Alec’s fellowship project has two key stands: 1. Finding ways to assess different forms of evidence (e.g., the scientific literature and indigenous and local community knowledge) to help improve evidence-informed decision-making; and 2. Developing new approaches to assessing the reliability and relevance of evidence to different decision-making contexts. Alec’s work helps to ensure that in the future, evidence-informed decision-making combines diverse sources of knowledge for decision-making, whilst making sure to assess the reliability and relevance of each piece of evidence so we can provide tailored recommendations to decision-makers based on their local context. Alec is also particularly interested in how the effectiveness of conservation interventions varies geographically, taxonomically, and socioeconomically, and whether we can predict this – i.e., how can we predict whether a conservation intervention is likely to work in a given local context?
A key and rewarding part of his work is co-designing and developing online tools to help practitioners in the field to determine the best conservation interventions for them to use in their local patch for a given issue using a structured evidence-based process. This has involved a fruitful collaboration with over a dozen different conservation organisations in the UK and abroad called ‘Evidence Champions’ that promote and deliver evidence-based conservation. Alec is continuing to work to create decision support and evidence assessment tools that combine different forms of evidence and knowledge to make better evidence-based decisions in conservation. He works within the Conservation Evidence project and was recently part of the team that won the Vice-Chancellor’s Award for Research Impact and Engagement 2023 for their work on transforming conservation through promoting and facilitating evidence-based practice and decision-making.
A newer element of Alec’s research focuses on Artificial Intelligence and whether machine learning can help to accelerate the evidence synthesis pipeline, translating scientific evidence into useful recommendations for practice and policy more quickly and rigorously. He is also working on applying AI to invasive species surveillance to see whether Open Source Intelligence can help us better stop biological invasions before they become too difficult to control. Overall, Alec’s work is tied together by the unifying theme of applying the patchy global evidence base to inform more effective, local conservation actions.
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The dynamics of infectious disease (ID) require fast accurate diagnosis for effective management and treatment. Without affordable, accessible diagnostics, syndromic or presumptive actions are often followed, where positive cases may go undetected in the community, or mistreated due to wrong diagnosis. In many low and middle income countries (LMICs), this undermines effective clinical decision-making and infectious disease containment.
Unsteady effects occur in many natural and technical flows, for example around flapping wings or during aircraft gust encounters. If the unsteadiness is large, the resulting forces can be quite considerable. However, the exact physical mechanisms underlying the generation of unsteady forces are complex and their accurate prediction remains challenging. One strategy is to identify the dominant effects and describe these with simple analytical models, first proposed a hundred years ago. When used successfully, this approach has the advantage that it also gives us a conceptual understanding of unsteady fluid mechanics.
In this lecture I will explain some of these ideas and demonstrate how they can still be useful today. As a practical example, I will show how the forces experienced in a wing-gust encounter can be predicted – and how the predictions can be used to mitigate the gust effects. The lecture will be illustrated with images and videos from simple, canonical, experiments.
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