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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]

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.)

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]

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]

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]

• 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]

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]

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]

• 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]

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]

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]

Background: Recent research has shown that insecure attachment, especially attachment anxiety, is associated with poor mental health outcomes, especially during the COVID‐19 pandemic. Other research suggests that insecure attachment may be linked to nonadherence to social distancing behaviours during the pandemic. Aims: The present study aims to examine the causal links between attachment styles (secure, anxious, avoidant), mental health outcomes (depression, anxiety, loneliness) and adherence to social distancing behaviours during the first several months of the UK lockdown (between April and August 2020). Materials & Methods: We used a nationally representative UK sample (cross‐sectional n = 1325; longitudinal n = 950). The data were analysed using state‐of‐the‐art causal discovery and targeted learning algorithms to identify causal processes. Results: The results showed that insecure attachment styles were causally linked to poorer mental health outcomes, mediated by loneliness. Only attachment avoidance was causally linked to nonadherence to social distancing guidelines. Discussion: Future interventions to improve mental health outcomes should focus on mitigating feelings of loneliness. Limitations include no access to pre‐pandemic data and the use of categorical attachment measure. Conclusion: Insecure attachment is a risk factor for poorer mental health outcomes. [ABSTRACT FROM AUTHOR]

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]

Objectives Computer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a computerized algorithm designed to code free-text job descriptions to US SOC-2010 system based on free-text job titles and work tasks, to evaluate its accuracy. Methods SOCcer v2 was updated by expanding the training data to include jobs from several epidemiologic studies and revising the algorithm to account for nonlinearity and incorporate interactions. We evaluated the agreement between codes assigned by experts and the highest scoring code (a measure of confidence in the algorithm-predicted assignment) from SOCcer v1 and v2 in 14,714 jobs from three epidemiology studies. We also linked exposure estimates for 258 agents in the job-exposure matrix CANJEM to the expert and SOCcer v2-assigned codes and compared those estimates using kappa and intraclass correlation coefficients. Analyses were stratified by SOCcer score, score distance between the top two scoring codes from SOCcer, and features from CANJEM. Results SOCcer's v2 agreement at the 6-digit level was 50%, compared to 44% in v1, and was similar for the three studies (38%–45%). Overall agreement for v2 at the 2-, 3-, and 5-digit was 73%, 63%, and 56%, respectively. For v2, median ICCs for the probability and intensity metrics were 0.67 (IQR 0.59–0.74) and 0.56 (IQR 0.50–0.60), respectively. The agreement between the expert and SOCcer assigned codes linearly increased with SOCcer score. The agreement also improved when the top two scoring codes had larger differences in score. Conclusions Overall agreement with SOCcer v2 applied to job descriptions from North American epidemiologic studies was similar to the agreement usually observed between two experts. SOCcer's score predicted agreement with experts and can be used to prioritize jobs for expert review. [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]

The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distribution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on pressure and leakage management. This paper considers the control of diurnal flow velocities to maximize the self-cleaning capacity (SCC) of WDNs. We formulate a new optimal design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. The problem formulation includes a nonconvex objective function, nonconvex hydraulic conservation law constraints, and binary variables for modelling valve placement, resulting in a nonconvex mixed integer nonlinear programming (MINLP) optimization problem. Considering the challenges with solving nonconvex MINLP problems, we propose a heuristic algorithm which combines convex relaxations (with domain reduction), a randomization technique, and a multi-start strategy to compute feasible solutions. We evaluate the proposed algorithm on case study networks with varying size and degrees of complexity, including a large-scale operational network in the UK. The convex multi-start algorithm is shown to be a more robust solution method compared to an off-the-shelf genetic algorithm, finding good-quality feasible solutions to all design-for-control numerical experiments. Moreover, we demonstrate the implemented multi-start strategy to be a fast and scalable method for computing feasible solutions to the nonlinear SCC control problem. The proposed method extends the control capabilities and benefits of dynamically adaptive networks to improve water quality in WDNs. [Display omitted] • Operational strategies needed to manage discolouration risk in water networks. • Optimization problem formulated to maximize network self-cleaning capacity. • Location and operation of pressure control and flushing valves are optimized. • Solution algorithm developed using convex optimization and a multi-start heuristic. • Good-quality feasible solutions found for all numerical experiments. [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]

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]

• Probabilistic flexibility quantification to compare the performance of different control methods on TCLs. • A small change on temperature limits of synchronized TCLs increases the provision of power flexibility. • A comparison between homogeneous and heterogeneous loads to offer ancillary services is provided. • The heterogeneity of the loads may affect the power flexibility offered to the power grid. The need for flexible networks is an emerging challenge for power system operators (SO). The use of additional support, such as demand response (DR), must be quantified in order to offer a reliable service, given that this information is vital for demand aggregators. Thermostatically controlled loads (TCLs) are one of the most promising options among DR solutions; due to TCLs' thermal characteristics their power may be increased or reduced accounting as ancillary services. However, TCLs tend to synchronize their behavior, which may affect their capacity to provide flexibility. This paper proposes a method for quantifying TCLs' power flexibility, taking into account different scenarios, types of controllers and loads. Two control methods are compared, and a modified control algorithm is applied to the controllers under analysis to avoid TCL synchronization. The analysis was validated by simultaneously using real demand data from the UK National Grid and temperature data for the same region and time frame. [ABSTRACT FROM AUTHOR]

We describe a practical two-stage algorithm, BootComp, for multi-objective optimization via simulation. Our algorithm finds a subset of good designs that a decision-maker can compare to identify the one that works best when considering all aspects of the system, including those that cannot be modeled. BootComp is designed to be straightforward to implement by a practitioner with basic statistical knowledge in a simulation package that does not support sequential ranking and selection. These requirements restrict us to a two-stage procedure that works with any distributions of the outputs and allows for the use of common random numbers. Comparisons with sequential ranking and selection methods suggest that it performs well, and we also demonstrate its use analyzing a real simulation aiming to determine the optimal ward configuration for a UK hospital. [ABSTRACT FROM AUTHOR]

• Design of a novel SOFC-based CCHP system. • Incorporation of solar panels, heat pump, battery, EV charging and grid connection. • New MOPSO-GRA control algorithm provides objective multi-criteria optimization of system. • Simulation of UK residential conditions shows efficiency, emission & cost benefits. In the quest to achieve home comfort with the highest possible efficiency, there is an increasing interest in combined cooling, heating and power (CCHP) systems. These can be fuelled by natural gas and potentially by hydrogen enriched natural gas and ultimately by hydrogen. The optimised designs largely depend on the energy dispatch algorithms that take into account aspects of economic and environmental impacts as well as system efficiency. This paper details a new algorithm to optimise the operation of a novel hybrid solid oxide fuel cell (SOFC)-solar hybrid CCHP residential system. The proposed algorithm is based on the multi-objective particle swarm optimization (MOPSO) and the grey relationship analysis (GRA) (named as MOPSO-GRA) with the capability of resolving the objective energy dispatch issues within the system by effectively avoiding local optimum problem. More specifically, the grey incidences are first integrated into MOPSO to analyse the degree of closeness between non-ideal solutions and the ideal solution. Then, the relationship of degree is introduced to give objective energy dispatch in order to maximise the efficiency of energy utilization while minimising the system capital cost, operating costs, maintenance costs, fuel costs and emissions. Finally, the new algorithm is validated on the proposed new design of CCHP system. Ten cases are studied to evaluate the technical, economic and environmental performance of the MOPSO-GRA algorithm when being applied to an SOFC-based CCHP system under both the grid-connected and the island modes. A comparison is made with the conventional MOPSO method and system structure, and the impact of a plug-in EV is also evaluated. Based on a detailed cost and emissions analysis, the results indicate the environmental and economic advantages, in addition to the higher efficiency of the proposed methodology and system structure. The impact of these results and observations leads to the promotion of intelligent FC-based multi-energy system technologies for residential use. [ABSTRACT FROM AUTHOR]

Objectives X-Linked hypophosphataemic rickets (XLH) is a rare multi-systemic disease of mineral homeostasis that has a prominent skeletal phenotype. The aim of this study was to describe additional comorbidities in XLH patients compared with general population controls. Methods The Clinical Practice Research Datalink (CPRD) GOLD was used to identify a cohort of XLH patients (1995–2016), along with a non-XLH cohort matched (1 : 4) on age, sex and GP practice. Using the CALIBER portal, phenotyping algorithms were used to identify the first diagnosis (and associated age) of 273 comorbid conditions during patient follow-up. Fifteen major disease categories were used and the proportion of patients having ≥1 diagnosis was compared between cohorts for each category and condition. Main analyses were repeated according to the Index of Multiple Deprivation (IMD). Results There were 64 and 256 patients in the XLH and non-XLH cohorts, respectively. There was increased prevalence of endocrine [OR 3.46 (95% CI: 1.44, 8.31)] and neurological [OR 3.01 (95% CI: 1.41, 6.44)] disorders among XLH patients. Across all specific comorbidities, four were at least twice as likely to be present in XLH cases, but only depression met the Bonferroni threshold: OR 2.95 (95% CI: 1.47, 5.92). Distribution of IMD among XLH cases indicated greater deprivation than the general population. Conclusion We describe a higher risk of mental illness in XLH patients compared with matched controls, and greater than expected deprivation. These findings may have implications for clinical practice guidelines and decisions around health and social care provision for these patients. [ABSTRACT FROM AUTHOR]