We study the properties of nonequilibrium systems modelled as spin models without defined Hamiltonian as the majority voter model. This model has transition probabilities that do not satisfy the condition of detailed balance. The lack of detailed balance leads to entropy production phenomena, which are a hallmark of the irreversibility. By considering that voters can diffuse on the lattice we analyze how the entropy production and how the critical properties are affected by this diffusion. We also explore two important aspects of the diffusion effects on the majority voter model by studying entropy production and entropy flux via time-dependent and steady-state simulations. This study is completed by calculating some critical exponents as function of the diffusion probability.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevE.101.012130 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Secure privacy-preserving record linkage system from re-identification attack.
PLoS One
January 2025
Department of Computer Science and Engineering at Hanyang University ERICA, Ansan-si, Gyeonggi-do, South Korea.
Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems' resistance to re-identification attacks.
View Article and Find Full Text PDFInborn errors of immunity (primary immunodeficiencies).
Allergy Asthma Clin Immunol
January 2025
Division of Allergy and Clinical Immunology, Department of Pediatrics, Montreal Children's Hospital, McGill University Health Centre, Montreal, QC, Canada.
Primary immunodeficiencies (PID), now often referred to as inborn errors of immunity (IEI), are a large heterogeneous group of disorders that result from deficiencies in immune system development and/or function. IEIs can be broadly classified as disorders of adaptive immunity (e.g.
View Article and Find Full Text PDFMulti-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.
J Imaging Inform Med
January 2025
Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades.
View Article and Find Full Text PDFA Multi-Source Circular Geodesic Voting Model for Image Segmentation.
Entropy (Basel)
December 2024
Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters.
View Article and Find Full Text PDFTCellPredX: A Novel Approach for Accurate Prediction of Hepatitis C Virus Linear T Cell Epitopes.
ACS Omega
December 2024
College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
Article Synopsis
- Hepatitis C Virus (HCV) is a serious bloodborne virus causing liver disease, and currently, there are no effective vaccines to prevent its spread, highlighting the importance of understanding T cell epitopes (TCEs) in immune response.
- TCellPredX is a new prediction tool designed to identify TCEs related to HCV by using advanced machine learning techniques and a variety of feature encodings, which together improve the predictive accuracy of TCE identification.
- The tool has demonstrated high accuracy scores (0.900 and 0.897) in identifying relevant peptides for vaccine development, establishing TCellPredX as a significant resource for future research aimed at creating an effective HCV vaccine.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!