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Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

The article provides a preview of forthcoming papers accepted for publication in the Health Information and Libraries Journal. Topics include the effectiveness of citation searching in urgent and emergency care configuration studies, an alternative screening approach for Google Search in systematic reviews, and the analysis of health information seeking behavior among adolescents.

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer‐reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.Practitioner notesWhat is already known about this topicEmotions play a pivotal role in shaping the interaction process, making it essential to consider them when designing methodological strategies or learning tools.Empathic Pedagogical Conversational Agents (PCAs) have emerged as a crucial approach for enhancing and personalizing the learning experience (24/7) for pupils and supporting human teachers in their teaching process.Despite the creation of numerous empathic PCAs, there is a scarcity of Systematic Literature Reviews (SLRs) on their application in the educational field, particularly concerning the integration of emotional abilities in combination with the competencies of each subject.What this paper addsIt offers new insights into the design principles underlying the integration of the empathic field.It reviews different approaches for incorporating students' prior knowledge in real time.It provides a comprehensive and up‐to‐date overview of the research designs used for implementation, including quantitative, qualitative and mixed methods.It examines the factors that influence the effectiveness of empathic PCA in teaching and learning.It evaluates the types of feedback that enhance the impact of the empathic field on learning outcomes.Implications for practice and/or policyIt is crucial to grasp the topics that this paper introduces in order to effectively integrate new learning tools into any context.Techno‐pedagogical designers seeking to gain insights into empathic PCAs will find immense value in this SLR, as it comprehensively covers each stage of the process.For future research endeavours, this study offers a wealth of ideas to draw upon, enabling researchers to address the challenges outlined and explore new avenues of investigation. [ABSTRACT FROM AUTHOR]

In the ever‐evolving AI‐driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre‐service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre‐service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre‐service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self‐Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre‐service STEM teachers' willingness to integrate AI into their teaching practices. Practitioner notes What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology is constantly developing and changing. Due to the continuous development of technology and production of carbon emissions, various problems have occurred, forcing various regions to start promoting low‐carbon work. What kind of method can effectively and quickly improve the performance of carbon emissions has become one of the current research topics that has attracted much attention. In response to this problem, it is of great significance to study in the field of carbon emission performance improvement methods. With the in‐depth research on carbon emissions, the research on the intervention of water conservancy policies in carbon emissions has gradually been carried out, and its functional effects are of great significance to solve the problem of improving carbon emissions performance. This paper has aimed to study the impact and mechanism of water conservancy policy on carbon emission performance in the context of artificial intelligence. Through the analysis and research of the water conservancy policy under artificial intelligence, the double difference model (DID) is used to evaluate it, so that it can show the impact and mechanism on carbon emission performance, so as to solve the problem of improving carbon emission performance. This paper has analysed water policy, carbon emission performance and DID model under artificial intelligence. Its influence and mechanism have been experimentally analysed, and related theoretical formulas have been used to explain. The results have shown that the water conservancy policy has a significant and continuous driving effect on the reduction of carbon emission intensity, and its interaction coefficient under the control variable is −0.934, which is significantly negative. Further analysis of variables has shown that the energy intensity coefficient is 0.714, and the secondary industry coefficient is 0.924, which are both positive and significant. Water conservancy policies mainly reduce carbon intensity through the improvement of energy efficiency and industrial structure upgrading. It can be seen that water conservancy policies under artificial intelligence can meet the needs of improving carbon emission performance, and the level of economic growth and green development has been greatly improved. [ABSTRACT FROM AUTHOR]

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well‐known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI‐based models for drug–target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development. [ABSTRACT FROM AUTHOR]

Background: Driven by the ongoing need to provide high‐quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to evaluate the maturity of research discussing LA‐driven LD improvements in higher education. Methods: The systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA‐LD integration theoretical backgrounds) and substantial (LA‐driven LD improvements, types of data used, LA software development) features of the papers. Results and Conclusions: The findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments. Takeaways: The research area discussing LA‐driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA‐driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software. Lay Description: What is already known about this topic: Universities show an increased interest in using LA for improving LD.The evidence of such improvements being efficient is scarce.Learning analytics is weakly grounded in learning and teaching. What this paper adds: LA‐LD research presented in the reviewed papers cannot yet be considered fully mature.Almost half of the research was carried out using a case study approach, intrinsically challenging in terms of replication and validity. As few as three out of the reviewed studies applied an experimental approach, capable of stating the cause–effect relationship between LA and LD.All the studied papers mentioned improvements in LD, but only around a third of them reported actual improvements, another one‐third suggested potential improvements, and the rest did not come down to describing any.The LA software presented in the papers was mostly at the testing or implementation stages, and the impact of implemented software on LD has yet to be evaluated. Implications for practice and/or policy: Future LA‐LD research to become more mature may well elaborate on theoretical footing, contribute to the cause–effect relationship between LA and LD utilising experimental research design, ensure sufficient sample size.Practitioners' efforts should be directed to operationalising LD for efficient measurements, addressing pedagogical challenges when applying LA, and assessing LA‐driven improvements introduced into LD. [ABSTRACT FROM AUTHOR]

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead‐times of producing high‐quality peer‐reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health‐related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health‐ and computer‐science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single‐screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health‐related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research. [ABSTRACT FROM AUTHOR]

Background: The emergence of the artificial intelligence chatbot ChatGPT in November 2022 has garnered substantial attention across diverse disciplines. Despite widespread adoption in various sectors, the exploration of its application in libraries, especially within the medical domain, remains limited. Aims/Objectives: Many areas of interest remain unexplored like ChatGPT in medical libraries and this review aims to synthesise what is currently known about it to identify gaps and stimulate further research. Methods: Employing Cooper's integrative review method, this study involves a comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts. Results: A systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. After abstract reviews and methodological assessments, 136 articles were selected. Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers, forming the basis for the present study. The literature analysis reveals diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations and facilitating access to resources. Potential challenges and ethical considerations associated with ChatGPT in this context are also highlighted. Conclusion: Positioned as a review, our study elucidates the applications of ChatGPT in medical libraries and discusses relevant considerations. The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community. [ABSTRACT FROM AUTHOR]

This study investigated the added value of real‐time adaptive feedback on seventh graders' performances in tablet‐based geometry learning. To isolate the effects of the medium (ie, tablet) from those of the feedback, three groups were compared: paper‐and‐pencil, pen‐based tablet without feedback and pen‐based tablet with feedback. The feedback was provided by a tutoring system based on an artificial intelligence that automatically interpreted students' pen strokes on the screen. A total of 85 French students drew three geometric shapes, either on paper or on a tablet, and then performed a transfer task on paper. Results showed that using a tablet without feedback did not improve learning but seemed to enhance interest in the task compared to the paper‐and‐pencil group. Students in the tablet with feedback group performed significantly better than the other two groups on learning, as well as on transfer. This study was the first to combine media comparison and added‐value approaches to test the effects on students' geometry performances of using a new educational app on a pen‐based tablet in a naturalistic classroom environment. Results showed that it was not the medium used but the intelligent tutoring system‐based feedback that improved students' performance. Our data therefore indicate that artificial intelligence is a promising way of providing learners with real‐time adaptive feedback in order to improve their performances. Practitioner notes What is already known about this topic Previous meta‐analyses have investigated the effects of tablet‐based learning. Tablet computers have been proven to increase students' motivation. Yet, the influence of tablet computers on learning outcomes remains inconclusive. Other studies show that certain features of environments, such as feedback, have positive effects on learning. What this paper adds Most of the previous studies adopted a media comparison approach (paper‐ vs. tablet‐based instruction). We combine this approach with an added‐value approach by adding or not real‐time AI‐based feedback. Results showed that tablet use increased children's interest but not their learning outcomes. Feedback improved children's performance in a training task and a later transfer paper task. Implications for practice and/or policy Tablet computers can promote students' interest in the task during geometry instruction. App features play a critical role in improving students' learning. Specifically, IA‐based adaptive feedback helps children to perform better on a geometry task. What is already known about this topic Previous meta‐analyses have investigated the effects of tablet‐based learning. Tablet computers have been proven to increase students' motivation. Yet, the influence of tablet computers on learning outcomes remains inconclusive. Other studies show that certain features of environments, such as feedback, have positive effects on learning. What this paper adds Most of the previous studies adopted a media comparison approach (paper‐ vs. tablet‐based instruction). We combine this approach with an added‐value approach by adding or not real‐time AI‐based feedback. Results showed that tablet use increased children's interest but not their learning outcomes. Feedback improved children's performance in a training task and a later transfer paper task. Implications for practice and/or policy Tablet computers can promote students' interest in the task during geometry instruction. App features play a critical role in improving students' learning. Specifically, IA‐based adaptive feedback helps children to perform better on a geometry task. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison. [ABSTRACT FROM AUTHOR]

Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation. [ABSTRACT FROM AUTHOR]

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

The article provides an overview of the journal Molecular Ecology (MEC) and its achievements in the fields of ecology and evolution. It discusses the journal's high ranking and impact factor, as well as its policies on data and code archiving, indigenous perspectives, and the use of artificial intelligence. The article also mentions the journal's social media presence and blog. It concludes with information about prizes and notable papers published in the journal. The text discusses various research articles, opinion papers, and special issues covering a range of topics in molecular ecology. The journal aims to provide valuable insights and promote discussion in the field. The given text is an editorial written by several authors, but no information about its content is provided. [Extracted from the article]

Artificially intelligent systems (AISs) are being created by software developing companies (SDCs) to influence clinical decision‐making. Historically, clinicians have led healthcare decision‐making, and the introduction of AISs makes SDCs novel actors in the clinical decision‐making space. Although these AISs are intended to influence a clinician's decision‐making, SDCs have been clear that clinicians are in fact the final decision‐makers in clinical care, and that AISs can only inform their decisions. As such, the default position is that clinicians should hold responsibility for the outcomes of the use of AISs. This is not the case when an AIS has influenced a clinician's judgement and their subsequent decision. In this paper, we argue that this is an imbalanced and unjust position, and that careful thought needs to go into how personal moral responsibility for the use of AISs in clinical decision‐making should be attributed. This paper employs and examines the difference between prospective and retrospective responsibility and considers foreseeability as key in determining how personal moral responsibility can be justly attributed. This leads us to the view that moral responsibility for the outcomes of using AISs in healthcare ought to be shared by the clinical users and SDCs. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions ChatGPT's role in medical writing is a topic of discussion. I experimented whether ChatGPT almost automatically generates Correspondence or Letter addressed to a “translated” article, and thereby wish to arouse discussion regarding ChatGPT use in medical writing.I input an English article of mine into ChatGPT, tasking it with generating an English Disagreement Letter (Letter 1). Next, I tasked ChatGPT with translating the manuscript addressed to from English‐French‐Spanish‐German. Then, I once again tasked ChatGPT with generating an English Disagreement Letter addressed to a German manuscript (triplicate translated manuscript) (Letter 2).Letters 1 and 2 are readable and reasonable, shooting the point that the author (myself) felt as the weakness of the article. Letters addressed to French (single translation) and to Spanish (double translation) and longer Letters (corresponding to Letters 1 and 2) are also readable, and thus stand.Solely based on this experiment, one may be able to write a letter even without understanding the meaning of the paper being addressed, let alone the language of the paper. Although this humble experiment does not conclude anything, I plea for a comprehensive discussion on the implications of these findings. [ABSTRACT FROM AUTHOR]

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the center stage of philosophical debates about cognition. This development is directly relevant to long‐standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.This article is categorized under: Philosophy > Artificial Intelligence Computer Science and Robotics > Machine Learning [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is influencing different aspects of human life. An AI‐powered technology, which has been recently released, is ChatGPT. It is a cutting‐edge technology that influences second/foreign language (L2) education. Although there is increasing research on the benefits and misfits of this chatbot in different disciplines, L2 education lacks a thorough investigation. To fill this lacuna, this phenomenographic study examined the perceptions of research‐active English as a Foreign Language (EFL) teachers regarding the potentials and pitfalls of ChatGPT for L2 learning, teaching, assessment, and research. To this end, a semistructured interview was held with 30 Iranian EFL teachers with varying educational backgrounds and AI integration experiences. The results of content and thematic analysis indicated that ChatGPT is a double‐edged sword that can both benefit and hurt these areas of L2 education. The most notable potentials were augmenting learner autonomy, providing personalized learning, reducing teachers’ teaching workload, designing assessment rubrics, and summarizing lengthy papers and theses to save L2 researchers’ time and energy. Concerning pitfalls, it was reported that ChatGPT might kill creativity and academic integrity, encourage cheating in online exams, spread fake and misinformation into the world of research, and cherish high‐tech plagiarism. Some practical suggestions are made to empower L2 educators and researchers to survive in the world of AI. [ABSTRACT FROM AUTHOR]

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction. [ABSTRACT FROM AUTHOR]

Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Most existing multi‐view stereo (MVS) methods fail to consider global context information in the stage of feature extraction and cost aggregation. As transformers have shown remarkable performance on various vision tasks due to their ability to perceive global contextual information, this paper proposes a transformer‐based feature enhancement network (TF‐MVSNet) to facilitate feature representation learning by combining local features (both 2D and 3D) with long‐range contextual information. To reduce memory consumption of feature matching, the cross‐attention mechanism is leveraged to efficiently construct 3D cost volumes under the epipolar constraint. Additionally, a colour‐guided network is designed to refine depth maps at a coarse stage, hence reducing incorrect depth predictions at a fine stage. Extensive experiments were performed on the DTU dataset and Tanks and Temples (T&T) benchmark and results are reported. [ABSTRACT FROM AUTHOR]

Artificial neural network‐based computing is anticipated to surpass the von Neumann bottleneck of traditional computers, thus dramatically boosting computational efficiency and showing a wide range of promising applications. In this paper, sol−gel deposition was used to prepare thin films of samarium‐doped hafnium dioxide (Sm:HfO2). When Sm is doped at a concentration of 4%, it mimics biological synapses; meantime, by voltage scanning, an obvious mimicry of resistive switching can be detected, demonstrating that the technology may be applied to simulate biological synapse characteristics, including long‐term potentiation (depression), short‐term potentiation (depression), paired‐pulse facilitation, and learning rules of spike‐time‐dependent plasticity. Additionally, a pulsed neural network is built on the MNIST dataset to test the memristor's capacity to handle visual input. The findings show the possibility of synthetic synapses in artificial intelligence systems that integrate neuromorphic computing with synaptic brain activity. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy.Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy.Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes.What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions.Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy.Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy.Practitioners are able to use the AILQ to assess students' AI literacy development. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notesWhat is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.Describes how we adapted six design methods to design AI features.Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.Our work also includes a reflection on our design process and implications for future design processes. [ABSTRACT FROM AUTHOR]

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,…), the results either lack variety or ease of control.In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. [ABSTRACT FROM AUTHOR]

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

Deep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type of deep generative models, called conditional Generative Adversarial Networks (cGAN), has been used since a few years in the context of image‐to‐image (I2I) translation on solar observations. These algorithms have however hyperparameters whose values might influence the quality of the synthetic image. In this work, we use magnetograms produced by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and EUV images from the Atmospheric Imaging Assembly (AIA) for the problem of generating Artificial Intelligence (AI) synthetic magnetograms from multiple SDO/AIA channels using cGAN, and more precisely the Pix2PixCC algorithm. We perform a systematic study of the most important hyperparameters to investigate which hyperparameter might generate magnetograms of highest quality with respect to the Structural Similarity Index. We propose a structured way to perform training with various hyperparameter values, and provide diagnostic and visualization tools of the generated versus targeted image. Our results shows that when using a larger number of filters in the convolution blocks of the cGAN, the fine details in the generated magnetogram are better reconstructed. Adding several input channels besides the 304 Å channel does not improve the quality of generated magnetogram, but the hyperparameters controlling the relative importance of the different loss functions in the optimization process have an influence on the quality of the results. Plain Language Summary: Performance of space weather forecasting methods relies on the availability of data to be ingested in physical models, which is scarce in space weather as compared to terrestrial weather. Since a few years, deep learning methods have produced algorithms capable of performing image‐to‐image‐translation that are used in solar physics to mitigate this scarcity issue. In this work, we consider a deep generative model called the Pix2PixCC algorithm, which is based on conditional Generative Adversarial Networks. One needs to fix some hyperparameters in the Pix2PixCC algorithm, such as for example, the number of filters used to build the features that characterize the information contained in the data. Our aim in this paper is to make an extensive study of these hyperparameters. We propose a structured way to perform training with various hyperparameter values and provide diagnostic and visualization tools of the generated versus real image. Our results show that a higher number of filters will improve the quality of the synthetic magnetogram and that the respective contribution of each loss function in the global objective function to be minimized also has an influence on the quality of the result. Key Points: We study an image to image translation algorithm that generates solar vector magnetograms from multichannel extreme ultraviolet observationsWe provide insight into the architecture and optimization of the Pix2PixCC algorithm, a conditional Generative Adversarial NetworkOur experimental design highlights relevant hyperparameters, and our interactive tool analyses the quality of the generated magnetograms [ABSTRACT FROM AUTHOR]

The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved. [ABSTRACT FROM AUTHOR]

Background: The increasing prevalence of Artificial Intelligence (AI) language models, exemplified by ChatGPT, has sparked inquiries into their influence on creative writing skills in educational contexts. This study aims to quantitatively investigate whether ChatGPT's use negatively affects university students' creative writing abilities, focusing on originality, content presentation, accuracy, and elaboration in essays. The research adopts an experimental approach to shed light on this concern. Objective: This study aims to quantitatively investigate whether the utilization of ChatGPT, an AI chatbot, adversely affects specific dimensions of creative writing skills among university students, with an emphasis on originality, content presentation, accuracy, and elaboration. Method: The experimental study involves 600 students from 10 universities, divided into a control and an experimental group (EGp). The EGp incorporates ChatGPT in their creative writing process as an intervention. The study evaluates originality, content presentation, accuracy, and elaboration, utilizing the Wilcoxon Signed‐Rank Test for analysis. Results and Conclusion: The findings reveal a detrimental association between ChatGPT use and university students' creative writing abilities. Analysing both machine‐based and human‐based assessments substantiates earlier qualitative observations regarding ChatGPT's adverse impact on creative writing. This study highlights the necessity of approaching AI integration, particularly in creative writing disciplines, with caution. While AI tools have merits, their integration should be thoughtful, considering the potential drawbacks. These insights inform future research and educational practices, guiding the effective incorporation of AI while nurturing students' writing skills. Lay Description: What is already known about this topic: ChatGPT poses an ethical dilemma regarding its use in the field of academiaQualitative claims and opinions have been raised in prior studies regarding its use in the creative writing processPrior studies have both supported and opposed its use but with very limited quantitative approaches while most of the opinions remain qualitativeSome prior studies opine in support of ChatGPT's ability as an authorSeveral factors measuring creativity has been identified by previous studies but a constructive approach in the light of advanced Artificial Intelligence (AI) based chatbots like ChatGPT is missing in such literature What this paper adds: An experimental approach to provide a valid quantitative proof of the qualitative claims over ChatGPT's detrimental effect towards creativity in writing, which was absent in prior studiesA multifactor‐based formula to measure creativity in a quantitative formA quantitative view of the factors that are affected in either a positive way or a negative way in a user by ChatGPT, providing a holistic picture to determine its extent of useA statistical and theoretical understanding over an unexplored topic like creative writing in the light of ChatGPTA quantitative proof why ChatGPT should not be considered as an author Implications for practice and/or policy: Educators may implement changes in assigning tasks to students compared to their earlier practices, based on the identified factors that are being affected negatively, to ensure ChatGPT does not hinder a student's creativity at a greater extentThe extent of using ChatGPT should be limited to self‐learning as positive effect was experienced through the experimentPolicymakers may use the findings of the study to impose strict policies in academia for ensuring academic integrity (Example: must use of plagiarism detecting software for checking scripts, assigning tasks to students which require more analytical abilities, providing tasks which are not properly readable by LLM's like ChatGPT such as image‐based questions, case studies etc.) [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by investigating how children were engaged in AI literacy activities that are supported by intelligent agents. These activities were implemented in a Hong Kong kindergarten with a class of six 5‐year‐olds (Mage = 62.83 months, SD = 2.91) over a 6‐week period. Methods: The study gathered data from multiple sources, including classroom observations, teacher interviews, and documents/artefacts. Results and Conclusions: The results showed that children could learn about AI through interaction with intelligent agents in embodied learning contexts. The findings of this study have implications for the broader field of digital technology education, particularly in the context of early childhood education. Lay Description: What is already known about this topic: Artificial Intelligence (AI) literacy is essential in today's world.Little research exists on engaging young children in learning AI literacy. What this paper adds: Technology‐enhanced embodied learning approach affords multimodality in enabling young children's learning with AI‐related agents.Project‐based learning and real‐life examples can enhance children's learning. Implications for practice and/or policy: Early exposure to AI can help children navigate the increasingly digital world.Practical approaches, such as embodied and multimodal learning, seem to be effective.Teachers can enhance children's digital learning by using pedagogical strategies like project‐based learning and real‐life examples. [ABSTRACT FROM AUTHOR]

The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

This paper explains the importance and benefits of using AI in fluid mechanics, emphasizing its capability of finding the solution of fluid flow systems through iterative optimization techniques. It explores how AI can enhance the understanding of fluid dynamics to improve engineering processes. In the presented studies, the heat and mass transport of the Casson nanofluidic flow model on a nonlinear slanted extending sheet (HMT‐CNFM) via AI‐based Levenberg Marquard methodology with backpropagated trained neural networks (TNN‐BLMM) is studied. By applying an appropriate transformation, the governing PDEs representing HMT‐CNFM are converted into a system of nonlinear ODEs. The dataset for Levenberg Marquard methodology with backpropagated trained neural network (TNN‐BLMM) for all six scenarios of this proposed model via computational power of the Lobatto IIIA scheme using the "bvp4c" package in MATLAB and then graphically illustrate all these six scenarios through nftool to attain mean square error, regression, error histogram, performance, and fit curve. Training, testing, and validation processes of NN‐BLMM are organized for the investigation of the HMT‐CNFM model. [ABSTRACT FROM AUTHOR]

Data extraction is a time‐consuming and resource‐intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently. [ABSTRACT FROM AUTHOR]

Software‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%. [ABSTRACT FROM AUTHOR]

Multiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests. [ABSTRACT FROM AUTHOR]

Parametric modeling and generative deep learning for bridge design Given the significant environmental impact of the construction industry, the analysis and, above all, optimization of the sustainability of structures while maintaining established levels of reliability are becoming increasingly important. While there are initial tools for life cycle analysis in the building sector, these are not directly transferable to bridge structures. This paper introduces a deep learning‐based approach to explore and model the design space of parametric bridge models and their performance evaluations, illustrating its application for multi‐objective optimization of reinforced concrete frame bridges. Initially, data is synthetically generated using a parametric bridge model and coupling analysis software, followed by training a conditional variational autoencoder (CVAE) as a metamodel. In the context of conceptual bridge design, the CVAE serves as an efficient co‐pilot for both forward and inverse analysis. The sensitivity analysis performed with the CVAE reveals relationships between design parameters and/or performance metrics, highlighting optimization potentials. The integrated framework presented here has the potential to realize efficient bridge design, particularly focusing on sustainability and structural safety criteria, and can be easily extended to other parametric inquiries. [ABSTRACT FROM AUTHOR]

Image recognition methods for the partial inspection of fire safety equipment – a study considering machine learning techniques and the integration of expert knowledge This paper explores the potential application of artificial intelligence (AI) and computer vision in the context of Building Information Modeling (BIM) for documenting fire safety equipment. Detection algorithms, particularly current You Only Look Once (YOLO) models, are utilized to automatically identify fire safety equipment such as fire extinguishers, fire blankets, smoke detectors, and fire alarms in images. The results demonstrate high detection accuracy in both images. Future work aims to expand automated detection by directly integrating it into BIM models. In addition, the importance of expert knowledge in determining the extinguishing agent type of a fire extinguisher is addressed to verify and, if necessary, correct the prediction of an AI‐based fire safety inspection. The developed AI applications aim to increase the level of automation in fire safety inspections to improve the safety of buildings in the operational phase. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Objective: In community health, there is great demand but limited time for the delivery of staff and patient education. During the COVID‐19 pandemic, evolving needs necessitated that health education be provided in an accurate and timely manner. This paper describes the development, implementation, and evaluation of a YouTube channel designed to disseminate health education to a wide audience of public health workers and patient populations. Methods: This project, divided into three phases, originated within shelter‐based care, providing education to shelter staff (Phase 1) and overtime has evolved to provide education within the Community Health Worker Hub at a major teaching hospital for community health workers (Phase 2) and the populations they serve (Phase 3). Further, during phase 3, the project developer used an artificial intelligence (AI) platform to increase the reach of the YouTube channel. Results: Over a span of 21 months, 18 unique videos have garnered 489 views. Conclusions: Clinicians and educators can leverage technology platforms for health education delivery, increasing the reach of their work while meeting the demands of the profession. While the quality of some information on YouTube may be poor, patients and students turn to this platform for health education. It is imperative that public health nurses embrace this medium, rather than push against it. By creating high‐quality content, educating students and patients about DISCERN and PEMAT tools, and guiding patients to credible sources, public health nurses may ameliorate the standards of health education on YouTube. [ABSTRACT FROM AUTHOR]

This paper aims to explore the possibility of employing large language models (LLMs) – a type of artificial intelligence (AI) – in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation and potential risk reduction measures are analysed. The existing literature is reviewed to investigate the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the OECD.ai platform was used. The review highlights the dual‐use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework. The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual‐use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability and safety. Additionally, AI's potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks. [ABSTRACT FROM AUTHOR]

Concerns about runaway artificial intelligence (AI) – including large language models (LLMs) like ChatGPT – are at the forefront of contemporary political, social, and scientific discourse. This commentary provides a first look at ChatGPT's capabilities and limitations in supporting geographic research, critical thinking, learning, and curriculum development. We assessed ChatGPT's geographic knowledge, synthesising abilities, and potential for extrapolation. ChatGPT was employed for writing assistance, research evaluation, curriculum material creation, and content generation. Despite achieving scores of 47% to 55% on an actual exam paper, ChatGPT exhibited shortcomings including the generation of false references. Ethical concerns regarding academic misconduct, model bias, robustness, and toxic output were also identified. We assert that AI and LLMs like ChatGPT have transformative potential in Geography education and knowledge production but demand critical usage. Accordingly, we urge geographers to enhance AI literacy to enable responsible and effective use of these assistive technologies in our academic practice. This commentary provides a first look at ChatGPT's capabilities and limitations in supporting geographic research, critical thinking, learning, and curriculum development. We assessed ChatGPT's geographic knowledge, synthesising abilities, and potential for extrapolation as well as its use as a supportive technology for writing assistance, research evaluation, curriculum material creation, and content generation. We close by urging geographers to enhance AI literacy to enable responsible and effective use of these assistive technologies in our academic practice. [ABSTRACT FROM AUTHOR]