The project proposes a novel way to numerically model delamination or debonding in layered structures using beam-type finite elements for the layers, which can be geometrically linear or nonlinear, and mixed-mode, rate-dependent cohesive-zone models (CZMs) for the interface, both based on recent cutting-edge research. In this way, the project shall provide new, more accurate, more intuitive and computationally much cheaper techniques than those currently available, that will be implemented in open-source user-friendly software and experimentally validated for mode-I, mode-II and mixed-mode tests on aluminium-epoxy adhesive joints. At the end of the project, engineers will be able to numerically simulate tests with different dimensions and material properties to characterise the fracture energy and its rate dependence for existing or new adhesives or other interfaces, with applications including but not limited to metal joints, composite delamination, reinforced elastomers or dissection of soft tissues in biomedical engineering. The research builds on complementary and internationally highly recognised expertise of the researcher and his PhD supervisor at the University of Rijeka (on geometrically nonlinear beam models) and the supervisor at Brunel University (on CZMs and nonlinear finite-element analysis). The researcher will have the opportunity to (a) develop world-leading knowledge and expertise in a research topic of significant importance for industrial and real-life applications, (b) transfer it to a country where such expertise is limited and (c) boost his scientific career and international profile through high-quality publications and via his leadership in the development of the software. This will also provide numerous networking opportunities with other research groups and industries worldwide for all parties involved in the action.

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.

The turnover of mining and quarrying in Europe reaches up to 224 billion Euros and has generated EUR 64.9 billion of value added, 1% of the non-financial business economy total. The rise of key enabling technologies and the urbanisation and industrialisation of emerging economies in combination with increase in population and living standards will continue to drive growing demand for raw materials. The need to extract raw materials in a profitable, environmentally sound, and safe way for both mining workforce and communities is driving the mining industry towards innovative approaches to transform operations. Even though Industry 4.0 offers a wide spectrum of solutions, and intelligent technologies to address respective challenges, the mining industry hesitates to adopt such innovative approaches when compared to downstream industries. In addition, the need for a human-centred, environmentally oriented and society-driven approach is emerging in developing Industrial Internet of Things technologies for mining. Dig_IT will address the needs of the mining industry to move forward towards a sustainable use of resources while keeping people and environment at the forefront of their priorities. In order to achieve that Dig_IT proposes the development of a smart Industrial Internet of Things platform (IIoTp) that will improve the efficiency and sustainability of mining operations by connecting cyber and physical systems. The platform will collect data from sensors at 3 levels: (i) human, (ii) assets, (iii) environment and will also incorporate both market real time and historical data. The impact of Dig_IT to the European Mining industry, but also the society itself, can be summarised in the following (with a horizon of 4 years after project ends): (i) increase of the mining efficiency by 17%, (ii) increased OEE for machines and loading by 20% and 18% respectively, (iii) 19% reduction of CO2eq, (iv) about 310 new jobs created and (v) over 28M EUR ROI for the consortium.