based quantization at a Frame Error Rate (FER) of and a polar code of compared to state-of-the-art decoders.
Full article
(This article belongs to the Special Issue Intelligent Information Processing and Coding for B5G Communications)
Open AccessArticle
Leveraging Data Locality in Quantum Convolutional Classifiers
by
Mingyoung Jeng, Alvir Nobel, Vinayak Jha, David Levy, Dylan Kneidel, Manu Chaudhary, Ishraq Islam, Audrey Facer, Manish Singh, Evan Baumgartner, Eade Vanderhoof, Abina Arshad and Esam El-Araby
Entropy 2024, 26(6), 461; https://doi.org/10.3390/e26060461 - 28 May 2024
Abstract
Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than a fully connected
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Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than a fully connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multifeatures in multidimensional data. In this paper, we present an multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multifeature quantum convolution with average and Euclidean pooling, thus adapting the CNN structure to a variational quantum algorithm (VQA). The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results show the favorable characteristics of our proposed techniques compared with existing work with respect to a number of quantitative metrics, such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.
Full article
(This article belongs to the Special Issue Quantum Computation, Communication and Cryptography)
Open AccessArticle
Testing the Conjecture That Quantum Processes Create Conscious Experience
by
Hartmut Neven, Adam Zalcman, Peter Read, Kenneth S. Kosik, Tjitse van der Molen, Dirk Bouwmeester, Eve Bodnia, Luca Turin and Christof Koch
Entropy 2024, 26(6), 460; https://doi.org/10.3390/e26060460 - 28 May 2024
Abstract
The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass
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The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest—pain generates intense feelings and absorbs all our conscious awareness, yet nothing particularly intelligent is going on. In the hard sciences, this topic is frequently met with skepticism because, to date, no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here, we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human conscious experience through brain–quantum computer interfaces.
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(This article belongs to the Section Quantum Information)
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Ising’s Roots and the Transfer-Matrix Eigenvalues
by
Reinhard Folk and Yurij Holovatch
Entropy 2024, 26(6), 459; https://doi.org/10.3390/e26060459 - 28 May 2024
Abstract
Today, the Ising model is an archetype describing collective ordering processes. As such, it is widely known in physics and far beyond. Less known is the fact that the thesis defended by Ernst Ising 100 years ago (in 1924) contained not only the
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Today, the Ising model is an archetype describing collective ordering processes. As such, it is widely known in physics and far beyond. Less known is the fact that the thesis defended by Ernst Ising 100 years ago (in 1924) contained not only the solution of what we call now the ‘classical 1D Ising model’ but also other problems. Some of these problems, as well as the method of their solution, are the subject of this note. In particular, we discuss the combinatorial method Ernst Ising used to calculate the partition function for a chain of elementary magnets. In the thermodynamic limit, this method leads to the result that the partition function is given by the roots of a certain polynomial. We explicitly show that ‘Ising’s roots’ that arise within the combinatorial treatment are also recovered by the eigenvalues of the transfer matrix, a concept that was introduced much later. Moreover, we discuss the generalization of the two-state model to a three-state one presented in Ising’s thesis, which is not included in his famous paper of 1925 (E. Ising, Z. Physik 31 (1925) 253). The latter model can be considered as a forerunner of the now-abundant models with many-component order parameters.
Full article
(This article belongs to the Section Statistical Physics)
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Embedded Complexity of Evolutionary Sequences
by
Jonathan D. Phillips
Entropy 2024, 26(6), 458; https://doi.org/10.3390/e26060458 - 28 May 2024
Abstract
Multiple pathways and outcomes are common in evolutionary sequences for biological and other environmental systems due to nonlinear complexity, historical contingency, and disturbances. From any starting point, multiple evolutionary pathways are possible. From an endpoint or observed state, multiple possibilities exist for the
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Multiple pathways and outcomes are common in evolutionary sequences for biological and other environmental systems due to nonlinear complexity, historical contingency, and disturbances. From any starting point, multiple evolutionary pathways are possible. From an endpoint or observed state, multiple possibilities exist for the sequence of events that created it. However, for any observed historical sequence—e.g., ecological or soil chronosequences, stratigraphic records, or lineages—only one historical sequence actually occurred. Here, a measure of the embedded complexity of historical sequences based on algebraic graph theory is introduced. Sequences are represented as system states S(t), such that S(t − 1) ≠ S(t) ≠ S(t + 1). Each sequence of N states contains nested subgraph sequences of length 2, 3, …, N − 1. The embedded complexity index (which can also be interpreted in terms of embedded information) compares the complexity (based on the spectral radius λ1) of the entire sequence to the cumulative complexity of the constituent subsequences. The spectral radius is closely linked to graph entropy, so the index also reflects information in the sequence. The analysis is also applied to ecological state-and-transition models (STM), which represent observed transitions, along with information on their causes or triggers. As historical sequences are lengthened (by the passage of time and additional transitions or by improved resolutions or new observations of historical changes), the overall complexity asymptotically approaches λ1 = 2, while the embedded complexity increases as N2.6. Four case studies are presented, representing coastal benthic community shifts determined from biostratigraphy, ecological succession on glacial forelands, vegetation community changes in longleaf pine woodlands, and habitat changes in a delta.
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(This article belongs to the Special Issue Entropy and Information in Biological Systems)
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Geometrothermodynamics of 3D Regular Black Holes
by
Nurzada Beissen
Entropy 2024, 26(6), 457; https://doi.org/10.3390/e26060457 - 28 May 2024
Abstract
We investigate a spherically symmetric exact solution of Einstein’s gravity with cosmological constant in (2 + 1) dimensions, non-minimally coupled to a scalar field. The solution describes the gravitational field of a black hole, which is free of curvature singularities in the entire
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We investigate a spherically symmetric exact solution of Einstein’s gravity with cosmological constant in (2 + 1) dimensions, non-minimally coupled to a scalar field. The solution describes the gravitational field of a black hole, which is free of curvature singularities in the entire spacetime. We use the formalism of geometrothermodynamics to investigate the geometric properties of the corresponding space of equilibrium states and find their interpretation from the point of view of thermodynamics. It turns out that, as a result of the presence of thermodynamic interaction, the space of equilibrium states is curved with two possible configurations, which depend on the value of a coupling constant. In the first case, the equilibrium space is completely regular, corresponding to a stable thermodynamic system. The second case is characterized by the presence of two curvature singularities, which are shown to correspond to locations where the system undergoes two different phase transitions, one due to the breakdown of the thermodynamic stability condition and the second one due to the presence of a divergence at the level of the response functions.
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(This article belongs to the Special Issue Geometrothermodynamics and Its Applications)
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The Interplay between Tunneling and Parity Violation in Chiral Molecules
by
Daniel Martínez-Gil, Pedro Bargueño and Salvador Miret-Artés
Entropy 2024, 26(6), 456; https://doi.org/10.3390/e26060456 - 27 May 2024
Abstract
In this review, the concepts of quantum tunneling and parity violation are introduced in the context of chiral molecules. A particle moving in a double well potential provides a good model to study the behavior of chiral molecules, where the left well and
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In this review, the concepts of quantum tunneling and parity violation are introduced in the context of chiral molecules. A particle moving in a double well potential provides a good model to study the behavior of chiral molecules, where the left well and right well represent the L and R enantiomers, respectively. If the model considers the quantum behavior of matter, the concept of quantum tunneling emerges, giving place to stereomutation dynamics between left- and right-handed chiral molecules. Parity-violating interactions, like the electroweak one, can be also considered, making possible the existence of an energy difference between the L and R enantiomers, the so-called parity-violating energy difference (PVED). Here we provide a brief account of some theoretical methods usually employed to calculate this PVED, also commenting on relevant experiments devoted to experimentally detect the aforementioned PVED in chiral molecules. Finally, we comment on some ways of solving the so-called Hund’s paradox, with emphasis on mean-field theory and decoherence.
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(This article belongs to the Special Issue Tunneling in Complex Systems)
Open AccessArticle
Linear Codes Constructed from Two Weakly Regular Plateaued Functions with Index (p − 1)/2
by
Shudi Yang, Tonghui Zhang and Zheng-an Yao
Entropy 2024, 26(6), 455; https://doi.org/10.3390/e26060455 - 27 May 2024
Abstract
Linear codes are the most important family of codes in cryptography and coding theory. Some codes only have a few weights and are widely used in many areas, such as authentication codes, secret sharing schemes and strongly regular graphs. By setting
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Linear codes are the most important family of codes in cryptography and coding theory. Some codes only have a few weights and are widely used in many areas, such as authentication codes, secret sharing schemes and strongly regular graphs. By setting , we constructed an infinite family of linear codes using two distinct weakly regular unbalanced (and balanced) plateaued functions with index . Their weight distributions were completely determined by applying exponential sums and Walsh transform. As a result, most of our constructed codes have a few nonzero weights and are minimal.
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(This article belongs to the Section Information Theory, Probability and Statistics)
Open AccessArticle
Systems and Methods for Transformation and Degradation Analysis
by
Jude A. Osara and Michael D. Bryant
Entropy 2024, 26(6), 454; https://doi.org/10.3390/e26060454 - 27 May 2024
Abstract
Modern concepts in irreversible thermodynamics are applied to system transformation and degradation analyses. Phenomenological entropy generation (PEG) theorem is combined with the Degradation-Entropy Generation (DEG) theorem for instantaneous multi-disciplinary, multi-scale, multi-component system characterization. A transformation-PEG theorem and space materialize with system and process
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Modern concepts in irreversible thermodynamics are applied to system transformation and degradation analyses. Phenomenological entropy generation (PEG) theorem is combined with the Degradation-Entropy Generation (DEG) theorem for instantaneous multi-disciplinary, multi-scale, multi-component system characterization. A transformation-PEG theorem and space materialize with system and process defining elements and dimensions. The near-100% accurate, consistent results and features in recent publications demonstrating and applying the new TPEG methods to frictional wear, grease aging, electrochemical power system cycling—including lithium-ion battery thermal runaway—metal fatigue loading and pump flow are collated herein, demonstrating the practicality of the new and universal PEG theorem and the predictive power of models that combine and utilize both theorems. The methodology is useful for design, analysis, prognostics, diagnostics, maintenance and optimization.
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(This article belongs to the Special Issue Trends in the Second Law of Thermodynamics)
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Combining Exergy and Pinch Analysis for the Operating Mode Optimization of a Steam Turbine Cogeneration Plant in Wonji-Shoa, Ethiopia
by
Shumet Sendek Sharew, Alessandro Di Pretoro, Abubeker Yimam, Stéphane Negny and Ludovic Montastruc
Entropy 2024, 26(6), 453; https://doi.org/10.3390/e26060453 - 27 May 2024
Abstract
In this research, the simulation of an existing 31.5 MW steam power plant, providing both electricity for the national grid and hot utility for the related sugar factory, was performed by means of ProSimPlus® v. 3.7.6. The purpose of this study is
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In this research, the simulation of an existing 31.5 MW steam power plant, providing both electricity for the national grid and hot utility for the related sugar factory, was performed by means of ProSimPlus® v. 3.7.6. The purpose of this study is to analyze the steam turbine operating parameters by means of the exergy concept with a pinch-based technique in order to assess the overall energy performance and losses that occur in the power plant. The combined pinch and exergy analysis (CPEA) initially focuses on the depiction of the hot and cold composite curves (HCCCs) of the steam cycle to evaluate the energy and exergy requirements. Based on the minimal approach temperature difference (∆Tlm) required for effective heat transfer, the exergy loss that raises the heat demand (heat duty) for power generation can be quantitatively assessed. The exergy composite curves focus on the potential for fuel saving throughout the cycle with respect to three possible operating modes and evaluates opportunities for heat pumping in the process. Well-established tools, such as balanced exergy composite curves, are used to visualize exergy losses in each process unit and utility heat exchangers. The outcome of the combined exergy–pinch analysis reveals that energy savings of up to 83.44 MW may be realized by lowering exergy destruction in the cogeneration plant according to the operating scenario.
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(This article belongs to the Special Issue Thermodynamic Optimization of Industrial Energy Systems)
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Extended Regression Analysis for Debye–Einstein Models Describing Low Temperature Heat Capacity Data of Solids
by
Ernst Gamsjäger and Manfred Wiessner
Entropy 2024, 26(6), 452; https://doi.org/10.3390/e26060452 - 26 May 2024
Abstract
Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye–Einstein integrals in the temperature range from to . The parameters of the Debye–Einstein approach are either obtained by a Markov chain Monte
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Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye–Einstein integrals in the temperature range from to . The parameters of the Debye–Einstein approach are either obtained by a Markov chain Monte Carlo (MCMC) global optimization method or by a Levenberg–Marquardt (LM) local optimization routine. In the case of the MCMC approach the model parameters and the coefficients of a function describing the residuals of the measurement points are simultaneously optimized. Thereby, the Bayesian credible interval for the heat capacity function is obtained. Although both regression tools (LM and MCMC) are completely different approaches, not only the values of the Debye–Einstein parameters, but also their standard errors appear to be similar. The calculated model parameters and their associated standard errors are then used to derive the enthalpy, entropy and Gibbs energy as functions of temperature. By direct insertion of the MCMC parameters of all computer runs the distributions of the integral quantities enthalpy, entropy and Gibbs energy are determined.
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(This article belongs to the Special Issue Computational Thermodynamics and Its Applications)
Open AccessArticle
An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information
by
Yanbing Liu, Liping Chen, Yu Chen and Jianwan Ding
Entropy 2024, 26(6), 451; https://doi.org/10.3390/e26060451 - 26 May 2024
Abstract
Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress
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Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress of adaptive sampling techniques, addressing the inadequacy of existing algorithms to fully leverage the spatial location information of sample points, and introduces an innovative adaptive sampling method. This approach incorporates the Dual Inverse Distance Weighting (DIDW) algorithm, embedding the spatial characteristics of sampling points within the probability sampling process. Furthermore, it introduces reward factors derived from reinforcement learning principles to dynamically refine the probability sampling formula. This strategy more effectively captures the essential characteristics of PDEs with each iteration. We utilize sparsely connected networks and have adjusted the sampling process, which has proven to effectively reduce the training time. In numerical experiments on fluid mechanics problems, such as the two-dimensional Burgers’ equation with sharp solutions, pipe flow, flow around a circular cylinder, lid-driven cavity flow, and Kovasznay flow, our proposed adaptive sampling algorithm markedly enhances accuracy over conventional PINN methods, validating the algorithm’s efficacy.
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(This article belongs to the Special Issue Physics-Informed Neural Networks)
Open AccessArticle
A Direct Entropic Approach to the Thermal Balance of Spontaneous Chemical Reactions
by
Michele D’Anna, Paolo Lubini, Hans U. Fuchs and Federico Corni
Entropy 2024, 26(6), 450; https://doi.org/10.3390/e26060450 - 26 May 2024
Abstract
When working with, and learning about, the thermal balance of a chemical reaction, we need to consider two overlapping but conceptually distinct aspects: one relates to the process of reallocating entropy between reactants and products (because of different specific entropies of the new
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When working with, and learning about, the thermal balance of a chemical reaction, we need to consider two overlapping but conceptually distinct aspects: one relates to the process of reallocating entropy between reactants and products (because of different specific entropies of the new substances compared to those of the old), and the other to dissipative processes. Together, they determine how much entropy is exchanged between the chemicals and their environment (i.e., in heating and cooling). By making explicit use of (a) the two conjugate pairs chemical amount (i.e., amount of substance) and chemical potential, and entropy and temperature, respectively, (b) the laws of balance of amount of substance on the one hand and entropy on the other, and (c) a generalized approach to the energy principle, it is possible to create both imaginative and formal conceptual tools for modeling thermal balances associated with chemical transformations in general and exothermic and endothermic reactions in particular. In this paper, we outline the concepts and relations needed for a direct approach to chemical and thermal dynamics, create a model of exothermic and endothermic reactions, including numerical examples, and discuss how to relate the direct entropic approach to traditional models of these phenomena.
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(This article belongs to the Section Thermodynamics)
Open AccessArticle
Interference of Particles with Fermionic Internal Degrees of Freedom
by
Jerzy Dajka
Entropy 2024, 26(6), 449; https://doi.org/10.3390/e26060449 - 26 May 2024
Abstract
The interference of fermionic particles, specifically molecules comprising a small number of fermions, in a Mach–Zehnder interferometer is being investigated under the influence of both classical and non-classical external controls. The aim is to identify control strategies that can elucidate the relationship between
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The interference of fermionic particles, specifically molecules comprising a small number of fermions, in a Mach–Zehnder interferometer is being investigated under the influence of both classical and non-classical external controls. The aim is to identify control strategies that can elucidate the relationship between the interference pattern and the characteristics of internal fermion–fermion interactions.
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(This article belongs to the Special Issue Matter-Aggregating Systems at a Classical vs. Quantum Interface)
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Multi-Server Multi-Function Distributed Computation
by
Derya Malak, Mohammad Reza Deylam Salehi, Berksan Serbetci and Petros Elia
Entropy 2024, 26(6), 448; https://doi.org/10.3390/e26060448 - 26 May 2024
Abstract
The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a
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The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of distributed servers, we establish the communication cost upper bounds for a variety of data statistics, function classes, and data placements across the servers. To do so, we proceed to apply, for the first time here, Körner’s characteristic graph approach—which is known to capture the structural properties of data and functions—to the promising framework of multi-server multi-task distributed computing. Going beyond the general expressions, and in order to offer clearer insight, we also consider the well-known scenario of cyclic dataset placement and linearly separable functions over the binary field, in which case, our approach exhibits considerable gains over the state of the art. Similar gains are identified for the case of multi-linear functions.
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(This article belongs to the Special Issue Foundations of Goal-Oriented Semantic Communication in Intelligent Networks)
Open AccessArticle
Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
by
Hassan Termos
Entropy 2024, 26(6), 447; https://doi.org/10.3390/e26060447 - 26 May 2024
Abstract
This study introduces a novel approach to bolstering quantum key distribution (QKD) security by implementing swift classical channel authentication within the SARG04 and BB84 protocols. We propose mono-authentication, a pioneering paradigm employing quantum-resistant signature algorithms—specifically, CRYSTALS-DILITHIUM and RAINBOW—to authenticate solely at the conclusion
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This study introduces a novel approach to bolstering quantum key distribution (QKD) security by implementing swift classical channel authentication within the SARG04 and BB84 protocols. We propose mono-authentication, a pioneering paradigm employing quantum-resistant signature algorithms—specifically, CRYSTALS-DILITHIUM and RAINBOW—to authenticate solely at the conclusion of communication. Our numerical analysis comprehensively examines the performance of these algorithms across various block sizes (128, 192, and 256 bits) in both block-based and continuous photon transmission scenarios. Through 100 iterations of simulations, we meticulously assess the impact of noise levels on authentication efficacy. Our results notably highlight CRYSTALS-DILITHIUM’s consistent outperformance of RAINBOW, with signature overheads of approximately 0.5% for the QKD-BB84 protocol and 0.4% for the QKD-SARG04 one, when the quantum bit error rate (QBER) is augmented up to 8%. Moreover, our study unveils a correlation between higher security levels and increased authentication times, with CRYSTALS-DILITHIUM maintaining superior efficiency across all key rates up to 10,000 kb/s. These findings underscore the substantial cost and complexity reduction achieved by mono-authentication, particularly in noisy environments, paving the way for more resilient and efficient quantum communication systems.
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(This article belongs to the Special Issue Quantum Optics: Trends and Challenges)
Open AccessArticle
Impact of Combined Modulation of Two Potassium Ion Currents on Spiral Waves and Turbulent States in the Heart
by
Jing Bai, Chunfu Zhang, Yanchun Liang, Adriano Tavares and Lidong Wang
Entropy 2024, 26(6), 446; https://doi.org/10.3390/e26060446 - 26 May 2024
Abstract
In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving
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In the realm of cardiac research, the control of spiral waves and turbulent states has been a persistent focus for scholars. Among various avenues of investigation, the modulation of ion currents represents a crucial direction. It has been proved that the methods involving combined control of currents are superior to singular approaches. While previous studies have proposed some combination strategies, further reinforcement and supplementation are required, particularly in the context of controlling arrhythmias through the combined regulation of two potassium ion currents. This study employs the Luo–Rudy phase I cardiac model, modulating the maximum conductance of the time-dependent potassium current and the time-independent potassium current, to investigate the effects of this combined modulation on spiral waves and turbulent states. Numerical simulation results indicate that, compared to modulating a single current, combining reductions in the conductance of two potassium ion currents can rapidly control spiral waves and turbulent states in a short duration. This implies that employing blockers for both potassium ion currents concurrently represents a more efficient control strategy. The control outcomes of this study represent a novel and effective combination for antiarrhythmic interventions, offering potential avenues for new antiarrhythmic drug targets.
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(This article belongs to the Section Complexity)
Open AccessArticle
Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images
by
Hongli Li, Xuhui Chen, Wei Yang, Jian Huang, Kaimin Sun, Ying Wang, Andong Huang and Liye Mei
Entropy 2024, 26(6), 445; https://doi.org/10.3390/e26060445 - 25 May 2024
Abstract
Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well
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Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.
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(This article belongs to the Section Multidisciplinary Applications)
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Dependent Competing Failure Processes in Reliability Systems
by
Jewgeni H. Dshalalow, Hend Aljahani and Ryan T. White
Entropy 2024, 26(6), 444; https://doi.org/10.3390/e26060444 - 25 May 2024
Abstract
This paper deals with a reliability system hit by three types of shocks ranked as harmless, critical, or extreme, depending on their magnitudes, being below , between and , and above , respectively. The system’s
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This paper deals with a reliability system hit by three types of shocks ranked as harmless, critical, or extreme, depending on their magnitudes, being below , between and , and above , respectively. The system’s failure is caused by a single extreme shock or by a total of N critical shocks. In addition, the system fails under occurrences of M pairs of shocks with lags less than some ( -shocks) in any order. Thus, the system fails when one of the three named cumulative damages occurs first. Thus, it fails due to the competition of the three associated shock processes. We obtain a closed-form joint distribution of the time-to-failure, shock count upon failure, -shock count, and cumulative damage to the system on failure, to name a few. In particular, the reliability function directly follows from the marginal distribution of the failure time. In a modified system, we restrict -shocks to those with small lags between consecutive harmful shocks. We treat the system as a generalized random walk process and use an embellished variant of discrete operational calculus developed in our earlier work. We demonstrate analytical tractability of our formulas which are also validated, through Monte Carlo simulation.
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(This article belongs to the Section Multidisciplinary Applications)
Open AccessArticle
Entropy Production of Run-and-Tumble Particles
by
Matteo Paoluzzi, Andrea Puglisi and Luca Angelani
Entropy 2024, 26(6), 443; https://doi.org/10.3390/e26060443 - 24 May 2024
Abstract
We analyze the entropy production in run-and-tumble models. After presenting the general formalism in the framework of the Fokker–Planck equations in one space dimension, we derive some known exact results in simple physical situations (free run-and-tumble particles and harmonic confinement). We then extend
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We analyze the entropy production in run-and-tumble models. After presenting the general formalism in the framework of the Fokker–Planck equations in one space dimension, we derive some known exact results in simple physical situations (free run-and-tumble particles and harmonic confinement). We then extend the calculation to the case of anisotropic motion (different speeds and tumbling rates for right- and left-oriented particles), obtaining exact expressions of the entropy production rate. We conclude by discussing the general case of heterogeneous run-and-tumble motion described by space-dependent parameters and extending the analysis to the case of d-dimensional motions.
Full article
(This article belongs to the Collection Disorder and Biological Physics)
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