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Rigorously calibrating dynamic models with time-series data can pose roadblocks. Oftentimes, the problem is ill-posed and one has to rely on appropriate regularization techniques to ensure stable parameter estimation from which forward projections with quantified uncertainty could be generated. If the inversion procedure is cast as nonlinear least squares constrained by a system of nonlinear differential equations, then the system has to be solved numerically at every step of the iterative process and the corresponding parameter-to-data map cannot be used to evaluate the Fréchet derivative analytically. To address challenges related to both instability and Jacobian approximation, we propose a novel regularized Levenberg-Marquardt algorithm with iterative rank-one updates for computation of the derivative operator. In order to test the efficiency of this scheme, we conduct numerical experiments using a mathematical model of infectious disease transmission and real incidence data of historic measles outbreaks in the UK.

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]

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]

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 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]

Knee arthritis occupies a significant proportion of the musculoskeletal burden in the UK. Total knee arthroplasty currently comprises the mainstay of treatment. There has been a shift towards treating isolated unicompartmental osteoarthritis with bone-preserving surgical techniques, in the form of realignment osteotomy or unicompartmental arthroplasty. There are significant data regarding the survivorship of unicompartmental knee arthroplasty from the National Joint Registry data. Similar registry data are not available for osteotomy surgery yet, but the evidence suggests that unicompartmental knee arthroplasty has greater survivorship. Osteotomies can, however, deliver higher functional return. For both techniques to succeed, it is imperative that rigorous surgical decision-making, with regards to patient selection, should be followed. This paper discusses the basis for these principles and their importance in delivering optimal care. Often, these two surgical techniques are promoted as being mutually exclusive; this paper argues that, in fact, they are part of a complementary algorithm that can deliver the best outcome to the appropriately selected patient. [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]

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]

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]

• This paper proposed a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. • The parametric algorithm accounted for diverse types of bridge members and produced BIM bridge models using shape information automatically extracted per parameter. • The parametrically generated bridge BIM model was converted into PCD, serving as deep learning train data for automatic segmentation of bridge members. • The trained deep learning model showed high accuracy for segmenting superstructures, indicating promise for automated segmentation of bridge members. • The segmentation accuracy increased when accounting for the point density of members corresponding to the different sizes of bridge members. This paper proposes a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. For this, a parametric algorithm was developed to accommodate diverse superstructures, piers, abutments, and bearings. Each structural member in a bridge was classified to designate its shape information as parameters. The range of each parameter was defined to account for various dimensions of the shape of the bridge member. Therefore, libraries for each member were established to produce BIM bridge models using shape information automatically extracted per parameter. Then, the framework includes an algorithm that is developed to convert bridge BIM models generated by the parametric algorithm into PCD data. The virtually generated PCD is used as deep learning train data for automatic member segmentation based on the Point-Net algorithm. The trained algorithm using the virtual PCD was applied to a real bridge PCD. The segmentation of the bridge members showed high accuracy for the superstructure but low accuracy for the bearing. The segmentation accuracy of the algorithm including the bearing member could increase by modifying the density of PCD to account for the different sizes of bridge members. Furthermore, the proposed framework was applied to a UK bridge PCD and showed its applicability for use in bridges in different countries. [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]

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]

Correct pedigree is essential to produce accurate genetic evaluations of livestock populations. Pedigree validation has traditionally been undertaken using microsatellites and more recently, based on checks on opposing homozygotes using single nucleotide polymorphisms (SNPs). In this study, the genomic relationship matrix was examined to see whether it was a useful tool to forensically validate pedigree and discover unknown pedigree. Using 5,993 genotyped Limousin animals which were imputed to a core set of 38,907 SNPs, the genomic relationships between animals were assessed to validate the reported pedigree. Using already pedigree-verified animals, the genomic relationships between animals of different relationships were shown to be on average 0.58, 0.59, 0.32, 0.32, 0.19, and 0.14 between animals and their parents, full siblings, half siblings, grandparents, great grandparents, and great great grandparents, respectively. Threshold values were defined based on the minimum genomic relationship reported between already pedigree-verified animals; 0.46, 0.41, 0.17, 0.17, 0.07, and 0.05, respectively for animals and their parents, full siblings, half siblings, grandparents, great grandparents, and great great grandparents. Using the wider population and the above genomic relationship threshold values, potential pedigree conflicts were identified within each relationship type. Pedigree error rates of between 0.9% (animal and great great grandparent) and 4.0% (full siblings) were identified. A forensic genomic pedigree validation and discovery system was developed to enable pedigree to be verified for individual genotyped animals. This system verifies not just the parents, but also a wide number of other genotyped relatives and can therefore identify more potential errors in the pedigree than current conventional methods. A novel aspect to this algorithm is that it can also be used to discover closely related animals on the basis of their genomic relationships although they are not recorded as such in the pedigree. This functionality enables missing pedigree information to be discovered and corrected in the pedigree of livestock populations. The methods in this paper demonstrate that the genomic relationship matrix can be a useful tool in the validation and discovery of pedigree in livestock populations. However, the method does rely on being able to define threshold values appropriate to the specific livestock population, which will require sufficient number of animals to be genotyped and pedigree validated before it can be used.

Recent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed. In this paper, we propose the correlated sequence kernel association test (CSKAT) to address such correlations using the linear mixed model. Specifically, random effects are used to account for the outcome correlations and a variance component test is used to examine the microbiome effect. Compared to existing genetic association tests for longitudinal and family samples, we implement a correction procedure to better calibrate the null distribution of the score test statistic to accommodate the small sample size nature of data collected from a typical microbiome study. Comprehensive simulation studies are conducted to demonstrate the validity and efficiency of our method, and we show that CSKAT achieves a higher power than existing methods while correctly controlling the Type I error rate. We also apply our method to a microbiome data set collected from a UK twin study to illustrate its potential usefulness. A free implementation of our method in R software is available at https://github.com/jchen1981/SSKAT., (© 2018 Wiley Periodicals, Inc.)

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]

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]

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]

Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems. [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]

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]

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]

Background: To address if the long-standing association between maternal infection, depression/anxiety in pregnancy, and offspring neurodevelopmental disorder (NDD) is causal, we conducted two negative-control studies. Methods: Four primary care cohorts of UK children (pregnancy, 1 and 2 years prior to pregnancy, and siblings) born between 1 January 1990 and 31 December 2017 were constructed. NDD included autism/autism spectrum disorder, attention-deficit/hyperactivity disorder, intellectual disability, cerebral palsy, and epilepsy. Maternal exposures included depression/anxiety and/or infection. Maternal (age, smoking status, comorbidities, body mass index, NDD); child (gender, ethnicity, birth year); and area-level (region and level of deprivation) confounders were captured. The NDD incidence rate among (1) children exposed during or outside of pregnancy and (2) siblings discordant for exposure in pregnancy was compared using Cox-regression models, unadjusted and adjusted for confounders. Results: The analysis included 410 461 children of 297 426 mothers and 2 793 018 person-years of follow-up with 8900 NDD cases (incidence rate = 3.2/1000 person years). After adjustments, depression and anxiety consistently associated with NDD (pregnancy-adjusted HR = 1.58, 95% CI 1.46–1.72; 1-year adj. HR = 1.49, 95% CI 1.39–1.60; 2-year adj. HR = 1.62, 95% CI 1.50–1.74); and to a lesser extent, of infection (pregnancy adj. HR = 1.16, 95% CI 1.10–1.22; 1-year adj. HR = 1.20, 95% CI 1.14–1.27; 2-year adj. HR = 1.19, 95% CI 1.12–1.25). NDD risk did not differ among siblings discordant for pregnancy exposure to mental illness HR = 0.97, 95% CI 0.77–1.21 or infection HR = 0.99, 95% CI 0.90–1.08. Conclusions: Maternal risk appears to be unspecific to pregnancy: our study provided no evidence of a specific, and therefore causal, link between in-utero exposure to infection, common mental illness, and later development of NDD. [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 paper provides a reference for the hierarchical response to railway accidents. • Scenario-response mode is integrated into the dynamic grading of railway accidents. • An improved DPC algorithm is present for dynamical evaluation of accident severity. • Experiments are conducted on the real-world case of railway accidents in the UK. The timely and scientific assessment of railway accident severity can provide effective support for making a suitable rescue plan. This paper proposes an improved density peaks clustering algorithm based on scenario construction for dynamically tackling the severity ranking problem of railway accidents. Firstly, the scenario is introduced into the selection of grading indicators of railway accidents, and a five-dimensional railway accident scenario expression model based on knowledge element is proposed to dynamically select and express the grading indicators. Secondly, an improved density peaks clustering algorithm based on k nearest neighbors and turning point (IDPC-KNN-TP) as the grading model of railway accidents is established, which optimizes the local density measurement, the selection of cluster center points, and the assignment strategy of density peaks clustering (DPC). Finally, the railway accidents from 2010 to 2021 in the UK as a case study are analyzed. The results show that the performance of IDPC-KNN-TP is better than others, and the grading algorithm in this paper has good practical performance. The dynamic grading strategy and the emergency disposal suggestions are put forward to provide a reference for the hierarchical response to railway accidents. [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.

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]

"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]

• A novel model of loop metro service driven by stochastic demand of general distribution. • A Markov decision process for optimal schedule with circulation of limited rolling stock. • An actor-critic deep reinforcement learning framework with an off-policy training algorithm. • A case study demonstrating the efficiency and robustness of the proposed control system. This paper presents a novel actor-critic deep reinforcement learning approach for metro train scheduling with circulation of limited rolling stock. The scheduling problem is modeled as a Markov decision process driven by stochastic passenger demand. As in most dynamic optimization problems, the complexity of the scheduling process grows exponentially with the amount of states, decisions, and uncertainties involved. This study aims to address this 'curses of dimensionality' issue by adopting an actor-critic deep reinforcement learning solution framework. The framework simplifies the evaluation and searching process for potential optimal solutions by parameterizing the original state and decision spaces with the use of artificial neural networks. A deep deterministic policy gradient algorithm is developed for training the artificial neural networks via simulated system transitions before the actor-critic agent can be applied for online schedule control. The proposed approach is tested with a real-world scenario configured with data collected from the Victoria Line of London Underground, UK. Experiment results illustrate the advantages of the proposed method over a range of established meta-heuristics in terms of computing time, system efficiency, and robustness under different stochastic environments. This study innovates urban transit operations with state-of-the-art computer science and dynamic optimization techniques. [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 2002 a paper was presented at the 43rd Annual Meeting of the Institute of Nuclear Materials Management (INMM) on the assay of low level plutonium (Pu) in soft drummed waste (Miller 2002 INMM Ann. Meeting (Orlando, FL, 23-27 July 2002)). The technique described enabled the Atomic Weapons Establishment (AWE), at Aldermaston in the UK, to meet the stringent Low Level Waste Repository at Drigg (LLWRD) conditions for acceptance for the first time. However, it was initially applied to only low density waste streams because it relied on measuring the relatively low energy (60 keV) photon yield from Am-241 during growth. This paper reviews the results achieved when using the technique to assay over 10,000 waste packages and presents the case for extending the range of application to denser waste streams.

Objectives: to describe the development and testing of an algorithm for diagnosis of active labour in primiparous women., Design: qualitative and quantitative methods were used. A literature review was first conducted to identify the key cues for inclusion in the algorithm. Focus groups of midwives were then conducted to assess content validity, finally a vignette study assessed the inter-rater reliability of the algorithm., Setting: midwives from two study sites were invited to participate. Data were collected during 2002 and 2003., Participants: midwives from the first site took part in the focus groups (n=13), completed vignettes (n=19), or both. Midwives from the second site then completed vignettes (n=17)., Findings: an algorithm, developed from the key informational cues reported in the literature, was validated in relation to content validity by the findings from the focus groups. Inter-rater reliability was tested using vignettes of admission case histories and was found to be moderate in the first test (K=0.45). However, after modifying the algorithm the kappa score was 0.86, indicating a high level of agreement., Key Conclusions: diagnosis of labour may be straightforward on paper but is frequently problematic in practice. This may be because the diagnosis of labour is made in a high pressured environment where conflicting pressures of workload, limited resources and emotional pressures add to the complexity of the judgement., Implications for Practice: we offer a valid and reliable decision-support tool as an aid for diagnosis of labour. The evaluation of the implementation of this tool is under way and will determine whether it is effective in reducing unnecessary admissions and improving clinical outcomes for women.

The paper describes the need for the introduction of an anaemia management algorithm. It discussed the problems which the unit had in constant reviewing and re-prescribing ESA to maintain optimum haemoglobin levels for the unit's patients. The method used to create and use the algorithm is explained. The findings demonstrate the beneficial effects of using the algorithm. The paper concludes with the recommendation that algorithms should be more widely used for better treatment outcomes.

The Amyotrophic Lateral Sclerosis Assessment Questionnaire (ALSAQ-40) is the most widely validated measure of health status for use with patients diagnosed with motor neuron disease/amyotrophic lateral sclerosis (MND/ALS). The questionnaire was designed to be used in studies evaluating treatment regimes where missing data may cause problems with data analyses. The purpose of this paper is to evaluate an algorithm for the imputation of missing dimension scores on the ALSAQ-40. We used a postal survey of patients diagnosed with MND/ALS. One thousand, nine hundred and seventy-nine patients were surveyed and 1093 (55.2%) questionnaires returned. Of these, complete ALSAQ-40 data was available in 854 (85.8%) cases. Data were deleted from this complete dataset, and in a randomly selected subset of 100 cases, and then imputed using the Expectation Maximization (EM) algorithm: results were then compared to the dataset prior to data deletion. Descriptive statistics, mean scores and spread of scores were almost identical between original and imputed datasets. Furthermore, the two datasets were highly correlated (intra-class correlation coefficient = 0.95 or greater), and mean differences were small (+/-1.00). We concluded that EM imputation for the ALSAQ-40 provides data that closely mirrors the original when this has been deliberately removed. Consequently, EM is likely to be appropriate for studies using the ALSAQ-40 that contain missing data points.

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]

This paper aims to demonstrate how knowledge acquired from occupants' manual window operations can be implemented into BMS automated window operation algorithms. Ten single-occupant offices were selected in a university building in the UK. More than 28,000 hourly data points on indoor and outdoor temperature and open window area (OWA) were analysed from 2015 to 2020. The BMS had adopted nine different automated window operation algorithms during the 5 years. The automated window algorithms could be manually overridden by the office occupants. Automated algorithms were compared against manual window operations. The results showed that the slope and gradient of the regression lines for occupants' manual window operations are smaller than automated operations. OWA of automated window operations increased 20% per 1 °C increase in indoor temperature, however, occupants opened windows 6–8% per 1 °C increase. Occupants react slower to temperature changes than assumed by BMS, which could be considered in BMS automated window operations. • BMS automated window algorithms operate based on indoor temperature in this study. • Automated window operations by BMS impact occupants' manual window operations. • Occupants open windows at higher temperatures on the third floor than first floor. • BMSs need to consider building-related differences and seasonal changes. • A BMS system architecture is proposed to reduce the behaviour gap. [ABSTRACT FROM AUTHOR]