All students will be engaged in the co-design of their own research projects in collaboration with the UK Food Systems Academy which is the gateway for students to supervisors and core project ideas (project kernels). Early in Year 1, students will select from a catalogue of project kernels that will form the basis of their rotations with potential supervisors. Following the rotations, thesis proposals will be finalised in a capstone two-day Project and Thesis Proposal Intensive Workshop with partners from the Food System Academy. PhD research projects topics will be finalised at the end of year 1, and will initiate at the beginning of year 2.

Fake news is defined as false stories that appear to be news, spread on the internet or other media, usually created to influence political views or for satire. It has been argued that fake news is one of the greatest threats to democracy today and a study by Pew Research Centre found 64% of US adults believe fake news has caused a 'great deal of confusion' about current events. Social media platforms often act as a catalyst for these kinds of articles to spread around the world quickly, where around 1 in 4 US adults have shared fake news either knowingly or unknowingly (Pew Research, 2016). More worryingly, around 59% of articles shared on social media have never been clicked by the sharer - often as a result of misleading (or 'clickbait') headlines. In some cases, companies such as Cambridge Analytica, have leveraged these platforms and news articles to target particular social groups thus influencing people's views around elections and referendums. It is therefore evident that there are a number of issues surrounding the media we consume and a number of questions emerge when assessing the validity of news articles: Is the article factually accurate? Does the headline correspond to the message outlined in the body of the article? Does the publisher have an agenda and if so, what is it? Is it an older article that has been reposted? (IFLA.org, n.d.) To address the stated problem and questions, the proposed PhD work aims to facilitate the verification of online articles and mitigate the propagation of fake news. To meet this aim, the project will involve the: Investigation and development of state-of-the-art ML algorithms and NLP techniques that will help determine: the accuracy of an online article, whether the article title matches its content, whether the publishing source is trustworthy and the date of the original article. Investigation and application of interaction design principles and HCI practices to ensure that the proposed system is transparent, trustworthy and usable to non-expert users. This work draws insight and experience from preliminary work conducted as part of my dissertation which focused on the development of a ML-enabled mobile app to determine the political bias of news content using ML and then provide alternative articles on the same topic. In brief, the proposed work will seek to leverage modern advancements in ML and Human-Computer Interaction to create a platform to prevent the spread of fake news. In particular, the project aligns exceptionally well with the research carried out by three research groups in the Department of Computer Science: i) the Intelligent Data Analysis group; ii) the Human-Computer Interaction group; and iii) the Interactive Multimedia Systems group. In addition, the project is well-situated within the Brunel Digital Science and Technology Hub of CEDPS and, in particular, the work conducted under the Industrial and Applied AI research theme. Moreover, it is congruent with the agenda of the newly formed Institute for Digital Futures of the University. More widely, it aligns with the Government's Industrial Strategy, namely "AI and data". Finally, 'Artificial Intelligence Technologies' is an EPSRC research area of growing interest, while 'Human-Computer Interaction' is an area of maintained focus.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Concrete filled stainless steel columns (CFSSC) combine the advantages of both concrete and stainless steel columns to offer a durable and high load carrying solution. Concrete-filled stainless steel columns are used in many engineering applications including: columns, beams, trusses and many forms of structural components. The stainless steel grades most commonly encountered in construction industry are Austenitic, Duplex/Lean duplex and Ferritic grades. Austenitic stainless steels are generally selected for structural applications which require a combination of good strength, corrosion resistance, formability and weldability. Where higher strength and/or higher levels of corrosion resistance are required, the duplex stainless steels or lean duplex stainless steels, with lower material cost, are most suitable. Ferritic stainless steels, containing no or very low nickel content, provide a cost effective solution to applications where strong and moderately durable structural elements with attractive metallic surface finish are required.

Current research is looking at the creation of customised blockchain architecture to represent and perform functionality of a Central Bank's Balance Sheet (Blockchain Balance Sheet - BBS). The aim of the research is to design, build and evaluate a prototype of BBS, that could be utilised as unconventional monetary policy tool for early detection (Early Warning System (EWS)), management and mitigation of future fiscal crises (fiscal QE). Objectives Investigate and evaluate theory and practice, through existing theoretical work, in relation to the utilisation of blockchain technology for banking business, and central bank's balance sheet application for fiscal EWS and QE; Design and build BBS prototype, that is customised for blockchain-based representation of the central bank's balance sheet to be used as a framework for modelling and simulation of: In a blockchain compatible environment develop an analytical framework for associated data mining and analytics that could be utilised for effective fiscal EWS and QE; Design and conduct experiments (through modelling and simulation) for theory synthesis and algorithm evaluation; Reflect on the findings and implications for analytics concerned for the central bank's balance sheet and effectiveness of EWS and fiscal QE using BBS. What questions does the project intend to answer? How to develop a new design of blockchain based architecture that is customised for functionality of a central bank's balance sheet (BBS)? How to create an effective monetary policy decisions support system, that utilises BBS-based data mining and analytics, to enable timely responses to future fiscal shocks? What demonstrates its effectiveness? What other utility (if any) does the new architecture of this unconventional monetary policy tool, can provide? What demonstrates that utility? The novel physical science/engineering methodology that will be carried out during the course of the project. What will the student be doing? Current study utilises combination of "Positivist Approach", "Design Science Research" and "Modelling and Simulation". In order to design customised BBS and identify variables that are required for effective fiscal EWS and QE using BBS, this study applies the "heuristic search technique", outlined by Hevner, A. R. et al., 2004, where the use of heuristic to find "good" design solutions. The blockchain architecture will be coded, utilising existing blockchain practices, but tailored to the needs of the central bank's balance sheet: i.e. an artefact that is custom-made for a specific set of problems, mainly dynamic signal extraction and funds flow analytics, to identify potential future fiscal deficit on the government accounts in the central bank