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ChatGPT could allow students to plagiarize the content of their coursework with little risk of detection. Little is known about undergraduate willingness to use AI tools. In this study, psychology undergraduates (N = 160) from the United Kingdom, indicated their willingness to use, and history of using, ChatGPT to write university assignments. Almost a third (32%) indicated that they would use such tools; 15% indicated that they had used them already. Neither personality (conscientiousness, agreeableness, Machiavellianism, narcissism), academic performance, nor study skills self-efficacy could predict future use of AI tools. A novel Degree Apathy Scale was the only significant predictor. Willingness to use AI tools was greater when the risk of getting caught was low, and punishment was light, particularly for those high in degree apathy. Findings suggest that degree apathy is a key risk factor in academic misconduct. Wider research and pedagogical applications of degree apathy are discussed. • Thirty-two percent of students surveyed reported that they would use ChatGPT on future academic assignments. • Future use of AI tools was significantly predicted by a new Degree Apathy Scale. • Big Five and Dark Triad scores were not significant predictors, nor was previous academic performance. • Likelihood of cheating using ChatGPT was reduced with increasing risk of detection or severity of punishment. [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]

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]

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]

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]

• 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]

[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]

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]

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]

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]

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]