Search

In this paper I discuss the affordances and popularity of the short-video app TikTok from an audience studies point of view. I do so by drawing on findings from ethnographic fieldwork with young adult TikTok users based in the United Kingdom that was conducted in 2020 and 2021. I trace how using the app, specifically scrolling through the TikTok For You Page, the app's algorithmic content feed, became a fixed part of the everyday routines of young adults. I show how TikTok appealed to them as a convenient means of escape and relief that they were unable to find elsewhere during and beyond times of lockdown. Further, I highlight the complex nature of TikTok as an app and the active role that users play in imagining and appropriating the app's affordances as meaningful parts of their everyday social life. Closing the paper, I reflect on future directions of TikTok scholarship by stressing the importance of situated audience studies. [ABSTRACT FROM AUTHOR]

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models. [ABSTRACT FROM AUTHOR]

Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic. Findings: Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. Conclusions: There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper determines the assessment of publications submitted to the UK research evaluation carried out in 2014, the REF, which would have resulted if they had been assessed with the bibliometric algorithm used by the Italian evaluation agency, ANVUR, for its evaluation of the research of Italian universities. We find extremely high correlations between the two assessment approaches. [ABSTRACT FROM AUTHOR]

The global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care. [ABSTRACT FROM AUTHOR]

Graph clustering acts as a critical topic for solving decision situations in networks. Different node clustering methods for undirected and directed graphs have been proposed in the literature, but less attention has been paid to the case of attributed weighted multi-edge digraphs (AWMEDiG). Nowadays, multi-source and multi-attributed data are used increasingly in decision sciences; however, traditional methods usually consider single-attributed and single-view data as the input. This type of directed network, whose nodes are described by a list of attributes and directed links are viewed as directed multi-edge, is a new challenge to graph clustering. This paper proposes a new approach to detecting and evaluating clusters of AWMEDiG based on the maximum clique method. Our algorithm first converts the given AWMEDiG into a new weighted graph. This transformation is carried out through a new structural-attributed similarity measurement, an improved version of our previous model. Then, the concept of the maximum clique is adopted to complete the clustering task. The main objective of this new approach is to improve the clustering quality by grouping the highly cohesive and homogeneous vertices. So, the entropy strongly tends to be zero. Moreover, since the number of clusters is not predefined, it has the ability to find out the natural number of clusters within a graph. The performance of the proposed algorithm is tested on a synthetic, provided for a three-view attributed weighted directed network including 50 nodes, and two real-world datasets: UK faculty and Seventh graders. The clustering results are analyzed based on the clusters' entropy and density, meanwhile the precision and F-score indexes measure the accuracy of them. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm to identify the potential effect of mergers by comparing the outcomes of interest in areas of overlap for the merging parties vis‐à‐vis areas where no overlap exists within a difference‐in‐differences estimation framework. Utilizing our proposed algorithm enables researchers and policymakers to perform retrospective merger evaluation studies that look at the effects of mergers on both price and non‐price aspects. We demonstrate the applicability and value of our proposed methodology by examining the effects on price and product variety of four mergers of the late 1980s and the 1990s on the U.K. car market. [ABSTRACT FROM AUTHOR]

In the last few years, research studies and opinion pieces have tried to account for the new polarisation and dealignment of US politics after Trump and the post-Brexit UK politics. It is now well-established both by academic research and by Facebook's own research that Facebook leads to more polarisation in its users' political views, but rhetorical analysis has not yet accounted for the role played by algorithms in political communication and persuasion. What does social media do to rhetoric? The situation of speech in social media is often treated like in a public sphere when it should not be. This misconception prevents rhetorical studies to take into consideration the question of technology. By using the recent literature in critical algorithm studies, I develop a new approach in rhetorical criticism. I argue here that the increasing agency that algorithms have acquired in delivering and mediating rhetoric means that we must consider the role played by intermediaries when examining rhetorical situations. This paper sheds light on what I call the four conditionalities of algorithms on rhetoric: (1) programmed speech content, (2) the verticalisation of political communication, (3) the new biases produced by digital media, and (4) the rhetorical machine learning. [ABSTRACT FROM AUTHOR]

The performance of the power system state estimation (SE) is influenced by the configuration of the meters and measurement redundancy. Therefore, the measurement set needs to be updated by installing new SCADA meters and phasor measurement units for improving the quality of the SE solution. However, the potential inconsistency between the existing meters and the new meters should be addressed. Otherwise, the additional meters may lead to numerical problems such as collinearity (linear dependence due to duplicated measurements) and the existence of high leverage points (HLPs) (influential measurements). Hence, this paper proposes an incremental meter placement method. The proposed method utilizes the HLPs and aims to improve the numerical performance of the SE and facilitate the elimination of bad data. The cuckoo search optimization is used for selecting the optimal locations and the numbers of the new meters. The performance of the proposed algorithm is tested on UK 18-bus, the IEEE 30-bus, and 118-bus systems and simulation results show improvements in the quality of the SE solution. [ABSTRACT FROM AUTHOR]

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case–control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case–control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype. [ABSTRACT FROM AUTHOR]

Abstract: The long‐standing debate into the potential benefit of developing mathematical thinking skills through learning to program has been reignited with the widespread introduction of programming in schools across many countries, including England where it is a statutory requirement for all pupils to be taught programming from 5 years old. Algorithm is introduced early in the English computing curriculum, yet there is limited knowledge of how young pupils view this concept. This paper explores pupils' (aged 10–11) understandings of algorithm following their engagement with 1 year of ScratchMaths, a curriculum designed to develop computational and mathematical thinking skills through learning to program. A total of 181 pupils from 6 schools undertook a set of written tasks to assess their interpretations and evaluations of different algorithms that solve the same problem, with a subset of these pupils subsequently interviewed to probe their understandings in greater depth. We discuss the different approaches identified, the evaluation criteria they used, and the aspects of the concept that pupils found intuitive or challenging, such as simplification and abstraction. The paper ends with some reflections on the implications of the research, concluding with a set of recommendations for pedagogy in developing primary pupils' algorithmic thinking. [ABSTRACT FROM AUTHOR]

Since the first launch of the new engineering contract (NEC) in 1993, early warning of problems has been widely recognized as an important approach of proactive management during a construction or engineering project. Is early warning really effective for the improvement of problem solving and project performance? This is a research question that still lacks a good answer. For this reason, an empirical investigation was made in the United Kingdom (U.K.) to answer the question. This study adopts a combination of literature review, expert interview, and questionnaire survey. Nearly 100 questionnaire responses were collected from the U.K. construction industry, based on which the use of early warning under different forms of contract is compared in this paper. Problem solving and project performance are further compared between the projects using early warning and the projects not using early warning. The comparison provides clear evidence for the significant effect of early warning on problem solving and project performance in terms of time, cost, and quality. Subsequently, an input-process-output model is developed in this paper to explore the relationship among early warning, problem solving, and project performance. All these help construction researchers and practitioners to better understand the role of early warning in ensuring project success. [ABSTRACT FROM AUTHOR]

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety. [ABSTRACT FROM AUTHOR]

Background Evidence suggests a correlation of blood pressure (BP) level with presence of diabetic microvascular complications (DMCs), but the effect of BP on DMCs incidence is not well-established. We aimed to explore the associations between BP and DMCs (diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy) risk in participants with diabetes. Methods This study included 23030 participants, free of any DMCs at baseline, from the UK Biobank. We applied multivariable-adjusted Cox regression models to estimate BP-DMCs association and constructed BP genetic risk scores (GRSs) to test their association with DMCs phenotypes. Differences in incidences of DMCs were also compared between the 2017 ACC/AHA and JNC 7 guidelines (traditional criteria) of hypertension. Results Compared to systolic blood pressure (SBP)<120 mm Hg, participants with SBP≥160 mm Hg had a hazard ratio (HR) of 1.50 (95% confidence interval (CI)=1.09, 2.06) for DMCs. Similarly, DMCs risk increased by 9% for every 10 mm Hg of higher SBP at baseline (95% CI=1.04, 1.13). The highest tercile SBP GRS was associated with 32% higher DMCs risk (95% CI=1.11, 1.56) compared to the lowest tercile. We found no significant differences in DMCs incidence between JNC 7 and 2017 ACC/AHA guidelines. Conclusions Genetic and epidemiological evidence suggests participants with higher SBP had an increased risk of DMCs, but hypertension defined by 2017 ACC/AHA guidelines may not impact DMCs incidence compared with JNC 7 criteria, contributing to the care and prevention of DMCs. [ABSTRACT FROM AUTHOR]

This paper examines the dynamics of self-employment rates overall and by gender across the UK during the period 2004–2016. Specifically, using the panel convergence methodology suggested by Phillips and Sul (Econometrica 75:1771–1855, 2007) we investigate whether self-employment rates can be characterised by a process where all regions tend to the same equilibrium (global or full convergence) or, if not, whether there are one or more clusters of regions with the same equilibrium (convergence clubs). We find that there is no global regional convergence in total and gender-specific self-employment rates. However, two convergence clubs of regions with lower self-employment rates are found along with a group of non-convergent regions which have higher self-employment rates and somewhat higher rates of growth in self-employment. We also show that gender differences in convergence patterns across UK exist. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that uses forecast encompassing tests for combining forecasts when there are a large number of forecasts that might enter the combination. The algorithm excludes a forecast from the combination if it is encompassed by another forecast. To assess the usefulness of this approach, an extensive empirical analysis is undertaken using a US macroeconomic dataset. The results are encouraging; the algorithm forecasts outperform benchmark model forecasts, in a mean square error (MSE) sense, in a majority of cases. The paper also compares the empirical performance of different approaches to forecast combination, and provides a rule-of-thumb cut-off point for the thick-modeling approach. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aims: This study is the first to systematically document histological features of fractures of known age in infants (≦12 months). It has been used to develop a tabulated database specifically to guide histopathologists to age fractures in children considered to have suffered accidental or non‐accidental injury (NAI). Currently in the United Kingdom there are insufficient pathologists with experience in histological ageing of fractures to meet the medicolegal need for this examination. This study provides a practical tool that will allow those skilled paediatric and forensic pathologists currently involved in assessing infants for evidence of accidental or non‐accidental injury a basis for extending their assessment into this area of unmet need. Methods and results: One hundred and sixty‐nine fractures of known age at death were obtained from 52 anonymised infants over a period of 32 years (1985–2016 inclusive). Sections stained using haematoxylin and eosin (H&E) and Martius scarlet blue (MSB) were used to identify specific histological features and to relate them to fracture age. In 1999 the data were entered into a tabulated database for fractures accumulated between from 1985 to 1998 inclusive. Thereafter cases were added, and at 2‐yearly intervals the accumulated data were audited against the previous database and adjustments made. Conclusions: This paper describes the final data set from the 2017 audit. The study was terminated at the end of 2016, as there had been no material changes in the data set for three consecutive audits. [ABSTRACT FROM AUTHOR]

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. [ABSTRACT FROM AUTHOR]

Anderson Gray McKendrick's 1926 paper, 'Applications of mathematics to medical problems', was the earliest reference cited in Dempster et al.'s 1977 paper that defined and popularized the EM algorithm. McKendrick's paper was prominently featured by Joseph Oscar Irwin in his 1962 inaugural address as the President of the Royal Statistical Society (in the UK), entitled 'The place of mathematics in medical and biological statistics'. The link of McKendrick's work to the EM algorithm is due to an improvement made by Irwin on a novel method McKendrick used for estimating an infection rate when the observed data do not distinguish between those individuals who are not susceptible to the infection and those who are susceptible, but do not develop symptoms. This article examines this link, along the way illustrating the central ideas underlying the EM algorithm as well as its properties; the examination also suggests a profiling strategy for speeding up EM, which may be worthy of general investigation. McKendrick's data on an epidemic of cholera are used for illustration and to compare EM with Irwin's method as well as the Newton-Raphson algorithm. Issues beyond computation are also discussed whenever appropriate. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) is a common occurrence in the UK, with 60,000 cases reported annually. To improve outcomes among these patients it is necessary to improve links in the cardiac arrest chain of survival. Cardiopulmonary resuscitation (CPR) is one of the main elements of this chain because it reduces further ischaemic insult in the brain and heart by contributing to blood flow. However, even the best manual CPR provides only 20%-30% of the normal cardiac output. Despite the emphasis on timely CPR at the correct rate and depth, delivery of compressions is suboptimal for many patients and mechanical devices may improve outcomes. This article critically appraises two important papers on the use of mechanical CPR for OHCA. It also suggests potential uses for this treatment option and areas for future research. [ABSTRACT FROM AUTHOR]

Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual's habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual's free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments. [ABSTRACT FROM AUTHOR]

Mathematics can be perceived as being a difficult subject to learn due to the conceptual leaps required to understand particular mathematical topics. In some areas of mathematics, part of the difficulty may be associated with applying sufficient imagination to visualize a particular mathematical concept, and applying sufficient visio-spatial abilities to apply the concept to practical use. In this paper we examine the potential use of animation as an aid for the teaching of mathematical concepts at undergraduate level. An experiment was conducted with a group of UK undergraduate computing students to ascertain if and how animation can potentially be used to support the teaching of mathematical concepts in areas such as rotational symmetry and matrices. In general, the results of the experiment appeared to indicate that animated learning materials can prove more useful than static representations for such topics. [ABSTRACT FROM AUTHOR]

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learners CN2, RIPPER, and APRIORI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance. APRIORI-SD is also compared to subgroup discovery algorithms CN2-SD and SubgroupMiner. The comparisons performed on U.K. traffic accident data show that APRIORI-SD is a competitive subgroup discovery algorithm. [ABSTRACT FROM AUTHOR]

The author reflects on the risk of algorithmic injustice for interactive learning environments in education and the role of the "Journal of Interactive Learning Environment" (ILE) in the context of algorithms and artificial intelligence (AI). Other topics include the increasing sophistication of machine learning algorithms, the use by the British government of algorithm in the calculation of student grades, and the relationship between school funding and students' examination scores.

"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]

Purpose - The purpose of this paper is to calculate the rate of suicide in different religious groups in people of South Asian origin in the UK. Design/methodology/approach - A cross-sectional, secondary analysis of a national data set. A name recognition algorithm was used to identify people of South Asian origin and their religion. Standardized mortality ratios (SMRs) were calculated using this data and data from the national census. Setting: a population study of all those who died by suicide in England and Wales in 2001. Participants: all cases of suicide and undetermined intent identified by the Office for National Statistics for England and Wales. Findings - There were 4,848 suicides in the UK in 2001 of which 125 (2.6 percent) were identified as people of South Asian origin by the algorithm. The suicide rate for all people of South Asian origin was 5.50/100,000 compared to 9.31/100,000 for the population of England and Wales. The age SMR for those whose names were of Hindu, Muslim or Sikh origin were 0.88, 0.47 and 0.85, respectively. Female South Asians have lower rates of suicide, than their South Asian male counterparts. Research limitations/implications - Religious classification by the computerized program does not guarantee religious affiliation. The data set were confined to one year because religion was not collected prior to the 2001 census. Originality/value - The rates of suicide for South Asian sub-populations in the UK differ by gender and religion. [ABSTRACT FROM AUTHOR]

Previous evidence suggested a significant variation in the testing algorithms used across the United Kingdom for the diagnosis of Clostridium difficile infection (CDI) and new national guidelines were issued in 2012. The main aim of this paper was to explore if such variation in testing and reporting is still present, to compare the management of CDI cases, and to investigate if there is any significant variation in the antibiotic policies among different hospitals. Using London hospitals as a sample, results show that there is still a wide variation of testing methods and reporting used, making comparisons difficult. It is likely that the overall variability in practices would be greater at a national and, even more, at international level. The relationship between broad-spectrum antibiotics and C. difficile incidence and alternative approaches in antibiotic guidelines may require further studies. [ABSTRACT FROM AUTHOR]

Many real matching markets are subject to distributional constraints. These constraints often take the form of restrictions on the numbers of agents on one side of the market matched to certain subsets on the other side. Real-life examples include restrictions on regions in medical matching, academic master's programs in graduate admission, and state-financed seats for college admission. Motivated by these markets, we study design of matching mechanisms under distributional constraints. We show that existing matching mechanisms suffer from inefficiency and instability, and propose a mechanism that is better in terms of efficiency, stability, and incentives while respecting the distributional constraints. (JEL C70, D61, D63) [ABSTRACT FROM AUTHOR]

The article highlights a comment on the report on parameter estimation of the nonlinear Muskingum Flood-Routing model using a hybrid harmony search algorithm. Topics discussed include a review of the first case study presented in the report that can be considered as a benchmark problem in estimation of the nonlinear Muskingum model parameters and another on the second case study focused on a real flood event in Great Britain in 1960.

In August 2020, the UK government and regulation body Ofqual replaced school examinations with automatically computed A Level grades in England and Wales. This algorithm factored in school attainment in each subject over the previous three years. Government officials initially stated that the algorithm was used to combat grade inflation. After public outcry, teacher assessment grades used instead. Views concerning who was to blame for this scandal were expressed on the social media website Twitter. While previous work used NLP-based opinion mining computational linguistic tools to analyse this discourse, shortcomings included accuracy issues, difficulties in interpretation and limited conclusions on who authors blamed. Thus, we chose to complement this research by analysing 18,239 tweets relating to the A Level algorithm using Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), underpinned by social actor representation. We examined how blame was attributed to different entities who were presented as social actors or having social agency. Through analysing transitivity in this discourse, we found the algorithm itself, the UK government and Ofqual were all implicated as potentially responsible as social actors through active agency, agency metaphor possession and instances of passive constructions. According to our results, students were found to have limited blame through the same analysis. We discuss how this builds upon existing research where the algorithm is implicated and how such a wide range of constructions obscure blame. Methodologically, we demonstrated that CL and CDA complement existing NLP-based computational linguistic tools in researching the 2020 A Level algorithm; however, there is further scope for how these approaches can be used in an iterative manner. [ABSTRACT FROM AUTHOR]

ABSTRACT: The purpose of this paper is to introduce the conceptualisation and evolution of a trialed Doppler traffic light system for healed venous leg ulceration. This tool aims to facilitate clinical decision-making and provides a systematic approach to the ongoing assessment of arterial disease in patients with healed venous leg ulcers when used in conjunction with other purposely-designed assessment documentation to ensure the novice registered and unregistered nurse contributes safely and effectively to the care of patients. The validation and trial of this clinical decision tool is discussed in terms of the use of a recognised methodology determining its fitness for purpose and robustness. [ABSTRACT FROM AUTHOR]

There is a discernable mood in macro-level public evaluations of party issue competence. This paper argues that voters use heuristics to transfer issue competence ratings of parties between issues, therefore issue competence ratings move in common. Events, economic shocks and the costs of governing reinforce these shared dynamics. These expectations are analysed using issue competence data in Britain 1950–2008, and using Stimson's dyad ratios algorithm to estimate ‘macro-competence’. Effects on macro-competence are found for events and economic shocks, time in government, leader ratings, economic evaluations and partisanship, but macro-competence also accounts for unique variance in a model of party choice. The article presents an aggregate-level time-series measure to capture the long-term dynamics of ‘valence’. [ABSTRACT FROM PUBLISHER]

Five years of UK Met Office global model analyses from 1 January 2004 to 31 December 2008 have been used to generate a global hail climatology at 1° latitude by 1° longitude resolution. Hail has been diagnosed from model vertical profiles of temperature and humidity using a simple algorithm based on a paper by Fawbush and Miller (). Results have been compared with published observational climatologies on both a global and regional basis. The model diagnoses compare favourably with the observed data with both orographic and seasonal signals. However, there are differences, and possible reasons for these are discussed. These include limitations of the simple algorithm, the difficulty of observing hail in sparsely populated areas and the problem of population growth influencing the number of hail reports. Copyright © 2011 Royal Meteorological Society and Crown Copyright [ABSTRACT FROM AUTHOR]

Multi-reservoir operation planning is a complex task involving many variables, objectives, and decisions. This paper applies a hybrid method using genetic algorithm (GA) and linear programming (LP) developed by the authors to determine operational decisions for a reservoir system over the optimization period. This method identifies part of the decision variables called cost reduction factors (CRFs) by GA and operational variables by LP. CRFs are introduced into the formulation to discourage reservoir depletion in the initial stages of the planning period. These factors are useful parameters that can be employed to determine operational decisions such as optimal releases and imports, in response to future inflow predictions. A part of the Roadford Water Supply System, UK, is used to demonstrate the performance of the GA-LP method in comparison to the RELAX algorithm. The proposed approach obtains comparable results ensuring non zero final storages in the larger reservoirs of the Roadford Hydrosystem. It shows potential for generating operating policy in the form of hegging rules without a priori imposition of their form. [ABSTRACT FROM AUTHOR]

Objectives. Pain assessment in older adults with cognitive impairment is often challenging, and paramedics are not given sufficient tools/training to assess pain. The development of a mobile app may improve pain assessment and management in this vulnerable population. We conducted usability testing of a newly developed iPhone pain assessment application with potential users, in this case as a tool for clinical paramedic practice to improve pain assessment of older adults with cognitive impairment. Methods. We conducted usability testing with paramedic students and a Delphi panel of qualified paramedics. Participants studied the app and paper-based algorithm from which the app was developed. The potential use for the app was discussed. Usability testing focus groups were recorded, transcribed verbatim, and analyzed using a thematic approach. Proposed recommendations were disseminated to the Delphi panel that reviewed and confirmed them. Results. Twenty-four paramedic students from two UK ambulance services participated in the focus groups. Usability of the app and its potential were viewed positively. Four major themes were identified: 1) overall opinion of the app for use in paramedic services; 2) incorporating technological applications into the health care setting; 3) improving knowledge and governance; and 4) alternative uses for the app. Subthemes were identified and are presented. Discussion. Our results indicate that the pain assessment app constitutes a potentially useful tool in the prehospital setting. By providing access to a tool specifically developed to help identify/assess pain in a user-friendly format, paramedics are likely to have increased knowledge and confidence in assessing pain in patients with dementia. [ABSTRACT FROM AUTHOR]

Electoral analysis using aggregate data relies on the availability of accurate voting statistics. One vital piece of information, often missing from official electoral returns, particularly British local government elections, is the total number of valid ballot papers. This figure is essential for the calculation of electoral turnout. When voters have a single vote and official information about the number of ballot papers issued is missing, a figure for the total vote can still be derived. However, local elections in Britain frequently use a system of multiple-member wards, where voters have as many votes as there are seats to be filled. In such cases, calculating the total vote and, hence, the turnout does present a real problem. It cannot be assumed that all voters will use their full quota of votes or that voters will cast a ballot in favour of a single party. This paper develops and tests diff erent algorithms for calculating the total vote in such circumstances. We conclude that the accuracy of an algorithm is closely related to the structure of party competition. The findings of this paper have a number of important implications. First, the difficulties in calculating the turnout in multiple-member wards are identified. This will inform the debate about public participation in the local electoral process. Second, the method for deriving a figure for the total vote has an important bearing on a number of other statistics widely employed in electoral analysis. [ABSTRACT FROM AUTHOR]

This article develops a new algorithm named TTRISK to solve high‐dimensional risk‐averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so‐called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large‐scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID‐19. The results indicate that the risk‐averse framework is feasible with the tensor approximations under tens of random variables. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

This paper describes the result of carrying out such a maximisation of a social welfare function prescribed for a model of the United Kingdom economy over the period 1957-66. The choice of social welfare function was a crucial part of the exercise and is discussed in some detail. Optimisation of the welfare function was achieved using modern control theory.[1] The next section of this paper describes the model; its dynamic properties are then evaluated in a subsequent section. Next, the optimisation goal is presented in terms of the model, and a social welfare function is chosen. The optimisation procedure is then applied and the results obtained are presented and discussed. Finally, consideration is given to the ways in which the procedures might in later research be modified as a result of the lessons learnt from this experiment. The results presented in this paper have shown that this model gives a good picture of how controls influence the economic system, despite the restrictions placed upon its size. The major cycles of the fitting period were reproduced and the model's poor ability at forecasting was a direct result of the primitive assumption of a fixed exchange rate. One advantage of its structure was that, apart from the control variables, it had only two exogenous variables. Although there was no quantitative way of assessing the dynamic properties of the model, the results of the dynamic multiplier tests were qualitatively satisfactory. On the other hand the attempt to use a control algorithm in order to suggest a policy which might have given better economic results at the end of 1961 must be admitted to have been a failure. The procedure correctly reflected the instructions given to it in the form of the social welfare function which was selected, but this function treated public and consumers' expenditure as perfect substitutes and omitted to take account of the desirability of certain items and overstated the undesirability of certain oth... [ABSTRACT FROM AUTHOR]

Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK's renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log. [ABSTRACT FROM AUTHOR]

In this paper, we apply Bayesian averaging of classical estimates (BACE) and Bayesian model averaging (BMA) as an automatic modeling procedures for two well-known macroeconometric models: UK demand for narrow money and long-term inflation. Empirical results verify the correctness of BACE and BMA selection and exhibit similar or better forecasting performance compared with a non-pooling approach. As a benchmark, we use Autometrics—an algorithm for automatic model selection. Our study is implemented in the easy-to-use gretl packages, which support parallel processing, automates numerical calculations, and allows for efficient computations. [ABSTRACT FROM AUTHOR]

Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N = 2675), and a third external cohort using the UK Biobank Validation Study (N = 181) who underwent maximal VO2max testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80–0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model's latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests. [ABSTRACT FROM AUTHOR]

Purpose: Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments (EDs). This study validates an AI algorithm to triage patients presenting with Intracranial Haemorrhage (ICH) or Acute Infarct whilst also identifying a subset of patients as Normal, with the potential to function as a rule-out test. Methods: In total, 390 CT head scans were collected from 3 institutions in the UK, US and India. Ground-truth labels were assigned by 3 FRCR consultant radiologists. AI performance, as well as the performance of 3 independent radiologists, was measured against ground-truth labels. Results: The algorithm showed AUC values of 0.988 (0.978–0.994), 0.933 (0.901–0.961) and 0.939 (0.919–0.958) for ICH, Acute Infarct and Normal, respectively. Sensitivity/specificity for ICH and Acute Infarct were 0.988/0.925 and 0.833/0.927, respectively, compared to 0.907/0.991 and 0.618/0.977 for radiologists. AI rule-out of Normal scans achieved 0.93% negative predictive value (NPV) for the removal of 54.3% of Normal cases, compared to 86.8% NPV for radiologists. Conclusion: We show our algorithm can provide effective triage of ICH and Acute Infarct to prioritise acutely unwell patients. AI can also benefit clinical accuracy, with the algorithm identifying 91.3% of radiologist false negatives for ICH and 69.1% for Acute Infarct. Rule-out of Normal scans has huge potential for workload management in busy EDs, in this case removing 27.4% of all scans with no acute findings missed. [ABSTRACT FROM AUTHOR]

With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peerto-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning (DML) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The proposed model involves the design of oppositional coyote optimization algorithm (OCOA) based feature selection technique in which the OCOA is derived by the integration of oppositional based learning (OBL) concept with COA for improved convergence rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In order to additional improve the predictive performance of the DBN model, a hyperparameter optimizer is introduced using chicken swarm optimization (CSO) algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome. The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training, testing, and validation accuracy of 90.17%, 87.39%, and 87.86%. [ABSTRACT FROM AUTHOR]

This project was developed to set up a nurse-led service based on the needs of patients diagnosed with 'red legs'. These patients are often wrongly admitted into hospital for treatment of cellulitis. Representatives from the specialties involved in caring for those individuals with red legs were invited to participate with patients to create a stakeholder group whose purpose was to develop integrated care pathways focused on referral criteria, diagnostics and treatment to inform a new nurse-led service. There was a commitment to utilising a number of facilitation techniques and practicedevelopment methods in the progression of the project with the support of the Foundation of Nursing Studies. Much of the prescribed care can be carried out by the patients at home and only 25% patients have required a follow-up appointment within the new service. The service has now been fully commissioned and a secondment opportunity has been developed to lead the new service. Significant savings have been demonstrated and regular revision of the integrated care pathways with all groups, including the patients, will take place. [ABSTRACT FROM AUTHOR]

GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a Bayesian solution in O(T) computational operations, where T denotes the sample size. We assess the performance of our proposed algorithm in the context of both artificial examples and an empirical application to 26 UK sectorial stock returns, and compare it to existing approximate solutions. [ABSTRACT FROM AUTHOR]