We study a coevolving nonlinear voter model describing the coupled evolution of the states of the nodes and the network topology. Nonlinearity of the interaction is measured by a parameter q. The network topology changes by rewiring links at a rate p. By analytical and numerical analysis we obtain a phase diagram in p,q parameter space with three different phases: Dynamically active coexistence phase in a single component network, absorbing consensus phase in a single component network, and absorbing phase in a fragmented network. For finite systems the active phase has a lifetime that grows exponentially with system size, at variance with the similar phase for the linear voter model that has a lifetime proportional to system size. We find three transition lines that meet at the point of the fragmentation transition of the linear voter model. A first transition line corresponds to a continuous absorbing transition between the active and fragmented phases. The other two transition lines are discontinuous transitions fundamentally different from the transition of the linear voter model. One is a fragmentation transition between the consensus and fragmented phases, and the other is an absorbing transition in a single component network between the active and consensus phases.
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http://dx.doi.org/10.1038/s41598-017-13047-2 | DOI Listing |
Background: Early detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi-modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer's disease.
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View Article and Find Full Text PDFSci Rep
January 2025
School of Information Engineering, Changji University, Changji, 831100, Xinjiang, China.
Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach consists of three key stages: data preprocessing, model training and integration, and result analysis with feature interpretation.
View Article and Find Full Text PDFWorld J Mens Health
December 2024
Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Purpose: Information retrieval (IR) and risk assessment (RA) from multi-modality imaging and pathology reports are critical to prostate cancer (PC) treatment. This study aims to evaluate the performance of four general-purpose large language model (LLMs) in IR and RA tasks.
Materials And Methods: We conducted a study using simulated text reports from computed tomography, magnetic resonance imaging, bone scans, and biopsy pathology on stage IV PC patients.
Bone Joint J
January 2025
Division of Informatics, Imaging & Data Sciences, The University of Manchester, Manchester, UK.
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