To be confirmed

Austerity, technology, vulnerability: the role of digital technologies for young people in situations of vulnerability and the organisations which support them The 2008 financial crisis became the justification for some of the deepest cuts to public services in the history of the United Kingdom (UK) (Lowndes and Pratchett, 2012), enabling the relatively unimpeded enactment of austerity policies since 2010. Whilst this is often claimed as a retreat of the state and a stripping back of its responsibilities, in practical terms, the state became more, yet 'selectively' active (Harvey, 2007) in favour of the interests of capital. The impacts of this were wide-reaching, and intensified the transformation of the politico-economic and sociocultural environment of the country in aid of the neoliberal project. Whilst no area of UK policy remained untouched by austerity policies, one of the key areas of financial cuts and policy transformations have been services delivered to children and young people, which have seen larger reductions in levels of funding than public services generally (Youdell and McGimpsey, 2015). Austerity has altered the dominant political condition into one of scarcity and precarity (Berlant, 2011), fundamentally altering the values, practices and experiences of young people and the organisations which work to support them. Increasingly, these organisations are charities and voluntary sector organisations, who have aimed to fill the gap in provision left by the state in the wake of austerity (Clayton, Donovan and Merchant, 2015). As charities become increasingly responsible for the provision of services to young people in situations of vulnerability, they find themselves operating in a heavily marketized and financialized environment, competing against each other for 'tenders' for contracts to deliver vital services (Buckingham, 2012) or for funding from grantmaking organisations. The neoliberal, marketized mindset which this results in means that charitable decisions cannot ever be made solely for a public good but instead must have an eye towards keeping the charity 'competitive' or to demonstrate the 'impact' of the organisation's work. In parallel to these developments, digital technologies have become more widely used. Whilst this affords young people in situations of vulnerability quicker, easier access to services, connections to people they care about, and things that they enjoy, the rise of platform and surveillance capitalism via these technologies has resulted in the creation and extraction of data for the purposes of generating wealth for private companies (Srnicek, 2016). In the specific case of young people in situations of vulnerability - who are more prone to having data created about them that they are not privy to - these technologies may be used to make decisions about them which fail to contextually understand 'the whole story'. In such a situation, digital technologies may actively reproduce inequalities rather than mitigate them. As organisations who work with these young people become increasingly encouraged to engage with the digital, this PhD aims to understand what happens when these two worlds meet. Firstly, what are these organisations like, post-austerity? What are their practices and processes? How do they (and their young people) imagine their worlds? Secondly, how are the cultures of the digital beginning to affect these organisations? How do organisations see and engage with the digital, and how do designers and developers imagine both their own work and the work of these organisations? Finally, in light of these points, how can we design and use technologies with young people in situations of vulnerability that respect their agency and which meaningfully contribute towards their lives?

Many older people feel that creative arts activities have had a positive impact on their health. Recently, research has focussed on the value and impact of the arts on particular groups of older people; especially those suffering from dementia. The results of these studies, however are primarily anecdotal and lack rigorous methodology; therefore, it is difficult to draw firm conclusions about the impact of different arts activities on wellbeing. ?Ageing Creatively? will establish a robust range of methodologies, enabling us to collect meaningful data and evaluate the relative benefits of creative arts interventions on the wellbeing of older people. There is a need to ask some fundamental questions: can the creative arts really foster a sense of wellbeing, reducing the sense of isolation for many older people? Are there greater benefits from active participation rather than more passive experiences (eg song-writing versus attending a concert)? Are some artforms more effective than others? Are the benefits significantly different from those of other regular social contact? It is vital to achieve greater understanding of the processes and potential therapeutic outcomes of creative activity and especially to evaluate the appropriateness of different methodologies. This is the fundamental aim of the current proposal. An initial critical literature review will build a comprehensive picture of all previous and relevant research already available in this field. ?Ageing Creatively? will offer value for money by using established infrastructures where appropriate and developing approaches for further research. Different methodological approaches will be examined, building not only on Newcastle University?s own recent experience, but on the latest international research. Secondly, a series of weekly 2-hour sessions involving groups of around 10 participants aged over 55 and not currently in the workforce will cover three creative areas: creative writing and reading, music (song-writing and concert-going), and fine art (painting/ sculpting and exhibition visits), with a further ?control? group experiencing non-creative activities (eg board games). ?Ageing Creatively? will develop and evaluate the processes and methodologies around a range of creative activities designed to promote mental health and wellbeing in later life which can inform relevant future policy and practice.

The past decade has seen Machine Learning emerge as a prominent new tool in molecular modelling. In particular, Machine Learning Interatomic Potentials (MLIPs) are methods that can reproduce potential energy surfaces with ab initio accuracy at a fraction of the cost of DFT and Quantum Chemistry methods. The resulting lower cost of the MLIP models enables the simulation of larger systems (100x) and longer simulation times scales (100x) to be routinely studied. This promises to revolutionize research sectors that benefit from molecular simulations such as novel material discovery (energy), drug design and protein modelling (medical). In the recent years, the field has seen multiple developments taking place. While earlier MLIPs were based on Bayesian statistics (e.g. linear models, kernel models), recent advances in computing (e.g. powerful GPUs) led to the development of neural network models (NN). These models treat data in a very high-dimensional space, making use of millions of parameters, which allows them to capture complex patterns and interpolate on massive amounts of data. The critical process behind NN-MLIP effectiveness is training which involves feeding the network different configurations labelled with DFT evaluated properties (energy, forces etc.) and tuning the network's internal parameters until it predicts these properties accurately. Training generally requires non-trivial and meticulous selection of data and the resulting models often display poor transferability to systems they have not been trained on. This project aims to improve our understanding in this area by exploring the capabilities of newly developed MACE foundational MLIPs (FMLIPs) for modelling complex battery materials and interfaces - a particularly challenging and interesting system to study. MACE is a message-passing graph-neural network MLIP that has proven very effective in learning the chemical space and producing stable molecular dynamics in a wide variety of systems ranging from inorganic crystals and molecular liquids to complex organic-inorganic interfaces. The MACE foundational models have been pretrained on massive datasets containing various systems and the hypothesis (which will be thoroughly tested in this PhD project) is that they can be used to model novel systems with little retraining or fine-tuning. If proven true, FMLIPs have the potential to dramatically reduce the amount and complexity of training, making models even cheaper and accessible to a wider scientific audience. This project will investigate how fine-tuning can be optimized to ensure models are stable in MD, and accurate not only on reproducing energies and forces but also the thermodynamics and kinetics of the systems under study. This project will focus on a particularly challenging system for FMLIPs: battery materials and interfaces. Specifically, the electrolyte mixture of Li-Ion batteries will be studied with the aim to improve our understanding of battery properties such as charging speed and lifetime which are underpinned by atomic-level processes. The electrolyte mixture is a crucial component of batteries that facilitates ion transport between electrodes. The long timescales required to capture important properties (10-100ns) and the complexity of the molecular liquid make the electrolytes a difficult system to simulate. Special-purpose MACE MLIPs have recently been shown to produce stable dynamics for the desired timescales and accurately predict properties both within and between molecules of the electrolyte. This work will explore whether fine-tuning general-purpose MACE FMLIPs for electrolytes can yield the same results with significantly less human effort. If true, this will open the possibility to easily retrain on different levels of ab initio theory and compare first-principle predictions directly to experiment.

My project investigates the process as well as the structural implications of the decline in the rate of occurrence of finite pronominal enclisis in fifteenth-century Spanish. I will collect and quantitatively analyse data from authentic medieval texts. I will use the R software environment to perform logistic regression analysis and subsequently construct and compare best-fit logistic curves. My research will provide linguists with a more thorough understanding of enclisis and proclisis at all periods of Spanish. Furthermore, given the similarities across Romance, my results could be used in further research by scholars of cliticisation and pertinent systems in related languages.