ERA is a digital repository of original research produced at The University of Edinburgh. The archive contains documents written by, or affiliated with, academic authors, or units, based at Edinburgh that have sufficient quality to be collected and preserved by the Library, but which are not controlled by commercial publishers. Holdings include full-text digital doctoral theses, masters dissertations, project reports, briefing papers and out-of-print materials.
Information on current research activity including staff, projects and publications is available via the Edinburgh Research Explorer.
Communities in ERA
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Modelling bacterial biofilms in spatially heterogeneous environments
(The University of Edinburgh, 2023)Biofi lms are communities of one or more species of microorganism which have adhered both together and to a surface. Biofi lms are ubiquitous in nature, with up to 80% of bacterial life on earth estimated to be found in ... -
Evolutionary ecology of parasite strategies for within-host survival
(The University of Edinburgh, 2023-02-08)Plasmodium parasites, the causal agents of malaria, engage in complex interactions with their hosts, however despite decades of research much of their life cycle remains unexplored. A deeper understanding of the strategies ... -
Using the Integrative Behavioural Model to explore the factors influencing nurse adherence towards personal protective equipment (PPE)
(The University of Edinburgh, 2023-02-08)BACKGROUND: Infectious disease has become an increasing field of interest due to the recent COVID-19 pandemic on a long-standing background of nosocomial infections, which have contributed to excess and largely avoidable ... -
Absence of God: case studies on the use and value of Nietzsche in avant-gardist thought 1905-1945
(The University of Edinburgh, 2023-02-08)This thesis considers how Nietzsche was interpreted and misinterpreted by a range of artists and writers who were prominent in avant-garde circles in the first half of the twentieth century. Through a series of case ... -
Meta-learning to optimise: loss functions and update rules
(The University of Edinburgh, 2023-02-07)Meta-learning, aka “learning to learn”, aims to extract invariant meta-knowledge from a group of tasks in order to improve the generalisation of the base models in the novel tasks. The learned meta-knowledge takes various ...
