IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-27 DOI: 10.3390/e27070687

Ralph D Lorenz

引用次数: 0

Impact of Calcium and Potassium Currents on Spiral Wave Dynamics in the LR1 Model. LR1模型中钙钾电流对螺旋波动力学的影响。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-27 DOI: 10.3390/e27070690

Xiaoping Yuan, Qianqian Zheng

引用次数: 0

Transverse Self-Propulsion Enhances the Aggregation of Active Dumbbells. 横向自推进增强活动哑铃的聚集。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-27 DOI: 10.3390/e27070692

Pasquale Digregorio, Claudio Basilio Caporusso, Lucio Mauro Carenza, Giuseppe Gonnella, Daniela Moretti, Giuseppe Negro, Massimiliano Semeraro, Antonio Suma

{"title":"Transverse Self-Propulsion Enhances the Aggregation of Active Dumbbells.","authors":"Pasquale Digregorio, Claudio Basilio Caporusso, Lucio Mauro Carenza, Giuseppe Gonnella, Daniela Moretti, Giuseppe Negro, Massimiliano Semeraro, Antonio Suma","doi":"10.3390/e27070692","DOIUrl":"10.3390/e27070692","url":null,"abstract":"We investigate a two-dimensional system of active Brownian dumbbells using molecular dynamics simulations. In this model, each dumbbell is driven by an active force oriented perpendicular to the axis connecting its two constituent beads. We characterize the resulting phase behavior and find that, across all values of activity, the system undergoes phase separation between dilute and dense phases. The dense phase exhibits hexatic order, and for large enough activity, we observe a marked increase in local polarization, with dumbbells predominantly oriented towards the interior of the clusters. Compared to the case of axially self-propelled dumbbells, we find that the binodal region is enlarged towards lower densities at all activities. This shift arises because dumbbells with transverse propulsion can more easily form stable cluster cores, serving as nucleation seeds, and show a highly suppressed escaping rate from the cluster boundary. Finally, we observe that clusters exhibit spontaneous rotation, with the modulus of the angular velocity scaling as ω∼rg-2, where rg is the cluster's radius of gyration. This contrasts with axially propelled dumbbells, where the scaling follows ω∼rg-1. We develop a simplified analytical model to rationalize this scaling behavior.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12294323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728930","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}

引用次数: 0

Nonlinear Dynamics and Applications. 非线性动力学及其应用。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-27 DOI: 10.3390/e27070688

José M Amigó, Fernando Montani

引用次数: 0

Jointly Optimizing Resource Allocation, User Scheduling, and Grouping in SBMA Networks: A PSO Approach. 联合优化SBMA网络中的资源分配、用户调度和分组：一种粒子群算法。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-27 DOI: 10.3390/e27070691

Jianjian Wu, Chanzi Liu, Xindi Wang, Chi-Tsun Cheng, Qingfeng Zhou

{"title":"Jointly Optimizing Resource Allocation, User Scheduling, and Grouping in SBMA Networks: A PSO Approach.","authors":"Jianjian Wu, Chanzi Liu, Xindi Wang, Chi-Tsun Cheng, Qingfeng Zhou","doi":"10.3390/e27070691","DOIUrl":"10.3390/e27070691","url":null,"abstract":"Blind Interference Alignment (BIA) and Sparse Code Multiple Access (SCMA) offer the potential for massive connectivity but face limitations. Our recently proposed Sparsecode-and-BIA-based Multiple Access (SBMA) scheme synergizes their strengths, promising enhanced performance. SBMA leverages flexible user grouping (UG) strategies to effectively manage its unique combination of sparse code constraints and interference alignment requirements, thereby facilitating the fulfillment of diverse Quality of Service (QoS) demands. However, realizing SBMA's full potential requires efficient joint resource allocation (RA), user scheduling (US), and user grouping (UG). The inherent coupling of these factors within the SBMA framework complicates this task significantly, rendering RA/US solutions designed purely for SCMA or BIA insufficient. This paper addresses this critical open issue. We first formulate the joint RA, US, and UG problems specifically for SBMA systems as an integer optimization task, aiming to maximize the number of users meeting QoS requirements. To tackle this NP-hard problem, we propose an effective algorithm based on Particle Swarm Optimization (PSO), featuring a carefully designed update function tailored specifically for the joint US and UG decisions required in SBMA. Comprehensive simulations demonstrate show that the proposed algorithm significantly outperforms the random-based scheme. Under certain conditions, it serves approximately 280% more users who meet their QoS requirements in high-SNR scenarios.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12294732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728868","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}

引用次数: 0

An Aliasing Measure of Factor Effects in Three-Level Regular Designs. 三水平规则设计中因子效应的混叠度量。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-26 DOI: 10.3390/e27070680

Qiuying Chen, Zhiming Li, Zhi Li

引用次数: 0

Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series. 具有未知偏微分方程的物理信息神经网络：在多元时间序列中的应用。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-26 DOI: 10.3390/e27070682

Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari

{"title":"Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series.","authors":"Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari","doi":"10.3390/e27070682","DOIUrl":"10.3390/e27070682","url":null,"abstract":"A significant advancement in Neural Network (NN) research is the integration of domain-specific knowledge through custom loss functions. This approach addresses a crucial challenge: How can models utilize physics or mathematical principles to enhance predictions when dealing with sparse, noisy, or incomplete data? Physics-Informed Neural Networks (PINNs) put this idea into practice by incorporating a forward model, such as Partial Differential Equations (PDEs), as soft constraints. This guidance helps the networks find solutions that align with established laws. Recently, researchers have expanded this framework to include Bayesian NNs (BNNs) which allow for uncertainty quantification. However, what happens when the governing equations of a system are not completely known? In this work, we introduce methods to automatically select PDEs from historical data in a parametric family. We then integrate these learned equations into three different modeling approaches: PINNs, Bayesian-PINNs (B-PINNs), and Physical-Informed Bayesian Linear Regression (PI-BLR). To assess these frameworks, we evaluate them on a real-world Multivariate Time Series (MTS) dataset related to electrical power energy management. We compare their effectiveness in forecasting future states under different scenarios: with and without PDE constraints and accuracy considerations. This research aims to bridge the gap between data-driven discovery and physics-guided learning, providing valuable insights for practical applications.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12294105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728911","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}

引用次数: 0

Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle. 将最小自由能原理改进为最大信息效率原理。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-26 DOI: 10.3390/e27070684

Chenguang Lu

{"title":"Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle.","authors":"Chenguang Lu","doi":"10.3390/e27070684","DOIUrl":"10.3390/e27070684","url":null,"abstract":"Friston proposed the Minimum Free Energy Principle (FEP) based on the Variational Bayesian (VB) method. This principle emphasizes that the brain and behavior coordinate with the environment, promoting self-organization. However, it has a theoretical flaw, a possibility of being misunderstood, and a limitation (only likelihood functions are used as constraints). This paper first introduces the semantic information G theory and the R(G) function (where R is the minimum mutual information for the given semantic mutual information G). The G theory is based on the P-T probability framework and, therefore, allows for the use of truth, membership, similarity, and distortion functions (related to semantics) as constraints. Based on the study of the R(G) function and logical Bayesian Inference, this paper proposes the Semantic Variational Bayesian (SVB) and the Maximum Information Efficiency (MIE) principle. Theoretic analysis and computing experiments prove that R - G = F - H(X|Y) (where F denotes VFE, and H(X|Y) is Shannon conditional entropy) instead of F continues to decrease when optimizing latent variables; SVB is a reliable and straightforward approach for latent variables and active inference. This paper also explains the relationship between information, entropy, free energy, and VFE in local non-equilibrium and equilibrium systems, concluding that Shannon information, semantic information, and VFE are analogous to the increment of free energy, the increment of exergy, and physical conditional entropy. The MIE principle builds upon the fundamental ideas of the FEP, making them easier to understand and apply. It needs to combine deep learning methods for wider applications.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12293917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728863","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}

引用次数: 0

A Dual-Encoder Contrastive Learning Model for Knowledge Tracing. 知识追踪的双编码器对比学习模型。

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-26 DOI: 10.3390/e27070685

Yanhong Bai, Xingjiao Wu, Tingjiang Wei, Liang He

{"title":"A Dual-Encoder Contrastive Learning Model for Knowledge Tracing.","authors":"Yanhong Bai, Xingjiao Wu, Tingjiang Wei, Liang He","doi":"10.3390/e27070685","DOIUrl":"10.3390/e27070685","url":null,"abstract":"Knowledge tracing (KT) models learners' evolving knowledge states to predict future performance, serving as a fundamental component in personalized education systems. However, existing methods suffer from data sparsity challenges, resulting in inadequate representation quality for low-frequency knowledge concepts and inconsistent modeling of students' actual knowledge states. To address this challenge, we propose Dual-Encoder Contrastive Knowledge Tracing (DECKT), a contrastive learning framework that improves knowledge state representation under sparse data conditions. DECKT employs a momentum-updated dual-encoder architecture where the primary encoder processes current input data while the momentum encoder maintains stable historical representations through exponential moving average updates. These encoders naturally form contrastive pairs through temporal evolution, effectively enhancing representation capabilities for low-frequency knowledge concepts without requiring destructive data augmentation operations that may compromise knowledge structure integrity. To preserve semantic consistency in learned representations, DECKT incorporates a graph structure constraint loss that leverages concept-question relationships to maintain appropriate similarities between related concepts in the embedding space. Furthermore, an adversarial training mechanism applies perturbations to embedding vectors, enhancing model robustness and generalization. Extensive experiments on benchmark datasets demonstrate that DECKT significantly outperforms existing state-of-the-art methods, validating the effectiveness of the proposed approach in alleviating representation challenges in sparse educational data.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 7","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12294018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728812","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}

引用次数: 0

A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones. 基于博客和智能手机GPS数据的COVID-19传播趋势预测模型

IF 2 3区物理与天体物理

Entropy Pub Date : 2025-06-26 DOI: 10.3390/e27070686

Ryosuke Susuta, Kenta Yamada, Hideki Takayasu, Misako Takayasu

引用次数: 0

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