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This paper tests whether visual nudges help direct attention towards existing instructions designed to increase waste sorting accuracy. The study was conducted in a quasi-experimental setting over a period of 9 weeks in two buildings of a large UK university campus. Two treatments on recycling behaviour were tested against a control group: one considered the impact of visual nudges in the form of human eyes; the other one combined human eye with pre-existing sorting instructions. Results show that for mixed recycling the combination of visual and information nudges decreased sorting errors by 7 percentage points. In contrast, visual nudges alone increased sorting errors by 4.5 percentage points. These findings indicate that, when combined, information and a visual nudge are cost-effective tools to significantly improve waste sorting behaviour. This paper adds to existing experimental evidence based on neuroscientific theories. [ABSTRACT FROM AUTHOR]

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production animal veterinary medicine, comparable frameworks for companion animals are lacking. First-opinion veterinary electronic health records (EHRs) have the potential to reveal disease signals and often represent the initial reporting of clinical syndromes in animals presenting for medical attention, highlighting their possible significance in early disease detection. Yet despite their availability, there are limitations surrounding their free text-based nature, inhibiting the ability for national-level mortality and morbidity statistics to occur. This paper presents PetBERT, a large language model trained on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the additional training of PetBERT as a multi-label classifier for the automated coding of veterinary clinical EHRs with the International Classification of Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, outperforming current clinician-assigned point-of-care labelling strategies up to 3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in veterinary medicine represents a promising avenue for advancing animal health and improving public health outcomes. [ABSTRACT FROM AUTHOR]

Objective: Mental health problems are a leading and increasing cause of health-related burden in children across the world. Peer support interventions are increasingly used to meet this need using the lived experience of people with a history of mental health problems. However, much of the research underpinning this work has focused on adults, leaving a gap in knowledge about how these complex interventions may work for different children in different circumstances. Realist research may help us to understand how such complex interventions may trigger different mechanisms to produce different outcomes in children. This paper reports on an important first step in realist research, namely the construction of an embryonic initial programme theory to help 'focus' realist evaluation exploring how children's peer support services work in different contexts to produce different outcomes in the West Midlands (UK). Methods: A survey and preliminary semi-structured realist interviews were conducted with 10 people involved in the delivery of peer support services. Realist analysis was carried out to produce context-mechanism-outcome configurations (CMOC). Results: Analysis produced an initial programme theory of peer support for children's mental health. This included 12 CMOCs. Important outcomes identified by peer support staff included hope, service engagement, wellbeing, resilience, and confidence; each generated by different mechanisms including contextualisation of psychoeducation, navigating barriers to accessing services, validation, skill development, therapeutic relationship, empowerment, and reducing stigma. Conclusion: These data lay the groundwork for designing youth mental health realist research to evaluate with nuance the complexities of what components of peer support work for whom in varying circumstances. [ABSTRACT FROM AUTHOR]

With many of the world's governments committing to achieve net-zero greenhouse gas (GHG) emissions by mid-century, with well-defined milestones along the road, it is important to investigate how each sector can contribute towards achieving this global goal. The manufacturing sector, with its energy-intensive processes, large amounts of wastes, and hazardous and harmful emissions, is one of the main contributors to global GHG emissions, as well as other sustainability aspects, and, thus, it has great potential to contribute substantially to achieve net-zero objectives. This paper presents a techno-environmental-economic analysis of technologies that can play a key, enabling and leading role in the quest towards net-zero. Such technologies typically bring modest improvement in the environmental performance; however, the aim of this paper is to demonstrate how such small changes, when implemented in an industrial setting, can contribute significantly to the collective improvement in the environmental performance. In order to put the potential improvements into perspective, a real case study from the UK aerospace manufacturing sector is conducted. In the case study, metrics measuring potential improvements from the installation of a low-to-medium waste heat recovery system, and the upgrade of electric motors in the shopfloor to more energy efficient ones, are calculated through environmental and economic models. The models are then subject to a series of sensitivity analyses experiments to help understand the impact of different sources of uncertainty on the perceived GHG emissions, and economic and energy savings. The techno-environmental-economic analysis results revealed that these small changes, when implemented in an industrial setting, can indeed bring valuable improvements in the environmental performance of a manufacturing institute. Further, the sensitivity analysis experiments demonstrated how the environmental and economic performances are not adversely affected by different levels of fluctuations in key, likely to fluctuate, input parameters. [ABSTRACT FROM AUTHOR]

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of 0. 63 (0.02) and for height a correlation of 0. 648 (0.009) for a Taiwanese population. This is above the measured values of 0. 61 (0.01) and 0. 631 (0.008) , respectively, for UK Biobank trained predictors applied to a European population. [ABSTRACT FROM AUTHOR]

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur. [ABSTRACT FROM AUTHOR]

The vehicle emissions testing programme was conducted by the UK Department of Transport in 2016 in response to emissions tampering exposed in the Volkswagen (VW) emissions scandal. The programme identified large emissions discrepancies between real-world and in-lab testing across a range of Euro 5 and Euro 6 diesel passenger vehicles. The large vehicle test fleet reflects the current challenges faced in controlling vehicle emissions. This paper presents the following findings: NOx emissions are altered due to exhaust gas recirculation mismanagement. A new Real-Life Emissions methodology is introduced to improve upon the current Real Driving Emissions standard. A large and concerning emissions divergence was discovered between the achieved NOx improvement and deterioration of CO2. The findings act as catalysts to improve vehicle emissions testing beyond standards established since the VW scandal, aiding in the development of better climate change mitigation strategies and bring tangible air quality improvements to the environment. [ABSTRACT FROM AUTHOR]

To achieve sustainable development worldwide, the United Nations set 17 Sustainable Development Goals (SDGs) for humanity to reach by 2030. Society is involved in the challenge, with firms playing a crucial role. Thus, a key question is to what extent firms engage with the SDGs. Efforts to map firms' contributions have mainly focused on analysing companies' reports based on limited samples and non-real-time data. We present a novel interdisciplinary approach based on analysing big data from an online social network (Twitter) with complex network methods from statistical physics. By doing so, we provide a comprehensive and nearly real-time picture of firms' engagement with SDGs. Results show that: (1) SDGs themes tie conversations among major UK firms together; (2) the social dimension is predominant; (3) the attention to different SDGs themes varies depending on the community and sector firms belong to; (4) stakeholder engagement is higher on posts related to global challenges compared to general ones; (5) large UK companies and stakeholders generally behave differently from Italian ones. This paper provides theoretical contributions and practical implications relevant to firms, policymakers and management education. Most importantly, it provides a novel tool and a set of keywords to monitor the influence of the private sector on the implementation of the 2030 Agenda. [ABSTRACT FROM AUTHOR]

The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests. [ABSTRACT FROM AUTHOR]

Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies. [ABSTRACT FROM AUTHOR]

Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences. [ABSTRACT FROM AUTHOR]

This paper investigates the variation in nonperforming loans over the economic cycle and the effect of past returns based on a nonparametric quantile analysis of the largest Islamic banks in the United Kingdom and Turkey from 2010 to 2019. The findings show a weak variation in nonperforming loans that increases with an increasing return on assets and a decreasing return on equity and decreases in an inverse scenario. As a result, the credit risk of Islamic banks is countercyclical. We suggest that the inverse relationships evidence the existence of trade-offs within bank returns and credit risk. Thus, banks' past profitability and risk mitigation are determinants of asset quality. These findings provide support for risk-taking and risk-sharing principles in which flight-to-safety mirrors the calibration of risk factors in a disruptive economy. Our estimates indicate that nonparametric quantile regression captures considerably more variation in a risk-return analysis. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the impact of Brexit on the dependence between European financial markets. We use the Diebold and Yilmaz (2009, 2012) approach to better map the relationship between the three main European markets and we propose an Optimal portfolio weighting to gain insights into the portfolio design dynamics in the period between 2013 and 2019, in particular any changes due to uncertainty surrounding Brexit. First, the findings show that between September 2015 and September 2016, the high level of volatility and spillover confirms the strong degree of market integration, with uncertainty surrounding the referendum outcome having a clear impact on the three main European markets. Second, the direction of spillover in the pre-Brexit period was from the UK market to the French and German markets in anticipation of uncertainty regarding the outcome of the referendum and the period immediately following it. Third, only the conditional correlation between the pair (CAC40–FTSE100) is characterized by an asymmetric effect. [ABSTRACT FROM AUTHOR]

This paper presents a brief survey of the Series of Scientific Monographs "Stability and Control: Theory, Methods and Applications" published during 1995–2002 at the Gordon and Breach Science Publishers (UK) and the Taylor and Frances Publisher (USA). The Series comprises 22 volumes and is encyclopedic in the field of stability and control theories. Moreover, the history of appearance and realization of the idea of publishing the Series is outlined. [ABSTRACT FROM AUTHOR]

This paper simulates the likely fiscal and employment effects of a vast public annual investment programme of free universal high-quality early childhood education and care (ECEC) services in the UK. It examines the extent to which it would pay for itself fiscally for different scenarios of pay increases. Investing in high-quality universal ECEC benefits all children by improving their life chances, especially for those living in lower income families. It also generates larger employment effects than other more typical investment policies such as construction projects and fosters gender equality in employment: not only it provides many high-quality jobs for women, it also allows many mothers to improve their lifetime earnings prospects by freeing up their childcare constraints. This in turn has beneficial fiscal revenue effects for the government. Estimations of annual public expenditure for a system of highly qualified and well-paid childcare staff with low child-to-staff ratios are performed, with universal coverage for all pre-school children aged 6 months to 4.5 years. Labour demand and matching supply effects are also simulated using input–output methods, for different take-up rates of the programme. A microsimulation tool is used to calculate increases in household income and tax liabilities and decreases in social security benefits spending. This results in a net annual funding requirement of between 28 and 39% of the gross investment. Two funding methods are then explored: raising taxation in a progressive way and recouping the cost over time from persistent mothers' increased earnings. The former would entail a net additional contribution by the richest 20% of households of at most 0.4% of their income; the latter would require 21 to 31 years to offset the programme on average, which is within a typical working life-course following a first child's birth, of 35 years. [ABSTRACT FROM AUTHOR]

Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques. [ABSTRACT FROM AUTHOR]

With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km 2 . Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk/. [ABSTRACT FROM AUTHOR]

In this paper we revisit the concept of mobility entropy. Over time, the structure of spatial interactions among urban centres tends to become more complex and evolves from centralised models to more scattered origin and destination patterns. Entropy measures can be used to explore this complexity, and to quantify the degree of structural diversity of in- and out-flows at different scales and across the system. We use toy models of commuting networks to examine global and local measures, allowing the comparison to occur between different parts of the system. We show that entropy at the link and node level give different insights on the characteristics of the systems, enabling us to identify employment hubs and interdependencies between and within different parts of the system. We compute the measures in the commuting networks of the Northern Powerhouse and Greater South East regions in the UK to examine their relevance when studying real systems of cities. Finally we discuss how these can be used to inform planning and policy decisions oriented towards decentralisation and resilience. [ABSTRACT FROM AUTHOR]

In this paper we revisit the concept of mobility entropy. Over time, the structure of spatial interactions among urban centres tends to become more complex and evolves from centralised models to more scattered origin and destination patterns. Entropy measures can be used to explore this complexity, and to quantify the degree of structural diversity of in- and out-flows at different scales and across the system. We use toy models of commuting networks to examine global and local measures, allowing the comparison to occur between different parts of the system. We show that entropy at the link and node level give different insights on the characteristics of the systems, enabling us to identify employment hubs and interdependencies between and within different parts of the system. We compute the measures in the commuting networks of the Northern Powerhouse and Greater South East regions in the UK to examine their relevance when studying real systems of cities. Finally we discuss how these can be used to inform planning and policy decisions oriented towards decentralisation and resilience. [ABSTRACT FROM AUTHOR]

The United Kingdom (UK) case R v Lee (2010) EWCA Crim 1404 resulted in a pharmacist being convicted for an inadvertent dispensing error and paved way for the decriminalisation of such errors by way of a due diligence defence enacted in 2018. In relation to Hong Kong (HK), what is its legal position for dispensing errors, and can it follow the decriminalising steps of UK? The primary objective of this paper is to explore whether and how HK can reach the normative position for a dispensing error legal regime: (1) I posit that the normative position for healthcare professional (HCP) liability for dispensing errors should prioritise the public interest of minimisation of future dispensing errors over the retribution of past wrongs; (2) I illustrate HK's current position for the liabilities of HCPs on dispensing errors, focusing analysis on the relatively controversial aspects of HK's criminal liability, referencing the landmark cases Hin Lin Yee v HKSAR (2009) 13 HKCFAR 142 and Kulemesin v HKSAR (2013) 16 HKCFAR 195 to assist my analysis of the requisite mental element for relevant statutory offences; (3) through comparison with UK's development post-R v Lee and application of Rule of Law principles, HK's current position is critiqued, coming to the conclusion that while there are compelling reasons for the decriminalisation of dispensing errors in HK, the prerequisite for this to happen is an overhaul of regulatory frameworks by significantly increasing levels of accountability. [ABSTRACT FROM AUTHOR]

Housing is a paradigmatic example of a social determinant of health, as it influences and is influenced by structural determinants, such as social, macroeconomic, and public policies, politics, education, income, and ethnicity/race, all intersecting to shaping the health and well-being of populations. It can therefore be argued that housing policy is critically linked to health policy. However, the extent to which this linkage is understood and addressed in public policies is limited and highly diverse across and within countries. This analysis seeks to describe the linkages between housing policies and health and well-being using examples from three countries at different levels of the wealth spectrum: Singapore, the UK, and Kenya. We conducted a comparative policy analysis across three country contexts (Singapore, the UK, and Kenya) to document the extent to which housing policies address health and well-being, highlighting commonalities and differences among them. To guide our analysis, we used the United Nations (UN) definition of adequate housing as it offers a broad framework to analyze the impact of housing on health and well-being. The anatomy of housing policies has a strong correlation to the provision of adequate housing across Singapore, the UK, and Kenya, especially for vulnerable groups. The paper demonstrates that contextual factors including population composition (i.e., aging versus youthful), political ideologies, legal frameworks (i.e., welfare versus market-based provision of housing), and presence (or absence) of adequate, quality, timely, reliable, robust data systems for decision-making, which are taken up by stakeholders/state, have strong implications of the type of housing policies developed and implemented, in turn directly and indirectly impacting the overall health and well-being of populations. This analysis demonstrates the value of viewing housing policies as public health policies that could significantly impact the health and well-being of populations, especially vulnerable groups. Moreover, the findings highlight the importance of the Health in All Policies approach to facilitate integrated policy responses to address social determinants of health such as housing. This is more critical than ever, given the context of the global pandemic that has led to worsening overall health and well-being. [ABSTRACT FROM AUTHOR]

Green Infrastructure (GI) research tends to focus on the need for GI to enhance ecological processes, its potential to provide health and economic benefits, and on the barriers preventing its uptake. Yet there has been inadequate focus on the social aspects of GI. In the United Kingdom (UK) the need for GI is well established, such that policymakers and planners are now turning to the question of how GI should be implemented. Drawing on a mixed method research approach centring on practitioner experience, this paper identifies potential social principles that underpin GI and questions the extent to which these are being implemented in the UK. Results highlight the hitherto unexplored complexities of GI maintenance, the effects of austerity politics, and the role of local-level power dynamics on the implementation of GI. Findings have implications for international literature on GI as well as nature-based solutions more broadly. [ABSTRACT FROM AUTHOR]

A major challenge that TBM performance is requested to deal with for a successful and effective progress is tunnelling through lithologically and geomechanically heterogeneous rock masses. Such heterogeneous environments are common and recent tunnel examples in the UK include the Hinckley Point C offshore cooling tunnels being driven through interbedded carbonaceous mudstone/shales and argillaceous limestone and the Anglo American's Woodsmith Mine Mineral Transport System tunnel in Redcar Mudstone with beds of ironstone. This inherent geological heterogeneity leads to difficult tunnelling conditions that initially stem from predicting a sound and representative ground model that can be used to preliminary assess the TBM performance. In this work, an exhaustive review of existing TBM Penetration Rate (PR) methods identified that no models address the issue of parameter selection for heterogeneous rock masses comprising layers with different rock strengths. Consequently, new approaches are required for estimating rock mass behaviour and machine performance in such environments. In the presented work the Blue Lias Formation (BLI), which is characterised by its layered rock mass, comprising very strong limestone, interbedded with weak mudstone and shales, is investigated. BLI formation is considered herein being a representative example of lithological heterogeneity. Based on the fieldwork carried out in three localities in the Bristol Channel Basin (S. Wales and Somerset), geological models are produced based on which a geotechnical model is developed, and four ground types are determined. Implications of the current findings for TBM performance are assessed, including faulting, groundwater inflow and excavation stability with a particular focus on both PR and advance rate. A modified approach using the existing empirical models is proposed, developed and presented in this paper that can be used as a guide to determine TBM performance in heterogeneous rock masses reducing the risk of cost and time overruns. [ABSTRACT FROM AUTHOR]