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University of Leeds

Country: United Kingdom

University of Leeds

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4,274 Projects, page 1 of 855
  • Funder: UKRI Project Code: 2277497

    Existing models for transport demand are not refined for disruptions. The work described here is intended to make use of emerging big data sources to improve the ability of transport providers to respond to changed transport requirements in these circumstances. It will establish how well existing travel demand models perform in predicting the response to major disruptions based on historical data. Candidate datasets include Oyster card data from Transport for London (TFL), mobile phone data from Telefonica (made available to academics by Transport System Catapult (TSC), etc . The disruptions to be tested will include predictable short duration events such as a sporting event or adverse weather, predictable long-term disruption such as station or line closures, as well as unpredictable events such as points failure or terrorism. Special focus will be on mathematically modelling the demand and supply variations with different influencing factors that can be used for predicting the impact of future disruptions. The importance of this work will be seen primarily in capacity planning and designing crisis-response plans. When constructing new infrastructure, it will be very useful to be able to see what parts of the old network might be stretched if any disruption occurs and plan for such scenarios, possibly by adapting the new infrastructure plans. In traditional approaches, the behaviour of people in the event of a disruption is determined using surveys - either recall surveys after a disruptive event and/or questionnaires on potential behaviour in hypothetical scenarios. Both have limitations. The recall data may not be accurate while the data on hypothetical scenarios may not be realistic. There is thus a dearth of reliable data available to study behavioural choices in stressful situations created by disruption. Improved more robust models would be developed using insights to be obtained by mining alternative data sources such as mobile phone GPS or call detail record (CDR) data and TFL Oyster card or wi-fi log data. These data sources may provide valuable insights into the reactions of the traveling public to sudden disruptions. Other researchers have suggested there may be a large difference in the responses of Twitter users as opposed to respondents to the Household Interview Travel Survey. This may reflect the different weaknesses in each data source. Being able to collate the two sources of information will help give a better picture of what the population dynamics are in response to any given disruption. Furthermore, geo-located Twitter data could be useful when used in conjunction with smart-card (oyster) data. Cities like London have one tap smart-cards on their bus network which captures only the starting point of a journey. Combining this data with geo-location tweets may better help us identify how commuters respond to disruption when trains are out of service. The use of smart card data alone may be misleading. In order to use twitter data effectively, it might be desirable to use machine learning algorithms to tag tweets, allocating each to a categories according to the sentiment expressed. This creates the data to be used in a model of how commuters are responding to disruption. Unfortunately, statistical models and machine learning are currently ill equipped on their own to deal with human responses to disruption. This sentiment analysis is a way of gauging the general mood of commuters which will impact on how they choose to respond to the situation at hand. Choice modelling may be appropriate for this purpose. Using a measure of how upset different sets of people are during the disruption may help, in real time, decide what the best course of action might be.

  • Funder: UKRI Project Code: 2105267

    The manufacture of high value materials requires high reproducibility of physical characteristics and performance. However, changes in manufacturing conditions are commonplace in commercial production and can lead to unpredictable changes in material properties that may disrupt product manufacture. These are often manifested as poor filtration, bulk powder consolidation, poor granule/tablet dispersion or sedimentation in suspensions. These shifts in behaviour, outside of the designed process operating window, are not easily rectified on commercial timescales and can inflict serious financial and operational consequences ranging from low productivity (e.g. throughput, down time and re-work), potentially hazardous interventions, to catastrophic product quality failure and withdrawal. Controlling surface and interface properties appears to be of central importance, but a lack of analytical techniques that determine molecular level properties has prevented progress in this field. With the advent of modern X-ray technologies for molecular-level surface and interface analysis of particulate products under environmental control we can now realistically hope to make progress in this field. This project will employ experimental techniques such as environmental XPS at Leeds and synchrotron radiation techniques such as environmental near-edge X-ray absorption fine-structure (NEXAFS), pair-distribution function (PDF) determination by total scattering and high resolution X-ray imaging and tomography to obtain insight into changes in the interfacial properties of particles in powders as a function of time while varying process and storage conditions. In situ experiments will be designed to understand the molecular basis better, and an important part of this work will be to relate observed behaviour to models that can be used for the predictive process design.

  • Funder: UKRI Project Code: 2745608

    This project will look at some aspect of medical diagnosis through the application of Artificial Intelligence which will be determined in more detail by the end of year one

  • Funder: EC Project Code: 299255
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  • Funder: UKRI Project Code: 1947962

    Diesel fuel contains long chain n-alkanes which can phase separate at low temperatures and result in formation of large flat wax crystals. Such wax crystals can block fuel filters which results in fuels starvation and vehicle failure. These problems have become exacerbated by the introduction of biofuel, with thousands of vehicles failing in winter conditions. To-date, these issues have been addressed by developing chemistries to nucleate wax (and hence produce more smaller crystals) and also by introducing polymers which incorporate in a plane of crystallization and hence produce needle-like crystals. Both approaches reduce filter blockages, through modification of crystal structure. However, wax crystals do still form and the possibility of filter blockage and deposit formation throughout the fuel system (including in fuel injectors) does still exist. If wax nucleation could be effectively understood and controlled, then tailored cold weather properties could be designed. Recent research has highlighted the presence of ordering prior to nucleation of crystals. If the factors affecting such pre-nucleation ordering were understood, it may be possible to prevent (or control) nucleation and hence wax crystal formation. Nucleation of wax then occurs via two possible mechanisms: instantaneous and progressive, which are influenced by the composition of the fuel (Paraffinic, Olefinics, Napthenics, Aromatics etc).The aim of the project will be to develop an understanding of the fundamental principles involved in pre-nucleation ordering of n-alkanes and biofuel components and to link instantaneous and progressive nucleation to the chemical composition of the fuel. This work will be used as a way of automating additive selection on 'Fuel tiles' This project is mostly experimental with some complementary molecular, solid-state and morphological modelling required. The majority of experimental facilities are available at the University of Leeds in the School of Chemical and Process Engineering. Various techniques will be used throughout the project including polythermal (metastable zone widths and KHBR analysis) and isothermal (induction time) crystallisation studies, x-ray diffraction, crystal 16, thermochemical methods, optical microscopy and small angle x-ray (SAXS) both in Leeds and at Diamond Light Source.


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