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ChatGPT, a general artificial intelligence, has been recognized as a powerful tool in scientific writing and programming but its use as a medical tool is largely overlooked. The general accessibility, rapid response time and comprehensive training database might enable ChatGPT to serve as a diagnostic augmentation tool in certain clinical settings. The diagnostic process in neurology is often challenging and complex. In certain time-sensitive scenarios, rapid evaluation and diagnostic decisions are needed, while in other cases clinicians are faced with rare disorders and atypical disease manifestations. Due to these factors, the diagnostic accuracy in neurology is often suboptimal. Here we evaluated whether ChatGPT can be utilized as a valuable and innovative diagnostic augmentation tool in various neurological settings. We used synthetic data generated by neurological experts to represent descriptive anamneses of patients with known neurology-related diseases, then the probability for an appropriate diagnosis made by ChatGPT was measured. To give clarity to the accuracy of the AI-determined diagnosis, all cases have been cross-validated by other experts and general medical doctors as well. We found that ChatGPT-determined diagnostic accuracy (ranging from 68.5% ± 3.28% to 83.83% ± 2.73%) can reach the accuracy of other experts (81.66% ± 2.02%), furthermore, it surpasses the probability of an appropriate diagnosis if the examiner is a general medical doctor (57.15% ± 2.64%). Our results showcase the efficacy of general artificial intelligence like ChatGPT as a diagnostic augmentation tool in medicine. In the future, AI-based supporting tools might be useful amendments in medical practice and help to improve the diagnostic process in neurology. [ABSTRACT FROM AUTHOR]

Introduction: The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. Methods: The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis. Results: The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project. Discussion: Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios. [ABSTRACT FROM AUTHOR]

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Copyright of e-Education Research is the property of Northwest Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent advancements in generative artificial intelligence raise fundamental questions about its impact on the journalistic profession and the future role of journalists in news production. However, the attitudes of regular journalists towards artificial intelligence remain largely unexplored. We address this gap by investigating the potentials and concerns that Danish journalists attribute to the impact of artificial intelligence on journalism. Journalists largely acknowledge its potential to streamline news production. While they also express broader concerns about the trustworthiness of journalism and the risk of job displacement, their more immediate worry lies in the impact of artificial intelligence on job meaningfulness and professional identity. These findings reveal a differentiated perspective on the profession-wide and individual impacts of artificial intelligence on journalism. Furthermore, journalists emphasise their human qualities and skills such as nuanced judgement, empathy, and creativity as beyond artificial intelligence’s capabilities. This indicates that artificial intelligence prompts journalists to reshape their expertise to prioritise the human touch in journalism, suggesting a broadened view of the journalistic profession. By examining these perceptions among regular journalists, this study contributes to a better understanding of the multifaceted challenges that artificial intelligence poses to both individual journalistic practices and the broader profession. [ABSTRACT FROM AUTHOR]

This theme issue brings together researchers from diverse fields to assess the current status and future prospects of embodied cognition in the age of generative artificial intelligence. In this introduction, we first clarify our view of embodiment as a potentially unifying concept in the study of cognition, characterizing this as a perspective that questions mind–body dualism and recognizes a profound continuity between sensorimotor action in the world and more abstract forms of cognition. We then consider how this unifying concept is developed and elaborated by the other contributions to this issue, identifying the following two key themes: (i) the role of language in cognition and its entanglement with the body and (ii) bodily mechanisms of interpersonal perception and alignment across the domains of social affiliation, teaching and learning. On balance, we consider that embodied approaches to the study of cognition, culture and evolution remain promising, but will require greater integration across disciplines to fully realize their potential. We conclude by suggesting that researchers will need to be ready and able to meet the various methodological, theoretical and practical challenges this will entail and remain open to encountering markedly different viewpoints about how and why embodiment matters. This article is the part of this theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'. [ABSTRACT FROM AUTHOR]

Human learning essentially involves embodied interactions with the material world. But our worlds now include increasing numbers of powerful and (apparently) disembodied generative artificial intelligence (AI). In what follows we ask how best to understand these new (somewhat 'alien', because of their disembodied nature) resources and how to incorporate them in our educational practices. We focus on methodologies that encourage exploration and embodied interactions with 'prepared' material environments, such as the carefully organized settings of Montessori education. Using the active inference framework, we approach our questions by thinking about human learning as epistemic foraging and prediction error minimization. We end by arguing that generative AI should figure naturally as new elements in prepared learning environments by facilitating sequences of precise prediction error enabling trajectories of self-correction. In these ways, we anticipate new synergies between (apparently) disembodied and (essentially) embodied forms of intelligence. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'. [ABSTRACT FROM AUTHOR]

AbstractRecent advancements in Generative Artificial Intelligence (GAI) have paved the way for developing sophisticated language models such as ChatGPT, which are widely employed across various areas, including marketing. However, despite its benefits in marketing, little attention has been paid to understanding the factors influencing ChatGPT adoption among marketing professionals. Also, existing literature tends to focus more on the advantages of ChatGPT, while there is a paucity of studies focusing on potential barriers to its adoption. To fill these gaps, a framework was developed using Behavioral Reasoning Theory and validated through a survey of 390 marketing experts. The study finds that marketers’ reasons for (against) positively (negatively) influence their attitude and adoption intention toward ChatGPT, and uncertainty avoidance as a cultural value significantly affects marketers’ attitudes and reasoning. Furthermore, this study highlights digital literacy’s moderating role, intensifying the negative relationship between “reasons against” and the intention to adopt ChatGPT, though it does not significantly affect the association between “reasons against” and attitude. Additionally, the findings offer managerial guidance for facilitating the adoption of GAI tools. [ABSTRACT FROM AUTHOR]

The identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module's constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4's role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) software has recently increased exponentially. Generative AI capabilities have moved from fiction to fact. This technology is changing the way we engage in Entrepreneurship, research it, and teach it. The significant impact on Entrepreneurship teaching is the focus of this paper. Any instructor, regardless of tech background, can and should be integrating AI into their courses right now. We describe and discuss our experience with this process and our deliberative approach to creating value for students. We also provide several templates of Entrepreneurship exercises that will appeal to a variety of students and can be integrated in virtually any Entrepreneurship course. [ABSTRACT FROM AUTHOR]

Regenerative AI is transforming breast cancer diagnosis and treatment through enhanced imaging analysis, personalized medicine, drug discovery, and remote patient monitoring. AI algorithms can detect subtle patterns in mammograms and other imaging modalities with high accuracy, potentially leading to earlier diagnoses. In treatment planning, AI integrates patient-specific data to predict individual responses and optimize therapies. For drug discovery, generative AI models rapidly design and screen novel molecules targeting breast cancer pathways. Remote monitoring tools powered by AI provide real-time insights to guide care. Examples include Google's LYNA for analyzing pathology slides, Kheiron's Mia for mammogram interpretation, and Tempus's platform for integrating clinical and genomic data. While promising, challenges remain, including limited high-quality training data, integration into clinical workflows, interpretability of AI decisions, and regulatory/ethical concerns. Strategies to address these include collaborative data-sharing initiatives, user-centered design, explainable AI techniques, and robust oversight frameworks. In developing countries, AI tools like MammoAssist and Niramai's thermal imaging systemare improving access to screening. Overall, regenerative AI offers significant potential to enhance breast cancer care, but judicious implementation with awareness of limitations is crucial. Coordinated efforts across the healthcare ecosystem are needed to fully realize AI's benefits while addressing challenges. [ABSTRACT FROM AUTHOR]

This article explores the potential uses of artificial intelligence (AI) in managing lung cancer. AI algorithms, such as machine learning and deep learning, have shown promise in detecting and characterizing lung nodules, improving screening and diagnosis. AI models can also predict treatment responses and guide therapy selection, optimize radiation therapy, and assist in bronchoscopy and thoracic surgery. However, the introduction of AI in healthcare raises ethical and legal concerns, such as patient autonomy, bias, and liability. More research is needed to evaluate the effectiveness of AI-assisted techniques and their impact on patient outcomes. Overall, integrating AI into clinical practice has the potential to improve patient outcomes and redefine modern medicine. [Extracted from the article]

Pseudo human settlements in the information age is an important part of human settlements geography, which has a comprehensive and profound impact on urban production, life and ecology in China and even the world, but the existing research on pseudo human settlements is still unclear, and cannot meet the requirements of China's "digital-realistic fusion", "network power" and the construction of a beautiful China. Therefore, this paper starts from the perspective of pseudo, based on the theory of the coupling system of human settlements, and applies the entropy weight method, coupling coordination model, geodetector and other methods and spatial technologies to deeply analyze the spatial and temporal evolution of the pseudo human settlements and the driving mechanism of the 14 cities in Liaoning Province from 2011 to 2020. The results show that: 1) Liaoning Province's pseudo human settlements in the time evolution of the overall quality of the main theme of the rise, with the "Internet festival" node effect and significant stage. 2) The spatial distribution is characterized by the geographical layout of "1"-type high-quality development centers and concentric-circle spatial distribution; and the trend surface shows a low uniformity pattern of "high in the south-central part and low in the surrounding area". 3) The degree of system coupling and the degree of coupling coordination both show a fluctuating upward trend, with the spatial heterogeneity characteristic of "high in the central part and low in the surrounding area". 4) Tool system has become the emerging driving force, communication and social chat has become the basic driving force, and life service is the invisible driving force; communication class and community class are the important leading driving factors. The study aims to enrich the research system of human settlements, promote the development of the discipline of human settlements geography, provide a new basis for the construction of human settlements in the new era, and respond to the demand for decision-making in the strategy of network power. [ABSTRACT FROM AUTHOR]

Background/Objectives: Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). Methods: By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Results: Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. Conclusions: These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows. [ABSTRACT FROM AUTHOR]

Introduction: While the global medical graduate and student population is approximately 50% female, only 13–15% of cardiologists and 20–27% of training fellows in cardiology are female. The potentially transformative use of text-to-image generative artificial intelligence (AI) could improve promotions and professional perceptions. In particular, DALL-E 3 offers a useful tool for promotion and education, but it could reinforce gender and ethnicity biases. Method: Responding to pre-specified prompts, DALL-E 3 via GPT-4 generated a series of individual and group images of cardiologists. Overall, 44 images were produced, including 32 images that contained individual characters and 12 group images that contained between 7 and 17 characters. All images were independently analysed by three reviewers for the characters' apparent genders, ages, and skin tones. Results: Among all images combined, 86% (N = 123) of cardiologists were depicted as male. A light skin tone was observed in 93% (N = 133) of cardiologists. The gender distribution was not statistically different from that of actual Australian workforce data (p = 0.7342), but this represents a DALL-E 3 gender bias and the under-representation of females in the cardiology workforce. Conclusions: Gender bias associated with text-to-image generative AI when using DALL-E 3 among cardiologists limits its usefulness for promotion and education in addressing the workforce gender disparities. [ABSTRACT FROM AUTHOR]

This paper seeks to illuminate new perspectives on the concept of crisis in globalised higher education (HE) by focusing on knowledge generated by doctoral research. Doctoral research is a significant part of research and knowledge building in HE, particularly in science, and doctoral students contribute to the research capacity and knowledge building of institutions. This source of knowledge offers alternative perspectives on crisis in HE, providing a rich source of research which is often under‐consulted. Using the British Library's digital repository EThOS, a collection of around 637,000 doctoral studies carried out in British universities, the research harnesses Generative Artificial Intelligence approaches in order to analyse the ways in which crisis is defined and constructed in doctoral research since 2000. Through a pilot study using a prototype of a new AI tool, the paper offers both conceptual and methodological insights into constructions of crisis in this under‐used field of research. [ABSTRACT FROM AUTHOR]

The article discusses the role of rejection in scholarly knowledge production, emphasizing its importance in filtering out less credible scientific works. It explores how rejection helps reduce entropy and increase the dissemination of valuable knowledge, despite the subjectivity involved in the process. The text also highlights the necessity of intellectual humility among editors and reviewers to enhance the efficiency of knowledge production and ensure that valuable scientific contributions are not overlooked. [Extracted from the article]

The Harbingers study of early career researchers (ECRs), their work life and scholarly communications, began by studying generational—Millennial—change (c.2016), then moved to pandemic change (c.2020) and is now investigating another potential agent of change: artificial intelligence (2024–). We report here on a substantial scoping pilot study that looks at the impact of AI on the scholarly communications of international ECRs and, extends this to the arts and humanities. It aims to fill the knowledge gap concerning ECRs whose millennial mindset may render them especially open to change and, as the research workhorses they are, very much in the frontline. The data was collected via in‐depth interviews in China, Malaysia, Poland, Portugal, Spain and (selectively) the United Kingdom/United States. The data show ECRs to be thinking, probing and, in some cases, experimenting with AI. There was a general acceptance that AI will be responsible for the growth of low‐quality scientific papers, which could lead to a decline in the quality of research. Scholarly integrity and ethics were a big concern with issues of authenticity, plagiarism, copyright and poor citation practices raised. The most widespread belief was AI would prove to be a transformative force and would exacerbate existing scholarly disparities and inequalities. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into academic publishing has raised concerns about AI-generated anomalies in scholarly articles, highlighting the need for enhanced editorial scrutiny to maintain research integrity. Current methodologies for detecting AI-generated content are insufficient, necessitating the development of more robust detection technologies. Academic journals are urged to implement rigorous editorial standards, provide comprehensive training for reviewers and editors, and adopt advanced AI detection tools to uphold the quality of scholarly publications. The challenges posed by AI in scholarly publishing require comprehensive training, specialized software tools for AI detection, regular policy review, ethics and transparency in AI usage, scholarly dialogue, and collaboration between academia and the tech industry to ensure the integrity and reliability of academic outputs. [Extracted from the article]

In this conceptual article, we argue that artificial intelligence (AI) systems are contributing to the generation of an environment of affordances for everyday information practices through which they exert influence on people and the planet in ways that often are left unrecognized. We illustrate our insights by focusing on the practices of information seeking in everyday life, suggesting that the affordances of AI systems integrated into search engines, social media platforms, streaming services, and media generation, shape such practices in ways that may, paradoxically, result both in the increase and reduction of diversity of and access to information. We discuss the potential implications of these developments in terms of the sustainability of information ecosystems and suggest solutions for addressing them through regulation and education. Drawing from the fields of library and information science and science and technology studies and research on affordances, everyday information practices, and sustainability, the article seeks to respond to the need for more nuanced theoretical insights on the impact and implications of AI on information practices and to develop conceptual tools with which to examine the co‐evolution of humans and information systems from a systemic perspective. [ABSTRACT FROM AUTHOR]

The article discusses the potential impact of generative AI on science and medicine, likening it to a cognitive revolution similar to the industrial revolution. Large language models (LLMs) are transforming various scientific domains, including diagnostics, drug discovery, and personalized patient care. However, challenges such as model development, multi-disciplinary collaboration, AI education, and societal integration need to be addressed to achieve true symbiosis between AI and science/medicine. It is crucial to safeguard human elements in scientific progress and healthcare delivery while integrating AI as a tool to enhance human expertise, not replace it. [Extracted from the article]

The convergence of artificial intelligence (AI) and biology has given rise to increasing concerns regarding generative AI, such as large language models (LLMs), and their potential for harm and misuse focused on biosecurity. President Biden's Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (Executive Order 14110) has generated numerous discussions that may lead to recommendations from government agencies and others tasked with investigating these issues in the United States. For example, the National Academies of Sciences, Engineering, and Medicine (NASEM) is assessing the ways in which AI can increase these biosecurity risks and especially pandemic threats. Bringing together scientists from industry and academia, a recent meeting illustrated how generative AI has been used to design proteins, small molecules, CRISPRs, and to showcase the clear potential for AI to aid in innovation. On the other hand, misuse of AI is secondary to the science and most scientists do not consider misuse potential unless they need or must. The meeting discussed several steps that can be taken to add to the security of LLMs that may prevent or slow misuse in addition to their limitations. The general ease with which these generative AI technologies can be used, opens their use to the public, although a college education may be necessary to benefit from and understand their capabilities. At this stage, it is unclear what recommendations will be forthcoming from NASEM or the US government and what impact these will have on the growing AI field. [ABSTRACT FROM AUTHOR]

The article from GEN Biotechnology discusses Recursion's innovative approach to AI and machine learning in drug development. Recursion aims to revolutionize the drug discovery process by utilizing its Recursion Operating System, which integrates hardware, software, datasets, and proprietary tools to explore biology's complex search space. The company plans to announce clinical data from several pipeline candidates, including REC-994 for cerebral cavernous malformation, and is set to merge with Exscientia to further enhance its capabilities. Recursion's CEO, Chris Gibson, emphasizes the importance of AI in improving the efficiency and success rate of drug development, aiming to challenge the industry's low success rate through innovative approaches. [Extracted from the article]

This review illustrates how language ideologies about correctness, in speaking and writing, have been discussed in research on the role of language in education. Research illustrates a give-and-take between the interests of multilingual speakers and advocates of language diversity on the one hand and, on the other, the correctness ideologies embedded in institutional demands for correctness and standardization (in schools and language policies) and commodification (in the global educational marketplace). More subtle than ideologies of correctness, language ideologies about "appropriate" language emerge as related to race, ethnicity, gender, and other embodied biases, and the nuanced mechanisms of language socialization illuminate the persistent dynamics of these appropriateness ideologies. Finally, we discuss the relevance of postcolonial epistemologies, the collaborative participatory research methods that are reframing what counts as correct and appropriate for the study of language and education, and the emerging role of generative artificial intelligence for language in education. [ABSTRACT FROM AUTHOR]

With the breakthrough development of generative artificial intelligence (AI), its usage in academic articles is rapidly increasing, and the risk of the lack of research transparency arises with that use. To address this risk, the sources, mechanisms, and quality of AI-generated scholarly content are studied to calibrate our expectations for this technology. The authors find that generative AI has great potential to improve the efficiency of researchers and to enhance research articles but also has significant inherent limitations. Then, they examine the use of generative AI in academic articles from three perspectives: AI-assisted research issue development, AI-assisted addressing of research questions, and AI-assisted research findings communication. On this basis, the authors propose a tiered disclosure strategy based on the generative AI usage context for researchers to transparently use generative AI. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GenAI) models have attracted tremendous interest since the advent of ChatGPT, raising numerous opportunities and challenges. However, their generative power has not yet been studied, leaving open the question of what is truly generated by these tools. This paper addresses this question and precisely characterizes the generativity behind GenAI models. Owing to the latest advancements in engineering design, we first propose a framework for uncovering the various types of generativity. Then, we consider the main families of GenAI models and systematically analyze them to characterize their generativity within this framework. By doing so, we highlight the existence of two distinct generative levels in GenAI: one leading to the generation of new artifacts and the other leading to the generation of GenAI models themselves. We are also able to characterize the generativity of both of these levels, thus specifically confirming the generative power of GenAI and opening research avenues toward human-GenAI collaboration. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Integrating remote Internet of Things (IoT) laboratories into project-based learning (PBL) in higher education institutions (HEIs) while exploiting the approach of technology-enhanced learning (TEL) is a challenging yet pivotal endeavor. Our proposed approach enables students to interact with an IoT-equipped lab locally and remotely, thereby bridging theoretical knowledge with practical application, creating a more immersive, adaptable, and effective learning experience. This study underscores the significance of combining hardware, software, and coding skills in PBL, emphasizing how IoTRemoteLab (the remote lab we developed) supports a customized educational experience that promotes innovation and safety. Moreover, we explore the potential of IoTRemoteLab as a TEL, facilitating and supporting the understanding and definition of the requirements of remote learning. Furthermore, we demonstrate how we incorporate generative artificial intelligence into IoTRemoteLab's settings, enabling personalized recommendations for students leveraging the lab locally or remotely. Our approach serves as a model for educators and researchers aiming to equip students with essential skills for the digital age while addressing broader issues related to access, engagement, and sustainability in HEIs. The practical findings following an in-class experiment reinforce the value of IoTRemoteLab and its features in preparing students for future technological demands and fostering a more inclusive, safe, and effective educational environment. [ABSTRACT FROM AUTHOR]

Along with the growing renewable energy sources sector, energy storage will be necessary to stabilize the operation of weather-dependent sources and form the basis of a modern energy system. This article presents the possibilities of using energy storage in the energy market (day-ahead market and balancing market) in the current market conditions in Poland after reforming the balancing market in June 2024. The current state of the markets is characterized by high price volatility, which can ensure the high profitability of storage operations. However, very flexible and self-adaptive algorithms for charging and discharging are required, taking advantage of market price spreads. This study aimed to see if, through a solution based on ChatGPT 4o, energy storage operations can be planned by taking maximum advantage of the existing price spreads in the market. Previous analyses in this area have focused on complex models that predicted prices in the markets and planned the plant's operation on this basis. In this case, the simple model used (charging and discharging based on historical prices) resulted in profits of EUR 90/MWh, while in the second case, when holidays, weather, and demand forecasts were taken into account, the profit was EUR 150–180/MWh, which exceeds the current Levelized Cost of Electricity of storage estimated at around EUR 100/MWh. These analyses indicated that modern genAI tools are appropriate for further study, especially as the technology dramatically increases its capabilities. [ABSTRACT FROM AUTHOR]

The use of generative artificial intelligence (AI) in higher education design programmes is expanding, yet there is little formalized approach to its integration. Professionally, generative AI is starting to become an indispensable tool for ideation and prototyping, two fundamental skills taught in design's studio pedagogy. Yet this digital leap into the future risks leaving design educators behind unless they take a proactive approach to its implementation and present its strengths and weaknesses. This study surveyed 74 design students from an Australian university, exploring their current utilization of generative AI and their projections for its future application in design practice. Findings confirm that generative AI is being used in an ad hoc way by students to speed up the ideation process tempered by a sceptical view of its creative output. A list of generative AI training for integration into the design curricula based on current research and survey results is proposed. [ABSTRACT FROM AUTHOR]

The International Journal of Humanities & Arts Computing (IJHAC) has a special issue on "Spatial Humanities and the Global South," which focuses on research related to historical geographic information systems (GIS) and spatial humanities. The issue includes articles that explore topics such as the territorial reconfiguration of a religious unit in colonial Mexico, Indigenous circulation networks in the Orinoco region of Colombia, European cultural heritage through the voices of Latin American women writers, and the preservation of Armenian toponyms in conflict zones. The journal also features two additional studies on Bob Dylan's song lyrics and the virtual reconstruction of an eighteenth-century funerary ceremony. The editorial team has announced a future special issue on "Artificial Intelligence and the Digital Humanities." [Extracted from the article]

While the uses of generative artificial intelligence (AI) are vast, one capability it lacks is true creativity. This requires originality and surprise, neither of which is possible without the human process. Yet students and others increasingly rely on AI, so it seems likely to shape public discourse as well as everyday life. Social-media groupthink and insular academic communities have already narrowed the range of intellectual discourse. AI stands to exacerbate the trend, filtering out unconventional prose and content in languages and dialects that are not widely spoken. Yet AI companies have limited motivation to enlighten consumers about its shortcomings. As with other sweeping technological innovations, complacency and overreliance on AI appears inevitable absent intervention. Without proper guidelines, AI could distil writing and idea formulation into a few predicted outcomes and extinguish true creative thought. [ABSTRACT FROM AUTHOR]

Objective: This research aims to explore the significant impact of Information and Communication Technology (ICT) on literary representations of diasporic experiences, particularly focusing on its role in connecting immigrants worldwide. Methodology: The study employs an analytical approach to examine the influence of ICT on diasporic literature. Key Findings: The findings reveal that ICT has played a crucial role in reshaping the concept and portrayal of diasporic experiences in literature. In Inaam Kachachi's novel The Dispersal (2023), Iskander's technological skills highlight ICT's potential to maintain family connections. He uses technology to preserve his family's history by creating a virtual graveyard, allowing for the sharing of cultural practices and traditions. Conclusions: The research concludes that ICT is a transformative tool that helps preserve cultural identities, enhance resilience, and strengthen the diasporic experience by facilitating communication and fostering a sense of belonging. Recommendations: The study recommends applying the same analytical approach to other novels within diasporic literature to further explore how technology influences diasporic experiences in literature. [ABSTRACT FROM AUTHOR]

We present the last part of this special issue on Use of Artificial Intelligence in Communication: Ethical Implications for Media. This editorial examines the role that humans should play in the responsible use of generative artificial intelligence (GenAI) in the media. It provides an overview of transparency as an ethical obligation in relation to trust and truthfulness when using GenAI to create different kinds of content. Finally, it enters into the debate about the moral status of machines as we anticipate ethical questions in the study of ethics and GenAI in the media. Six articles complete this special issue with studies examining implications of GenAI for semiotics research, public relations, advertising, fact checking, information literacy and journalism education. [ABSTRACT FROM AUTHOR]

The controversy surrounding ChatGPT has reopened the debate about the impact of new technologies in many fields of activities, including communication and PR. This study mapped Romanian PR practitioners' use of generative AI and their perception of it, placing a special focus on the ethical concerns involved and the implications for the profession itself. We took a quantitative-qualitative approach by using both a survey and semi-structured interviews. Our goal was to determine the impact of generative artificial intelligence (AI) in the Romanian PR industry and to understand the reasons and challenges behind integrating generative AI in PR practice. The survey findings revealed a substantial adoption (73.5%) of AI within the Romanian PR community, with an overwhelming 91.6% of them using ChatGPT. The satisfaction level was remarkably high, with 92% expressing satisfaction with generative AI application efficacy. Benefits included timesaving, work simplification, and the reduction of repetitive tasks. Surprisingly, not only did 67.3% of respondents not perceive AI as an immediate threat to PR jobs, but 80.5% believed AI represents an opportunity for the industry. Indeed, almost all our interviewees admitted relief and satisfaction when using generative AI tools to complete their tasks. However, some concerns were expressed regarding the quality of the generative AI content and, in particular, the need to always check this kind of content by a human editor before using it. Moreover, PR professionals' main ethical concerns are related to the issue of transparency towards their clients when using AI tools to produce different types of content. [ABSTRACT FROM AUTHOR]

The article delves into the symbiotic relationship between sociosemiotics and AI research, specifically focusing on Generative Artificial Intelligences (GenAI). As GenAI gain traction in verbal and iconic creation, in the context of various cultural and communication theories, sociosemiotics emerges as a key framework for understanding these processes. The article addresses questions such as: How have the relationships between AI research and sociosemiotics evolved? How can sociosemiotics contribute to the understanding of GenAI? If Umberto Eco (1986) distinguished between general semiotics and specific (or applied) semiotics, what would be the main characteristics of a semiotics of Generative AI? In this context, the article's objectives encompass three dimensions: 1) Tracing the intertwined evolution of sociosemiotics and AI research over 75 years, spotlighting the textual and sociosemiotics contributions; 2) Proposing a series of possible sociosemiotic interventions in the realm of GenAI; and 3) Reflecting on the emergence of an applied semiotics of GenAI. Ultimately, the article encapsulates the intricate interplay between sociosemiotics and GenAI, paving the way for novel insights into AI's semiotic foundations and its multifaceted implications in contemporary digital creation. [ABSTRACT FROM AUTHOR]

The document provides a schedule of events for the XXXII Congress of the Spanish Society of Clinical Pharmacology. The congress includes various workshops, seminars, round table discussions, oral communications, and a closing conference. Topics covered include research ethics, next-generation medicine, competitive projects, professional opportunities in clinical pharmacology, therapeutic value of medicines, medicines in special situations, medicine authorization processes in Europe, chronic diseases and polytherapy, and artificial intelligence in medicine. The congress also includes a welcome cocktail, lunch, coffee breaks, poster sessions, networking opportunities, and an awards ceremony. [Extracted from the article]

This article explores the impact of artificial intelligence (AI) on literacy learning and living. It discusses the increasing presence of AI in our lives and the concerns it raises, such as academic integrity and copyright law. The article also examines how AI is transforming teaching practices and assessment methods. It emphasizes the need for scholars and educators to understand and respond to the disruptions caused by AI in literacy education. The article addresses the social and political implications of AI, including issues of bias, inequality, and environmental impact. It argues for a critical and humanizing approach to literacy studies in the age of AI, considering both the potential for harm and transformation. The article concludes by presenting various themes that explore the philosophical, critical, and speculative aspects of literacy in relation to AI. It references other scholarly contributions that delve into the challenges and opportunities of AI in literacy education. [Extracted from the article]

Transmission electron microscopy (TEM) plays a crucial role in heterogeneous catalysis for assessing the size distribution of supported metal nanoparticles. Typically, nanoparticle size is quantified by measuring the diameter under the assumption of spherical geometry, a simplification that limits the precision needed for advancing synthesis‐structure‐performance relationships. Currently, there is a lack of techniques that can reliably extract more meaningful information from atomically resolved TEM images, like nuclearity or geometry. Here, cycle‐consistent generative adversarial networks (CycleGANs) are explored to bridge experimental and simulated images, directly linking experimental observations with information from their underlying atomic structure. Using the versatile Pt/CeO2 (Pt particles centered ≈2 nm) catalyst synthesized by impregnation, large datasets of experimental scanning transmission electron micrographs and physical image simulations are created to train a CycleGAN. A subsequent size‐estimation network is developed to determine the nuclearity of imaged nanoparticles, providing plausible estimates for ≈70% of experimentally observed particles. This automatic approach enables precise size determination of supported nanoparticle‐based catalysts overcoming crystal orientation limitations of conventional techniques, promising high accuracy with sufficient training data. Tools like this are envisioned to be of great use in designing and characterizing catalytic materials with improved atomic precision. [ABSTRACT FROM AUTHOR]

Background: Large Language Models (LLMs) provide clinical guidance with inconsistent accuracy due to limitations with their training dataset. LLMs are "teachable" through customization. We compared the ability of the generic ChatGPT-4 model and a customized version of ChatGPT-4 to provide recommendations for the surgical management of gastroesophageal reflux disease (GERD) to both surgeons and patients. Methods: Sixty patient cases were developed using eligibility criteria from the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) & United European Gastroenterology (UEG)-European Association of Endoscopic. Surgery (EAES) guidelines for the surgical management of GERD. Standardized prompts were engineered for physicians as the end-user, with separate layperson prompts for patients. A customized GPT was developed to generate recommendations based on guidelines, called the GERD Tool for Surgery (GTS). Both the GTS and generic ChatGPT-4 were queried July 21st, 2024. Model performance was evaluated by comparing responses to SAGES & UEG-EAES guideline recommendations. Outcome data was presented using descriptive statistics including counts and percentages. Results: The GTS provided accurate recommendations for the surgical management of GERD for 60/60 (100.0%) surgeon inquiries and 40/40 (100.0%) patient inquiries based on guideline recommendations. The Generic ChatGPT-4 model generated accurate guidance for 40/60 (66.7%) surgeon inquiries and 19/40 (47.5%) patient inquiries. The GTS produced recommendations based on the 2021 SAGES & UEG-EAES guidelines on the surgical management of GERD, while the generic ChatGPT-4 model generated guidance without citing evidence to support its recommendations. Conclusion: ChatGPT-4 can be customized to overcome limitations with its training dataset to provide recommendations for the surgical management of GERD with reliable accuracy and consistency. The training of LLM models can be used to help integrate this efficient technology into the creation of robust and accurate information for both surgeons and patients. Prospective data is needed to assess its effectiveness in a pragmatic clinical environment. [ABSTRACT FROM AUTHOR]

Background: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy. Methods: Video clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to train three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The primary outcome was the average precision of the performance of AI in detecting parathyroid glands. Results: 152 Fine-needle aspiration-confirmed parathyroid gland images were acquired from 150 patients who underwent unilateral lobectomy. The average precision of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods, with the geometric transformation data augmentation dataset showing a higher average precision (79%) than the image inpainting model (78.6%). When this model was subjected to external validation using a completely different thyroidectomy approach, the image inpainting method was more effective (46%) than both the geometric transformation (37%) and baseline (33%) methods. Conclusion: This AI model was found to be an effective and generalizable tool in the intraoperative identification of parathyroid glands during thyroidectomy, especially when aided by appropriate augmentation methods. Additional studies comparing model performance and surgeon identification, however, are needed to assess the true clinical relevance of this AI model. [ABSTRACT FROM AUTHOR]

Background: Recently, there has been a growing interest in exploring AI-driven chatbots, such as ChatGPT, as a resource for disease management and education. Objective: The study aims to evaluate ChatGPT's accuracy and quality/clarity in providing nutritional management for Type 2 Diabetes (T2DM), the Metabolic syndrome (MetS) and its components, in accordance with the Academy of Nutrition and Dietetics' guidelines. Methods: Three nutrition management-related domains were considered: (1) Dietary management, (2) Nutrition care process (NCP) and (3) Menu planning (1500 kcal). A total of 63 prompts were used. Two experienced dietitians evaluated the chatbot output's concordance with the guidelines. Results: Both dietitians provided similar assessments for most conditions examined in the study. Gaps in the ChatGPT-derived outputs were identified and included weight loss recommendations, energy deficit, anthropometric assessment, specific nutrients of concern and the adoption of specific dietary interventions. Gaps in physical activity recommendations were also observed, highlighting ChatGPT's limitations in providing holistic lifestyle interventions. Within the NCP, the generated output provided incomplete examples of diagnostic documentation statements and had significant gaps in the monitoring and evaluation step. In the 1500 kcal one-day menus, the amounts of carbohydrates, fat, vitamin D and calcium were discordant with dietary recommendations. Regarding clarity, dietitians rated the output as either good or excellent. Conclusion: Although ChatGPT is an increasingly available resource for practitioners, users are encouraged to consider the gaps identified in this study in the dietary management of T2DM and the MetS. [ABSTRACT FROM AUTHOR]

We are confronted with the concept of intelligence every day. Starting with human intelligence to artificial intelligence. Some animals are also attested to be intelligent based on specific problems they solve. We also come across terms such as swarm intelligence, emotional intelligence or even physical intelligence. But there is still a lack of a clear definition of what intelligence actually is and, in particular, how it could be measured. Intelligence tests that provide quantitative information have so far only been available from psychology and only for people. There is a lack of criteria for what makes a system an intelligent system. This became particularly clear with the question of whether generative AI, such as ChatGPT, can be considered intelligent at all. So can intelligence be derived from the cognitive abilities of a system, or is it ultimately decisive how these abilities come about? This paper suggests a definition of the term intelligence and suggests an explanation for what constitutes intelligence and to what extent intelligence is required to gain knowledge. And finally it is questioned whether artificial systems are intelligent and have any knowledge at all. [ABSTRACT FROM AUTHOR]

Generative AI is increasingly being used, among others also in the professional media industry. This article provides an overview of the main ethical and legal challenges regarding content generated by, or with the help of generative AI. Among the ethical challenges, special attention is devoted to the development of journalistic standards. The legal discussion highlights the questions of civil and criminal responsibility regarding content, with a primary focus on the attribution of liability. It scrutinises the roles of deployers, assessing their contributions to the final product. It suggests that all human participants' contributions should be accordingly acknowledged, potentially necessitating the creation of novel categories such as hybrid authorship and shared responsibility. [ABSTRACT FROM AUTHOR]

Aim: Artificial intelligence, especially ChatGPT, has been used in various aspects of medicine; however, whether ChatGPT can be used in case report writing is unknown. This study aimed to provoke discussion and provide a platform for it. Methods: I wrote a theoretical case report where cyst aspiration cured a twisted ovarian cyst (Manuscript 4). I tasked ChatGPT with generating case reports by inputting information at three different levels: (1) key message and case profile, (2) addition of key introduction information (including known facts and problems to be solved), and (3) further addition of main discussion points. These inputs resulted in the creation of Manuscripts 1–3, which were subjected to analysis. Manuscript 3, generated by ChatGPT with the deepest information input, was compared with Manuscript 4, the human‐authored counterpart. Results: With the least information, Manuscript 1 can stand on its own, but its content is superficial. The more detailed data input, the more readable and reasonable the manuscripts become. A human‐written manuscript involves personal experience and viewpoints other than obstetrics‐gynecology. Conclusions: Better input produced more reasonable and readable case reports. Human‐written paper, compared with ChatGPT‐generated one, can involve "human touch." Whether such human touch enriches the case report awaits further discussion. Whether ChatGPT can be used in case report writing, and if it can, to what extent, should be worthy of further study. I encourage every doctor to form their own stance towards ChatGPT use in medical writing. [ABSTRACT FROM AUTHOR]

This study aimed to determine the perceptions and attitudes of preservice teachers of Chinese as a foreign language (CFL) toward applying information and computer technology (ICT) and image-generative artificial intelligence (image-GAI) tools in CFL teaching from the perspective of drawings. This 2-week study involved 20 preservice CFL teachers from a university in northern Taiwan. Various data were collected, including questionnaire responses and drawings (both hand-drawn and artificial-intelligence-assisted) by preservice teachers. The collected data were analyzed to determine how the experience of using image-GAI tools influenced how preservice CFL teachers perceive ICT-assisted CFL teaching, and how they perceive the potential of image-GAI tools in enhancing their creativity. The results indicated that most conceptions held by the preservice CFL teachers of ICT-assisted Chinese language teaching involved teachers and learners as the primary people involved, and that they tended to adopt a teacher-centered teaching mode. The preservice CFL teachers had positive attitudes toward using image-GAI tools in CFL teaching. However, they were more reserved regarding whether generative AI tools can promote diversity in CFL teaching and enhance the creativity of preservice teachers. The most common elements in the drawings by the preservice teachers were the activities and the people involved in CFL teaching. Two other noteworthy findings were (1) most preservice CFL teachers believed that this new technology (image-GAI tools) better reflected their understanding of ICT and CFL teaching, and (2) that their drawings indicated that having experience of using image-GAI tools influenced their perception of ICT and CFL teaching. [ABSTRACT FROM AUTHOR]

Fostering students’ problem-solving abilities and design thinking has been recognized as a crucial educational objective. In the conventional collaborative project-based learning approach, co-regulative learning (CRL) has been adopted to guide learners to plan and monitor the progress of their projects. However, without providing adequate supports or feedback to individual teams, the innovation and design quality of the project outcomes could be disappointing. In this study, a generative AI (artificial intelligence)-based co-regulative learning (GAI-CRL) approach is proposed to address this issue. Moreover, an experiment was conducted in a digital game development (DGD) to assess this approach. A total of 46 university students from two classes were recruited in this study. The experimental group adopted the GAI-CRL approach, while the control group adopted the conventional CRL (C-CRL) approach. The results showed that the GAI-CRL approach significantly enhanced students’ project design performance, learning motivation, self-efficacy, and creative thinking compared to the C-CRL approach. This study serves as a reference not only for implementing DGD projects but also for applying CRL to other educational domains. [ABSTRACT FROM AUTHOR]

This study examines the potentials and challenges of integrating conversational AI (Artificial Intelligence) tools in higher education. Specifically, the study delves into how ChatGPT is reshaping teaching, learning, research, and student evaluation while addressing ethical concerns such as academic integrity. We employ a qualitative research design based on semi-structured interviews with 12 highly accomplished academicians from North America, Asia, and Europe’s higher education institutions. Our analysis reveals ChatGPT’s potential to uplift educational productivity, learning outcomes, creativity, and idea generation but raises apprehensions about academic honesty, authenticity, and over-reliance. While ChatGPT can enhance educational practices such as brainstorming, analysis, and skills acquisition, its unchecked use may impede learning, critical thinking, and research quality. The study advocates for the responsible AI use in academia, underscoring educators and policymakers to establish policies guiding conversational AI ethical applications. Thus, training faculty and executing institutional policies are essential for effective and ethical integration of ChatGPT and other AI tools in higher education. Finally, this research contributes to ongoing research on AI in education by providing valuable insights that can harness potential of these transformative tools while upholding academic integrity and standards. [ABSTRACT FROM AUTHOR]

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine. [ABSTRACT FROM AUTHOR]