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Working closely with scientists at the United States Environmental Protection Agency (USEPA), the University of Southampton has developed new methods for space-time modelling that have trebled the accuracy of air pollution forecasts. The USEPA has adopted the research as its official forecasting method to protect the American public and agriculture. More than 19 million children and 16 million adult Americans suffering from respiratory conditions such as asthma now benefit by being able to adjust their outdoor activities based on the forecasts, and improved data has fed into policy debates on carbon emission regulations. Success in the USA has led the EPSRC to fund a similar project in the UK and Australia's national science agency is using Southampton-developed software for its air pollution forecasts.
The Climate Change Act, 2008, constructed a legally-binding long-term framework for the UK to cut greenhouse gas emissions and a framework for building the UK's ability to adapt to a changing climate. The Act requires a UK-wide climate change risk assessment (CCRA) that must take place every five years and a national adaptation programme (NAP), setting out the Government's objectives, proposals and policies for responding to the risks identified in the CCRA. The CCRA, and thus the NAP, drew heavily on the uncertainty analysis for future climate outcomes, published in 2009 by the Met Office as the UK Climate Projections UKCP09, which in turn drew heavily on research into the Bayesian analysis of uncertainty for physical systems modelled by computer simulators carried out at Durham University. A wide range of industries and public sector organisations likely to be affected by climate change have consulted with the Met Office on UKCP09 to inform decisions on policy and investment, involving billions of pounds, in sectors as diverse as flood defence, transport, energy supply and tourism.
Pratt & Whitney (one of the world's largest makers of aircraft engines) has developed a process, "Design for Variation" (DFV), that uses Bayesian methods developed at Sheffield for analysing uncertainty in computer model predictions within the design, manufacture and service of aircraft engines. The DFV process significantly improves cost efficiency by increasing the time an engine stays operational on the wing of an aircraft, so reducing the time that the aircraft is unavailable due to engine maintenance. DFV also saves costs by identifying design and process features that have little impact on engine performance, but are expensive to maintain. Pratt & Whitney estimate the DFV process to generate savings, for a large fleet of military aircraft, of [text removed for publication].
The UK Food and Environment Research Agency (Fera) has used these methods in their risk analyses, for example in assessing risks of exposure to pesticides.
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.
Research conducted at the School of Mathematics at Cardiff University has engineered lifesaving, improvements to UK healthcare systems. New mathematical models, accounting for the complexity and diversity of the health system, have been created and applied in a variety of contexts to markedly enhance the efficiency and effectiveness of a wide range of healthcare services — at policy, commissioning and operational levels. The extensive benefits include:
This work has been disseminated nationally and internationally, in the media and at a range of events designed to engage the public with Mathematics. Therefore the impacts claimed in this case study are health, economic benefits and public engagement.
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
Researchers at the University of St Andrews have changed the way environmental monitoring and impact assessment data are collected and analysed, particularly in the marine environment. We have developed new statistical models of wildlife population dynamics that, for example, form the basis for population assessment of most of the world's grey seals, allowing the UK and Canadian governments to implement effective management of the populations. Other research carried out by us has led to reformulation of the recommended standard statistical practice for impact assessment in the UK marine renewables industry, enabling marine regulators such as Marine Scotland to make better-informed licensing decisions concerning large-scale offshore renewable energy developments.
The safe operation of ships is a high priority task in order to protect the ship, the personnel, the cargo and the wider environment. Research undertaken by Professor Alexander Korobkin in the School of Mathematics at UEA has led to a methodology for the rational and reliable assessment of the structural integrity and thus safety of ships and their cargos in severe sea conditions. Central to this impact is a set of mathematical models, the conditions of their use, and the links between them, which were designed to improve the quality of shipping and enhance the safety of ships. The models, together with the methodology of their use, are utilised by the ship certification industry bringing benefits through recognised quality assurance systems and certification.
The School of Mathematics at Cardiff University has developed important statistical and mathematical models for forecasting consumer buying behaviour. Enhancements to classical models, inspired by extensively studying their statistical properties, have allowed us to exploit their vast potential to benefit the sales and marketing strategies of manufacturing and retail organisations. The research has been endorsed and applied by Nielsen, the #1 global market research organisation that provides services to clients in 100 countries. Nielsen has utilised the models to augment profits and retain their globally leading corporate position. This has led to a US$30 million investment and been used to benefit major consumer goods manufacturers such as Pepsi, Kraft, Unilever, Nestlé and Procter & Gamble. Therefore the impact claimed is financial. Moreover, impact is also measurable in terms of public engagement since the work has been disseminated at a wide range of national and international corporate events and conferences. Beneficiaries include Tesco, Sainsbury's, GlaxoSmithKline and Mindshare WW.
The research has enabled industrial simulation users to investigate and develop larger scale systems faster and cheaper and thus to explore a wider variety of cost-saving options with more precision, and industrial simulation providers to offer new high-performance simulation (HPS) products and services. As a direct result of this work: Ford has made £150,000 cost savings in consultancy and significant process improvements to engine manufacture globally; Saker Solutions (UK SME) has created the first ever HPS system for production and logistics; Sellafield PLC has used this system to make significant process improvements and savings in the management of nuclear waste reprocessing of around £200,000 per year; and Whole Systems Partnership (a UK SME) used a spin-off from this research to generate a £200,000 per year revenue stream from interoperable healthcare decision support systems. Globally, several other companies are adopting the standardisation efforts and other outcomes of the research as the foundation for future innovation.