Entropy最新文献 - Book学术

Epidemic Dynamics and Intervention Measures in Campus Settings Based on Multilayer Temporal Networks. 基于多层时间网络的校园疫情动态及干预措施

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-21 DOI: 10.3390/e27050543

Xianyang Zhang, Ming Tang

{"title":"Epidemic Dynamics and Intervention Measures in Campus Settings Based on Multilayer Temporal Networks.","authors":"Xianyang Zhang, Ming Tang","doi":"10.3390/e27050543","DOIUrl":"10.3390/e27050543","url":null,"abstract":"This study simulates the spread of epidemics on university campuses using a multilayer temporal network model combined with the SEIR (Susceptible-Exposed-Infectious-Recovered) transmission model. The proposed approach explicitly captures the time-varying contact patterns across four distinct layers (Rest, Dining, Activity, and Academic) to reflect realistic student mobility driven by class schedules and spatial constraints. It evaluates the impact of various intervention measures on epidemic spreading, including subnetwork closure and zoned management. Our analysis reveals that the Academic and Activity layers emerge as high-risk transmission hubs due to their dynamic, high-density contact structures. Intervention measures exhibit layer-dependent efficacy: zoned management is highly effective in high-contact subnetworks, its impact on low-contact subnetworks remains limited. Consequently, intervention measures must be dynamically adjusted based on the characteristics of each subnetwork and the epidemic situations, with higher participation rates enhancing the effectiveness of these measures. This work advances methodological innovation in temporal network epidemiology by bridging structural dynamics with SEIR processes, offering actionable insights for campus-level pandemic preparedness. The findings underscore the necessity of layer-aware policies to optimize resource allocation in complex, time-dependent contact systems.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12110693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149739","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

Multi-Level Thresholding Based on Composite Local Contour Shannon Entropy Under Multiscale Multiplication Transform. 多尺度乘变换下基于复合局部轮廓香农熵的多级阈值分割。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-21 DOI: 10.3390/e27050544

Xianzhao Li, Yaobin Zou

{"title":"Multi-Level Thresholding Based on Composite Local Contour Shannon Entropy Under Multiscale Multiplication Transform.","authors":"Xianzhao Li, Yaobin Zou","doi":"10.3390/e27050544","DOIUrl":"10.3390/e27050544","url":null,"abstract":"Image segmentation is a crucial step in image processing and analysis, with multi-level thresholding being one of the important techniques for image segmentation. Existing approaches predominantly rely on metaheuristic optimization algorithms, which frequently encounter local optima stagnation and require extensive parameter tuning, thereby degrading segmentation accuracy and computational efficiency. This paper proposes a Shannon entropy-based multi-level thresholding method that utilizes composite contours. The method selects appropriate multiscale multiplication images by maximizing the Shannon entropy difference and constructs a new Shannon entropy objective function by dynamically combining contour images. Ultimately, it automatically determines multiple thresholds by integrating local contour Shannon entropy. Experimental results on synthetic images and real-world images with complex backgrounds, low contrast, blurred boundaries, and unbalanced sizes demonstrate that the proposed method outperforms six recently proposed multi-level thresholding methods based on the Matthew's correlation coefficient, indicating stronger adaptability and robustness for segmentation without requiring complex parameter tuning.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12111540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150012","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

Multi-Branch Network with Multi-Feature Enhancement for Improving the Generalization of Facial Forgery Detection. 基于多特征增强的多分支网络提高人脸伪造检测的泛化。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-21 DOI: 10.3390/e27050545

Siyu Meng, Quange Tan, Qianli Zhou, Rong Wang

{"title":"Multi-Branch Network with Multi-Feature Enhancement for Improving the Generalization of Facial Forgery Detection.","authors":"Siyu Meng, Quange Tan, Qianli Zhou, Rong Wang","doi":"10.3390/e27050545","DOIUrl":"10.3390/e27050545","url":null,"abstract":"The rapid development of deepfake facial technology has led to facial fraud, posing a significant threat to social security. With the advent of diffusion models, the realism of forged facial images has increased, making detection increasingly challenging. However, the existing detection methods primarily focus on identifying facial forgeries generated by generative adversarial networks; they may struggle to generalize when faced with novel forgery techniques like diffusion models. To address this challenge, a multi-branch network with multi-feature enhancement (M2EH) model for improving the generalization of facial forgery detection is proposed in this paper. First, a multi-branch network is constructed, wherein diverse features are extracted through the three parallel branches of the network, allowing for extensive analysis into the subtle traces of facial forgeries. Then, an adaptive feature concatenation mechanism is proposed to integrate the diverse features extracted from the three branches, obtaining the effective fused representation by optimizing the weights of each feature channel. To further enhance the facial forgery detection ability, spatial pyramid pooling is introduced into the classifier to augment the fused features. Finally, independent loss functions are designed for each branch to ensure the effective learning of specific features while promoting collaborative optimization of the model through the overall loss function. Additionally, to improve model adaptability, a large-scale deepfake facial dataset, HybridGenFace, is built, which includes counterfeit images generated by both generative adversarial networks and diffusion models, addressing the limitations of existing datasets concerning a single forgery type. Experimental results show that M2EH outperforms most of the existing methods on various deepfake datasets.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12110902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150010","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

Advancing Traditional Dunhuang Regional Pattern Design with Diffusion Adapter Networks and Cross-Entropy. 用扩散适配器网络和交叉熵推进传统敦煌区域格局设计。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-21 DOI: 10.3390/e27050546

Yihuan Tian, Tao Yu, Zuling Cheng, Sunjung Lee

{"title":"Advancing Traditional Dunhuang Regional Pattern Design with Diffusion Adapter Networks and Cross-Entropy.","authors":"Yihuan Tian, Tao Yu, Zuling Cheng, Sunjung Lee","doi":"10.3390/e27050546","DOIUrl":"10.3390/e27050546","url":null,"abstract":"To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large numbers due to their limited availability. However, existing text-to-image methods are computationally intensive and struggle to capture fine details and complex semantic relationships in text and images. To address these challenges, this paper proposes the Diffusion Adapter Network (DANet). It employs a lightweight adapter module to extract visual structural information, enabling the diffusion model to generate Dunhuang patterns with high accuracy, while eliminating the need for expensive fine-tuning of the original model. The attention adapter incorporates a multihead attention module (MHAM) to enhance image modality cues, allowing the model to focus more effectively on key information. A multiscale attention module (MSAM) is employed to capture features at different scales, thereby providing more precise generative guidance. In addition, an adaptive control mechanism (ACM) dynamically adjusts the guidance coefficients across feature layers to further enhance generation quality. In addition, incorporating a cross-entropy loss function enhances the model's capability in semantic understanding and the classification of Dunhuang patterns. The DANet achieves state-of-the-art (SOTA) performance on the proposed Diversified Dunhuang Patterns Dataset (DDHP). Specifically, it attains a perceptual similarity score (LPIPS) of 0.498, a graph matching score (CLIP score) of 0.533, and a feature similarity score (CLIP-I) of 0.772.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12111379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149678","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

Will You Become the Next Troll? A Computational Mechanics Approach to the Contagion of Trolling Behavior. 你会成为下一个巨魔吗？网络喷子行为传染的计算力学方法。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-21 DOI: 10.3390/e27050542

Qiusi Sun, Martin Hilbert

{"title":"Will You Become the Next Troll? A Computational Mechanics Approach to the Contagion of Trolling Behavior.","authors":"Qiusi Sun, Martin Hilbert","doi":"10.3390/e27050542","DOIUrl":"10.3390/e27050542","url":null,"abstract":"Trolling behavior is not simply a result of 'bad actors', an individual trait, or a linguistic phenomenon, but emerges from complex contagious social dynamics. This study uses formal concepts from information theory and complexity science to study it as such. The data comprised over 13 million Reddit comments, which were classified as troll or non-troll messages using the BERT model, fine-tuned with a human coding set. We derive the unique, minimally complex, and maximally predictive model from statistical mechanics, i.e., ε-machines and transducers, and can distinguish which aspects of trolling behaviors are both self-motivated and socially induced. While the vast majority of self-driven dynamics are like flipping a coin (86.3%), when social contagion is considered, most users (95.6%) show complex hidden multiple-state patterns. Within this complexity, trolling follows predictable transitions, with, for example, a 76% probability of remaining in a trolling state once it is reached. We find that replying to a trolling comment significantly increases the likelihood of switching to a trolling state or staying in it (72%). Besides being a showcase for the use of information-theoretic measures from dynamic systems theory to conceptualize human dynamics, our findings suggest that users and platform designers should go beyond calling out and removing trolls, but foster and design environments that discourage the dynamics leading to the emergence of trolling behavior.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12111620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149883","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

Research on Cold Chain Logistics Joint Distribution Vehicle Routing Optimization Based on Uncertainty Entropy and Time-Varying Network. 基于不确定性熵和时变网络的冷链物流联合配送车辆路线优化研究。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-20 DOI: 10.3390/e27050540

Huaixia Shi, Yu Hong, Qinglei Zhang, Jiyun Qin

{"title":"Research on Cold Chain Logistics Joint Distribution Vehicle Routing Optimization Based on Uncertainty Entropy and Time-Varying Network.","authors":"Huaixia Shi, Yu Hong, Qinglei Zhang, Jiyun Qin","doi":"10.3390/e27050540","DOIUrl":"10.3390/e27050540","url":null,"abstract":"The sharing economy is an inevitable trend in cold chain logistics. Most cold chain logistics enterprises are small and operate independently, with limited collaboration. Joint distribution is key to integrating cold chain logistics and the sharing economy. It aims to share logistics resources, provide collective customer service, and optimize distribution routes. However, existing studies have overlooked uncertainty factors in joint distribution optimization. To address this, we propose the Cold Chain Logistics Joint Distribution Vehicle Routing Problem with Time-Varying Network (CCLJDVRP-TVN). This model integrates traffic congestion uncertainty and constructs a time-varying network to reflect real-world conditions. The solution combines simulated annealing strategies with genetic algorithms. It also uses the entropy mechanism to optimize uncertainties, improving global search performance. The method was applied to optimize vehicle routing for three cold chain logistics companies in Beijing. The results show a reduction in logistics costs by 18.3%, carbon emissions by 15.8%, and fleet size by 12.5%. It also effectively addresses the impact of congestion and uncertainty on distribution. This study offers valuable theoretical support for optimizing joint distribution and managing uncertainties in cold chain logistics.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12110850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149813","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

Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits. 基于组合多臂强盗的成本高效分布式学习。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-20 DOI: 10.3390/e27050541

Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz

{"title":"Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits.","authors":"Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz","doi":"10.3390/e27050541","DOIUrl":"10.3390/e27050541","url":null,"abstract":"We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among n workers. By assigning tasks to all workers and waiting only for the k fastest ones, the main node can trade off the algorithm's error with its runtime by gradually increasing k as the algorithm evolves. However, this strategy, referred to as adaptive k-sync, neglects the cost of unused computations and of communicating models to workers that reveal a straggling behavior. We propose a cost-efficient scheme that assigns tasks only to k workers, and gradually increases k. To learn which workers are the fastest while assigning gradient calculations, we introduce the use of a combinatorial multi-armed bandit model. Assuming workers have exponentially distributed response times with different means, we provide both empirical and theoretical guarantees on the regret of our strategy, i.e., the extra time spent learning the mean response times of the workers. Furthermore, we propose and analyze a strategy that is applicable to a large class of response time distributions. Compared to adaptive k-sync, our scheme achieves significantly lower errors with the same computational efforts and less downlink communication while being inferior in terms of speed.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12111685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149721","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 Two-State Random Walk Model of Sperm Search on Confined Domains. 限定域上精子搜索的两态随机游走模型。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-19 DOI: 10.3390/e27050539

Martin Bier, Maciej Majka, Cameron Schmidt

{"title":"A Two-State Random Walk Model of Sperm Search on Confined Domains.","authors":"Martin Bier, Maciej Majka, Cameron Schmidt","doi":"10.3390/e27050539","DOIUrl":"10.3390/e27050539","url":null,"abstract":"Mammalian fertilization depends on sperm successfully navigating a spatially and chemically complex microenvironment in the female reproductive tract. This process is often conceptualized as a competitive race, but is better understood as a collective random search. Sperm within an ejaculate exhibit a diverse distribution of motility patterns, with some moving in relatively straight lines and others following tightly turning trajectories. Here, we present a two-state random walk model in which sperm switch from high-persistence-length to low-persistence-length motility modes. In reproductive biology, such a switch is often recognized as \"hyperactivation\". We study a circularly symmetric setup with sperm emerging at the center and searching a finite-area disk. We explore the implications of switching on search efficiency. The first proposed model describes an adaptive search strategy in which sperm achieve improved spatial coverage without cell-to-cell or environment-to-cell communication. The second model that we study adds a small amount of environment-to-cell communication. The models resemble macroscopic search-and-rescue tactics, but without organization or networked communication. Our findings provide a quantitative framework linking sperm motility patterns to efficient search strategies, offering insights into sperm physiology and the stochastic search dynamics of self-propelled particles.","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12110710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149676","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

Bootstrap Confidence Intervals for Multiple Change Points Based on Two-Stage Procedures. 基于两阶段过程的多变化点自举置信区间。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-17 DOI: 10.3390/e27050537

Li Hou, Baisuo Jin, Yuehua Wu, Fangwei Wang

引用次数: 0

Fixed-Time Cooperative Formation Control of Heterogeneous Systems Under Multiple Constraints. 多约束条件下异构系统的固定时间协同编队控制。

IF 2.1 3区物理与天体物理

Entropy Pub Date : 2025-05-17 DOI: 10.3390/e27050538

Yandong Li, Wei Zhao, Ling Zhu, Zehua Zhang, Yuan Guo

引用次数: 0