This proof of concept proposal will build on an innovative monolithic diamond Raman laser, with near quantum limited efficiency demonstrated during the ERC starting grant DiaL. A range of important applications will be scoped and the technology tailored to meet the most promising market demands. The result will be a platform approach to compact laser design taking existing Nd:YAG-based lasers to wavelengths with a strong applications pull that cannot be met with current technology. This will extend the product lines and market penetration of EU laser companies. This programme has two complementary tracks: working with specialists, we will identify the market pull and the required laser specifications; in parallel, we will engineer the science developed within DiaL to address these needs in a compact, robust and cost-effective way.

Parasitic diseases, including malaria, leishmaniasis, and trypanosomiasis are amongst the most prevalent diseases world wide collectively with the poorest availability of effective drugs. Human African trypanosomiasis (HAT) has been reported to have a greater morbidity and mortality than HIV/AIDS in some locations. Parasitic disease is difficult to treat because the parasites become closely connected with the living host and it is therefore difficult to find drugs that attack only the parasite. Available drugs are few and are also toxic to humans. In laboratory experiments, we have shown that novel compounds designed and prepared in at the University of Strathclyde and University of Dundee are potentially able to fill the gap in drug availability. Our project is to optimise the effectiveness of our compounds to provide candidate drugs for full clinical development.

A key element of understanding the health of nuclear power plant reactors is the analysis of condition monitoring data generated during routine operation. As reactors age, there is the desire to extend their operational lives, provided it is still safe to do so, to ensure a continued supply of low-carbon base load generation. This project is examining the use of machine learning techniques to supplement existing diagnostic knowledge and operational expertise to provide enhanced understanding of reactor core component health. Case studies are being explored from two different reactor designs to develop novel semi-supervised machine learning techniques to aid with assessment of existing health and make predictions of future condition of key plant components. The specific novel challenges for the research focuses on a) dealing with the imbalance of data where the vast majority of the data represents normal, routine operation and little faulty data exists and therefore techniques to incorporate human expertise is required and b) providing suitable explicability of results within the regulated, safety related nuclear industry.