Search

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]

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]

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]

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]

It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised. [ABSTRACT FROM AUTHOR]

The Rosebank field, located in the Faroe-Shetland Basin, contains producible hydrocarbons in intra-basaltic siliciclastic reservoirs. The volcanic-reservoir interface is poorly studied and the geochemical system, as a function of distance from the basalt, is largely unknown. The current paper applies a geochemical model coupling mineral dissolution and precipitation with element diffusion to investigate the geochemical system in time and space from the basalt-sandstone interface. Earlier studies indicate few negative effects on reservoir properties despite the proximity to a reactive volcanic lithology. The causes of this minimal impact have not been studied. The numerical simulations in this study expand on the knowledge demonstrating that precipitation of authigenic phases at the basalt-sandstone interface buffer the formation water solution for key elements, which hamper the transport of solutes and subsequent precipitation of secondary minerals within the reservoir. Saturation index values over the simulated period indicate that precipitation of authigenic phases should not extend beyond the basalt-sandstone interface. This shows that diffusion alone is not enough to reduce the reservoir quality due to increased precipitation of secondary phases. The basalt dissolution rate varies according to the silica concentration. The combined effects on silica concentration by diffusional fluxes, mineral precipitation and dissolution, control the basalt dissolution rate, and there are no differences in the results between high and low basalt reactive surface area. [ABSTRACT FROM AUTHOR]

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system's performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals. [ABSTRACT FROM AUTHOR]

Current climate projections suggest that the UK will experience warmer and wetter winters and warmer and dryer summers. This change in the climate could affect the incidence or severity of microbiological attack on exposed timber and have significant impact on buildings and construction. One method of assessing the geographical climate based hazard is to use the Scheffer Climate index, which relates temperature and rain variables. There was a considerable increase in the Scheffer climate indices for various locations of the UK from 1990 to 2019. The highest index values are seen in the Northern and western areas of the United Kingdom, but increases are seen across the country. The paper also uses representative concentration pathway (RCP) scenarios to project future climate decay indices for the United Kingdom until the end of the twenty-first century. The projections show an increase in the Scheffer index even in the lowest RCP scenario, with indices in all regions of the UK increasing to indicate very high hazard of decay. The major implication is that to ensure serviceability of wood and wooden structures exposed to the environment the use of good design, durable woods and properly treated or modified woods will be paramount. [ABSTRACT FROM AUTHOR]