Non-Markovian dynamics pervades human activity and social networks and it induces memory effects and burstiness in a wide range of processes including interevent time distributions, duration of interactions in temporal networks, and human mobility. Here, we propose a non-Markovian majority-vote model (NMMV) that introduces non-Markovian effects in the standard (Markovian) majority-vote model (SMV). The SMV model is one of the simplest two-state stochastic models for studying opinion dynamics, and displays a continuous order-disorder phase transition at a critical noise. In the NMMV model we assume that the probability that an agent changes state is not only dependent on the majority state of his neighbors but it also depends on his age, i.e., how long the agent has been in his current state. The NMMV model has two regimes: the aging regime implies that the probability that an agent changes state is decreasing with his age, while in the antiaging regime the probability that an agent changes state is increasing with his age. Interestingly, we find that the critical noise at which we observe the order-disorder phase transition is a nonmonotonic function of the rate β of the aging (antiaging) process. In particular the critical noise in the aging regime displays a maximum as a function of β while in the antiaging regime displays a minimum. This implies that the aging/antiaging dynamics can retard/anticipate the transition and that there is an optimal rate β for maximally perturbing the value of the critical noise. The analytical results obtained in the framework of the heterogeneous mean-field approach are validated by extensive numerical simulations on a large variety of network topologies.
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http://dx.doi.org/10.1103/PhysRevE.102.062311 | DOI Listing |
Health Inf Sci Syst
December 2025
Division of Software, Yonsei University, Mirae Campus, Yeonsedae-gil 1, Wonju-si, 26493 Gangwon-do Korea.
Purpose: Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
View Article and Find Full Text PDFGenetically encoded calcium (Ca ) indicators (GECIs) are widely used for imaging neuronal activity, yet current limitations of existing red fluorescent GECIs have constrained their applicability. The inherently dim fluorescence and low signal-to-noise ratio of red-shifted GECIs have posed significant challenges. More critically, several red-fluorescent GECIs exhibit photoswitching when exposed to blue light, thereby limiting their applicability in all- optical experimental approaches.
View Article and Find Full Text PDFSpatial transcriptomics (ST) provides critical insights into the complex spatial organization of gene expression in tissues, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is essential for understanding tissue architecture and the mechanisms underlying various biological processes, including development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially aware dimensionality reduction method for spatial transcriptomics (ST) data.
View Article and Find Full Text PDFThe Circle of Willis (CW) is a critical cerebrovascular structure that supports collateral blood flow to maintain brain perfusion and compensate for eventual occlusions. Increased tortuosity of highrisk vessels within the CW has been implicated as a marker in the progression of cerebrovascular diseases especially in structures like the internal carotid artery (ICA). This is partly due to age-related plaque deposition or arterial stiffening.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
Bearings are critical in mechanical systems, as their health impacts system reliability. Proactive monitoring and diagnosing of bearing faults can prevent significant safety issues. Among various diagnostic methods that analyze bearing vibration signals, deep learning is notably effective.
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