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Purpose: This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode-specific training on ASR accuracy. Method: Recordings of read and conversational speech from 27 individuals with various speech disorders were analyzed using both (a) one speaker-independent ASR system trained and optimized for typical speech and (b) multiple ASR models that were personalized to the speech of the participants with disordered speech. Word error rates were calculated for each speech model, read versus conversational, and subject. Linear mixed-effects models were used to assess the impact of speech mode and disorder severity on ASR accuracy. We investigated nine variables, classified as technical, linguistic, or speech impairment factors, for their potential influence on the performance gap. Results: We found a significant performance gap between read and conversational speech in both personalized and unadapted ASR models. Speech impairment severity notably impacted recognition accuracy in unadapted models for both speech modes and in personalized models for read speech. Linguistic attributes of utterances were the most influential on accuracy, though atypical speech characteristics also played a role. Including conversational speech samples in model training notably improved recognition accuracy. Conclusions: We observed a significant performance gap in ASR accuracy between read and conversational speech for individuals with speech disorders. This gap was largely due to the linguistic complexity and unique characteristics of speech disorders in conversational speech. Training personalized ASR models using conversational speech significantly improved recognition accuracy, demonstrating the importance of domain-specific training and highlighting the need for further research into ASR systems capable of handling disordered conversational speech effectively. [ABSTRACT FROM AUTHOR]

This paper explores what the measures of utterance fluency typically employed in Automatic Speech Evaluation (ASE), i.e. automated speaking assessments, tell us about oral proficiency. 60 Chinese learners of English completed the second part of the speaking section of IELTS and six tasks designed to measure the linguistic knowledge and processing assumed to underpin second language speech production. A sample of eight native speakers rated the learners' oral productions for functional adequacy. Analyses of the data confirm: (1) articulation rate, mid-clause pause frequency, and repetition frequency predict functional adequacy, (2) breadth of lexical knowledge is the main predictor of articulation rate as well as functional adequacy, (3) speed of syntactic processing predicts end-clause pause duration and speed of lexical processing predicts mid-clause pause duration, and (4) measures of utterance fluency together account for 60% of the variation in functional adequacy scores. These findings suggest that articulation rate best reflects overall functional adequacy. Moreover, other measures of utterance fluency reflect different areas of underlying knowledge and processing, opening up the possibility of automating diagnostic speaking tests.

Purpose: Amyotrophic lateral sclerosis (ALS) is a progressive, ultimately fatal disease causing progressive muscular weakness. Most people living with ALS (plwALS) experience dysarthria, eventually becoming unable to communicate using natural speech. Many wish to use speech for as long as possible. Personalized automated speech recognition (ASR) model technology, such as Google's Project Relate, is argued to better recognize speech with dysarthria, supporting maintenance of understanding through real-time captioning. The objectives of this study are how plwALS and communication partners use Relate in everyday conversation over a period of up to 12 months and how it may change with any decline in speech over time. Method: This study videoed interactions between three plwALS and communication partners. We assessed ASR caption accuracy and how well they preserved meaning. Conversation analysis was used to identify participants' own organizational practices in the accomplishment of interaction. Thematic analysis was used to understand better the participants' experiences of using ASR captions. Results: All plwALS reported lower-than-expected ASR accuracy when used in conversation and felt ASR captioning was only useful in certain contexts. All participants liked the concept of live captioning and were hopeful that future improvements to ASR accuracy may support their communication in everyday life. Conclusions: Training is needed on best practices for customization and practical use of ASR technology and for the limitations of ASR in conversational settings. Support is needed for those less confident with technology and to reduce misplaced allocation of ownership of captioning errors, risking negative effects on psychological well-being. [ABSTRACT FROM AUTHOR]

Objectives: Teleconsultation is anticipated to have a long-term role in primary care. However, conducting virtual physical examinations is a well-known limitation. To anticipate unmet needs general practitioners (GPs) and patients may experience during teleconsultation, this study aims to automatically identify physical examinations typically conducted during in-person GP consultation., Material and Methods: This study utilizes 281 GP in-person consultations (de-identified transcripts & video recordings) within UK general practices, where 169 eligible ones were included in this study. We propose an automated text-based approach using regular expressions on keywords in GP-patient consultation dialogue (e.g., "roll up your sleeves") to identify physical examinations (e.g. blood pressure measurement). This approach involves the construction of conceptual diagrams to visually inspect the relationship between keywords and physical examinations, syntax analysis to identify patterns between keywords and generate regular expressions, and the use of these regular expressions in consultation transcripts to detect potential instances of physical examinations, where matching video frames were subsequently retrieved. The performance of our automated text-based approach is compared to manual classification by 2 independent researchers using 5-fold cross-validation (precision, recall, and F1-score)., Results: Among the 169 eligible GP in-person consultations, 133 (79%) required a physical examination, while the other 33 visits were for psychological reasons. Out of these 133 consultations, a total of 283 physical examinations were observed, with 21 instances conducted behind a curtain. We identified 42 distinct types of physical examinations from these 283 instances, grouped into 10 physical examination categories based on body areas and physical artefacts. The most frequent category of physical examinations is Vital Signs 26.80% (76/283). Overall, blood pressure measurement (also belonging to the Vital Signs category) is the most frequent physical examination at 59.2% (45/76). The comparison between manual classification and the regular expression model demonstrates an average precision of 88.3%, recall of 78.9%, and an F1-score of 83.3% from 5-fold cross-validation, providing significant insights into the frequency and types of physical examinations conducted during in-person GP consultations., Conclusion: By using regular expressions in consultation dialogues between GPs and patients, we can automatically identify physical examinations in GP consultations with a precision of 88.3%. Findings from this study, i.e. physical examinations during in-person GP consultations, provide insights into areas where GPs and patients may need support during teleconsultation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: Dizziness is a common symptom that can occur in an unpredictable and episodic manner leading to the imprecise reporting of symptoms. Patients will often see many specialists before receiving a diagnosis and treatments can vary in terms of risk and invasiveness which places a significant burden on health services. Achieving an early precise diagnosis could be key in reducing the impact of symptoms on patients and health services., Methods and Analysis: The continuous ambulatory vestibular assessment (CAVA) trial is a single-arm, non-randomised, multicentre diagnostic accuracy device trial that aims to quantify the extent to which the CAVA system can differentiate three common inner-ear causes of dizziness: Ménière's disease, vestibular migraine and benign paroxysmal positional vertigo. 85 participants with an established diagnosis from each of the three dizziness conditions, a total of 255 participants, will be recruited from ear, nose and throat, audiology and audiovestibular medicine departments in National Health Service (NHS) sites across the UK. The CAVA device is composed of two components: A set of bespoke single-use sensor arrays that adhere to the left and right side of the participant's face; and a small reusable module fitting over the ear that contains a battery, a data storage facility and connection ports for the arrays. The CAVA device will be worn by all participants for up to 30 days with the aim of capturing eye movement data during a dizzy attack. The first objective is to develop an algorithm that can discriminate between the three dizziness conditions listed above. The second is to quantify the financial and patient benefits of deployment in the NHS. The final objective is to expedite a plan to deploy the system in the NHS., Ethics and Dissemination: The trial was approved by the West Midlands-South Birmingham Research Ethics Committee and the Medicines and Healthcare products Regulatory Agency (MHRA). REC reference: 22/WM/0229, IRAS Project ID: 317899, and MHRA: CI/2022/0062 /GB. Participants will provide full informed consent and can withdraw for any reason without it affecting their standard care. Dissemination will include publication in peer-reviewed journals, presentations at academic and public conferences including patients and the public and to policymakers and practitioners., Trial Registration Number: ISRCTN81218533, trial protocol V.3.1 (25 January 2024)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Aim: Climate change poses a major threat to human health, with significant contributions from healthcare systems, with the UK National Health Service (NHS) accounting for 4% of national CO 2 emissions. Radiology departments, with high energy consumption from heating, ventilation, cooling (HVAC), and scanners, also contribute significantly. Workstations, though less power-intensive than scanners, are numerous and offer an avenue for emission reduction potential. This study investigates the impact of an automatic power-off/on protocol for radiology workstations at an acute hospital trust on energy consumption, carbon emissions, and financial savings., Materials and Methods: Data from 88 reporting workstations were collected on power usage, CO 2 emissions, and the associated energy cost before and after implementing an automatic shutdown protocol, which ensured workstations were turned off out of hours and over weekends., Results: Average weekly workstation on-time fell from 148 to 75.5 hours, resulting in an annual energy saving of 17 MWh, equivalent to a reduction of 3.4 tonnes CO 2 equivalent and a financial saving of £5000. No complaints or issues with workflow disruption were reported., Conclusion: This intervention demonstrates a significant reduction in emissions and energy costs without workflow disruption, offering an easy and replicable sustainability measure for radiology departments. While savings are modest compared to HVAC and scanner emissions, the protocol's simplicity and effectiveness in addressing human factors in power management highlight its potential. Broader application across hospital networks could yield substantial environmental and financial benefits. These findings contribute to the ongoing efforts to improve sustainability within radiology and health care., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Objectives: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). Methods: This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. Results: A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. Conclusion: State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. Clinical relevance: Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. Key Points: • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis. [ABSTRACT FROM AUTHOR]

Voice assistants (VAs), such as Alexa, Siri, or Google Now, are becoming increasingly ubiquitous. Consequently, the potential societal impacts of such systems are gaining relevance in public and academic discourses. Investigating the effects of VA voice features on user perception of VAs is one central aspect of research. Here, the potential effects of VAs on the perception of gender roles and the attribution of gender stereotypes stand out. Following the Computers as Social Actors paradigm, it is assumed that gendered voices have the potential to reproduce existing gender stereotypes. Yet, recently, the implementation of gender-ambiguous voices has been suggested to mitigate such effects and promote more diverse technology design. In this study, we set out to inquire about the relationship between gendered VA voice (male, female, gender-ambiguous) and gender stereotype activation. In two online surveys with an experimental manipulation (Study 1: N = 140, Study 2: N = 397), we test stereotype activation as a function of VA voice, user gender, and conversation topic. Empirical findings are mixed, with no effects for conversation topic but indications of small effects of VA voice. Implications for future research are discussed. [ABSTRACT FROM AUTHOR]

Designing an automatic loom that replenishes weft mechanically without operative intervention challenged the ingenuity of engineers and technologists from the middle of the 19th century until about 1970, when further development ceased in favour of shuttleless alternatives. Although well established in the United States for manufacturing basic cotton fabrics by 1914, and sufficiently well advanced for producing a wide range of fabrics by 1930, the diffusion of the automatic loom in the British cotton industry was sluggish until the 1950s. The reasons are best understood by examining the operational prerequisites, provision of which in Britain was confounded by longstanding rigidities in accounting policy and industrial relations at the level of the firm, and in strategic management at the level of the industry. At the heart of this was the longevity of the traditional non-automatic Lancashire loom, both in design and operation. The article is a counterpoise to the orthodox economic analyses that tend to be dismissive of technological constructs. [ABSTRACT FROM AUTHOR]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

People's natural tendencies to either approach or avoid different stimuli in their environment are considered fundamental motivators of human behaviour. There is a wealth of research exploring how changes in approach and avoidance motivational orientations impact behaviour with consequences for wellbeing. However, research has seldom explored this relationship in reverse. The COVID-19 pandemic offered a unique opportunity to explore whether widespread changes in social behaviour are associated with changes in automatic approach-avoidance tendencies over time. We gathered online survey data on people's adherence to 7 of the prescribed social restrictions set out by the UK government and people's automatic approach-avoidance tendencies in response to different stimuli (sad/happy faces and social scenes) at three time points during the COVID-19 pandemic. Reduced-overall-interaction (digital and in person) was found to be significantly associated with faster avoidance relative to approach of sad faces. The results suggest that automatic approach-avoidance tendencies may function to protect people against the typically negative experience of reduced social interaction, with important implications for understanding public resilience during times of crisis, and beyond. [ABSTRACT FROM AUTHOR]

A large body of evidence shows that individuals with poor mental health have lower income over the lifespan but a dearth of evidence exists on how poor mental health affects savings behaviour. In this paper, we provide novel evidence of a mental health gap in pension participation in the UK using nationally representative longitudinal data from Understanding Society (UKHLS). Beginning in 2012, the UK government introduced automatic enrolment enabling us to assess the impact of one of the largest pension policy reforms in the world on this mental health gap. We measure mental health using the General Health Questionnaire (GHQ-12) which is a commonly used tool for measuring psychological distress. Prior to automatic enrolment, we find that male private sector employees with poor mental health are 3.7 percentage points less likely to participate in a workplace pension scheme while female private sector employees with poor mental health are 2.9 percentage points less likely to participate after controlling for key observables including age, education, race, marital status, number of children, occupation type, industry type, presence of a physical health condition and cognitive ability. The implementation of automatic enrolment removes the mental health gap in pension participation, equalising the pension participation rates of individuals with and without poor mental health in the private sector., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Experience in the UK with auto enrolling workers in pensions indicates that once enrolled, most workers stay enrolled, suggesting that auto enrollment may be a desirable policy for other countries. However, the experience with auto enrollment is much different in Poland. This paper examines the argument that inertia is not a strong force when workers distrust in the security of future pension benefits. We present a simple conceptual framework where trust affects the subjective probability distribution of workers relating to future pension benefits. We investigate how this framework might explain the high opt-out rate in Poland. [ABSTRACT FROM AUTHOR]

Explains multimedia information systems (MMIS) and describes a pilot system with automatic content retrieval developed at the Victoria University of Manchester (England). Topics discussed include types of databases; requirements of the Manchester MMIS; system architecture and operation; semantic data and their representation of images; queries; implementation results; and future work. (46 references) (LRW)

Speech technology--especially automatic speech recognition--has now advanced to a level where it can be of great benefit both to able-bodied people and those with various disabilities. In this paper we describe an application "TalkMaths" which, using the output from a commonly-used conventional automatic speech recognition system, enables the user to dictate mathematical expressions in a relatively straightforward way. These then get converted into electronic formats, so that they can be embedded in a document and/or displayed in an editor or web browser. This process can be used for preparing teaching material, assignments, or entering mathematical content for online tests. Our system does not require the user to have extensive knowledge of the syntax of any markup language or mathematical document specification language, so that learning to use it should be relatively straightforward for non-specialists. The way in which our system analyses, converts and encodes the spoken mathematical expressions is a novel approach. (Contains 4 figures.)

The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking. (Contains 7 notes.)

Highlights • We present an automatic change detection processing chain for rapid flood mapping. • The reference image is selected automatically by Jensen-Shannon (JS) divergence. • Saliency-guided generalized Gaussian mixture model combined with fully-connected CRF. • It is capable of handling highly imbalanced data. • Fine structures in flooded area are preserved when the noise is removed. Abstract In this paper, a two-step automatic change detection chain for rapid flood mapping based on Sentinel-1 Synthetic Aperture Radar (SAR) data is presented. First, a reference image is selected from a set of potential image candidates via a Jensen-Shannon (JS) divergence-based index. Second, saliency detection is applied on log-ratio data to derive the prior probabilities of changed and unchanged classes for initializing the following expectation-maximization (EM) based generalized Gaussian mixture model (GGMM). The saliency-guided GGMM is capable of capturing the primary pixel-based change information and handling highly imbalanced datasets. A fully-connected conditional random field (FCRF) model, which takes long-range pairwise potential connections into account, is integrated to remove the ambiguities of the saliency-guided GGMM and to achieve the final change map. The whole process chain is automatic with an efficient computation. The proposed approach was validated on flood events at the Evros River, Greece and the Wharfe River and Ouse River in York, United Kingdom. Kappa coefficients (k) of 0.9238 and 0.8682 were obtained respectively. The sensitivity analysis underlines the robustness of the proposed approach for rapid flood mapping. [ABSTRACT FROM AUTHOR]

Within three years of the invention of the telephone in 1876 attempts were being made for the partial machine switching of calls. A step-by-step system was developed by A.B. Strowger of the USA in the years 1889 to 1892 and from the beginning of the twentieth century was the first automatic exchange system to come into widespread use there. Conditions in Britain at the time were not conducive to the adoption of machine switching while telephone service was divided between the Post Office and the National Telephone Company (NTC) but the Post Office undertook a review of USA practice. The Post Office completed the purchase of NTC in January 1912 and instituted a series of trials of available automatic systems. The first of these, the public exchange at Epsom, Surrey, opened in May 1912 using the Strowger system. The paper describes the installation and operation of the equipment at Epsom and how it was received by its users and the wider public. In 1922 the Post Office decided to standardize on a development of Strowger step-by-step for British requirements. Ironically, when Epsom exchange required enlargement in the early 1930s it was instead replaced by a new manual exchange. A few examples of equipment from the pioneer British automatic exchange systems survive in preservation. [ABSTRACT FROM AUTHOR]

Background: Online reviews may act as a rich source of data to assess the quality of dental practices. Assessing the content and sentiment of reviews on a large scale is time consuming and expensive. Automation of the process of assigning sentiment to big data samples of reviews may allow for reviews to be used as Patient Reported Experience Measures for primary care dentistry., Aim: To assess the reliability of three different online sentiment analysis tools (Amazon Comprehend DetectSentiment API (ACDAPI), Google and Monkeylearn) at assessing the sentiment of reviews of dental practices working on National Health Service contracts in the United Kingdom., Methods: A Python 3 script was used to mine 15800 reviews from 4803 unique dental practices on the NHS.uk websites between April 2018 -March 2019. A random sample of 270 reviews were rated by the three sentiment analysis tools. These reviews were rated by 3 blinded independent human reviewers and a pooled sentiment score was assigned. Kappa statistics and polychoric evalutaiton were used to assess the level of agreement. Disagreements between the automated and human reviewers were qualitatively assessed., Results: There was good agreement between the sentiment assigned to reviews by the human reviews and ACDAPI (k = 0.660). The Google (k = 0.706) and Monkeylearn (k = 0.728) showed slightly better agreement at the expense of usability on a massive dataset. There were 33 disagreements in rating between ACDAPI and human reviewers, of which n = 16 were due to syntax errors, n = 10 were due to misappropriation of the strength of conflicting emotions and n = 7 were due to a lack of overtly emotive language in the text., Conclusions: There is good agreement between the sentiment of an online review assigned by a group of humans and by cloud-based sentiment analysis. This may allow the use of automated sentiment analysis for quality assessment of dental service provision in the NHS., Competing Interests: The authors have declared that no competing interests exist.

Automatic facial recognition technology (AFR) is increasingly used in criminal justice systems around the world, yet to date there has not been an international survey of public attitudes toward its use. In Study 1, we ran focus groups in the UK, Australia and China (countries at different stages of adopting AFR) and in Study 2 we collected data from over 3,000 participants in the UK, Australia and the USA using a questionnaire investigating attitudes towards AFR use in criminal justice systems. Our results showed that although overall participants were aligned in their attitudes and reasoning behind them, there were some key differences across countries. People in the USA were more accepting of tracking citizens, more accepting of private companies' use of AFR, and less trusting of the police using AFR than people in the UK and Australia. Our results showed that support for the use of AFR depends greatly on what the technology is used for and who it is used by. We recommend vendors and users do more to explain AFR use, including details around accuracy and data protection. We also recommend that governments should set legal boundaries around the use of AFR in investigative and criminal justice settings. [ABSTRACT FROM AUTHOR]

Background: Given the lack of data about the nutritional value and other determinants of the consumption of foods and drinks sold in vending machines in European universities and the relevance of this sector in Spain, it is necessary to obtain scientific data on this topic. The present study aimed to assess the availability, nutritional profile and processing level of food products from vending machines at a Spanish public university and to investigate differences in nutritional profile according to the cost and promotion., Methods: Cross-sectional descriptive study. Data from all products available (3894) were collected and analysed using the criteria of the Spanish Agency for Consumption, Food Safety and Nutrition and the United Kingdom nutrient profiling model. The items were also classified according to the degree of industrial processing through the NOVA system. Promotion was assessed, taking into account where products were displayed in vending machines., Results: The most common products were sweets (23.4% of the total options), coffee (20.3%) and salty snacks (11.7%). According to the combination of the two criteria used to assess nutritional profile, 48.6% of the products were classified as with low nutritional quality (LNQ). In addition, 73.8% of the items were categorised as "ultra-processed". Foods (β = 0.31, 95% CI 0.24, 0.39, p < 0.001) and hot drinks (β = 0.46, 95% CI 0.39, 0.52, p < 0.001) with high nutritional quality (HNQ) were more likely to have higher prices than alternatives with LNQ. Both foods and cold drinks that support healthy dietary recommendations were promoted to a lesser extent than those with LNQ ( p < 0.001)., Conclusion: Almost half of the products were of LNQ and three-quarters had a high level of processing. Moreover, foods and cold drinks with LNQ were less expensive and more often promoted than alternatives with HNQ.

Introduction: Automatic dishwashing rinse aids are drying aids which contain non-ionic surfactants, usually ethoxylated alcohols, typically at concentrations of ≤30%., Objective: To assess the reported toxicity of rinse aids., Methods: Telephone enquiries to the UK National Poisons Information Service were analysed from January 2008 to June 2019., Results: Ingestion : Ingestion was involved in 976 cases and produced gastrointestinal features, coughing and central nervous system depression, particularly in young children. In those in whom the amount ingested was known, the majority (56%) of children <18 years and of adults (57%) ingested <50 mL of rinse aid. Although moderate or severe exposures (Poisoning Severity Score (PSS) ≥ 2) were uncommon, they occurred significantly ( p < 0.0008) more often in adults (9.0%) than in children (1.8%); however, three of the four adults with PSS ≥ 2 co-ingested other substances. Eye exposure : Ocular exposure was reported in 35 cases, of whom 29 developed features. Eye irritation ( n = 10, 28.6%) and eye pain ( n = 10, 28.6%) were reported most commonly, and three patients (8.6%) developed corneal abrasions (PSS 2). Dermal exposure : Thirty-four patients were exposed dermally, and six (17.6%) reported features, including rash, numbness, pruritus and burns (PSS 1)., Conclusions: Overall, clinical features developed in 47% of patients exposed to rinse aids, but more severe features (PSS ≥ 2) were rare (<3%) following exposure by any route.

Introduction: Detergents used in automatic dishwashing machines are of two main types: traditional tablets that require removal from an external wrapper and newer soluble film tablets., Objective: To determine the toxicity of automatic dishwashing tablets., Methods: Telephone enquiries to the UK National Poisons Information Service were analysed for the period January 2008 to June 2019., Results: Ingestion : Ingestion was involved in 798 traditional tablet exposures and 725 soluble film exposures. Clinical features (Poisoning Severity Score ≥ 1) developed in 22.2% of patients ingesting traditional tablets and in 28.8% ingesting soluble film tablets; moderate or severe toxicity was rare (<0.5% for both traditional and soluble film tablets). Children (≤5 years) significantly ( p < 0.0001) more often developed features following ingestion of soluble film ( n = 193, 28.2%) than traditional tablets ( n = 134, 19.1%). In contrast, adults more often developed features following ingestion of traditional than soluble film tablets, although this difference was not statistically significant. Eye exposure : The eye was involved in only 26 of 1539 exposures; 17 of 26 exposures resulted in ocular features. The most commonly reported features were conjunctivitis, eye pain and blurred vision, although one patient sustained a corneal abrasion and developed loss of vision. Skin exposure : Thirty-four of 1539 exposures involved the skin but only 3 developed dermal features which were minor., Conclusions: Children (≤5 years) significantly more often developed features following ingestion of soluble film than traditional tablets, although the likelihood of a child developing features was relatively low (<30%) and features that did develop were almost always mild. In contrast, adults more often developed features following the ingestion of traditional than soluble film tablets. Overall, the eye was involved in only 1.7% of exposures and only one patient sustained a corneal abrasion.

The Police and Criminal Evidence Act (PACE) 1984 was implemented in 1986, inter alia, to render routine police powers and procedures in England and Wales, and to improve protection for those suspected of committing a criminal offence. Yet, in the thirty years since its inception, various provisions within PACE have attracted fervent criticism for their ineffectiveness. Drawing upon empirical data collected at two custody states in England, this paper illustrates, in line with previous studies, that custody officers are still failing to act as a "check " on police powers and are routinely authorising detention. Perhaps more problematically, custody officers may go beyond authorisation and may assist arresting officers in finding sufficient grounds for detention. As such, in the 30 years post-PA CE, s.37 is still having little to no impact on custody practices. This paper explores the factors which influence the authorisation of detention and offers tentative conclusions about how the refusal or authorisation of detention may be regulated. [ABSTRACT FROM AUTHOR]

The 100 000 m 2 wave-cut pavement in the Bristol Channel near Lilstock, UK, is a world-class outcrop, perfectly exposing a very large fracture network in several thin limestone layers. We present an analysis based on manual interpretation of fracture generations in selected domains and compare it with automated fracture tracing. Our dataset of high-resolution aerial photographs of the complete outcrop was acquired by an unmanned aerial vehicle, using a survey altitude optimized to resolve all fractures. We map fractures and identify fracture generations based on abutting and overprinting criteria, and we present the fracture networks of five selected representative domains. Each domain is also mapped automatically using ridge detection based on the complex shearlet transform method. The automatic fracture detection technique provides results close to the manually traced fracture networks in shorter time but with a bias towards closely spaced Y over X nodes. The assignment of fractures into generations cannot yet be done automatically, because the fracture traces extracted by the automatic method are segmented at the nodes, unlike the manual interpretation in which fractures are traced as a path from fracture tip to fracture tip and consist of several connected segments. This segmentation makes an interpretation of relative age impossible, because the identification of correct abutting relationships requires the investigation of the complete fracture trace by following a clearly defined set of rules. Generations 1 and 2 are long fractures that traverse all domains. Generation 3 is only present in the southwestern domains. Generation 4 follows an ENE–WSW striking trend, is suborthogonal to generations 1 and 2, and abuts on them and generation 3, if present. Generations 5 is the youngest fracture set with a range of orientations, creating polygonal patterns by abutting at all other fracture generations. Our mapping results show that the northeastern domains only contain four fracture generations; thus, the five generations of the outcrop identified in the southwestern domains are either not all present in each of the five domains or vary locally in their geometry, preventing the interpreter from linking the fractures to their respective generation over several spatially separate mapping domains. Fracture intensities differ between domains where the lowest is in the NE with 7.3 m -1 and the highest is in the SW with 10 m -1 , coinciding with different fracture orientations and distributions of abutting relationships. Each domain has slightly different fracture network characteristics, and greater connectivity occurs where the development of later shorter fractures is not affected by the stress shadowing of pre-existing longer fractures. [ABSTRACT FROM AUTHOR]

Automatic calf feeders (AMFs) that supply milk for pre-weaning calves require group housing. This observational study assessed calf growth, health, feeding patterns and social behaviours prior to, during and following weaning in groups on the AMF. Calves were recruited from a single UK dairy farm and placed into two groups on the AMF between 3 and5 weeks of age (n = 19 and 13). They were fed milk near-ad libitum until 8 weeks of age when they entered a 2-week step-down weaning period. Daily milk consumption and the numbers of successful and refusal visits were collected from the AMF, along with weekly weights and health checks. Calf behaviour around the AMF was monitored through video analysis, and activity was assessed using tri-axial accelerometers. On average, the calves consumed approximately 3 L of milk three times a day from the AMF, confirming that limiting calves to twice daily feeding will restrict their desired milk consumption. The ADLG pre-weaning was 0.74 kg/d (SD 0.41 kg), rising to 0.99 kg/d (SD 0.46 kg) over weaning and 1.05 kg/d (SD 0.12 kg) post weaning. Refusal visits to the AMF, social interactions, displacements and cross sucking all increased over the weaning periods, suggesting that the calves were experiencing frustration due to a lack of expected milk rewards rather than hunger as ADLG continued to increase. Female calves also had more successful and refusal visits to the AMF than males, along with higher displacement indexes (p = 0.052), suggesting single-sex groups may be beneficial to AMFs. Calf activity had a diurnal pattern, with the highest standing times being during mid-morning and early evening, which may put pressure on limited resources if all calves are active during these particular time periods. [ABSTRACT FROM AUTHOR]

Automatic enrolment (AE) into a workplace pension is an important recent development in pension policy. An important question for this policy is why do people opt-out or not opt-out of AE? This question is important for understanding the power of suggestion associated with AE as well as responding to concerns that women might face undue pressure to opt-out. This article addresses this question through a focus group study into the United Kingdom’s new AE policy. Women were more likely than men to cite lack of affordability as a reason for opting out. Lack of information also seemed important for the power of suggestion associated with AE. Further research should explore how to make AE less gender blind as well as the types of information or advice that should be provided alongside AE. [ABSTRACT FROM AUTHOR]

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue, and prevalence of diagnosis has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in machine learning make it possible to attempt to diagnose ADHD based on the analysis of relevant data, and this could inform clinical practice. This paper reports on findings related to the mental health services of a specialist Trust within the UK's National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a diagnostic model for ADHD in adults. The results demonstrate that it is indeed possible to correctly diagnose ADHD patients with promising statistical accuracy. [ABSTRACT FROM AUTHOR]

Describes the operation of the Global Positioning System (GPS) in Great Britain. Comparison with other automatic vehicle location systems; Schematic of GPS receiver system; Use of the Global System Mobile cellular telephone system.

"Space allocation" was a central pursuit in postwar research on computing and architecture. Researchers sought an algorithm that could automatically design the most efficient floor plan for a set of activities. In this article, we connect an early algorithm for computing architectural floor plans to the postwar British hospital. We examine how researchers adapted algorithmic methods for floor layout design developed in industrial capitalist settings to the promotion of the British welfare state. Finally, we pay special attention to the agency that certain graphic inscriptions borrowed from mathematics had in validating these particular algorithmic methods as promising avenues for the algorithmic automation of all architectural work. This article situates the automation of hospital design in postwar U.K. at the intersection of building science and healthcare management, with the aim to contribute critical perspectives on algorithmic reifications of work in early computer-aided architectural design systems. [ABSTRACT FROM AUTHOR]

User comfort in higher-level Automated Vehicles (AVs, SAE Level 4+) is crucial for public acceptance. AV driving styles, characterised by vehicle kinematic and proxemic factors, affect user comfort, with "human-like" driving styles expected to provide natural feelings. We investigated a) how the kinematic and proxemic factors of an AV's driving style affect users' evaluation of comfort and naturalness, and b) how the similarities between automated and users' manual driving styles affect user evaluation. Using a motion-based driving simulator, participants experienced three Level 4 automated driving styles: two human-like (defensive, aggressive) and one machine-like. They also manually drove the same route. Participants rated their comfort and naturalness of each automated controller, across twenty-four varied UK road sections. We calculated maximum absolute values of the kinematic and proxemic factors affecting the AV's driving styles in longitudinal, lateral, and vertical directions, for each road section, to characterise the automated driving styles. The Euclidean distance between AV and manual driving styles, in terms of kinematic and proxemic factors, was calculated to characterise the human-like driving style of the AV. We used mixed-effects models to examine a) the effect of AV's kinematic and proxemic factors on the evaluation of comfort and naturalness, and b) how similarities between manual and automated driving styles affected the evaluation. Results showed significant effects of lateral and rotational kinematic factors on comfort and naturalness, with longitudinal kinematic factors having a less prominent effect. Similarities in vehicle metrics, such as speed, longitudinal jerk, lateral offset, and yaw, between manual and automated driving styles, enhanced user comfort and naturalness. This research facilitates an understanding of how control features of AVs affect user experience, contributing to the design of user-centred controllers and better acceptance of higher-level AVs. [ABSTRACT FROM AUTHOR]

Under the EU Air Quality Directive (AQD) 2008/50/EC member states are required to undertake routine monitoring of PM 2.5 composition at background stations. The AQD states for PM 2.5 speciation this should include at least: nitrate (NO 3 −), sulfate (SO 4 2−), chloride (Cl−), ammonium (NH4+), sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), elemental carbon (EC) and organic carbon (OC). Until 2017, it was the responsibility of each country to determine the methodology used to report the composition for the inorganic components of PM 2.5. In August 2017 a European standard method of measurement of PM 2.5 inorganic chemical components (NO 3 −, SO 4 2−, Cl−, NH 4 +, Na+, K+, Mg2+, Ca2+) as deposited on filters (EN16913:2017) was published. From August 2019 this then became the European standard method. This filter method is labour-intensive and provides limited time resolution and is prone to losses of volatile compounds. There is therefore increasing interest in the use of alternative automated methods. For example, the UK reports hourly PM 2.5 chemical composition using the Monitor for AeRosols and Gases in Ambient air (MARGA, Metrohm, NL). This study is a pre-assessment review of available data to demonstrate if or to what extent equivalence is possible using either the MARGA or other available automatic methods, including the Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research Inc. US) and the Ambient Ion Monitor (AIM, URG, US). To demonstrate equivalence three objectives were to be met. The first two objectives focused on data capture and were met by all three instruments. The third objective was to have less than a 50% expanded uncertainty compared to the reference method for each species. Analysis of this objective was carried out using existing paired datasets available from different regions around the world. It was found that the MARGA (2006–2019 model) had the potential to demonstrate equivalence for all species in the standard, though it was only through a combination of case studies that it passed uncertainty criteria. The ACSM has the potential to demonstrate equivalence for NH 4 +, SO 4 2−, and in some conditions NO 3 −, but did not for Cl− due to its inability to quantify refractory aerosol such as sea salt. The AIM has the potential for NH 4 +, NO 3 −, SO 4 2−, Cl− and Mg2+. Future investigations are required to determine if the AIM could be optimised to meet the expanded uncertainty criterion for Na+, K+ and Ca2+. The recommendation is that a second stage to demonstrate equivalence is required which would include both laboratory and field studies of the three candidate methods and any other technologies identified with the potential to report the required species. • Comparison of EN16913:2017 filter method for speciated inorganic PM 2.5 against automatic methods. • Automatic methods have the potential to demonstrate equivalence for some or all species. • Further field and laboratory studies are required to demonstrate equivalence for speciated PM 2.5 [ABSTRACT FROM AUTHOR]

• A framework for LRV detection, segmentation in large CMR studies. • Automatic initialization of bi-ventricles for the first time frame without any user intervention. • Automatic End-diastole and End-systole phase assessments. • Supervised deep neural network employed to train and segment the heart. • 3D-ASM image search procedure improved by combining image intensity models with derived distance maps for LV and RV edge localization. The sheer volume of data generated by population imaging studies is unparalleled by current capabilities to extract objective and quantitative cardiac phenotypes; subjective and time-consuming manual image analysis remains the gold standard. Automated image analytics to compute quantitative imaging biomarkers of cardiac function are desperately needed. Data volumes and their variability pose a challenge to most state-of-the-art methods for endo- and epicardial contours, which lack robustness when applied to very large datasets. Our aim is to develop an analysis pipeline for the automatic quantification of cardiac function from cine magnetic resonance imaging data. This work adopt 4,638 cardiac MRI cases coming from UK Biobank with ground truth available for left and RV contours. A hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (three-dimensional active shape models). The contributions of this paper are three-fold. First, a fully automatic method is proposed for left and right ventricle initialization and cardiac MRI segmentation by taking full advantage of spatiotemporal constraint. Second, a deeply supervised network is introduced to train and segment the heart. Third, the 3D-ASM image search procedure is improved by combining image intensity models with convolutional neural network (CNN) derived distance maps improving endo- and epicardial edge localization. The proposed architecture outperformed the state of the art for cardiac MRI segmentation from UK Biobank. The statistics of RV landmarks detection errors for Triscuspid valve and RV apex are 4.17 mm and 5.58 mm separately. The overlap metric, mean contour distance, Hausdorff distance and cardiac functional parameters are calculated for the LV (Left Ventricle) and RV (Right Ventricle) contour segmentation. Bland–Altman analysis for clinical parameters shows that the results from our automated image analysis pipelines are in good agreement with results from expert manual analysis. Our hybrid scheme combines deep learning and statistical shape modeling for automatic segmentation of the LV/RV from cardiac MRI datasets is effective and robust and can compute cardiac functional indexes from population imaging. [ABSTRACT FROM AUTHOR]

Abstract: Schemes for automatic history matching of reservoir modeling are studied for the Nelson oilfield in the Central North Sea. A complete workflow of automatic history matching involves selection of reservoir variables that require modification and parameter updating schemes, automatic history matching, data analysis, and combination of the best results to obtain an ensemble of best reservoir models. Automatic history matching of Nelson field is conducted using production and time-lapse seismic data, with global single-variable approach, regional multi-variable approach and local multi-variable approach as updating schemes, net to gross, horizontal and vertical permeability as updating parameters. It is revealed that local multi-variable approach can effectively improve history matched results by reducing the number of simulation models, saving computing time and increasing the simulation precision. Global single-variable approach is only a suitable parameter updating scheme for cases where the history matching parameters are independent. Regional multi-variable approach is suitable for the cases where there is strong dependency between properties chosen for updating, and there are wells very close together with strong interaction. Local multi-variable approach is very useful when the history matching parameters are dependent but each selected region for updating is independent of others. [Copyright &y& Elsevier]

• An algorithm to automatically detect motion artefacts in cardiac MR short axis images. • Use of k-space corruption to generate realistic motion artefacts to address data imbalance. • Training of a spatio-temporal convolutional neural network using curriculum learning. • Detailed comparison against a range of machine learning methods. • An area under ROC curve of 0.89 is achieved. Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89. [ABSTRACT FROM AUTHOR]

Background: Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice. Aims: We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies. Method: The analysis used data of patients who died by suicide in the period 2013-2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods. Limitations: Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues. Results and Conclusion: The results of this pilot study indicate that machine learning shows promise for predicting within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

The article offers information about the British court case Pioneer Freight Futures Co Ltd (In Liquidation) v Cosco Bulk Carrier Co Ltd which involving master agreements. It provides details of the case and discusses forward freight agreements, automatic early retention, and terminated transactions.

The article discusses a Court of Appeal case where the appellant pleaded guilty to 14 counts of theft and asked for the consideration of 20 other offences of theft in Great Britain. It notes that the appellant, who was a scholar and expert on cultural relations between Iran and the Western world, stole maps and other illustrations from books in various British libraries to improve his personal library. The appellant's liability to automatic deportation under the Great Britain Borders Act 2007 is also cited.