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Maintenance Scheduling With Delay-time Modelling - An Overview
No full textNoEffective maintenance is a key for infrastructures' high operational reliability. The integration of corrective repairs and schedule-based failure preventions has been a mainstream of modern maintenance, and an associated policy-making technique, delay-time modelling, is overviewed in this paper for optimising the maintenance cost-efficiency in different practical scenarios, including imperfect, opportunistic and nested maintenance. A few typical examples of its applications in minimising maintenance operating expenses are discussed in this paper and their results are explained to better demonstrate the benefits of the technique. This work aims to prepare for the future applications of the delay-time modelling in railway maintenance policy making
The role of cultural heritage in visitor narratives of peatlands: analysis of online user-generated reviews from three peatland sites in England
Get PDFYesUser-generated reviews of visitor attractions, on publicly available websites,
such as Tripadvisor, are frequently used in tourism research but
feature less often in published cultural heritage research. In this paper,
we describe a qualitative analysis of the text from user-generated reviews
of three peatland heritage landscapes in the United Kingdom – Ilkley
Moor, Thorne and Hatfield Moors, and Shapwick Heath – to better understand
the role tangible and intangible cultural heritage play in visitor
perceptions and narratives of these sites. Our analysis indicates that
visitors tend to emphasise natural over cultural heritage of peatland
landscapes and hold plural, highly contextual and sometimes dissonant
perceptions; there is no single story of peatlands. This presents both
challenges and opportunities for building public appreciation of peatland
cultural heritage. User-generated reviews offer, as-yet under-explored,
potential data for use by heritage researchers and managers who seek
to explore how visitors understand and use sites, and may also contribute
to the emerging intangible heritage of heritage landscapes.Research Development Fund Publication Prize Award winner, June 2021
An exploration of the impact of SARS-CoV-2 (COVID-19) restrictions on marginalised groups in the UK
Get PDFYesTo contain the spread of COVID-19 within the UK over the past year, there have been a series of local and national lockdowns. These restrictions are likely to have impacted upon the health and well-being of marginalised groups who rely on now closed social and community support services to stay healthy. An understanding of the experiences of marginalised people is important; therefore, this study aimed to explore the impact of the COVID-19 restrictions on the health and well-being of marginalised groups in the UK.
Methods: In summer 2020, a rapid telephone survey was conducted by trained, trusted volunteers with 76 participants who were from marginalised groups. As part of this survey, 64 participants consented to describe their experience of lockdown. These case studies were thematically analysed to identify patterns of meaning.
Results: Findings indicate that lockdown led to the deterioration of health of participants, impacted adversely on their socio-economic positions and affected access to food and essential supplies. In addition, government public health messaging was considered confusing and inadequate.
Conclusions: This study highlights the need for pathways into services which support marginalised groups to remain accessible during periods of restrictions and essential supplies and food to be mapped and protected for marginalised individuals within our local communities.NHS England; Improvemen
Self-efficacy, habit strength, health locus of control and response to the personalised nutrition Food4Me intervention study
Get PDFYesPurpose - Randomised controlled trials identify causal links between variables but not why an outcome has
occurred. This analysis sought to determine how psychological factors assessed at baseline influenced
response to personalised nutrition.
Design/methodology/approach – Web-based, randomised, controlled trial (RCT) was conducted across
seven European countries. Volunteers, both male and female, aged over 18 years were randomised to either a
non-personalised (control) or a personalised (treatment) dietary advice condition. Linear mixed model analysis
with fixed effects was used to compare associations between internal and external health locus of control
(HLoC), nutrition self-efficacy (NS-E) and self-report habit index (S-RHI) at baseline (N 5 1444), with healthy
eating index (HEI) and Mediterranean diet index (MDI) scores between conditions post-intervention (N 5 763).
Findings – An increase in MDI scores was observed between baseline and six months in the treatment group
which was associated with higher NS-E (pEU FP7 Project “Personalised nutrition: an integrated analysis of opportunities and challenges” (Contract No. KBBE. 2010.2.3–02, Project No. 265494
Flyback photovoltaic micro-inverter with a low cost and simple digital-analog control scheme
Get PDFYesThe single-stage flyback Photovoltaic (PV) micro-inverter is considered as a simple and small in size topology but requires expensive digital microcontrollers such as Field-Programmable Gate Array (FPGA) or Digital Signal Processor (DSP) to increase the system efficiency, this would increase the cost of the overall system. To solve this problem, based on a single-stage flyback structure, this paper proposed a low cost and simple analog-digital control scheme. This control scheme is implemented using a low cost ATMega microcontroller built in the Arduino Uno board and some analog operational amplifiers. First, the single-stage flyback topology is analyzed theoretically and then the design consideration is obtained. Second, a 120 W prototype was developed in the laboratory to validate the proposed control. To prove the effectiveness of this control, we compared the cost price, overall system efficiency, and THD values of the proposed results with the results obtained by the literature. So, a low system component, single power stage, cheap control scheme, and decent efficiency are achieved by the proposed system. Finally, the experimental results present that the proposed system has a maximum efficiency of 91%, with good values of the total harmonic distortion (THD) compared to the results of other authorsThis work was supported in-part by Innovate UK GCRF Energy Catalyst PiCREST project under Grant number 41358, in-part by British Academy GCRF COMPENSE project under Grant GCRFNGR3\154
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems
Get PDFYesThis article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-the loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software.
The application of artificial intelligence (AI) and
data-driven decision-making systems in autonomous vehicles is
growing rapidly. As autonomous vehicles operate in dynamic
environments, the risk that they can face an unknown observation
is relatively high due to insufficient training data, distributional
shift, or cyber-security attack. Thus, AI-based algorithms should
make dependable decisions to improve their interpretation of the
environment, lower the risk of autonomous driving, and avoid
catastrophic accidents. This paper proposes an approach named
SafeML II, which applies empirical cumulative distribution
function (ECDF)-based statistical distance measures in a designed
human-in-the-loop procedure to ensure the safety of machine
learning-based classifiers in autonomous vehicle software. The
approach is model-agnostic and it can cover various machine
learning and deep learning classifiers. The German Traffic
Sign Recognition Benchmark (GTSRB) is used to illustrate the
capabilities of the proposed approach.Secure and Safe MultiRobot Systems (SESAME) H2020 Project under Grant Agreement 101017258
Managing performance expectations in association football
Get PDFYesMotivated by excessive managerial pressure and sackings, together with associated questions over the inefficient use of scarce resources, we explore realistic
performance expectations in association football. Our aim is to improve management quality by accounting for information asymmetry. Results highlight
uncertainty caused both by football’s low-scoring nature and the intensity of
the competition. At a deeper level we show that fans and journalists are prone
to under-estimate uncertainties associated with individual matches. Further, we
quantify reasonable expectations in the face of unevenly distributed resources.
In line with the statactivist approach we call for more rounded assessments to be
made once the underlying uncertainties are adequately accounted for. Managing fan expectations is probably impossible though the potential for constructive
dialogue remains
Estimation of structural steel and concrete stocks and flows at urban scale-towards a prospective circular economy
Get PDFYesQuantification of stocks and flows of construction materials is a key first stage in assessing the potential for creating higher value at end-of-life decisions compared to destructive demolition. Steel and concrete are amongst the most widely used construction materials primarily in structural components. Such components are highly variable in design, type, and dimensions. In the absence of urban-scale digitised models of structural components or building plans, accurate assessment relies on either onsite inspection or modelling by material intensity (MI) co-efficient which can vary by up to a factor of 100. In this study, we extend previous stock modelling approaches through the development of a method that relies on building archetypes and produces MI coefficients of steel and concrete that are representative of frame types, temporally explicit and disaggregated at product level. This is compared to the common existent method of calculating MI to demonstrate the capabilities of the proposed method. Coupled with a spatiotemporal model of urban buildings, the developed MI of both methods are applied to a case study in the UK. The total in-use stock of steel and concrete within multi-storey buildings is estimated at 81,000 tonnes and 655,000 m3 respectively. The stocks of steel and concrete are disaggregated based on their functions as products, for instance steel beams are distinguished from reinforcement steel. Subsequently, the embodied carbon of the in-use stock is calculated as 350 kt CO2eq. The results show the proposed method enables a more granular assessment of the embodied carbon of the structural material quantities.Engineering and Physical Sciences Research Council (EPSRC) research grant 'REBUILD - REgenerative BUILDings and products for a circular economy' [Grant reference: EP/ P008917/1
Predictive accuracy of enhanced versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study
Get PDFYesThe novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19.
Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5.
The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5.
Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.The Health Foundation (Award No 7380) and the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC) (Award No PSTRC-2016-006)Research Development Fund Publication Prize Award winner, Aug 2021
Ethical Framework for Artificial Intelligence and Digital Technologies
Get PDFYesThe use of Artificial Intelligence (AI) in Digital technologies (DT) is proliferating a profound socio-technical transformation. Governments and AI scholarship have endorsed key AI principles but lack direction at the implementation level. Through a systematic literature review of 59 papers, this paper contributes to the critical debate on the ethical use of AI in DTs beyond high-level AI principles. To our knowledge, this is the first paper that identifies 14 digital ethics implications for the use of AI in seven DT archetypes using a novel ontological framework (physical, cognitive, information, and governance). The paper presents key findings of the review and a conceptual model with twelve propositions highlighting the impact of digital ethics implications on societal impact, as moderated by DT archetypes and mediated by organisational impact. The implications of intelligibility, accountability, fairness, and autonomy (under the cognitive domain), and privacy (under the information domain) are the most widely discussed in our sample. Furthermore, ethical implications related to the governance domain are shown to be generally applicable for most DT archetypes. Implications under the physical domain are less prominent when it comes to AI diffusion with one exception (safety). The key findings and resulting conceptual model have academic and professional implications