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Spectral theory to improve the accuracy of vibrational energy predictions in complex structures such as cars, aeroplanes and buildings

Summary of the impact

Designs for complex structures like cars, aeroplanes and modern buildings suffer from unpredictable vibrations that lead to anything from irritating noises to dangerous structural failures. Predicting the distribution of vibrational energy in large coupled systems is an important and challenging task of major interest to industry. Until recently there was no reliable method to predict vibrations at the important mid-to-high frequency ranges.

There is a need to gain accurate predictions of vibrations at the design stage. However, previous techniques developed in the context of Quantum Chaos are too cumbersome to be used in a fast-moving commercial design setting. Bandtlow has used his expertise to develop a novel method that computes a very close approximation to these predictions but in a reasonable time. Bandtlow's method of constructing an efficient mathematical model for spectral vibrations has informed inuTech's latest product and led to enhanced performance of automobiles and aircraft.

Submitting Institution

Queen Mary, University of London

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Pure Mathematics, Applied Mathematics, Statistics

Optimising Spacecraft Design for A World-leading Space Agency

Summary of the impact

Through close collaboration with scientists at the European Space Agency (ESA), research at the University of Southampton has developed new algorithms and an associated software tool that have contributed to more efficient spacecraft design. Now a standard component of the ESA's design technology, the tools have doubled the speed in which crucial design processes can be completed, resulting in increased efficiency over the REF period of 20 person-years — equivalent to €1 million in monetary terms — and maintaining the ESA's manufacturing competitiveness. The success of this work led to a €480,000 EU grant to adapt the tools for the avionics industry as part of efforts to meet ambitious environmental targets under the EU Clean Sky Initiative.

Submitting Institution

University of Southampton

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Applied Mathematics, Numerical and Computational Mathematics
Information and Computing Sciences: Computation Theory and Mathematics

Improving Met Office weather forecasting accuracy

Summary of the impact

Weather impacts all of our lives and we all take a close interest in it, with every news report finishing with a weather forecast watched by millions. Accurate weather forecasting is essential for the transport, agricultural and energy industries and the emergency and defence services. The Met Office plays a vital role by making 5-day forecasts, using advanced computer algorithms which combine numerical weather predictions (NWP) with carefully measured data (a process known as data assimilation). However, a major limitation on the accuracy of these forecasts is the sub- optimal use of this data. Adaptive methods, developed in a partnership between Bath and the Met Office have been employed to make better use of the data, thus improving the Met Office operational data assimilation system. This has lead to a significant improvement in forecast accuracy as measured by the `UK Index' [A] with great societal and economic impact. These forecasts, in particular of surface temperatures, are pivotal for the OpenRoad forecasting system used by local authorities to plan road clearing and gritting when snow or ice are predicted [B].

Submitting Institution

University of Bath

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Environmental

Research Subject Area(s)

Mathematical Sciences: Pure Mathematics, Applied Mathematics, Numerical and Computational Mathematics

MAT01 - Mathematical methods to improve food safety and traceability

Summary of the impact

Recent food crises show the importance of having effective means of food identification and analysis. Many tests have been developed to monitor food, but analysis of the resulting data is highly problematic. Mathematical techniques developed by Dr Julie Wilson at the University of York allow complex mixtures to be analysed and interpreted. They have enabled the Food and Environment Research Agency (Fera) to maximize the information available from food testing, resulting in improved food safety and authentication worldwide, and underpin the analytical testing services delivered by Fera. The techniques have been incorporated into a bespoke Matlab based solution which is now routinely used by Fera's Chemical and Biochemical Profiling section in the specialist testing services which Fera provides across the food storage and retail, agri-environment and veterinary sectors to over 7,500 customers in over 100 countries. In addition, the techniques are used in Fera's research, supporting around £8M worth of work to develop a wide range of global applications including the determination of disease-related biomarkers, contaminant detection, food traceability and the development of drought- and disease-resistant crop varieties.

Submitting Institution

University of York

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Statistics
Biological Sciences: Biochemistry and Cell Biology
Medical and Health Sciences: Neurosciences

Improving Social Care Call Centre Operational Effectiveness

Summary of the impact

Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.

Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.

As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.

Submitting Institution

Northumbria University Newcastle

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Statistics
Information and Computing Sciences: Artificial Intelligence and Image Processing, Information Systems

Improved estimation of mortality and life expectancy for each constituent country of the UK and beyond

Summary of the impact

Graduated period life tables for men and women, based on the mortality experience of the population of England and Wales, have been published by the Office for National Statistics (ONS) using data from the 2001 Census. These tables are the sixteenth in a series known as the English Life Tables which are associated with decennial population censuses, beginning with the Census of 1841. Errors in crude census data owing to the small numbers of deaths involved, particularly in childhood and at very advanced ages, can be reduced by a statistical process of smoothing. A smoothing methodology developed at Cass Business School, City University London has been used in the latest ONS Decennial Life Tables. The tables show the increasing longevity of the population of England and Wales over a long period. The impact of this research is broad as life tables are used extensively in pensions planning, demography, insurance, economics and medicine. Life tables using this statistical smoothing methodology have also been prepared for Scotland, Northern Ireland, the Republic of Ireland and Canada.

Submitting Institution

City University, London

Unit of Assessment

Business and Management Studies

Summary Impact Type

Political

Research Subject Area(s)

Mathematical Sciences: Statistics
Economics: Applied Economics, Econometrics

Intermittent demand categorization and forecasting

Summary of the impact

Our research team has developed new approaches to classifying demand series as `intermittent' and `lumpy', and devised new variants of the standard Croston's method for intermittent demand forecasting, which improve forecast accuracy and stock performance. These approaches have impacted the forecasting software of Syncron and Manugistics, through the team's consultancy advice and knowledge transfer. Subsequently, this impact has extended to Syncron International and JDA Software, which took over Manugistics. These companies' forecasting software packages have a combined client base turnover of over £200 billion per annum, and their clients benefit from substantial inventory savings from the new approaches adopted.

Submitting Institution

Buckinghamshire New University

Unit of Assessment

Business and Management Studies

Summary Impact Type

Economic

Research Subject Area(s)

Mathematical Sciences: Statistics
Economics: Applied Economics, Econometrics

Phase Unwrapping Software

Summary of the impact

Phase unwrapping is an essential algorithmic step in any measurement system or sensor that seeks to determine continuous phase. Instances of such devices are widespread: e.g. image reconstruction in magnetic resonance imaging (MRI), synthetic aperture radar (SAR) by satellite systems, analysis of seismic data in geophysics and optical instrumentation, to name but a few. Without successfully solving the phase unwrapping problem these instruments cannot function.

The topic is well developed and competition among algorithms is fierce. In 2012 alone, some 235 papers, most of which were describing potential new algorithms, were published in the area. But the continuing need for high-speed, automated and robust unwrapping algorithms poses a major limitation on the employability of phase measuring systems.

Working originally within the context of structured light 3D measurement systems, our research has developed new phase image unwrapping algorithms that constitute significance advances in speed, automation and robustness. The work has led to adoption by industry, as well as use in commercial and government research centres around the globe. Our approach since 2010 has been to make these algorithms freely available to end users. Third parties have gone on to translate our algorithms into other languages, widely used numerical software libraries have incorporated the algorithms and there are high profile industrial users.

Submitting Institution

Liverpool John Moores University

Unit of Assessment

General Engineering

Summary Impact Type

Technological

Research Subject Area(s)

Information and Computing Sciences: Artificial Intelligence and Image Processing
Medical and Health Sciences: Clinical Sciences

The Development of Commercial Optimization Software

Summary of the impact

Research led by Professor Roger Fletcher has resulted in the development of a suite of algorithms that are now widely used throughout industry. An algorithm of fundamental importance constructed by Fletcher and co-workers is the filter method — a radically different approach to solving large and complex nonlinear optimization problems typical of those faced by industry. This algorithm was developed with the principal aim of providing a computationally reliable and effective method for solving such problems. The filter method is now utilised by a variety of high-profile industry end-users including IBM, Schlumberger, Lucent, EXXON, Boeing, The Ford Motor Company, QuantiSci and Thomson CSF. The use of the filter method has had a significant economic and developmental impact in these companies through enhanced business performance and cost savings.

Submitting Institution

University of Dundee

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Applied Mathematics, Numerical and Computational Mathematics
Information and Computing Sciences: Computation Theory and Mathematics

Data maps with applications to medical diagnostics and monitoring

Summary of the impact

Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.

Submitting Institution

University of Leicester

Unit of Assessment

Mathematical Sciences

Summary Impact Type

Technological

Research Subject Area(s)

Mathematical Sciences: Pure Mathematics, Statistics
Information and Computing Sciences: Computation Theory and Mathematics

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