Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.
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http://dx.doi.org/10.1038/s41467-020-20398-4 | DOI Listing |
Environ Monit Assess
January 2025
Faculty of Water Supply and Environmental Engineering, Arba Minch University Water Technology Institute, P.O.B 21, Arba Minch, Ethiopia.
In developing nations, the biggest threat to public health is the quality of the water. The Kulfo River provides the majority demand of the domestic water and irrigation along its course; however, it is observed that wastes from anthropogenic and natural activities enter the river. Therefore, this study aimed to examine the pollution status by integrating conventional methods with benthic macroinvertebrates.
View Article and Find Full Text PDFAir conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Integrative Anatomy, Nagoya City University Graduate School of Medicinal Sciences.
Neurons in the cerebral cortex and hippocampus discharge synchronously in brain state-dependent manner to transfer information. Published studies have highlighted the temporal coordination of neuronal activities between the hippocampus and a neocortical area, however, how the spatial extent of neocortical activity relates to hippocampal activity remains partially unknown. We imaged mesoscopic neocortical activity while recording hippocampal local field potentials in anesthetized and unanesthetized GCaMP-expressing transgenic mice.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Information Engineering, Electronics and Telecommunications, University of Rome La Sapienza, Piazzale Aldo Moro 5, Rome, 00185, ITALY.
Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity in vivo remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. Approach: To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity.
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