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Structural Damage Identification of Low-Carbon Energy Infrastructure Using Convolutional Neural Networks: A Case Study of Wind Turbines
No full textThe development of low-carbon energy technologies, along with the modernization of energy infrastructure, plays a vital role in the transition towards a net-zero future. Energy infrastructures are susceptible to major disruptions caused by extreme weather events, natural disasters, technical failures, and man-made accidents. The structural monitoring of energy infrastructures is therefore necessary to ensure the health and safety of systems and secure a reliable power supply. In recent years, many remote-sensing technologies such as satellite and drone imagery have been introduced to monitor large geographical areas. These technologies can provide high resolution images from energy infrastructures such as power plants, pipelines, and wind turbines. Collecting and analyzing such data can help to identify any potential damage to the infrastructure before it turns into a major incident. This paper proposes an Artificial Intelligence (AI) enabled tool that can correlate image data from various sources to identify and locate faults as well as provide some useful details about the damage such as size, shape, and orientation. Our model utilizes a ResNet50 convolutional neural network (CNN) model to classify the faults and a Region CNN model to localize the faults such that the classified faults can be singled out, labelled and given a short description to aid with the maintenance process. The model is tested on a dataset containing hundreds of images taken by a drone during an inspection of wind turbine blades. The results show that the proposed methods improve the detectability of faults, reduce failure rates, and consequentially cut down on repair expenditures. The effect of cost reduction is derived in terms of levelized cost of energy (LCOE) for six wind farms across the UK and Europe, and an average reduction of 1.2% in LCOE was achieved
Sustainable Neighbourhoods for Ageing in Place: An Interdisciplinary Voice Against Global Crisis
No full textThis timely book provides an understanding of how an ageing population can maintain health in the ageing process in their preferred homes and neighbourhoods while coping with global crises of climate change events, infectious diseases, systemic violence, and radical or extreme industrialisation. It is the first-known volume to consider the four crises as health and social threats to healthy longevity from a sustainability perspective.
The book is a collection of commentaries, theoretical frameworks, case studies, and empirical evidence that: (1) provides an analysis of how the crises affect neighbourhood attributes and the ability of residents to use them to maintain health while living in their preferred neighbourhoods, and (2) suggests potential interventions for enabling residents to utilise these attributes for health while living at home in contexts experiencing the crises. Contributions are authored by scholars and practitioners from various disciplines including public health, health care, architecture, engineering, human resources development, information technology, and finance. Among the topics covered:
The Impact of Crises on Older Adults’ Health and Function: An Intergenerational Perspective
A Behavioural Approach to Sustainable Neighbourhoods: A Philosophical Construction of a Friendly Neighbourhood
Assistive Technologies for Ageing in Place: A Theoretical Proposition of Human Development Postulates
“Sustainable Ageing” in a World of Crises
Sustainable Neighbourhoods for Ageing in Place: An Interdisciplinary Voice Against Global Crises serves as both a primary and secondary text particularly suited for post-graduate level study (e.g., MSc, PhD). Each chapter richly describes events, phenomena and models in a way that fits contemporary curricula for students and instructors in sociology, gerontology, architecture, environmental science studies, sustainability, ageing studies, and public health. Researchers in a broad range of disciplines can use the book as a research guide to design their studies based on models and insights described in its contents. With theoretical frameworks and recommendations from this book, stakeholders can understand what a sustainable neighbourhood is in the context of crises by presenting problems and solutions from different countries and disciplines
The operation of urban water treatment plants: a review of smart dashboard frameworks
Get PDFBy locating useful characteristics and determining the perfect circumstances to meet ideal water quality criteria, this study seeks to improve the operation of a water treatment facility. The research comprises gathering data from personnel and exposure to system events, as well as from explicit and tacit knowledge sources. The problem at hand is a multi-objective, multi-criteria problem with many variables in spatial and temporal dimensions, requiring the use of powerful tools for analysis. All engineering problems have an objective function consisting of smaller sub-functions, typically in the form of cost or error minimization. To solve such problems, optimization methods based on natural patterns have been introduced, including genetic algorithms, evolutionary algorithms, and particle mass optimization. By optimizing the operation process of the water treatment plant, the quality of the water provided can be improved to meet standards set by organizations such as Iran 1053, WHO, and EPA. The study's findings could be used to implement changes to the plant's management and operation processes to achieve more ideal water quality conditions. Ultimately, the optimization of water treatment plant processes could have significant positive impacts on public health and well-being, as well as the environment
Association between fear of falling and self‐care behaviours of older people with hypertension
Get PDFAim: This study investigated the association between fear of falling and self-care behaviours of older people with hypertension.
Design: A cross-sectional study.
Methods: This study was conducted in 2019 on 301 older people with hypertension above the age of 60 years in Tehran, Iran. Data were collected using a demographic questionnaire, the Persian Falls Efficacy Scale-International, and a hypertension- related self-care behaviour questionnaire.
Results: Analyses revealed that gender, educational level and history of falling were significant factors associated with fear of falling; and marital status, educational level and income source were significant factors associated with self-care behaviours (p< 0.05). Partial correlations controlling for education revealed a significant positive correlation showing that high fear of falling is associated with worse health promotion self-care behaviours and significant inverse correlations with psycho-emotional, social and daily self-care behaviours (p< 0.05), meaning that high fear of falling is associated with better self-care for these dimensions.
Patient or Public Contribution: This study involved patients in order to evaluate the validity and reliability of the questionnaires. The study was conducted on older people with hypertension referred to hypertension clinics in hospitals
The Joy of Guests: a podcast journey. An interview with Jeremy Strong.
No full textThe podcast market continues to grow, with almost half of UK adults in their 20s and 30s now listening to podcasts at least once a week, and listenership in the UK predicted to grow from 9 million in 2017 to 28 million by 2026 (Statista 2022). Podcasts have developed an undeniable appeal to mass audiences, and as such have also found their way into Higher Education, where they are used to enrich and support learning and teaching (see, for example, Alison Hawkings’ article in this issue of New Vistas) and/or offer academics new ways of disseminating their scholarly work and reaching broader audiences
A Study on the Effect of Target Orientation on the GPR Detection of Tree Roots Using a Deep Learning Approach
No full textMonitoring and protection of natural resources have grown increasingly important in recent years, since the effect of emerging illnesses has caused serious concerns among environmentalists and communities. In this regard, tree roots are one of the most crucial and fragile plant organs, as well as one of the most difficult to assess [1].
Within this context, ground penetrating radar (GPR) applications have shown to be precise and effective for investigating and mapping tree roots [2]. Furthermore, in order to overcome limitations arising from natural soil heterogeneity, a recent study has proven the feasibility of deep learning image-based detection and classification methods applied to the GPR investigation of tree roots [3].
The present research proposes an analysis of the effect of root orientation on the GPR detection of tree root systems. To this end, a dedicated survey methodology was developed for compilation of a database of isolated roots. A set of GPR data was collected with different incidence angles with respect to each investigated root. The GPR signal is then processed in both temporal and frequency domains to filter out existing noise-related information and obtain spectrograms (i.e. a visual representation of a signal's frequency spectrum relative to time). Subsequently, an image-based deep learning framework is implemented, and its performance in recognising outputs with different incidence angles is compared to traditional machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the use of novel ways to enhance the interpretation of tree root systems.
Acknowledgements
The Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust. The Authors would also like to thank the Ealing Council and the Walpole Park for facilitating this research
Effect of using a passive rotor on the accuracy of flow measurements in sewer pipes using a slug tracer-dilution method
Get PDFFlow measurements in pipelines using slug tracer have proved highly accurate for turbulent flow. This study experimentally investigates the effectiveness of using a passive rotor on the accuracy of discharge measurements in sewer pipes based on a saline slug tracer technique. For this purpose, a saline injector stack was developed to help inject saline at selected injection points. A passive axial flow rotor was also proposed and encased in the injector stack to enhance the mixing of injected tracer with the transmitted downstream flow and to decrease the required minimum mixing length. It was found that adding the passive rotor significantly increased the accuracy of the flow measurements. Two tracer flow formulas were developed: one based on the dimensional analysis approach and the other based on a semi-empirical formula obtained from the mass conservation approach. The resultant formulas compared favourably with flow metering, especially when utilizing the passive fan unit
Safety and reliability in aviation – a systematic scoping review of normal accident theory, high-reliability theory, and resilience engineering in aviation
Get PDFAviation is a complex system with different interconnected and interdependent subsystems that rely on each other to ensure safety and reliability. The technological progress in the sector has increased safety, but incidents and accidents still happen. However, accident analyses and safety research have not paid equal attention to all aviation subsystems resulting in possibly undetected or underestimated risks. This study systematically investigates the literature on aviation safety from 1984 to 2021 with a particular focus on Normal Accident Theory (NAT), High-reliability Theory (HRT), and Resilience Engineering (RE) as their underpinning theoretical perspectives. The analysis of the 77 records that were screened as most relevant shows that the studies underpinned by these theories were mainly looking at the ‘primary operational aviation subsystems' such as air traffic control (ATC) and flight operations and significantly less at the 'secondary operational subsystems' such as ground operations and aircraft maintenance. In addition, the analysis showed that research building on RE has increased in recent years and is now the predominant theoretical framework in studies of this type. Nevertheless, NAT and HRT are still relevant and are often employed in conjunction with RE. Future research should pay more attention to the role of secondary subsystems and their impact on the safety, reliability, and efficiency of the aviation system. Moreover, there is perhaps a need for researchers to develop a more integrative framework that includes valuable components of all three theories and to create a set of safety and reliability strategies suitable for both primary and secondary aviation subsystems, hence, benefiting the entire aviation system
Mitigation of airborne contaminants dispersion in an educational building and investigate its impacts on indoor air quality and energy performance
Get PDFIn today's modern world, people spend most of their time inside buildings, highlighting the importance of indoor air quality (IAQ) and providing clean air to the occupants. In this regard, a simple educational building is modeled using CONTAM-EnergyPlus co-simulation to investigate IAQ enhancement strategies and their role in the building's energy performance. Three contaminants, including CO2, PM2.5, and SARS-CoV-2, are considered to be generated from various sources. The occupants generate CO2, a source of PM2.5 is assumed in the lunchroom to represent cooking activities, and a person who sheds SARS-CoV-2 moves around the zones. The main goal of this study is to apply various pollutant mitigation methods to the model, such as increasing ventilation rate and outdoor air (OA) percentage, natural ventilation, installing filters and air cleaners, and UVGI lights. Then, their performance and impact on the defined contaminants are studied individually and in combination.
In this regard, a scenario with 80% outdoor air (OA) and 100% ventilation rate has been shown to be effective in reducing all three contaminants' concentrations to acceptable levels in most zones, but this results in 50% higher energy consumption compared to the model with no outdoor air. However, to achieve a safe level of PM2.5 in the lunchroom, a combination of all the strategies presented in the (0.8OA+1Vent+NatVent+all) scenario is required. Furthermore, HEPA air cleaners are more effective in diluting contaminants in all zones than UVGI lights and MERV 13 filters. Additionally, this study has shown that HVAC systems operating with little or no outside air can increase the risk of pollutants being transmitted between adjacent zones through the ducts, making it necessary to install in-duct filters
Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks
Get PDFTissue Doppler imaging is an essential echocardiographic technique for the non-invasive assessment of myocardial blood velocity. Image acquisition and interpretation are performed by trained operators who visually localise landmarks representing Doppler peak velocities. Current clinical guidelines recommend averaging measurements over several heartbeats. However, this manual process is both time-consuming and disruptive to workflow. An automated system for accurate beat isolation and landmark identification would be highly desirable. A dataset of tissue Doppler images was annotated by three cardiologist experts, providing a gold standard and allowing for observer variability comparisons. Deep neural networks were trained for fully automated predictions on multiple heartbeats and tested on tissue Doppler strips of arbitrary length. Automated measurements of peak Doppler velocities show good Bland–Altman agreement (average standard deviation of 0.40 cm/s) with consensus expert values; less than the inter-observer variability (0.65 cm/s). Performance is akin to individual experts (standard deviation of 0.40 to 0.75 cm/s). Our approach allows for > 26 times as many heartbeats to be analysed, compared to a manual approach. The proposed automated models can accurately and reliably make measurements on tissue Doppler images spanning several heartbeats, with performance indistinguishable from that of human experts, but with significantly shorter processing time