The proposed research capitalizes on a newly discovered class of mathematical formulae underpinning the realisation of string theory solutions that unify all known forms of matter and forces. String theory has had a profound impact on the development of both mathematics and physics and, more recently, on the construction of machine learning algorithms. String theory is a high-energy, extra-dimensional and supersymmetric theory, in which many ideas about physics beyond the Standard Model can be incorporated in a natural way. Although difficult, making contact with experimental physics is an imperative for string theory, requiring a sustained effort in developing the existing models up to the point where they can communicate with experimental results such as the LHC data. The difficulty is not conceptual, but rather mathematical and computational in nature. String theory is geometrical par excellence and, as such, one needs to identify the specific geometry that reduces it to the Standard Model of particle physics at low energies. The project contributes in an essential way to the resolution of this problem. It uses experimental mathematics derived from string theory to uncover and understand new algebraic and geometric structures. The new structures feed back into string theory, providing unexpected shortcuts to incredibly hard computations. It is rare to find a new type of mathematical structure that has so much potential for problem solving. This interplay between mathematics and physics is characteristic to string theory and has crucially contributed to making it the principal driving force in fundamental particle physics. Machine learning techniques have seen a wide range of applications in numerous areas of science and in industry. String theory and, more broadly, physics require a qualitatively different kind of machine learning, focused not only on results, but also on uncovering the mechanisms underlying them. The proposal goes beyond the standard 'black box' approach that gives correct results but no explanations by using machine lerning for the formulation of mathematically precise conjectures that can subsequently be approached using methods of algebraic geometry, everything converging towards the ultimate goal of understanding the physical implications of string theory.

What determines whether a mosquito develops as a male or a female is largely unknown. This question is not just an academic curiosity. Only female mosquitoes feed on vertebrate blood and hence are capable of transmitting disease. And the variety of diseases transmitted by mosquitoes is staggering. This proposal focuses on the Anopheles mosquitoes that transmit malaria but mosquitoes also transmit a number or incurable viruses as well as a number of severe parasitic diseases of humans and animals. We will use a variety or approaches to disclose the processes that trigger the sex development of Anopheles mosquitoes. We have already identified one gene, doublesex, which switches on a different chain of events in males and females. Based on studies in other insects, and our own preliminary data, we believe that this gene is a key regulator of sexual differentiation. In this proposal we will use doublesex as an anchor to identify steps upstream and downstream of the sex differentiation pathway and thereby begin to build a picture of the sex determination process in mosquitoes. We believe that this pathway will prove to be a rich source of targets for novel mosquito intervention strategies.

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.

Infectious disease is the main thing that kills people. Some of the greatest improvements to human health have involved improvements in our understanding and control of germs - from John Snow's pioneering work on cholera in the 19th century to the eradication of smallpox in the 20th century. The 21st century sees a new set of challenges in the understanding and control of infections - while the eradication of polio progresses, we see new influenza strains causing or threatening pandemics, the continued progression of HIV and a massive health burden of often simply but expensively preventable diseases in the developing world.Epidemiology - the science of looking for significant patterns in cases of disease - has always been at the heart of controlling infectious diseases, and mathematics has always been central epidemiology.This project applies advanced mathematics to the science of epidemiology, making use of the large datasets and modern computational resources that are available. New insights about the structure of complex systems offer the promise of making massive advances in this field, through enhanced understanding of transmission routes of infection, risk factors and changes in the disease over time. These insights can in turn be combined with mathematical methods to design optimised interventions against infection so that diseases can be controlled in the most effective way.

The overall aim of this work is the investigation of the use of shape memory alloys in rotating machinery. It has emerged from work so far that it is possible to construct a bearing support whose stiffness can be modified by the application of an electric current. This is a highly desirable property as it implies that the vibrational response can be modified, and therefore controlled. With the appropriate loops, this lead to a so-called 'Smart Machine'.Working with colleagues at University of Glasgow, two distinct systems have so far been developed to address this problem. The approach followed at Swansea has been to support the actual bearing in a pair of elastomer O-rings, whose stiffness is a function of their compressive stress. The effective stiffness of the support is controlled by varying the loading on a pair of elastomer O-rings which support the rolling element bearings. The stiffness of the O-rings is strongly influenced by the (static) loading on the elastomer and the load is applied by two sets of SMA wires as shown. Ohmic heating is used to control the temperature of the SMA wires and this is monitored by means of thermocouples. The SMA wires, shown as a single wire in figure 3, actually comprise a multiloop of wire, so arranged in order to provide sufficient compressive pre-load for the elastomer O-ring. This is a simple geometry which is employed merely to prove the concept.Experimental results to date show very promising results, with natural frequencies being shifted by more than 50% as current is applied.To take this work forward it would be highly beneficial to discuss with members of the group at Virginia Tech, many of whom have extensive experience with SMA application.The subsidiary part of this visit, to a bearing manufacturer in Calgary is in the same general are of 'Smart Machines', albeit a somewhat different application.