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Background: Over 180,000 people use crack cocaine in England, yet provision of smoking equipment to support safer crack use is prohibited under UK law. Pipes used for crack cocaine smoking are often homemade and/or in short supply, leading to pipe sharing and injuries from use of unsafe materials. This increases risk of viral infection and respiratory harm among a marginalised underserved population. International evaluations suggest crack pipe supply leads to sustained reductions in pipe sharing and use of homemade equipment; increased health risk awareness; improved service access; reduction in injecting and crack-related health problems. In this paper, we introduce the protocol for the NIHR-funded SIPP (Safe inhalation pipe provision) project and discuss implications for impact. Methods: The SIPP study will develop, implement and evaluate a crack smoking equipment and training intervention to be distributed through peer networks and specialist drug services in England. Study components comprise: (1) peer-network capacity building and co-production; (2) a pre- and post-intervention survey at intervention and non-equivalent control sites; (3) a mixed-method process evaluation; and (4) an economic evaluation. Participant eligibility criteria are use of crack within the past 28 days, with a survey sample of ~ 740 for each impact evaluation survey point and ~ 40 for qualitative process evaluation interviews. Our primary outcome measure is pipe sharing within the past 28 days, with secondary outcomes pertaining to use of homemade pipes, service engagement, injecting practice and acute health harms. Anticipated impact: SIPP aims to reduce crack use risk practices and associated health harms; including through increasing crack harm reduction awareness among service providers and peers. Implementation has only been possible with local police approvals. Our goal is to generate an evidence base to inform review of the legislation prohibiting crack pipe supply in the UK. This holds potential to transform harm reduction service provision and engagement nationally. Conclusion: People who smoke crack cocaine in England currently have little reason to engage with harm reduction and drug services. Little is known about this growing population. This study will provide insight into population characteristics, unmet need and the case for legislative reform. Trial registration: ISRCTN12541454 https://doi.org/10.1186/ISRCTN12541454 [ABSTRACT FROM AUTHOR]

Background: The Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure. Methods: Service users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range. Results: Analyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior. Conclusions: This study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS. [ABSTRACT FROM AUTHOR]

Simple Summary: According to cancer research statistics, there are 7500 new ovarian cancer diagnoses in the UK each year. An earlier detection of ovarian cancer leads to a better prognosis; however, there is currently no screening programme for ovarian cancer, and detection using ultrasound examinations remains challenging. The use of artificial intelligence in gynaecological ultrasound examinations aims to improve the diagnostic accuracy of ultrasound for ovarian cancer and improve outcomes for patients. This review aims to collate current research on AI in the ultrasound diagnosis of ovarian cancer and suggests the usefulness of incorporating this into clinical care. Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80–0.82), and specificity was 92% (95% CI, 0.92–0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Selective serotonin reuptake inhibitors (SSRIs) are commonly prescribed to manage anxiety in adults with an autism diagnosis. However, their effectiveness and adverse effect profile in the autistic population are not well known. This trial aims to determine the effectiveness and cost-effectiveness of the SSRI sertraline in reducing symptoms of anxiety and improving quality of life in adults with a diagnosis of autism compared with placebo and to quantify any adverse effects. Methods: STRATA is a two-parallel group, multi-centre, pragmatic, double-blind, randomised placebo-controlled trial with allocation at the level of the individual. It will be delivered through recruiting sites with autism services in 4 regional centres in the United Kingdom (UK) and 1 in Australia. Adults with an autism diagnosis and a Generalised Anxiety Disorder Assessment (GAD-7) score ≥ 10 at screening will be randomised 1:1 to either 25 mg sertraline or placebo, with subsequent flexible dose titration up to 200 mg. The primary outcome is GAD-7 scores at 16 weeks post-randomisation. Secondary outcomes include adverse effects, proportionate change in GAD-7 scores including 50% reduction, social anxiety, obsessive-compulsive symptoms, panic attacks, repetitive behaviours, meltdowns, depressive symptoms, composite depression and anxiety, functioning and disability and quality of life. Carer burden will be assessed in a linked carer sub-study. Outcome data will be collected using online/paper methods via video call, face-to-face or telephone according to participant preference at 16, 24 and 52 weeks post-randomisation, with brief safety checks and data collection at 1–2, 4, 8, 12 and 36 weeks. An economic evaluation to study the cost-effectiveness of sertraline vs placebo and a QuinteT Recruitment Intervention (QRI) to optimise recruitment and informed consent are embedded within the trial. Qualitative interviews at various times during the study will explore experiences of participating and taking the trial medication. Discussion: Results from this study should help autistic adults and their clinicians make evidence-based decisions on the use of sertraline for managing anxiety in this population. Trial registration: ISRCTN, ISRCTN15984604. Registered on 08 February 2021. EudraCT 2019-004312-66. ANZCTR ACTRN12621000801819. Registered on 07 April 2021. [ABSTRACT FROM AUTHOR]

In the UK, guidance exists to aid clinicians and patients deciding when treatment for Parkinson's disease (PD) should be initiated and which therapies to consider. National Institute for Health and Care Excellence (NICE) guidance recommends that before starting PD treatment clinicians should discuss the following: the patient's individual clinical circumstances; lifestyle; preferences; needs and goals; as well as the potential benefits and harms of the different drug classes. Individualization of medicines and management in PD significantly improves patients' outcomes and quality of life. This article aims to provide simple and practical guidance to help clinicians address common, but often overlooked, co-morbidities. A multi-disciplinary group of PD experts discussed areas where clinical care can be improved by addressing commonly found co-morbidities in people with Parkinson's (PwP) based on clinical experience and existing literature, in a roundtable meeting organized and funded by Bial Pharma UK Ltd. The experts identified four core areas (bone health, cardiovascular risk, anticholinergic burden, and sleep quality) that, if further standardized may improve treatment outcomes for PwP patients. Focusing on anticholinergic burden, cardiac risk, sleep, and bone health could offer a significant contribution to personalizing regimes for PwP and improving overall patient outcomes. Within this opinion-based paper, the experts offer a list of guiding factors to help practitioners in the management of PwP. [ABSTRACT FROM AUTHOR]

Simple Summary: Existing cross-cultural research on animal welfare often overlooks the way that policy and law are not the exclusive domain of central government. This can result in an over-simplification or misrepresentation of the true situation. The political culture and institutional arrangements for governing the modern state are more complex than a "one-size-fits-all" approach. It is argued that cross-cultural research needs to give greater attention to differences within as well as between unitary states. Specifically, it needs to examine developments in constituent nations and territories. Here we illustrate this by drawing on new research in the United Kingdom, and examine how 'devolution'—or decentralized government for Wales and Scotland—is providing contrasting opportunities for NGOs, campaigners, and the public to lobby to improve animal welfare policy based on local practices, beliefs, and demands (collectively known as the "political culture"). Our findings show how this is important because it results in contrasting animal welfare policies and laws in the constituent nations of the UK. It is argued that extant cross-cultural research on animal welfare often overlooks or gives insufficient attention to new governance theory, civil society, politics, and the realities of devolved or (quasi-)federal, multi-level governance in the modern state. This paper synthesizes relevant social theory and draws on new empirical findings of civil society accounts of campaigning on animal welfare policies and law in the United Kingdom. It is presented as a corrective to arguably reductive, earlier unitary state-based analyses. Our core, evidence-based argument is that cognizance of civil society activism and the contrasting institutional governance structures and political cultures of constituent nations in unitary states—such as the UK—are providing opportunities for the territorialization of legally grounded animal welfare regimes, and culturally distinctive practices. [ABSTRACT FROM AUTHOR]

Background: Community-based volunteering supports outbreak management by extending reach into at-risk communities. This paper examines the application of a 'community champions' model in England, UK, during the COVID-19 pandemic. Evidence pre-pandemic shows that community champion interventions tap into social networks to strengthen connections with disadvantaged communities. During the pandemic, the UK government set up a COVID community champions funding award scheme for local authorities to develop local programmes that addressed emerging inequalities. The study aim was to identify transferable learning on community engagement in the pandemic by undertaking a secondary qualitative synthesis of practice-based case studies of local COVID community champion programmes. Methods: A systematic staged approach for synthesis of practice-based case studies was used. In total, 16 COVID community champion case studies, which were written by practitioners involved in local programme implementation and published by the Local Government Association, were included. Case studies covered aims, programme development and delivery, examples of activities and a discussion of learning. Framework qualitative analysis methods were used to code and organise data prior to cross case analysis. The final stage produced an overarching thematic framework that best represented descriptive and interpretive themes. Results: The results provide an overview of common features of COVID community champion programmes and emergent learning. All local programmes aimed to reduce health inequalities by involving at-risk communities in local prevention efforts, adapting the approach to local priorities. Two levels of community engagement were volunteer mobilisation and subsequent community-based outreach activities. Elements of capacity building, such as training and creation of networks, were common. The synthesis of practice-based learning found that stronger relationships with communities were regarded as a key mechanism to support more equitable prevention strategies. Other learning themes related to champion roles, community engagement strategies and programme implementation. Conclusions: By focusing on how community champion approaches were applied by local authorities in England during the COVID-19 pandemic, this study contributes to knowledge on volunteer mobilisation as a mechanism to improve public health communication and outreach. Notwithstanding the limitations of experiential evidence, the synthesis of practice-based learning highlights potentially transferable community engagement strategies for health protection and health improvement. [ABSTRACT FROM AUTHOR]

This paper describes a new seamless blended multi‐model ensemble configuration of an existing probabilistic medium‐ to extended‐range weather pattern forecasting tool (called Decider) run operationally at the Met Office. In its initial configuration, the tool calculated and presented probabilistic weather pattern forecast information for five individual ensemble forecasting systems, which varied in terms of their number of ensemble members, horizontal resolution, update frequencies and forecast lead time. This resulted in multiple forecasts for the same validity time which varied in terms of forecast skill depending on the lead time in question. This presented challenges for end‐users (e.g., operational meteorologists) in terms of knowing which forecast output is best to use and at which lead time, as well as knowing what to do in situations where forecasts varied between ensembles. To get around these challenges, a new seamless blended multi‐model ensemble configuration has been implemented operationally, comprising of output from five separate ensembles, and provides a single best forecast from day one out to day 45. Objective verification for a set of eight weather pattern groups covering forecasts initialized over a 6‐year period (2017–2022) shows that the seamless blended multi‐model ensemble forecasts are at least as good as, if not better than the best performing individual model. [ABSTRACT FROM AUTHOR]

Open innovation is a concept of collaboration of ideas, knowledge, and resources that originates from within the organization and involves external organizations. University-industry collaboration is a factor driving innovation and competitiveness. Most research on interactions between universities and industry concentrated on the industrial side meanwhile, universities, as knowledge producers, play a vital role in creating an open innovation ecosystem that encourages realizing innovations that benefit society. Therefore, the problem raised in this research is how university collaboration can support open innovation. This research aims to identify research trends, the latest technologies, and inter-university collaboration frameworks that can support open innovation. The methodology used is Kitchenham's Systematic Literature Review (SLR) and bibliometric. SLR consists of Identification Study, Selection Studies, Quality Assessment, Data Extraction and Study Synthesis. From the SLR stages, 21 papers published between 2019 and 2023 were obtained. Synthesis and additional literature review were carried out to identify trends, technologies, and frameworks related to the topic comprehensively. In terms of trend, based on the application bibliometric, it was found that there was an increase in the number of publications and the top list of open innovation journals. University collaboration in open innovation more frequently takes place in the UK, and the industries mostly involved are small-medium enterprises. Quantitative research methodology and data analyses, comprising of hypothesis test, regression test, and descriptive statistics, are mostly preferred. Meanwhile, descriptive data analysis is the most common means of qualitative data analysis. Numerous suggestions on topics for future research were also identified. In addition, data analysis using machine learning survival data is identified as a novelty among data analysis methods. Technology transfer and the use of digital platforms may support open innovation processes, and the use of blockchain technology may promote digital platforms development. Seven domains form the framework for university collaboration in open innovation, namely Social Behavior, People, Process, Organization, Environment, Technology, and Performance. The findings of trends, technologies, and frameworks in this research may serve as a foundation for research on similar topics, and the identified framework domains could serve as framework components that might be used by universities to collaborate with partners in implementing open innovation processes. The research have contributes to knowledge management theoretically and practically. Theoretically, open innovation can expand the knowledge base by supporting the concept that knowledge and expertise can be found outside organizational boundaries. Practically, it can enrich knowledge management practices by emphasizing the importance of utilizing digital platform technology in supporting knowledge management sourced from external knowledge and collaboration services in the open innovation process. [ABSTRACT FROM AUTHOR]

This paper examines the inner debate in Basque nationalism resulting from the Easter Rising between 1916 and 1918. Basque nationalists firmly believed that the Irish Revolution would affect the British war effort, the outcome of the Great War and hence Basque political vindication itself. The official nationalist leadership and its organ, Euzkadi, supported Great Britain, arguing a British military triumph in Ireland and in the Great War would bring a worldwide era of autonomy for national minorities. Pro-independence militants, however, not only expressed solidarity and outspoken support towards the rebels, but also associated their victory with a post-war scenario favourable to independence movements. This study focuses on the two competing Basque nationalist interpretations as presented in the media. Simultaneously, traditionalist parties (i.e. Carlists and Integrists) exacerbated nationalists’ differences in order to weaken a direct political rival. [ABSTRACT FROM AUTHOR]

Genetic research's growing importance in understanding pulmonary arterial hypertension (PAH) and developing effective treatments prompted the RAPID‐PAH study. This study sought feedback from stakeholders who participated in two genomic studies to enhance genetic study delivery and clinical integration. Stakeholders from nine UK PH centres, representing various roles, ages, genders, and mutation statuses, took part in 53 semi‐structured interviews and focus groups. Transcripts were thematically coded using inductive analysis. Clustering analysis was conducted to identify patient groups that shared attitudes. In this paper, we focus on patients', carers', and relatives' perspectives. The key interview themes revealed insights into participants' attitudes toward genetic research and testing more generally, expertise and knowledge of the disease itself, motivations and barriers to participating in genetic research, awareness of and interest in consent procedures and the use of personal and genetic data, as well as the process of communicating individual genetic results. Factors influencing genetic research participation included altruistic motives, personal diagnostic experiences, and family‐related hopes. Clustering analysis produced distinct clusters based on the presence of barriers and motivators for research participation; however, hardly any patients shared identical sets of attitudes, emphasising the need for personalised approaches to recruitment. Most patients reported poor engagement with study‐related materials. Patients who received individual genetic results expressed satisfaction with the process, whereas those who did not were disappointed with the lack of feedback. Reflecting on patient perspectives, we offer recommendations to improve the genetic study delivery process. Enhancing genetic research integration into clinical practice requires tailored engagement, clear communication, and support from healthcare stakeholders. [ABSTRACT FROM AUTHOR]

Summary: Electroencephalogram-based evidence was accepted in a UK law court for the first time in 1939. This paper gives an account of that case, not previously clinically reported, and the individuals involved. Why it was not published in the literature at the time is explored and parallels with more recent technologies are highlighted. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

Despite the transition towards a circular economy (CE) being a significant element in achieving the decarbonisation of the built environment, a clear and common pathway to applying CE principles to building design is still lacking in both industry practice and academia. The integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) has the potential to enable the identification of feasible pathways to increase circularity. This study aims to investigate its practical use to facilitate the application of circular economy principles at the early stage of building design via using LCA-based BIM plugins. Within this aim, the paper centres on a co-production approach and presents a gap analysis by identifying gaps in knowledge and implementation as well as addressing the pressing needs of the current practice in the UK. A series of semi-structured interviews with expert practitioners in the field were conducted for data collection, contributing to the following phases of the co-production by allowing for an in-depth investigation and reflection of the practice. The findings have revealed that: (a) there is still an insufficient level of contextual awareness and readiness in the implementation of CE principles in the built environment and LCA understanding and (b) the adoption of BIM at the early stages of building design is still limited in the current practice; BIM models with sufficient level of data details and quality to enable circularity assessment are rarely produced. Thus, the paper highlights the need for enabling mechanisms, including the introduction of legislative instruments, the involvement and commitment of the industry and key stakeholders, the support for training and skills improvement, and the establishment of effective communication and implementation process management frameworks. Future research agenda points to the need of formulation of a BIM protocol to enable integration of BIM and LCA to promote CE principles in the building design process. [Display omitted] • Co-production is a promising approach to capture and respond to the industry needs. • Lack of seamless integration between BIM and LCA tools. • Insufficient understanding of how to apply CE principles in the building design. • Lack of systematic guidance and standardised process to implement BIM and LCA integration. • A tailored BIM protocol needed for applying LCA from a CE perspective in the building design. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

Do 'green' environmental concerns – such as about biodiversity, climate change, pollution – deter citizens from having children? This paper reports the first longitudinal evidence consistent with that increasingly discussed hypothesis. It follows through time a random sample of thousands of initially childless men and women in the UK. The paper shows that those individuals who are committed to a green lifestyle are found to be substantially less likely to go on later to have offspring. Probit and Weibull survival models are estimated. The results are robust to controlling for people's age, education, income, marital status, mental health, life satisfaction, optimism, and physical health. The paper's key estimated effect-size is substantial. A person entirely unconcerned about environmental behaviour is estimated to be just over 50% more likely to go on to have a child than a deeply committed environmentalist. • Will fertility decline because humans become so concerned about environmental damage? • Using UK data on thousands of randomly selected young adults, we provide the first longitudinal evidence that those who have environmental concerns and act in a 'green' way go on to have fewer children. • The estimated effect-size is large. [ABSTRACT FROM AUTHOR]

This paper analyses the potential implications of the proposed European Union Deforestation Regulation (EUDR) and the recently adopted United Kingdom (UK) legislation on deforestation-free supply chains (henceforth 'the legislation') for different stakeholders in Brazil. These regulations intend to address global commodity-driven deforestation and forest degradation by ensuring that targeted commodities and products placed on (or exported from) markets are of minimal risk of being associated with - in the EU - deforestation and forest degradation or - in the UK - illegal deforestation. The paper examines potential compliance readiness in cattle, cocoa, coffee, palm oil, soybean and tropical timber supply chains in Brazil, indicating specific challenges that may arise. Through the construction of a "Compliance Likelihood Index", our research provides comparable indications to policymakers on sectors and stakeholders that may need stronger support to meet the requirements, in order to maintain Brazil's access to EU and UK markets. The paper indicates that coffee is the sector with the highest level of incentivization and smallest hurdles for compliance, while the cattle sector may face stronger challenges to rapidly adjust its production system towards a deforestation-free value chain and prove compliance. Results of our analysis also highlight the need for collaboration between the EU/UK and Brazil in order to promote alignment between domestic and demand-side legislations so that they are mutually reinforcing. Results of this exercise, which has a focus on the producer-country view of demand-side legislation, will contribute to discussions on the merits of different approaches to strengthen the governance of deforestation-risk commodity trade. [Display omitted] • Brazil's coffee sector may currently have the greatest likelihood of ready compliance. • Cattle may be the sector to which the regulations may pose the greatest challenges. • The EU and UK should focus on providing financial means and technical assistance for smallholders. • The EU and UK legislation will need to be accompanied by domestic policies and regulations in Brazil. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Sampling uncertainty of UK design flood estimation" by Anthony Hammond, published in Hydrology Research. The author acknowledges errors in Equation (7) and Figure 12 in the original paper and provides the correct versions. Additionally, there were errors in one of the paragraphs and Figure 14, which have been removed from the online version of the paper. The document also includes information about the distribution and usage of the article under the Creative Commons Attribution Licence. [Extracted from the article]

Meeting the goal of reaching zero emissions by 2050 requires profound changes in the production of bulk materials, cement and concrete. For construction sector, this transition is also dependent on the evolution of demand, stemming from changes in the population structure and practice in structural design. Many timelines have been produced suggesting pathways to zero emissions, but none have looked at the required investment level, or the feasibility of transition looking at the level of single plants being shut, transformed or built within a single country. In this paper, we have looked at transition pathways in the United Kingdom as a function of the available funding and the possible emergence of transformative technologies. We find that trade is a necessary component of the transition, allowing for continued construction while the production apparatus is upgraded. Our results further suggest that the current level of investment is consistent with today's carbon price but falls short of what is necessary to achieve net zero by 2050. Finally we find that pathways assuming that CCS will be available at scale are particularly susceptible to overshoot their emissions target if the technological deployment fails to materialise. This is because they combine heavy investments, immature technology and concentrated risk. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper aims to introduce an artificial intelligence approach to enhance short-term daily streamflow forecasts through the integration of machine learning (ML) techniques with meta -heuristic (MH) algorithms. To maximize performance, a comprehensive analysis involving the autocorrelation and partial autocorrelation functions of the observed streamflow was conducted to derive ten distinct input combinations, from which the most suitable set was selected for investigation. Subsequently, six distinct ML methods, namely, decision trees, support vector regression, long short-term memory, gated recurrent unit, multi-layer perceptron, and radial basis function (as the optimal input combination), were leveraged to predict one-day-ahead streamflow. The most promising outcomes from these six explored methods were synergistically amalgamated to enhance predictive precision. This amalgamation was then optimized by utilizing parameter values hyper-tuned by four different MH algorithms. The algorithms included the shuffled frog leaping algorithm, particle swarm optimization, ant colony optimization (ACO), and gray wolf optimization (GWO). In culmination, a case study centered around the "Wick" river in the UK was conducted to rigorously evaluate the performance of the proposed method, utilizing a range of statistical evaluation indices. The results underscored the efficacy of the proposed combined method in substantially refining streamflow predictions, exhibiting an impressive level of accuracy. Additionally, the outcomes emphasized that ACO and GWO stand out as the optimal algorithms for calibrating the parameters of the amalgamated model. For instance, the R2 values attained by the combined models optimized through the ACO and GWO algorithms were 0.69422 and 0.69444, respectively. These values notably surpass those observed in the initial models, substantiating the superiority of the applied optimization approaches. [ABSTRACT FROM AUTHOR]

Mixed sediment beaches are globally commonplace, yet little is understood of the extent and behaviour of their nearshore zones, potentially underestimating total cross-shore change. This paper is the first study to investigate the lateral and vertical extent of the active zone of the gravel-rich mixed beach in Pevensey Bay, a study site on the South East UK coastline. Morphodynamic change in the nearshore zone was studied at a range of timescales (days, months, years) suggesting that the width of the active nearshore zone correlated with the magnitude of the peak morphological change, whilst the depth of closure was influenced by bed slope, grain size and local variation in wave conditions. A conceptual model detailing the physical parameters responsible for local variations in the depth of closure was used to help understand differences between the observed and predicted depths of closure. Finally, ongoing chronic loss of sediment from below Mean Sea Level (MSL) was examined, which was shown to be independent of the depth of closure, but closely linked to the wider geomorphic setting of the bay. • Comparison of observational and predicted depth of closure for a mixed sediment beach • Local variations in the observed depth of closure examined using a conceptual model • Magnitude of subtidal bed elevation change may be controlled by sediment availability. • Loss of nearshore sediments over time reflects the trend in upper beach steepening. [ABSTRACT FROM AUTHOR]

• Load-related and non-load related planning is integrated into a single methodology. • The concept of probabilistic asset health indices (HIs) is developed. • Averages and pdfs of asset virtual ages and HIs are calculated along system indices. • Asset in-service time is modelled via proportional hazard and Kijima 2 models. • Proposed methodology gives lower investments that the UK deterministic approach. In the last price control review, the UK regulator has specified a common set of "network output measures" that includes both system-wide and individual asset metrics. This paper introduces a two-stage framework for the replacement planning and develops the second – simulation stage. It consists of two Monte Carlo procedures, where the latter is based on the proposed probabilistic health index (HI) methodology and application of several types of asset interventions, such as minimal, minor and major repairs and replacement. The probabilistic HI model makes use of proportional hazard models and Kijima II virtual – age model. The outputs are system-wide and nodal reliability indices, as well as asset interventions and asset profiles. IEEE test network is used to test the probabilistic HI methodology, which is then compared to the deterministic HI method applied in the UK. [ABSTRACT FROM AUTHOR]

The modern electricity network is seeing a trend in the replacement of fossil fuel power plants with converter interfaced generation as worldwide efforts are made to combat climate change. New converter control structures such as grid-forming are seen as a key building block for maintaining the stability of the future power system. Moreover, wind power is the fastest growing renewable technology in the UK with ambitious targets set for installed capacity in the coming decade. While the benefits and drawbacks of the technology have been explored, little attention has been given to how many grid-forming converters will be needed to stabilise the modern network. Is there such a thing as too much grid-forming? This paper utilises an impedance-based windfarm model with the capability to include unique control systems on each turbine to present a small-signal based methodology for determining the penetration limits of grid-forming technology. Key stability and screening metrics are applied to identify the penetration that provides the strongest and most stable system. Three key points are specified: the critical, optimal and maximum penetrations. Moreover, findings suggest providing enhanced system strength via converters is only applicable to a certain extent where further interactions cause increased stability issues. • The balance between grid-following and forming is of key concern. • Grid-forming is useful but too much can cause poor system behaviour. • A novel technique utilising small-signal impedances is suggested for analysis. • Three key penetrations have been specified minimum, optimal and maximum. • System strength must be considered as a function of frequency. [ABSTRACT FROM AUTHOR]

Modern economies rely on economic growth for stability and prosperity. Further, periods of stagnation and recession are currently associated with poor health and wellbeing outcomes for citizens. However, 50 years of research indicates that this state of dependency is ecologically unsustainable. It is therefore critical that we better understand the growth dependency of our economies and welfare systems, and how to overcome it. In this paper we first offer a working definition of growth dependency, clarifying its core dimensions. Next, taking the UK's adult social care sector as a case study, we argue that growth dependencies emerge in the welfare state as a result of three distinct dynamics: 1) growth in manifest needs; 2) labour productivity growth; 3) the pursuit of economic rents. Lastly, we propose a novel, sector-led framework for identifying, analysing and transforming growth dependencies. We make the case that the growth dependency of the welfare state is not inevitable, but is instead the product of specific social, economic and structural factors that can be disrupted and transformed, if we can find the political will to do so. [ABSTRACT FROM AUTHOR]

[Display omitted] • In-train air quality in UK diesel trains tested for 13 different train classes. • Means were PM 10 : 8–52; PM 2.5 : 4–36; BC: 1–11; NO 2 : 28–201 (µg/m3); UFP: 225–9131 #/cm3. • Large in-train variability was observed and associated with multiple factors. • Trains under 10 years old had the lowest in-train PM concentrations. • UK diesel trains measured lower PM 2.5 and PNC than other transport modes. Concentrations of particulate matter (PM 10 , PM 2.5), ultrafine (UFP), particle number (PNC), black carbon (BC), nitrogen dioxide (NO 2) and nitrogen oxides (NO X) were measured in train carriages on diesel and bi-mode trains on inter-city and long-distance journeys in the United Kingdom (UK) using a high-quality mobile measurement system. Air quality on 15 different routes was measured using highly-time resolved data on a total of 119 journeys during three campaigns in winter 2020 and summer 2021; this included 13 different train classes. Each journey was sampled 4–10 times with approximatively 11,000 min of in-train concentrations in total. Mean-journey concentrations were 7.552 µg m−3 (PM 10); 3.936 µg m−3 (PM 2.5); 333–11,300 # cm−3 (PNC); 225–9,131 # cm−3 (UFP); 0.6–11 µg m−3 (BC); 28–201 µg m−3 (NO 2); and 130–3,456 µg m−3 (NO X). The impact of different factors on in-train concentrations was evaluated. The presence of tunnels was the factor with the largest impact on the in-train particle concentrations with enhancements by a factor of 40 greater than baseline for BC, and a factor 6 to 7 for PM and PNC. The engine fuel mode was the factor with the largest impact on NO 2 with enhancements of up to 14-times larger when the train run on diesel compared to the times running on electric on hybrid trains. Train classes with an age < 10 years observed the lowest in-train PM, BC and NO X concentrations reflecting improvements in aspects of rail technology in recent years. Air quality on UK diesel trains is higher than ambient concentrations but has lower PM 2.5 and PNC than most other transport modes, including subway systems, diesel and petrol cars. This paper adds significantly to the evidence on exposure to poor air quality in transport micro-environments and provides the industry and regulatory bodies with reference-grade measurements on which to establish in-train air quality guidelines. [ABSTRACT FROM AUTHOR]

Metrology provides the underpinning science that supports the practical use of testing standards; ensuring that different users, be it academia, government or industry, develop a common language regarding the capabilities of many materials sectors. For the hardmetal community, after a century of fundamental and effective applications, it isn't surprising that there are already a considerable number of formal International Standards. For example, ISO and ASTM are well known [1], and over time these have evolved, with international consensus, from metrological studies. Currently important properties of hardmetals such as Stiffness, Density, Hardness, Toughness, Abrasion Resistance, Macroscopic Strength and Phase and Magnetic properties are comprehensively supported by these international standards. However, there are other testing strategies where improved knowledge could contribute to enhanced carbide functionality. These might include high resolution microscopy, indentation strength measurements, micro-tribology, thermal fatigue testing and modelling in all its diverse approaches which are not yet addressed through standards, though they often feature strongly in published academic papers. The hardmetal research community is international, diverse and productive in addressing these many facets and frequently embed metrological studies that are relevant to the practical characterisation of hardmetals. In the UK the Materials Group at the National Physical Laboratory, NPL, has a prime remit to focus on metrology and our contribution to the WC-Co 100 year anniversary publication provides some specifics of typical metrological issues that need further attention before addressing the formal stage of standardisation. In this celebratory paper we have chosen examples of recent metrological activities that fall under this remit and which have only partly been contributed to the scientific published domain. These five examples are High Hardness measurements, Dislocation Analysis, Micro-Tribology, Nanoindentation and Structural Characterisation. • Issues associated with high hardness metrologies. • Assessment of dislocations in WC using slip trace analysis. • Micro-tribology as a metrology tool for hardmetal wear. • Nanoindentation of WC crystals at ambient and high temperatures. • WC grain size issues using EBSD, especially in large data files. [ABSTRACT FROM AUTHOR]

Maritime transport is an indispensable element of the global logistics network. Most maritime loading-unloading operations are supported by quay cranes, making their availability and condition critical to port operations. This work identifies discordant trends arising during the vibration-based condition monitoring of these quay cranes in one of the busiest container ports in the United Kingdom. This work proposes an unsupervised and constrained discord detection approach for irregular but near real-time time series data obtained from multi-modal IoT-based condition monitoring sensors installed on these cranes and transmitted over a 5G network. Due to the live nature of the seaport's operations, the development of controlled anomaly signatures for a baseline reference was not possible. To address the challenges of incomplete asset health information, irregular and batched time-series sensor data, massive data volumes, and the lack of the assets' vibration signature baselines, this paper proposes an unsupervised, robust, and fast discord detection mechanism that can rapidly highlight discordant time-series chunks in the received vibration data at a central server. A Support Vector Machine based One-Class Classifier (OCC-SVM) is used to identify the discordant vibration signatures in the time series data. During the development of this approach, the timestamped data chunks from the add-on IoT-based vibration sensors were clustered into two weight classes (loaded and unloaded) based on the crane's default Programmable Logic Controller (PLC) sensors. The efficacy of this method is checked against the crane maintenance logs and data from a separate crane's vibration and PLC data. Further, a method for generating synthetic noise-embedded vibration signatures to test the effectiveness of the discord detection method has been devised. Finally, the practicality of the proposed OCC-SVM approach for discord detection was inspected in a constrained environment setting. [ABSTRACT FROM AUTHOR]

In the process of implementing circular economy within the built environment, quantifying progress and performance towards this effort has posed a substantial challenge. In response, this paper reviews the current state of building-specific circularity indicators, creates a taxonomy, and integrates them within the Royal Institute of British Architects (RIBA) Plan of Work, a commonly employed framework within the UK's built environment. Through a systematic literature review considering both academic and grey literature, 32 building circularity indicators, developed by academia, consulting agencies, and governments, are identified. The first contribution of this study is the creation of a taxonomy of building circularity indicators, which classifies the identified indicators into 8 categories, such as basic characteristics of indicators, circularity level, and sustainability pillars. The second contribution of this study is the alignment of circularity within the stages of Plan of Work framework. It is revealed that the framework does not currently incorporate circularity, resulting in the proposal of a new Stage 8: End of Life to encompass demolition, disassembly, and deconstruction activities. Alignment of the taxonomy to Plan of Work framework provides a guide in utilization of building circularity indicators throughout the process of designing, constructing and operating building projects. • Building-specific circularity indicators are reviewed for the built environment. • A taxonomy of 32 building circularity indicators is created across eight categories. • A new Stage 8: End of Life is proposed for RIBA framework to enhance circularity. • The circularity indicators are integrated with the RIBA Plan of Work framework. • A traffic light approach is employed to guide the use of circularity indicators. [ABSTRACT FROM AUTHOR]

Energy poverty is driven by four factors: poverty; high energy prices; energy-inefficiency; and specific household characteristics. Increasingly refined definitions of energy poverty (and/or vulnerability) identify types or locations of households most likely to be suffering it. But there is also a place for a different approach, considering poverty reduction directly, as poverty is a necessary component within all energy poverty definitions. This paper investigates how high the UK's top marginal tax rates would need to be, to provide funds to lift low-income UK households above the poverty threshold as defined by the low-income-high-cost (LIHC) and 10% energy poverty indicators. It finds that the increased top tax rates would still be in line with those of other European OECD countries. Using recent and new research on income elasticities of CO2 emissions, it then estimates the net direct effects on CO2 emissions of this redistribution. For both scenarios the most likely effect is a small reduction in net CO2 emissions. Further, indirect downward pressures on CO2 emissions due to reduced income among the highest earners are likely to lead to yet more favourable outcomes for net CO2 emission reduction. [ABSTRACT FROM AUTHOR]

Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Computational individual-based models are a popular tool for describing and forecasting the spread of tree diseases due to their flexibility and ability to reveal collective behaviours. In this paper we present a versatile individual-based model with a Gaussian infectivity kernel to describe the spread of a generic tree disease through a synthetic treescape. We then explore several methods of calculating the basic reproduction number R 0 , a characteristic measurement of disease infectivity, defining the expected number of new infections resulting from one newly infected individual throughout their infectious period. It is a useful comparative summary parameter of a disease and can be used to explore the threshold dynamics of epidemics through mathematical models. We demonstrate several methods of estimating R 0 through the individual-based model, including contact tracing, inferring the Kermack–McKendrick SIR model parameters using the linear noise approximation, and an analytical approximation. As an illustrative example, we then use the model and each of the methods to calculate estimates of R 0 for the ash dieback epidemic in the UK. • Computational individual-based models can simulate the spread of plant disease. • Disease progression can be summarised by the epidemiological parameter R 0. • R 0 can be calculated from an individual-based model in several ways. • Methods include: analytic expressions, parameter inference, and contact-tracing. • Each has (dis)advantages — the best choice will vary depending on requirements. [ABSTRACT FROM AUTHOR]

High-frequency financial data is more difficult to predict than low-frequency data because it possesses nonlinearity, nonstationarity, higher volatility, and long memory and is frequently accompanied by the jump phenomena. In this paper, the nonparametric regression (NR) model based on kernel function is used to fit the nonlinear relationship between the nonstationary series Y t and its lagging series to model the trend of high frequency financial time series. Furthermore, the deep learning LSTM (long short-term memory) model is applied to capture the high volatility and frequent jumps of high frequency financial data and to improve the forecasting accuracy of the residual series. The results demonstrate that the hybrid NR and LSTM model has greatly improved the forecasting accuracy in several evaluation criteria. In comparison to NR, support vector machine (SVM), LSTM, ARIMA and NR-SVM models, the mean absolute error (MAE) of NR-LSTM has reduced by 89.78%, 97.85%, 86.48%, 32.47% and 89%, respectively. In addition, we have constructed the trading strategy for the Shanghai-Shenzhen 300 index by using the NR-LSTM model. The NR-LSTM model can continue to provide good returns even during a bear market, which can serve as a guide for investors. Furthermore, the NR-LSTM model also exhibits the best forecasting effect when we model the high-frequency data of Ping An bank in China, the FTSE 100 index in the UK, and the S&P 500 index in the US. [ABSTRACT FROM AUTHOR]

State-estimation plays a vital role to monitor, observe and understand the combined heat and electric network. In this paper, a hybrid framework is presented to accurately estimate the system states of electric distribution network and heat network, using the limited non-redundant measurements obtained from supervisory control and data acquisition and advanced metering infrastructure systems. The presented hybrid framework involves two steps, namely, the state-forecasting and the state-estimation. The state-forecasting uses a deep neural network to forecast the system states at every fifteen minutes interval, while these forecasted states are further used by the hybrid estimator, which uses a robust extended Kalman filter to estimate the system states with help of both datasets corresponding to supervisory control and data acquisition and advanced metering infrastructure systems, at hourly interval. The proposed framework does not completely rely on the system model at different instants. The effectiveness of the method is validated through thorough comparisons with simulation studies carried out using the Barry Island test system, United Kingdom. Satisfactory performance is observed even with the presence of bad data in the measurements. • A framework to estimate states of electric and heat networks with SCADA and AMI data • A method of forecasting the CHEN states using only a limited SCADA dataset • A robust Kalman filter for accurate estimation under gross errors in data. • Test results that validate the method on Barry Island CHEN system. • Comparative results over the state-of-art WLS and ANN based estimators. [ABSTRACT FROM AUTHOR]

Carbon capture usage and storage (CCUS) is emerging as an important solution in delivering deep emissions reductions in energy-intensive industries, enabling hydrogen production, and possibly directly capturing existing carbon dioxide (CO 2) from the atmosphere. The potential for associated new industry activity – for example in CO 2 Transport and Storage (T&S) – could also be important in transitioning economies with legacy investment in oil and gas extraction. This paper addresses the question of how introducing a nascent T&S industry may impact the wider UK economy in the presence of persisting national labour supply constraints. It does so by refining a multi-sector economy-wide computable general equilibrium (CGE) model of the UK to run scenarios focussed on the emergence of a nascent sector, involving identification of benchmark activity – here, the existing oil and gas industry – where that nascent sector is not currently represented in national accounting data. Crucially, the CGE model embeds a theoretically and empirically tested wage bargaining function to consider how cost and price pressures triggered will condition dynamic outcomes for producers, consumers and government budgets. Results suggest that emergence of a new T&S industry is likely to deliver sustained net gains in UK employment and GDP. However, maximising T&S-linked jobs gains while minimising displacement of employment and price pressures elsewhere in the economy requires policy action to alleviate labour supply and skills constraints. This reinforces policy and industry recommendations around the need for net zero workforce planning and attention to the potential fiscal implications of taking action, or not/in different timeframes. • Introducing a new CO 2 Transport and Storage industry could boost the UK economy. • Wage responses in a supply constrained labour market substantially condition outcomes. • Wage-cost driven price impacts affect all producers, consumers and government budgets. • Mitigating wage bargaining pressure requires policy action on workforce planning. • Findings reinforce the need for economy-wide and fiscal analysis of net zero actions. [ABSTRACT FROM AUTHOR]

Transport-related activities represented 34% of the total carbon emissions in the UK in 2022 and heavy-duty vehicles (HGVs) accounted for one-fifth of the road transport greenhouse gas (GHG) emissions. Currently, battery electric vehicles (BEVs) and hydrogen fuel cell electric vehicles (FCEVs) are considered as suitable replacements for diesel fleets. However, these technologies continue to face techno-economic barriers, creating uncertainty for fleet operators wanting to transition away from diesel-powered internal combustion engine vehicles (ICEVs). This paper assesses the performance and cost competitiveness of BEV and FCEV powertrain solutions in the hard-to-abate HGV sector. The study evaluates the impact of battery degradation and a carbon tax on the cost of owning the vehicles. An integrated total cost of ownership (TCO) model, which includes these factors for the first time, is developed to study a large retailer's HGV fleet operating in the UK. The modelling framework compares the capital expenditures (CAPEX) and operating expenses (OPEX) of alternative technologies against ICEVs. The TCO of BEVs and FCEVs are 11% to 33% and 37% to 78% higher than ICEVs; respectively. Despite these differences, by adopting a longer lifetime for the vehicle it can effectively narrow the cost gap. Alternatively, cost parity with ICEVs could be achieved if BEV battery cost reduces by 56% or if FCEV fuel cell cost reduces by 60%. Besides, the pivot point for hydrogen price is determined at £2.5 per kg. The findings suggest that BEV is closer to market as its TCO value is becoming competitive, whereas FCEV provides a more viable solution than BEV for long-haul applications due to shorter refuelling time and lower load capacity penalties. Furthermore, degradation of performance in lithium-ion batteries is found to have a minor impact on TCO if battery replacement is not required. However, critical component replacement and warranty can influence commercial viability. Given the high costs, we propose financial incentives and vehicle tax reforms to reduce costs of critical components that will encourage the roll-out of zero emission HGVs. • TCO model employs real-world fleet data to compare HGV powertrain technologies. • Maximising vehicle lifetime is key to reduce the TCO gap and get closer to parity. • BEVs have a TCO 11%–33% higher than ICEVs. • FCEVs have a TCO 37%–78% higher than ICEVs. • Degradation in lithium-ion batteries is found to have a minor impact on TCO value. [ABSTRACT FROM AUTHOR]