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Non-invasive inspection for a hand-bound book of the 19th century: numerical simulations and experimental analysis of infrared, terahertz, and ultrasonic methods
Get PDFDue to fungal growth and mishandling in the book, there are various types of defects as they age such as foxing, tears, and creases. It is important to develop novel non-invasive inspection techniques and defect recognition algorithms. In this work, three non-invasive inspection techniques, including infrared thermography (IRT), terahertz time-domain spectroscopy (THz-TDS), and air-coupled ultrasound (ACU), were employed for the detection of defects in an ancient book cover. To improve the image quality and defect contrast, principal component analysis, fast Fourier transform, and partial least squares regression algorithms are used as the post-processing methods. Furthermore, the YOLOv7 network is deployed for defect automatic detection. Finite element analysis and finite-difference time-domain methods were employed for generating training dataset of YOLOv7 network. Experimental results demonstrate that IRT and THz-TDS has excellent detection capability for surface and subsurface defects, respectively. By employing YOLOv7 network with simulation datasets, defects can be effectively identified.Infrared Physics & Technolog
The impacts of systematic false alarms on air traffic controllers’ situation awareness
No full texthttps://ergonomics.org.uk/events-calendar/ehf2024.htmlThe safety net, made of a set of alarms, is considered the final Air Traffic Management (ATM) protection to prevent an accident. The prevalence and causes of false Short-term Conflict Alerts (STCA), an alarm intended to represent one of the final safety barriers, was investigated based on the occurrence of 315 STCA events generated by a Western African Upper Airspace ATM system over an 11-month time period. Based on subject matter expert review, 313 STCA events (99.9%) were classified as false alarms. False STCA were caused by a combination of technical (aircraft position sensor fusion misalignment) and human attributes within the system. Furthermore, a survey with 26 ATCOs on the cognitive and behaviour effects elicited by the experience of false STCAs revealed that 73.08% of ATCOs experienced increased workload. Whilst 38.46% reported a reduction in situation awareness. Results of the analysis of the retrieved data on the STCA suggest that implementing efficient system integration of different sensors and reducing human error will reduce workload, and improve ATCO’s situation awareness and overall ATM system efficiency.Ergonomics & Human Factors 202
The process of training ChatGPT using HFACS to analyse aviation accident reports
Get PDFhttps://ergonomics.org.uk/events-calendar/ehf2024.htmlThis study investigates the feasibility of a generative-pre-trained transformer (GPT) to analyse aviation accident reports related to decision error, based on the Human Factors Analysis and Classification System (HFACS) framework. The application of artificial intelligence (AI) combined with machine learning (ML) is expected to expand significantly in aviation. It will have an impact on safety management and accident classification and prevention based on the development of the large language model (LLM) and prompt engineering. The results have demonstrated that there are challenges to using AI to classify accidents related to pilots’ cognitive processes, which might have an impact on pilots’ decision-making, violation, and operational behaviours. Currently, AI tends to misclassify causal factors implicated by human behaviours and cognitive processes of decision-making. This research reveals the potential of AI's utility in initial quick analysis with unexpected and unpredictable hallucinations, which may require a domain expert’s validation.Ergonomics & Human Factors 202
Numerical investigation of the inviscid Taylor-Green Vortex using an adaptive filtering method for a modal Discontinuous Galerkin method
Get PDFImplicit Large Eddy Simulation and under-resolved Direct Numerical Simulation bypass the complexity and uncertainty of turbulence modelling by using the numerical dissipation of the scheme as a subgrid scale model. High-order methods allow for more accurate capturing of smaller scale structures but suffer from energy pile-up in the higher modes which leads to instability in under-resolved applications. This work presents a filtered modal Discontinuous Galerkin method which adaptively determines the filter strength, avoiding unnecessary degradation of accuracy while maintaining stability. The method is applied to the inviscid Taylor-Green Vortex, a challenging test case which exhibits under-resolved turbulence for which few published results exist. This goal of this work is to present the adaptive filtering method which achieves robustness and accuracy despite a low number of degrees of freedom, as well as to publish a quantity of relevant data for the inviscid TGV problem.The authors acknowledge the computing time on ARCHER2 through UK Turbulence Consortium EPSRC: grant number EP/X035484/1.
P.T also acknowledges the support provided by the EPSRC grant for ‘Adaptively Tuned High-Order Unstructured Finite-Volume Methods for Turbulent Flows’ EPSRC grant number EP/W037092/1.International Journal of Computational Fluid Dynamic
Delineating mastitis cases in dairy cows: development of an IoT-enabled intelligent decision support system for dairy farms
Get PDFMastitis, an intramammary bacterial infection, is not only known to adversely affect the health of a dairy cow but also to cause significant economic loss to the dairy industry. The severity and spread of mastitis can be restrained by identifying the early signs of infection in the cows through an intelligent decision support system. Early intervention and control of infection largely depend on the availability of on-site high throughput machinery, which can analyze milk samples regularly. However, due to limited resources, marginal and small farms usually cannot afford such high-end machinery, hence, the financial loss in such farms due to mastitis may become significant. To overcome such limitations, this article proposes a low-complexity yet affordable automated system for accurate prediction of early signs of clinical mastitis infection in dairy cows. In this work, behavioral data collected through Internet of Things (IoT)-enabled wearable sensors for cows is utilized to develop a support vector machine (SVM) model for the daily prediction of mastitis cases in a dairy farm. The dataset from the research herd utilizes the information of 415 cows collected in the span of 4.75 years in which 75 cows had mastitis. In addition to relevant behavioral features, other statistically significant features, such as daily milk yield, lactation period, and age are also utilized as features. Our study indicates that the SVM model comprising a subset of behavioral and nonbehavioral features can deliver a mastitis prediction accuracy of 89.2%.U.K. Research and Innovation (Grant Number: 104989)IEEE Transactions on Industrial Informatic
Marketisation and the public good: a typology of responses among museum professionals
Get PDFAcross Western democracies, the public sector has undergone significant changes following successive waves of marketisation. Such changes find material expression in an organisation’s logic and associated vocabulary. While marketisation may be adopted, a growing body of research explains how it is often resisted as public sector professionals reject its logic and vocabulary. We contribute to this debate by detailing additional, theoretically important responses. Rather than simply rejecting or adopting both the logic and vocabulary of marketisation, this article shows how UK museum professionals decouple these. Our analysis shows how museum professionals either fashion generic market vocabulary (e.g. customer, value) to pursue local projects or sustain terms such as public and culture to cling to longer-standing ideals of publicness. Partly because of the nature of cultural goods, we propose the museum sector as a paradigm case to illustrate this phenomenon, but our argument has broader implications for the public sphere.Work, Employment and Societ
The untargeted metabolomic analysis of Ammodaucus leucotrichus Coss. & Dur. seeds reveals previously undescribed polar lignans and terpenoids
Get PDFThe polar metabolome of the medicinal plant Ammodaucus leucotrichus Coss. & Dur. has not been comprehensively characterised. In this study, the chemical composition of a water/methanol (4:1) extract of seeds was determined through a combination of UHPLC-MS and NMR techniques. Sixty compounds were identified from the extract with 36 of these confirmed using UHPLC-MS and/or NMR data while the remaining 24 were given putative identifications based on UHPLC-MS data. The compounds included lignans, terpenes, phenolics, flavonoids, and alkaloid derivatives together with some amino- and organic acids. Of these 2 terpenoids and 3 lignans were found to be novel and were isolated and structures determined by comprehensive NMR studies. A further novel lignan glycoside was tentatively identified. Together these data represent the most comprehensive profile of this traditional medicinal plant, providing an annotated profile that can form the basis of future correlative metabolomic investigations to determine active principles behind its reported bioactivities.The FP05 project (Bioproducts for African Agriculture), which is funded by OCP Morocco. FP05 is a collaboration between Mohammed VI Polytechnic University, Rothamsted Research and Cranfield University.Phytochemistry Letter
Managing life extension process for safety critical elements on offshore oil and gas installations
Get PDFLife extension (LE) of Safety Critical Elements (SCEs) is one of the hottest
topics in the offshore Oil and Gas (O&G) industry today. Although LE is
considered as the most appropriate alternative among the end of life
management strategies (EOMLS), there are still several challenges confronting
asset managers during LE phase of operation. Key among these challenges
includes; lack of integrated technical and economic approach to assess the
current health conditions of SCEs for LE, over reliance on expert knowledge
and experience for selecting the most suitable LE strategy and the lack of
suitable approach to integrate all LE decision making elements for effective
operations of SCEs.
To overcome these challenges, this research aimed at developing an integrated
decision making framework to use for managing LE process for SCEs found on
offshore O&G installations. In order to overcome the first challenge, a techno-
economic framework which integrates technical and economic assessment
procedures for condition assessment of SCEs is developed. Furthermore,
approaches based on Life Cycle Cost Benefit (LCCB) concept and Multi Criteria
Decision Making (MCDM) method are subsequently proposed to overcome the
challenge of lack of suitable methodology for selecting the most appropriate LE
strategy. Lastly, a proposed multi-stage remanufacturing architecture capable of
integrating all LE decision making elements is developed. These decision
making models have been developed and analysed using data from literature
review, expert opinion, review of company internal documents as well as data
from manufacturers of SCEs in the offshore O&G industry.
The models are validated with a number of real life case studies which involves
water deluge system, industrial air compressors and three phase separation
systems to ascertain their efficacy. The outcome of validation processes
indicates that these decision making models provide cost effective solutions to
overcome the three key LE challenges outlined in this study. This research has
added to the scientific understanding of this research area through creation of
novel decision making models to support LE phase of operation in the offshore
O&G industry.PhD in Energy and Powe
Impact of indium doping in lead-free (CH3NH3)3Bi2-xInxI9 perovskite photovoltaics for indoor and outdoor light harvesting
Get PDFHybrid halide perovskites (HHPs) have revolutionized the field of solar cells due to their low cost, solution-processable synthesis, and exceptional device performance. Although lead (Pb)-based perovskites are currently the most efficient, their application in indoor photovoltaics and wearable electronics is limited by lead’s toxicity. This has intensified the search for Pb-free alternatives, particularly for use in portable electronic devices. In this study, we utilized a vapor-assisted solution process to systematically engineer the composition of bismuth-based perovskite-inspired materials (PIMs) through indium doping, forming homogeneous and pinhole-free (CH3NH3)3Bi2–xInxI9 (Bi–In) films. These bimetallic Bi–In perovskites exhibit enhanced properties, including high recombination resistance, reduced low-frequency capacitance, lower defect density, and minimal microstrain. Electrochemical impedance spectroscopy (EIS) shows significantly reduced ion migration in Bi–In compositions compared with pure bismuth-based counterparts. The optimized Bi–In-based solar cells achieved a power conversion efficiency (PCE) of 2.5% under outdoor illumination and 5.9% under indoor lighting, showcasing their potential as promising lead-free alternatives for photovoltaic applications.S.M.J. acknowledges commonwealth research funding.ACS Applied Electronic Material
Predicting the impact of underwater skimming on dissolved oxygen consumption in slow sand filters for potable water treatment
Get PDFIn a well-functioning slow sand filter (SSF), dissolved oxygen (DO) is crucial for enabling aerobic processes and microbiota growth. Given that DO supply is predominantly via the feed water, flow pauses (e.g., during cleaning) may trigger anoxic/anaerobic conditions in the stagnant filter bed. Underwater skimming (UWS) is an advanced cleaning technique that employs a skimmer with a shrouded blade, mounted on a mobile platform, to remove the fouling layer composed of sand and particles in order to improve the efficiency of slow sand filtration. As UWS results in changes to the flow pattern of the SSF, a mathematical model was developed to predict DO utilization after a flow perturbation associated with UWS operation. The model was based on a depth resolved measurement of specific oxygen utilization derived from a full scale SSF. Pilot plant experiments monitored DO in the feed and filtrate of SSFs cleaned using underwater and conventional dry skimming techniques. The highest oxygen utilization was in the Schmutzdecke layer, with additional demand imposed by the presence of a granular activated carbon (GAC) sandwich layer. It was observed that pseudo-steady state conditions occurred following filter ripening, where DO utilization, driven by biological activity, remained relatively constant regardless of filter cleaning technique. For flow pauses between three and 24 h, the pause duration's importance decreased, while the hydraulic loading rate became the critical factor for DO recovery in the filter. Additionally, introducing a 'sweetening flow' during UWS ensured a continuous DO supply, facilitating quicker DO replenishment post-cleaning. The model reliably predicted filtrate DO within ±0.6 mg/L, demonstrating its operational utility, especially in the optimisation of UWS methodology. As such, UWS can be applied to clean SSFs with the methodology modified to prevent any detrimental effects to DO management within the filter. This study predicted DO dynamics in SSFs, advancing UWS techniques and could be applied for enhancing water treatment strategies by filtration.The authors acknowledge the financial support of the Engineering and Physical Sciences Research Council (ESPRC), through the STREAM Industrial Doctorate Centre (EP/L015412/1), and financial support from Thames Water and Northumbrian Water Group.Science of The Total Environmen