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FF2021 -1049 - Physics -informed machine learning for rapid fatigue assessments in offshore wind farms Jun 1, 2021 - Oct 6, 2022
14 UK offshore wind farms (1.2 GW) are approaching their designed lifetime by 2030, and around 700 monopiles are to be decommissioned every five years onwards. Understanding the actual accumulated fatigue and the ability to predict fatigue risk in th... Read More about FF2021 -1049 - Physics -informed machine learning for rapid fatigue assessments in offshore wind farms.

TRANSFORM: The development of a mobile phone app to capture the journey time and patient experience throughout a day in the life of cancer patient attending hospital. Oct 1, 2021 - Oct 31, 2022
People with cancer have to travel to specialist cancer hospitals to access treatments such as radiotherapy. A number of studies have identified an association between patients living further away from a cancer centre and being less likely to be treat... Read More about TRANSFORM: The development of a mobile phone app to capture the journey time and patient experience throughout a day in the life of cancer patient attending hospital..

KTP R4 2022-23 Adopting a Circular Economy Business Model for Resource Efficiency in ICT industry (ACE4ICT) Jul 3, 2023 - Jul 2, 2025
Techbuyer, winner of the Queen's award, is an innovative ICT company specialised in enterprise IT for the system-level data centre sector. It now seeks to use its expertise in data centre refurbishments to move into B2B electronic devices such as PC... Read More about KTP R4 2022-23 Adopting a Circular Economy Business Model for Resource Efficiency in ICT industry (ACE4ICT).

Mortality Risk Prediction of ICU Patients with Sepsis Considering Dynamic Time Series Characteristics under Uncertainty Mar 31, 2023 - Mar 30, 2025
ICU patients with sepsis have rapidly changing conditions and high mortality. Because of it, early prediction and timely intervention are the keys to reducing the risk of death.
The project aims to provide critical care physicians with an int... Read More about Mortality Risk Prediction of ICU Patients with Sepsis Considering Dynamic Time Series Characteristics under Uncertainty.