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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]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

Purpose: Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal's literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation. Design/methodology/approach: The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio. Findings: With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that "Saritas" is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, "Blackman" and "Richardson" are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability. Research limitations/implications: Several noteworthy research implications are provided by the bibliometric study of Foresight J. "Saritas" is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that "Blackman" and "Richardson" are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects' ongoing relevance can reveal information about the consistency and development of scholarly interests over time. Practical implications: Foresight J's bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal's influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers. Social implications: The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal's articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development. Originality/value: This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J's history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J. [ABSTRACT FROM AUTHOR]

The global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [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]

This paper presents the results of a pilot interview study investigating the leisure reading habits of 20 practising AI researchers based in the United Kingdom. The interview analysis yields six areas in which literature plays a role in the field of AI: research focus, career choice, community formation, science communication, ethical thinking, and modelling of sociotechnical futures. These categories are proposed as the basis of a systematic taxonomy of the role of literature in AI research, evidencing literature's significance in AI laboratory and professional cultures. The paper presents the results of this preliminary investigation in combination with a synthesis of existing evidence in each category of influence. The aim of this hybrid approach is to cohere research and evidence in this relatively new area of study, and to present new findings contextually, in order to provide the foundations for further qualitative and quantitative research. [ABSTRACT FROM AUTHOR]

Right from the beginning, attendance has played an important role in the education systems, not only in student success but in the overall interest of the matter. Although all schools try to accentuate good attendance, still some schools find it hard to achieve the required level (96% in UK) of average attendance. The most productive way of increasing the pupils′ attendance rate is to predict when it is going to go down, understand the reasons—why it happened—and act on the affecting factors so as to prevent it. Artificial intelligence (AI) is an automated machine learning solution for different types of problems. Several machine learning (ML) models like logistic regression, decision trees, etc. are easy to understand; however, complicated (Neural Network, BART etc.) ML models are not transparent but are black-boxes for humans. It is not always evident how machine intelligence arrived at a decision. However, not always, but in critical applications it is important that humans can understand the reasons for such decisions. In this paper, we present a methodology on the application example of pupil attendance for constructing explanations for AI classification algorithms. The methodology includes building a model of the application in the Isabelle Insider and Infrastructure framework (IIIf) and an algorithm (PCR) that helps us to obtain a detailed logical rule to specify the performance of the black-box algorithm, hence allowing us to explain it. The explanation is provided within the logical model of the IIIf, thus is suitable for human audiences. It has been shown that the RR-cycle of IIIf can be adapted to provide a method for iteratively extracting an explanation by interleaving attack tree analysis with precondition refinement, which finally yields a general rule that describes the decision taken by a black-box algorithm produced by Artificial intelligence. [ABSTRACT FROM AUTHOR]

Many governments find it challenging to set up a regulatory regime to govern rapidly developing Autonomous Vehicles (AVs) technologies empowered by Artificial Intelligence. This paper analyzes flexible regulation as a tool for assessing regulatory reforms that govern disruptive innovations such as AVs. After defining flexible regulation as regulation that gives regulated entities choices for how to comply with the regulatory objectives, this paper develops Regulatory Flexibility Indicators (RFI) for rule structure, enforcement structure, and regulatory feedback. We study AVs regulatory reforms that took place recently in the United Kingdom and South Korea, focusing on how such reforms have enhanced regulation flexibility. This paper finds that regulatory governance in the United Kingdom is more flexible than in South Korea, indicating aspects of further reforms for improving regulatory flexibility. [ABSTRACT FROM AUTHOR]

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [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]

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]

Artificial intelligence (AI)-based healthcare applications (apps) are rapidly evolving, and radiology is a target specialty for their implementation. In this paper, we put the case for a national deployment registry to track the spread of AI apps into clinical use in radiology in the UK. By gathering data on the specific locations, purposes, and people associated with AI app deployment, such a registry would provide greater transparency on their spread in the radiology field. In combination with other regulatory and audit mechanisms, it would provide radiologists and patients with greater confidence and trust in AI apps. At the same time, coordination of this information would reduce costs for the National Health Service (NHS) by preventing duplication of piloting activities. This commentary discusses the need for a UK-wide registry for such apps, its benefits and risks, and critical success factors for its establishment. We conclude by noting that a critical window of opportunity has opened up for the development of a deployment registry, before the current pattern of localised clusters of activity turns into the widespread proliferation of AI apps across clinical practice. [ABSTRACT FROM AUTHOR]

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual 'agents', and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly. [ABSTRACT FROM AUTHOR]

This paper considers legal contestation in the UK as a source of useful reflections for AI policy. The government has published a 'National AI Strategy', but it is unclear how effective this will be given doubts about levels of public trust. One key concern is the UK's apparent 'side-lining' of the law. A series of events were convened to investigate critical legal perspectives on the issues, culminating in an expert workshop addressing five sectors. Participants discussed AI in the context of wider trends towards automated decision-making (ADM). A recent proliferation in legal actions is expected to continue. The discussions illuminated the various ways in which individual examples connect systematically to developments in governance and broader 'AI-related decision-making', particularly due to chronic problems with transparency and awareness. This provides a fresh and current insight into the perspectives of key groups advancing criticisms relevant to policy in this area. Policymakers' neglect of the law and legal processes is contributing to quality issues with recent practical ADM implementation in the UK. Strong signals are now required to switch back from the vicious cycle of increasing mistrust to an approach capable of generating public trust. Suggestions are summarised for consideration by policymakers. [ABSTRACT FROM AUTHOR]

This article engages with the legal regulation of end-of-existence decisionmaking for novel beings, specifically assisted nonexistence for such entities. The author explains the concept of a legal model for assisted death by reference to the substantive features of legal regimes in three jurisdictions in which assisted suicide or euthanasia is lawful. He considers how these models might fit novel beings who may require or prefer assistance to end their own existence by reference to the constituent features—abstract legal ingredients—that models for assisted death share. The author argues that extant models may block some novel beings' access to end-of-existence assistance or fail to track what matters to them. He then examines the merits of adopting a universal model for assisted nonexistence, that is, a legal framework whose substantive features capture the end-of-existence concerns of both human and novel beings. Consideration of a unified legal framework may illuminate the discussion of assisted nonexistence for humans and novel beings. However, the paper proposes that whereas novel beings may have similar interests to humans, they may be relevantly different also. The prima facie case for adopting a one regime to rule us all approach to assisted nonexistence may be defeated by reasons for divergent regulation. [ABSTRACT FROM AUTHOR]

[Display omitted] The environment where we live and recreate can have a significant effect on our well-being. More beautiful landscapes have considerable benefits to both health and quality of life. When we chose where to live or our next holiday destination, we do so according to some perception of the environment around us. In a way, we value nature and assign an ecosystem service to it. Landscape aesthetics, or scenicness, is one such service, which we consider in this paper as a collective perceived quality. We present a deep learning model called ScenicNet for the large-scale inventorisation of landscape scenicness from satellite imagery. We model scenicness with an interpretable deep learning model and learn a landscape beauty estimator based on crowdsourced scores derived from more than two hundred thousand landscape images in the United Kingdom. Our ScenicNet model learns the relationship between land cover types and scenicness by using land cover prediction as an interpretable intermediate task to scenicness regression. It predicts landscape scenicness and land cover from the Corine Land Cover product concurrently, without compromising the accuracy of either task. In addition, our proposed model is interpretable in the sense that it learns to express preferences for certain types of land covers in a manner that is easily understandable by an end-user. Our semantic bottleneck also allows us to further our understanding of crowd preferences for landscape aesthetics. [ABSTRACT FROM AUTHOR]

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease. [ABSTRACT FROM AUTHOR]

Multiphase flowmeters are important to monitor oil wells, as they allow operators to obtain a real-time estimate of the production. However, due to its high installation cost, uncertainty, and reading errors, the oil and gas industry started to invest in virtual meters, mathematical models that can obtain the oil and gas flow forecast using the instrumentation already available in the well, such as pressure and temperature sensors. The purpose of this article is to develop a virtual multiphase flow predictor using artificial intelligence models such as Long Short-Term Memory (LSTM), Gate Recurrent Units (GRU), Multi-Layer Perceptron (MLP) neural networks and deep learning. As input data, this paper uses a public database available on the Oil and Gas Authority (UK) to train the models and demonstrate the possibility of predicting multiphase flow with a reasonable error margin, proving that the method is efficient in estimating well production rates. • In the oil industry, it is important to measure the production from each well. • A Virtual Meter is a low-cost alternative. It can measure multiphase flow without any additional equipment. • This paper proposes a Virtual Meter using neural networks such as MLP, LSTM, GRU and deep learning combinations. • Prediction performance was evaluated using the Oil and Gas Authority (OGA) public database and it shows promising results. • Experiments using a combination of multiple layers shows this type of prediction takes advantage of deep learning techniques. [ABSTRACT FROM AUTHOR]