Open Access Institutional Repository at Robert Gordon University

    The social construction of 101 non-emergency video relay services for deaf signers.

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    How the police prepare for and engage with a citizen who is deaf and uses British Sign Language (BSL) is a national problem. From the perspective of deaf sign language users, the police remain largely inaccessible and unprepared in how to accommodate their linguistic needs. Four regional forces have responded to this issue by introducing a local solution, a bespoke 101 non-emergency video relay service (101VRS). Independent VRS companies function as the auxiliary service, mediating video calls to a 101 helpline. This service was identified as a simple solution that relied on minimal resourcing and input from the police. In using Pinch and Bijker’s social construction of technology (SCOT) framework, we look at competing interpretations of the 101VRS concept and how this has led to a range of intended and unintended solutions and problems (Pinch TJ and Bijker WE (1984) The social construction of facts and artefacts: or how the sociology of science and the sociology of technology might benefit each other. Social Studies of Science 14(3): 399–441). To maintain the investment in improving access to the police, we recommend harmonization of 101VRS nationally, and ongoing consultation with how front-line services can become better prepared at assisting deaf citizens

    Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning.

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    Quantifying phenolic compound in peated barley malt and discriminating its origin are essential to maintain the aroma of high-quality Scottish whisky during the manufacturing process. The content of the total phenol varies in peated barley malts, which is critical in measuring the associated peatiness level. Existing methods for measuring such phenols are destructive and/or time consuming. To tackle these issues, we propose in this paper a novel nondestructive system for fast and effective estimating the phenolic concentrations and discriminating their origins with the near-infrared hyperspectral imagery and machine learning. First, novel ways of data acquisition and normalization are developed for robustness. Then, the principal component analysis (PCA) and folded-PCA are fused for extracting the global and local spectral features, followed by the support vector machine (SVM) based origin discrimination and deep neural network based phenolic measurement. In total 27 categories of peated barley malts from eight suppliers are utilized to form thousands of spectral samples for modelling. A classification accuracy up to 99.5% and a squared-correlation-coefficient up to 98.57% are achieved in our experiments, outperforming a few state-of-the-art. These have fully demonstrated the efficacy of our system in automated phenolic measurement and origin discrimination to benefit the quality monitoring in the whisky industry

    A novel autoregressive rainflow-integrated moving average modeling method for the accurate state of health prediction of lithium-ion batteries.

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    The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample

    Interest organizations and European Union politics.

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    Interest representation plays a systemic role in EU policy making and integration, recognised as such in the Treaty on European Union. Interest organisations supply technical and political information to the EU institutions, and EU institutions use interest organisations as agents of political communication. Interest organisations act as a proxy for an otherwise largely absent civil society, with a teeming population of groups advocating for every imaginable cause. Where groups are absent, so EU institutions have stimulated their formation. The result is a pluralist system of checks and balances, although the literature includes findings of ‘islands’ resembling corporatist practice. EU institutions have designed a range of procedures in support of ‘an open and structured dialogue between the Commission and special interest groups,’ now largely packaged as a ‘Better Regulation’ programme. Measures include funding for NGOs, consultation procedures accompanied by impact assessments, a Transparency Register to provide lobbying transparency, and measures for access to documents that enable civil society organisations to keep EU institutions accountable. A multi-level governance system further strengthens pluralist design, making it impossible for any one type of interest to routinely capture the diversity of EU decision making. A key controversy in the literature is how to assess influence, and whether lobbying success varies across interest group type. EU public policy making is regulatory, making for competitive interest group politics, often between different branches of business whose interests are affected differently by regulatory proposals. There are striking findings from the literature, including that NGOs are more successful than business organisations in getting what they want from EU public policy making, particularly where issues reach the status of high salience where they attract the attention of the European Parliament. A key innovation of the Lisbon Treaty involves a European Citizens’ Initiative, which takes dialogue between civil society and EU institutions outside the ecosystem inhabited by civil society organisations and EU institutions known as the ‘Brussels bubble’ and into the member states

    Effect of blade faults on the performance characteristics of a vertical axis wind turbine.

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    Due to the diminishing reserves of fossil fuels and increased pollution from exploitation of these fuels, the world is focusing on the renewable energy sources. Wind is being considered as one of the prime next generation energy sources. Considerable amount of research is being carried out on the innovative designs for maximizing the performance of wind turbines. Furthermore, a lot of research is being carried out on maintenance and condition monitoring of such systems to improve the design of these systems. To predict likelihood of a fault in such systems a variety of fault situations are being examined either numerically or experimentally. Most of the available studies deal with the presence of a single fault in the blades/structure of the wind turbines such as missing blade, deformed blades, blades with slits etc. In the present study two different faulty conditions of the turbine blades have been investigated, both individually and in combination, in order to estimate the contribution of each fault on the performance output of a vertical axis wind turbine. The torque output is one of the most important performance parameters of a wind turbine which has been shown to be quite sensitive to the faults in the blades of the wind turbines. The results depict that the presence of faults on rotor blade/s adversely affects the torque output of a Vertical Axis Wind Turbine (VAWT) and its effects can be seen in variations in the amplitude of the torque output. The study further shows that Computational Fluid Dynamics can be used as an effective tool to evaluate and analyze the presence of faults in a vertical axis wind turbine and can be used as an add-on to novel model based condition monitoring systems

    Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification.

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    In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses

    Computational fluid dynamics based analysis of a closed thermo-siphon hot water solar system.

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    One of the alternative sources of energy is solar energy which is available in abundance throughout the world. The energy contained within the solar rays is capable of starting natural convection within closed mechanical systems containing a suitable working fluid. One such system is commonly known as a Thermo-Siphon which transfers solar energy into internal energy of the working fluid, commonly water. In the present study, an attempt has been made towards better understanding of the flow structure within a thermo-siphon by analysing the natural convection phenomenon using Computational Fluid Dynamics techniques. A commercial CFD package has been used to create a virtual domain of the working fluid within the thermo-siphon, operating under no-load condition. The effects of the length to diameter ratio of the pipes connecting the condenser and the evaporator, number of connecting pipes, angle of inclination of the thermo-siphon and the heat flux from the solar rays to the working fluid, on the performance of the thermo-siphon, have been critically analysed in this study. The results depict that the heat flux and the length to diameter ratio of the pipes have significant effects on the performance of a thermo-siphon, whereas, the angle of inclination has negligibly small effect. Furthermore, an increase in the number of connecting pipes increases the temperature of the working fluid by absorbing more solar energy. Hence, CFD can be used as a tool to analyse, design and optimise the performance output of a thermo-siphon with reasonable accuracy

    Evaluating the effects of oral contraceptive use on biomarkers and body composition during a competitive season in collegiate female soccer players. [Dataset]

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    High training demands throughout the competitive season in female collegiate soccer players have been shown to induce changes in biomarkers indicative of stress, inflammation, and reproduction, which may be exacerbated in athletes using oral contraceptives (OCs). The purpose of this study was to compare biomarkers and body composition between OC-using and non-using (CON) female soccer players throught a competative season. The file accompaning this record presents graphical output of the Bayesian hierarchical generalized linear models fitted to the biomarker data

    Body, sign and double: a parallel analysis of Elain Shemilt's "Doppelganger", Federica Marangoni's "The box of life", and Sanja Ivekovic's "Instructions no.1" and "Make up - make down".

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    Body; identity; self-representation; sexuality; stereotypical images of women as portrayed by the society and the media; the condition of female professional artists: these themes were expressed and developed in several early video works in the 1970s and early 1980s, by women artists that had neither direct knowledge of nor contact between each other. The simultaneous appearance of such themes in their work is therefore an unusual phenomenon, which reveals common, contemporary sensibilities within both Europe and the USA. Early experimentation with video by many women artists in Europe and the USA have been marginalized for years, and most of the artworks have been lost or fallen into oblivion. Only recently have a few of them been shown at exhibitions and re-evaluated in publications. However, the contribution of women artists to video has not yet been fully recovered and reassessed. Several themes and topics shared by early women artists' video pieces have not yet been analyzed and contextualized in the wider European scene. This chapter explores some of these recurrent themes using specific examples of work by three different artists

    Development of a clinical risk score for pain and function following total knee arthroplasty: results from the TRIO study.

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    Objectives: The aim was to develop and validate a simple clinical prediction model, based on easily collected preoperative information, to identify patients at high risk of pain and functional disability 6 months after total knee arthroplasty (TKA). Methods: This was a multicentre cohort study of patients from nine centres across the UK, who were undergoing a primary TKA for OA. Information on sociodemographic, psychosocial, clinical and quality-of-life measures were collected at recruitment. The primary outcome measure for this analysis was the Oxford knee score (OKS), measured 6 months postoperatively by postal questionnaire. Multivariable logistic regression was used to develop the model. Model performance (discrimination and calibration) and internal validity were assessed, and a simple clinical risk score was developed. Results: Seven hundred and twenty-one participants (mean age 68.3 years; 53% female) provided data for the present analysis, and 14% had a poor outcome at 6 months. Key predictors were poor clinical status, widespread body pain, high expectation of postoperative pain and lack of active coping. The developed model based on these variables demonstrated good discrimination. At the optimal cut-off, the final model had a sensitivity of 83%, specificity of 61% and positive likelihood ratio of 2.11. Excellent agreement was found between observed and predicted outcomes, and there was no evidence of overfitting in the model. Conclusion: We have developed and validated a clinical prediction model that can be used to identify patients at high risk of a poor outcome after TKA. This clinical risk score may be an aid to shared decision-making between patient and clinician
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