'Language Acts and Worldmaking' argues that language is a material and historical force, not a transparent vehicle for thought. Language empowers us, by enabling us to construct our personal, local, transnational and spiritual identities; it can also constrain us, by carrying unexamined ideological baggage. This dialectical process we call 'worldmaking'. If one language gives us a sense of place, of belonging, learning another helps us move across time and place, to encounter and experience other ways of being, other histories, other realities. Thus, our project challenges a widely held view about ML learning. While it is commonly accepted that languages are vital in our globalised world, it is too often assumed that language learning is merely a neutral instrument of globalisation-a commercialised skill set, one of those 'transferable skills' that are part of a humanities education. Yet ML learning is a unique form of cognition and critical engagement. Learning a language means recognising that the terms, concepts, beliefs and practices that are embedded in it possess a history, and that that history is shaped by encounters with other cultures and languages. To regenerate and transform ML we must foreground language's power to shape how we live, and realise the potential of ML learning to open pathways between worlds past and present. Our project realises this potential by breaking down the standard disciplinary approaches that constrain Spanish and Portuguese within the boundaries of national literary and cultural traditions. We promote research that explores the vast multilingual and multicultural terrain constituted by the Hispanic and Lusophone worlds, with their global empires and contact zones in Europe, the Americas, and Africa. Understanding Iberia as both the originator and the product of global colonising movements places Iberian Studies on a comparative, transnational axis and emphasizes diasporic identities, historic postcolonial thinking, modern decolonial movements and transcultural exchange. Our research follows five paths linked by an interest in the movement of peoples and languages across time and place. 'Travelling concepts' researches the stories and vocabularies that construct Iberia as a cultural crossroads, a border between East and West, a homeland for Jews, Muslims and Christians. We examine the ideological work performed by the cultural semantics Iberia, Al-Andalus, and Sefarad in Spanish, Portuguese, English, French, German, Arabic, Hebrew and ladino (Judeo-Spanish), from the Middle Ages to the present, in Europe and beyond. 'Translation acts' turns to the theatrical narrative, investigating how words, as performed speech and embodied language create a world on stage. Through translation, we travel across time and space, interrogating the original words and bringing them to our time and place. This strand exploits theatre's capacity to (re)generate known and imagined worlds. 'Digital Modelling as an act of translation' examines the effects of digital, mobile and networked technology upon our concept of 'global' culture, and what kinds of 'translation' are enacted as information enters and leaves the digital sphere in the context of Hispanic and Lusophone cultures. 'Loaded Meanings and their history' demonstrates the centrality of historical linguistics to cultural understanding, by investigating the process and significance of the learned borrowings in Ibero-Romance. Such borrowings acquire 'loaded' meanings that reflect and shape people's attitudes and worldviews. Finally, the agents of language learning-teachers-are the focus of the fifth strand, 'Diasporic Identities and the Politics of Language Teaching'. This strand analyzes the life stories of native teachers of Spanish, Portuguese and Catalan to identify the vocabularies and narrative patterns that help them make sense of and interrogate their professional and personal identities as transnational cultural agents in the UK.

We live in the "Era of Mathematics" (UKRI, 2018), in which mathematics research has deep and widespread impact. Medical imaging is enhanced using the theory of inverse problems. Predicting sewage contamination in waterways after storms requires solving complicated systems of hydrodynamic equations. Machine learning tools are revolutionising data-intensive computing and, handled with proper mathematical care, have vast potential benefits for science and society. These are examples of the ongoing explosion in mathematical innovation driving, and being driven by, the analysis and modelling of data running through every aspect of life. Cutting-edge research now sits at the interface of data science and mathematical modelling. Methods and fields such as compressed sensing, stochastic optimisation, neural networks, Bayesian hierarchical models - to name but a few - have become interwoven and contributed to the delivery of a new domain of research. We refer to this research interface as "statistical applied mathematics". Established in 2014, the Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa, samba.ac.uk) delivers leading research and training in this space. In the development of this bid, we have consulted widely with academic, industrial, and governmental partners, who consistently report a large and widening gap between demand and supply for highly skilled graduates. Our vision is to create a new generation of statistical applied mathematicians ready to lead high-impact, data-driven, mathematically-robust research in academia and industry. We will nurture a vibrant culture of cohort learning, enabling internationally-leading training in modern mathematical data science. A particularly important research focus will be the synthesis of data-driven methods with robust mathematical modelling frameworks. Tomorrow's industrial mathematicians and statisticians must understand when machine learning tools are (and are not) appropriate to use and be able to conduct the underpinning research to improve these tools by integrating scientific domain knowledge. This research challenge is informed by deep partnerships with a range of industry and government bodies. Our long-term partners such as BT, Syngenta, Novartis, the NHS, and the Environment Agency co-create our vision and our training. They are emphatic that we must address the urgent need for mathematical data science talent in this key strategic area for the UK economy. Many of our students will work directly on industry challenges during their PhD either in their core research or with internships. Our unique Integrative Think Tanks are the key mechanism for exploring new research ideas with industry. These are week-long events where SAMBa students, leading academics, and partners work together on industrial and societal problems. SAMBa graduates will be able to develop and apply new ideas and methods to harness the power of data to tackle challenges affecting society, the economy, and the environment. Our students will move into academia, providing sustainability to the UK's capacity in this field, as well as industry and government, providing impact through societal benefits and driving economic growth. Many alumni now hold permanent positions at leading UK universities and senior positions in a range of businesses. The CDT will be embedded within the University of Bath's Department of Mathematical Sciences, where 98% of the research is world leading or internationally excellent (REF2021). The CDT is supported by 58 academics in maths, with similar numbers of co-supervisors from industry and other departments. The centre will be co-delivered with 22 industry and government partners. A vital international perspective is provided by a worldwide network of 11 academic institutions sharing our scientific vision.

SAMBa aims to create a generation of interdisciplinary mathematicians at the interface of stochastics, numerical analysis, applied mathematics, data science and statistics, preparing them to work as research leaders in academia and in industry in the expanding world of big models and big data. This research spectrum includes rapidly developing areas of mathematical sciences such as machine learning, uncertainty quantification, compressed sensing, Bayesian networks and stochastic modelling. The research and training engagement also encompasses modern industrially facing mathematics, with a key strength of our CDT being meaningful and long term relationships with industrial, government and other non-academic partners. A substantial proportion of our doctoral research will continue to be developed collaboratively through these partnerships. The urgency and awareness of the UK's need for deep quantitative analytical talent with expert modelling skills has intensified since SAMBa's inception in 2014. Industry, government bodies and non-academic organisations at the forefront of technological innovation all want to achieve competitive advantage through the analysis of data of all levels of complexity. This need is as much of an issue outside of academia as it is for research and training capacity within academia and is reflected in our doctoral training approach. The sense of urgency is evidenced in recent government policy (cf. Government Office for Science report "Computational Modelling, Technological Futures, 2018"), through the EPSRC CDT priority areas of Mathematical and Computational Modelling and Statistics for the 21st century as well as through our own experience of growing SAMBa since 2014. We have had extensive collaboration with partners from a wide range of UK industrial sectors (e.g. agri-science, healthcare, advanced materials) and government bodies (e.g. NHS, National Physical Laboratory, Environment Agency and Office for National Statistics) and our portfolio is set to expand. SAMBa's approach to doctoral training, developed in conjunction with our industrial partners, will create future leaders both in academia and industry and consists of: - A broad-based first year developing mathematical expertise across the full range of Statistical Applied Mathematics, tailored to each incoming student. - Deep experience in academic-industrial collaboration through Integrative Think Tanks: bespoke problem-formulation workshops developed by SAMBa. - Research training in a department which produces world-leading research in Statistical Applied Mathematics. - Multiple cohort-enhanced training activities that maximise each student's talents and includes mentoring through cross-cohort integration. - Substantial international opportunities such as academic placements, overseas workshops and participation in jointly delivered ITTs. - The opportunity for co-supervision of research from industrial and non-maths academic supervisors, including student placements in industry. This proposal will initially fund over 60 scholarships, with the aim to further increase this number through additional funding from industrial and international partners. Based on the CDT's current track record from its inception in 2014 (creating 25 scholarships to add to an initial investment of 50), our target is to deliver 90 PhD students over the next five years. With 12 new staff positions committed to SAMBa-core areas since 2015, students in the CDT cohort will benefit from almost 60 Bath Mathematical Sciences academics available for lead supervisory roles, as well as over 50 relevant co-supervisors in other departments.

An eruption of Fuego volcano, Guatemala, on 3rd June 2018, had tragic outcomes when an entire village was inundated by pyroclastic flows. The eruption has prompted evacuations of around 12,000 people. This event resulted in changes to hazard, exposure and vulnerability, demonstrating the complex and dynamic nature of ongoing and future risk. This proposal seeks to characterise this dynamic risk observed in the natural environment, and understand the interactions between dynamic risk and society. Following the 3rd June eruption of Fuego, evacuations have resulted in reduced exposure in some regions, however, vulnerability (physical, systemic, functional, social, economic and political) remains high and is a key component of the evolving risk. In particular, systemic and functional vulnerability are believed to be highly dynamic. This provides an opportunity to investigate how the evolving hazard situation at Fuego, combined with changes in exposure and highly dynamic systemic and functional vulnerability, play out to affect risk in a context where both recovery and continued eruption risk management are ongoing. This opportunity is urgent: we must characterise changing hazard, exposure and vulnerability through time. Although the nature of the hazard can be investigated retrospectively, documenting changes to exposure (evacuations and reoccupations) and vulnerability as they respond to changing hazard and socio-economic conditions needs to be done as it occurs. For example, it is important to document physical vulnerability on buildings already impacted by the pyroclastic flows before further damage by weather or heavy machinery occurs, or document road closures next to affected drainages which can constitute a major element of the systemic vulnerability to lahars or pyroclastic flows of a community isolated by that road closure. Information on systemic vulnerability at this level of granularity is not normally documented in Guatemala, thus will not be available for later study. Through this proposed work, we will collect an unprecedented dataset on vulnerability, documenting physical vulnerability of buildings impacted by pyroclastic flows before any further damage. When considering risk to life by volcanic flow hazards and lahars however, physical vulnerability of infrastructure can be reduced to a binary effect (impacted or not. It is actually systemic and functional vulnerability that are the more important, and harder to ascertain, unknowns. A key research component, therefore, is to test the hypothesis that for volcanic flow related hazards, in contrast to tephra hazards, it is widespread systemic vulnerability and not physical vulnerability of the footprint of potential impact that is the root cause of risk. This is important because much of the work currently undertaken on risk in volcanology is led by frameworks used for tephra fall hazards, yet flow impacts and risk are very different. The project is will-aligned with the UN Sendai Framework for Disaster Risk Reduction, as well as recent initiatives in the wider volcanology community to engage and improve our capacity to do risk well. We will use a combination of volcanology field approaches, forensic approaches, and interviews to gather the information.

In order to satisfy the increasingly enhanced requirements for system performance, practical engineering systems are now frequently modelled as complex interconnected systems. The study of such classes of systems is both challenging and adventurous due to the presence of nonlinearity, interconnections between subsystems, time delay and uncertainty between such models and the real world. The research described in this proposal aims to develop a novel design approach and to provide practical but rigorous solutions to the problems of decentralised observer and controller design for complex interconnected systems using HOSM techniques. The project will strengthen the collaboration between two leading sliding mode research teams to create a framework for this new research area. In order to avoid packet problems with transfer of information between subsystems, decentralised strategies will be the focus of this research. A novel approach involving artificial interconnections will be developed to reduce the conservatism caused by interaction between subsystems when decentralised strategies are employed. One specific focus will be to remove/suppress the undesired chattering to improve closed-loop system performance. The investigator has been heavily involved in research on interconnected systems using classical sliding mode control for over 10 years and has a strong research track record. The project partner has obtained excellent results in HOSM control specifically in chattering suppression, which is internationally leading in the area of HOSM. The expertise of both research teams is considerable and complementary. The experimental equipment provided by the projector partner will be crucial for testing the results obtained. All these factors are very important to guarantee the project success. As the focus of this proposal is to remove chattering whilst preserving the advantages of HOSM techniques and decentralised strategies, this research will bring the advantages of high robustness to uncertainty, ease of implementation, high reliability and increased lifespan of moving mechanical components when implemented in real systems. The research has great potential to enhance economic efficiency.