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will examine how the configuration, connectivity and condition of these dynamic water systems, and their surrounding land cover, influence environmental buffering, biodiversity and social benefits
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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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function of urban blue spaces influence perceptions. It will subsequently explore and evaluate the types of information and knowledge required to improve the understanding and appreciation of urban blue
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academia and industry. Uncover and quantify critical degradation mechanisms to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through
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to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through clear communication to diverse audiences, including industry stakeholders and
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This project is sponsored by EPSRC, Cranfield University and a consortium led by ETN global. ESPRC will provide stipend (£22,000 per annum) and cover UK fees. The consortium will provide supervision
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using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new