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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Author "IRC"

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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Author "IRC"

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  • MacCarron, Pádraig; Maher, Paul J.; Fennell, Susan C.; Burke, Kevin; Gleeson, James P.; Durrheim, Kevin; Quayle, Michael (Public Library of Science, 2020)
    Shared opinions are an important feature in the formation of social groups. In this paper, we use the Axelrod model of cultural dissemination to represent opinion-based groups. In the Axelrod model, each agent has a set ...
  • Devine, Kevin M.; Vynnycky, Michael; Mitchell, Sarah L.; O’Brien, Stephen B. G. (Oxford University Press, 2020)
    This paper investigates the different possible behaviours of a recent asymptotic model for oscillation-mark formation in the continuous casting of steel, with particular focus on how the results obtained vary when the heat ...
  • Hurd, Thomas R; Cellai, Davide; Melnik, Sergey; Shao, Quentin, H (2016)
    The scope of financial systemic risk research encompasses a wide range of interbank channels and effects, including asset correlation shocks, default contagion, illiquidity contagion, and asset fire sales. This paper ...
  • Melnik, Sergey; Porter, Mason A; Mucha, Peter J; Gleeson, James P. (American Institute of Physics, 2014)
    We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each ...
  • Hurd, Thomas R; Gleeson, James P.; Melnik, Sergey (Public Library of Science, 2017)
    We introduce a probabilistic framework that represents stylized banking networks with the aim of predicting the size of contagion events. Most previous work on random financial networks assumes independent connections ...
  • Fennell, Susan C.; Burke, Kevin; Quayle, Michael; Gleeson, James P. (American Chemical Society, 2021)
    When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured ...

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