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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]

Inverse gas chromatography (IGC) has emerged as a highly sensitive, adaptable, and effective technology for material analysis. Through employing thermochemical approaches, IGC provides crucial insight into physicochemical information of materials such as dispersive surface free energy, Gibbs surface energy components and Guttamann Lewis acid-base parameters. In this comprehensive review, we delve into the historical background, instrumentation, and diverse applications of IGC. Researchers and practitioners will find valuable information on the selection and description of numerous models used in IGC experiments. The applications of IGC span various domains, including polymers, medicines, minerals, surfactants, and nanomaterials. Furthermore, IGC facilitates the measurement of important parameters such as sorption enthalpy and entropy, surface energy components (dispersive and specific), co/adhesion work, glass transition temperature, surface heterogeneity, miscibility, solubility parameters, and specific surface area. These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. Moreover, the integration of computer vision and image processing techniques with IGC has enhanced our understanding of materials intricate surface texture, roughness, and related properties. This convergence of IGC with computer vision and artificial intelligence (AI) presents exciting opportunities for future exploration of chemical materials, opening new avenues for research and discovery. This paper not only provides a comprehensive overview of IGC, its techniques, and applications but also highlights the synergistic potential of combining IGC with AI and computer vision. The informative content and insights presented here will benefit researchers, scientists, and professionals in the field of advanced materials, enabling them to leverage IGC and AI for innovative materials discovery and development. [Display omitted] • This review paper offers an extensive exploration of the historical background, instrumentation, and diverse applications of Inverse Gas Chromatography (IGC). • It provides valuable information for researchers and practitioners by presenting various models employed in IGC experiments. • The applications of IGC covered in this review span multiple domains like polymers, medicines, minerals, surfactants, and nanomaterials. • These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. • A complete overview of IGC, its techniques, and applications but also the synergistic potential of combining IGC with AI and computer vision. [ABSTRACT FROM AUTHOR]

This paper describes our creative responses to a surface assemblage (a scatter) of lithic artefacts encountered on either side of a worn track across a field early on in the pandemic. Our art/archaeology response takes place within a phygital nexus in which artefacts or assemblages can be instantiated either physically or digitally, or both. In the nexus we create, connect and explore an ontological multiplicity of – more or less – physical and digital skeuomorphs and other more standard forms of records for sharing (i.e. Latour's immutable mobiles, such as photographs), but rendered with radically different properties and affordances, at different scales, with different apparatus. These include interactive Reflectance Transformation Images, graphical surface models, machine intelligence style transfer, and 3D prints, all of which were produced in a variety of isolated analytical "bubble" settings and transmitted to and from (both digitally and physically) a home office in an isolated Hampshire village and a home studio in a London suburb. Our approach is to describe, diffractively, the ontological shifts and itineraries associated with some of these objects and assess how this assemblage came to matter as an art/archaeology installation. Ultimately, some of these deterritorialised, (re)colourised, affective, biodegradable, and diffractively born metamorphic instars, now inscribed with new meanings, are returned to the original findspot of the lithics to be (re)discovered. [ABSTRACT FROM AUTHOR]

[Display omitted] • Artificial intelligence framework is developed to operation performance of AD plants. • The framework comprises RNN, NARX, SFLA models and sensitivity analysis. • Optimal operation strategy improves the productivity of biogas by 43% • Optimal feeding pattern during a four-day cycle needs feeding over the last two days only. • Optimised biogas is strongly influenced by the content of oats and catering waste. Despite the advantages of the Anaerobic Digestion (AD) technology for organic waste management, low system performance in biogas production negatively affects the wide spread of this technology. This paper develops a new artificial intelligence-based framework to predict and optimise the biogas generated from a micro-AD plant. The framework comprises some main steps including data collection and imputation, recurrent neural network/ Non-Linear Autoregressive Exogenous (NARX) model, shuffled frog leaping algorithm (SFLA) optimisation model and sensitivity analysis. The suggested framework was demonstrated by its application on a real micro-AD plant in London. The NARX model was developed for predicting yielded biogas based on the feeding data over preceding days in which their lag times were fine-tuned using the SFLA. The optimal daily feeding pattern to obtain maximum biogas generation was determined using the SFLA. The results show that the developed framework can improve the productivity of biogas in optimal operation strategy by 43 % compared to business as usual and the average biogas produced can raise from 3.26 to 4.34 m3/day. The optimal feeding pattern during a four-day cycle is to feed over the last two days and thereby reducing the operational costs related to the labour for feeding the plant in the first two days. The results of the sensitivity analysis show the optimised biogas generation is strongly influenced by the content of oats and catering waste as well as the optimal allocated day for adding feed to the main digester compared to other feed variables e.g., added water and soaked liner. [ABSTRACT FROM AUTHOR]

• This paper focuses on daily natural gas consumption forecasting. • A novel hybrid model is developed for daily natural gas consumption forecasting. • The most recent datasets of four cities in three climate zones are collected. • An improved singular spectrum analysis is first proposed. In daily natural gas consumption forecasting, the accuracy of forecasting models is vulnerably affected by the noise data in historical time series. Singular spectrum analysis (SSA) is often introduced into hybrid models for denoising. However, as a deterministic-based algorithm, SSA does not give good results when a time series is contaminated with a high noise level. Considering this fact, this paper proposes an improved SSA (ISSA) that modifies the determination method of subseries selection in the reconstruction stage of SSA. Combining ISSA with long short-term memory (LSTM), a novel hybrid model, ISSA-LSTM, is thus developed. Additionally, for validating the robustness and superiority of ISSA-LSTM, the historical datasets of four representative cities located in three climate zones are collected as the training and testing datasets, and a comparison of ISSA-LSTM with five advanced models is performed. The results reveal that SSA would generate negative values when time series close to zero and the contribution of SSA in improving the forecasting accuracy of LSTM is insignificant. In contrast, ISSA avoids generating negative values and reduces the mean absolute range normalized error (MARNE) of LSTM by a range of 0.86–11.86%. Among the models, ISSA-LSTM achieves the best performance and its MARNEs for London (temperate zone), Melbourne (subtropical zone), Karditsa (subtropical zone), and Hong Kong (tropical zone) are 4.68%, 5.72%, 5.76%, and 14.10%, respectively. The MARNE of the tropical city is higher than that of others, which is caused by the complex natural gas consumption pattern of itself. [ABSTRACT FROM AUTHOR]

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli's (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell's (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field. [ABSTRACT FROM AUTHOR]

Much more than their machinic reality, current iterations of AI rely on imagined divisions of human and non-human properties and skills that have genealogical ties to colonization. For this reason, research efforts have recently been made to historicize these imaginaries, connecting them to colonial ideals that delegate black and brown colonized people into the realm of the non-human. Atanasoski and Vora (Surrogate humanity. Race, robots and the politics of technological futures, Duke, Durham and London, 2019) have called this a "surrogate humanity", where narratives of autonomous technologies function to disappear precisely the formerly colonized peoples that are enveloped in its production process. At the same time, the gendered and racialized roots of this maternal figure represents an opportunity to uncover and critique the invisibilization of embodied resources necessary to produce AI, precarious bodies labouring to produce algorithmic infrastructures in a manner that can be considered in a genealogy of carework and reproduction. These genealogies complicate the detachment suggested by the surrogate figure and go beyond it to proclaim a more generative function of the relationship between the black maternal figure and AI. The article analyses Tabita Rezaire's multi-media artwork Sugar Walls Teardom to think through decolonial and queer renderings of the black female bodies upon which technological imaginaries rest, to extend beyond AI surrogacy and towards notions of kinship, care and world-making by producing an AI aesthetics that is relational, embodied, and celebratory of other ways of liveness. [ABSTRACT FROM AUTHOR]

The article invites researchers to participate in the EKAW 91 Fifth European Knowledge Acquistion for Knowledge-Based Systems Workshop to be held in Crieff, Scotland on May 20-24, 1991 and in London, England on May 17, 1991. It is the objective of EKAW 91 to assemble theoreticians and practitioners of artificial intelligence who recognise the need for developing methods and systems to assist the preocess of acquiring and modeling knowledge for knowledge-based systems. To encourage vigorous inteaction and exchange of ideas the workshop will be limited to 40 participants.

Skin cancers are the most common cancers in the UK. Malignant Melanoma (MM) is the most dangerous type of skin cancer. Primary Care Physicians (GPs) often have difficulty in distinguishing harmless skin blemishes from MM, leading to many patients being referred to dermatology departments. There have been enormous advances recently in the application of computer‐based Artificial Intelligence (AI) to the field of image analysis. This paper from four authors in London (UK) explores the current position of AI systems in analysing skin lesions (affected patches of skin). Important AI concepts such as convolutional neural networks (CNN) and "deep learning" are explained. By exposing AI systems to hundreds of thousands of pictures of skin lesions, it is possible to "train" them to recognise lesions with a high degree of accuracy. Skin cancer detection, particularly melanoma detection, has now been tested by numerous different groups with multiple AI systems; the most successful ones are generally "convolutional neural networks". When comparing their performance to dermatologists who are presented with photographs of skin lesions, these AI systems perform similarly, if not better, at identifying whether something is a melanoma or a benign (non‐cancerous) mole. However, these programmes are limited by the data sets on which they have been "trained"; for example, a lot of the datasets are trained on Caucasian skin, and will perform less well when detecting melanoma in skin of colour, as they may not have been exposed to enough of this type of image to perform well. They have also not been shown to be particularly useful for diagnosing other types of skin conditions yet and they are not able to provide any kind of explanation for their classification. Because of these limitations, it is unlikely that they will replace dermatologists. The legal framework for AI systems also doesn't allow them to take responsibility for decisions, so responsibility for diagnosis still falls to a responsible clinician. However, in the near future they may well prove to be extremely useful in aiding GPs in making clinical decisions about skin lesions, particularly with regards to urgent referrals to dermatology. Linked Article: Du-Harpur et al. Br J Dermatol 2020; 183:423–430. Linked Article: Du-Harpur et al. Br J Dermatol 2020; 183:423–430. [ABSTRACT FROM AUTHOR]

The study utilises the International Labor Organization's SMEs COVID-19 pandemic business risks scale to determine whether Artificial Intelligence (AI) applications are associated with reduced business risks for SMEs. A new 10-item scale was developed to capture the use of AI applications in core services such as marketing and sales, pricing and cash flow. Data were collected from 317 SMEs between April and June 2020, with follow-up data gathered between October and December 2020 in London, England. AI applications to target consumers online, offer cash flow forecasting and facilitate HR activities are associated with reduced business risks caused by the COVID-19 pandemic for both small and medium enterprises. The study indicates that AI enables SMEs to boost their dynamic capabilities by leveraging technology to meet new types of demand, move at speed to pivot business operations, boost efficiency and thus, reduce their business risks. [ABSTRACT FROM AUTHOR]

Licensed London taxi drivers have been found to show changes in the gray matter density of their hippocampus over the course of training and decades of navigation in London (UK). This has been linked to their learning and using of the "Knowledge of London," the names and layout of over 26,000 streets and thousands of points of interest in London. Here we review past behavioral and neuroimaging studies of London taxi drivers, covering the structural differences in hippocampal gray matter density and brain dynamics associated with navigating London. We examine the process by which they learn the layout of London, detailing the key learning steps: systematic study of maps, travel on selected overlapping routes, the mental visualization of places and the optimal use of subgoals. Our analysis provides the first map of the street network covered by the routes used to learn the network, allowing insight into where there are gaps in this network. The methods described could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence systems to efficiently learn new environments. [ABSTRACT FROM AUTHOR]

In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient's therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results. [ABSTRACT FROM AUTHOR]

Background: The rapid uptake of digital technology into the operating room has the potential to improve patient outcomes, increase efficiency of the use of operating rooms, and allow surgeons to progress quickly up learning curves. These technologies are, however, dependent on huge amounts of data, and the consequences of their mismanagement are significant. While the field of artificial intelligence ethics is able to provide a broad framework for those designing and implementing these technologies into the operating room, there is a need to determine and address the ethical and data governance challenges of using digital technology in this unique environment. Objective: The objectives of this study are to define the term digital surgery and gain expert consensus on the key ethical and data governance issues, barriers, and future research goals of the use of artificial intelligence in surgery. Methods: Experts from the fields of surgery, ethics and law, policy, artificial intelligence, and industry will be invited to participate in a 4-round consensus Delphi exercise. In the first round, participants will supply free-text responses across 4 key domains: ethics, data governance, barriers, and future research goals. They will also be asked to provide their understanding of the term digital surgery. In subsequent rounds, statements will be grouped, and participants will be asked to rate the importance of each issue on a 9-point Likert scale ranging from 1 (not at all important) to 9 (critically important). Consensus is defined a priori as a score of 7 to 9 by 70% of respondents and 1 to 3 by less than 30% of respondents. A final online meeting round will be held to discuss inclusion of statements and draft a consensus document. Results: Full ethical approval has been obtained for the study by the local research ethics committee at Imperial College, London (20IC6136). We anticipate round 1 to commence in January 2021. Conclusions: The results of this study will define the term digital surgery, identify the key issues and barriers, and shape future research in this area. International Registered Report Identifier (IRRID): PRR1-10.2196/26552 JMIR Res Protoc 2021;10(2):e26552 doi:10.2196/26552 We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications. Submit Suggested contribution levels: $20/$50/$100 [ABSTRACT FROM AUTHOR]

What are the socio-economic effects of the widespread introduction of robots, algorithms and digital technologies like artificial intelligence and machine learning? Following Frey and Osborne (London futures agiletown : the relentless march of technology and London's response. Deloitte, 2014, Technol Forecast Social Change 114(C), 254–280, 2017, https://doi.org/10.1016/j.techfore.2016.08.019) we apply the computerization probabilities to occupations in Austria. We conclude that about 40% of the Austrian workforce is active in occupations that are very likely to undergo substantial changes regarding task structure, skill requirement and working environment in the future, causing challenges and opportunities. We also provide evidence that compared to men, women in Austria seem more likely to be affected by technological changes, with sectoral orientation playing a role. Following EBRD (Skills, employment and automation. Chapter 2 in: EBRD (2018): Transition Report 2018–19, European Bank for Reconstruction and Development, London, 2018),we see a broader move towards job polarization. We see this as distributive consequences of technological change and argue that the consequences of technology refashioning socio-economic development are influencing market processes, actors and inequalities. As in previous technological advances, coping with these changes will require efforts on the individual as well as on the political level. [ABSTRACT FROM AUTHOR]

BCS' London offices recently held a retrospective exhibition of digital artist Paul Brown, winner of the ACM SIGGRAPH Distinguished Artist Award for Lifetime Achievement in Digital Art 2023 for his pioneering efforts in the fields of computational art, generative systems, and cellular automata. Georgia Smith took the opportunity to chat with him about his career, the CAS, and his thoughts on the future of AI and the digital arts. [ABSTRACT FROM AUTHOR]

Summary: Iron deficiency anaemia (IDA) is the most common cause of anaemia and a frequent indication for colonoscopy, although the prevalence of colorectal cancer (CRC) in IDA is low. Measurement of faecal haemoglobin by immunochemical techniques (FIT) is used to detect symptomatic patients. We studied FIT in patients with anaemia attending a gastroenterology clinic in Plymouth and looked at an artificial intelligence (AI) learning algorithm (ColonFlag™) in these patients, together with a cohort who had undergone colonoscopy for IDA in London. Of 592 patients referred on the basis of haemoglobin concentration, 21 (3.5%) had CRC. Using ColonFlag™, rather than haemoglobin concentration, in combination with symptoms, would have resulted in prioritisation of 304 patients for urgent referral rather than 592. One CRC would have been missed but might have been detected by FIT, which was not available in this case. In patients aged <55 years in whom the incidence of CRC is low, 15 rather than 109 patients would have been prioritised for urgent referral with no cancers missed. FIT has a high negative predictive value in IDA so its use may enable some patients to avoid investigation and AI learning may be a more useful trigger than haemoglobin concentration for urgent referral for colonoscopy. [ABSTRACT FROM AUTHOR]

Integration of algorithms of investment theory and artificial intelligence allows one to create a support system for investors in exchange markets based on the ensemble of long-short-termmemory (LSTM) based recurrent neural networks (RNN). The proposed support system contains five stages: preparation of historical data, prediction by an ensemble of LSTM RNNs, assessment of prediction distributions, investment portfolio formation and verification. The prediction process outputs a multi-modal distribution, which provides useful information for investors. The research compares four different strategies based on a combination of distribution forecasting models. The high-low strategy helps decision-makers in exchange markets to recognize signals of transactions and fix limits for expectations. A combination of high-low-daily-weekly predictions helps investors to make daily transactions with knowing distribution of exchange rates during the week. The shift in time of five hours between London and New York inspired us to create a UK-NY strategy, which allows investors to recognize the signals of the market in a very short time. The joined high-low-UK-NY strategy increases the possibility of recognizing the signals of transactions in a very short time and of fixing the limits for day trading. So, this support system for investors is verified as a profitable tool for speculators in the relatively risky currency market. [ABSTRACT FROM AUTHOR]

The author reflects on the centenary of the birth of computer pioneer, code-breaker, and polymath Alan Turing who was born in London, England on June 23, 1912. He mentions the accomplishments of Turing who was also a marathon runner with a time of two hours and 46 minutes and notes his contributions to the fields of human, artificial, and military intelligence. He also cites article by mathematician Andrew Hodges which provides an account of Turing's role in the development of computers.

The Tower of London Test (TOL) used to assess executive functions was inspired in Artificial Intelligence tasks used to test problem-solving algorithms. In this study, we compare the performance of humans and of exploration algorithms. Instead of absolute execution times, we focus on how the execution time varies with the tasks and/or the number of moves. This approach used in Algorithmic Complexity provides a fair comparison between humans and computers, although humans are several orders of magnitude slower. On easy tasks (1 to 5 moves), healthy elderly persons performed like exploration algorithms using bounded memory resources, i.e., the execution time grew exponentially with the number of moves. This result was replicated with a group of healthy young participants. However, for difficult tasks (5 to 8 moves) the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially. A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result. The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test. [ABSTRACT FROM AUTHOR]