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This study employs a bibliometric approach to analyze the landscape of artificial intelligence (AI) components used in performance measurement. As organizations increasingly leverage AI for optimizing processes and decision-making, understanding the trends in AI components becomes imperative. The identified AI components are classified based on their roles in enhancing performance measurement, offering insights into the prevalent methodologies and emerging technologies. The bibliometric analysis encompasses a comprehensive review of scholarly articles, conference papers, and patents, systematically exploring the evolving field. In this research, the methodology involves data extraction from reputable academic databases and patent repositories, followed by applying bibliometric techniques to quantify and visualize key aspects. The findings of this study contribute to the existing knowledge by mapping the intellectual structure of AI components for performance measurement. [ABSTRACT FROM AUTHOR]

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]

In recent years, the healthcare industry has faced numerous challenges in ensuring efficient and reliable supply chain management. The emergence of novel supply chain-based frameworks has presented promising solutions to these challenges. This survey paper aims to explore and analyze the latest advancements in supply chain frameworks specifically tailored for the healthcare industry. We review the existing literature, identify key trends, and highlight the potential impact of these frameworks on improving patient care, optimizing inventory management, enhancing operational efficiency, and reducing costs. Additionally, we discuss the challenges and opportunities associated with the implementation of these frameworks and suggest future research directions. [ABSTRACT FROM AUTHOR]

ChatGPT is a generative language model tool launched by OpenAI on November 30, 2022, enabling the public to converse with a machine on a broad range of topics. In January 2023, ChatGPT reached over 100 million users, making it the fastest-growing consumer application to date. This interview with ChatGPT is part 2 of a larger interview with ChatGPT. It provides a snapshot of the current capabilities of ChatGPT and illustrates the vast potential for medical education, research, and practice but also hints at current problems and limitations. In this conversation with Gunther Eysenbach, the founder and publisher of JMIR Publications, ChatGPT generated some ideas on how to use chatbots in medical education. It also illustrated its capabilities to generate a virtual patient simulation and quizzes for medical students; critiqued a simulated doctor-patient communication and attempts to summarize a research article (which turned out to be fabricated); commented on methods to detect machine-generated text to ensure academic integrity; generated a curriculum for health professionals to learn about artificial intelligence (AI); and helped to draft a call for papers for a new theme issue to be launched in JMIR Medical Education on ChatGPT. The conversation also highlighted the importance of proper "prompting." Although the language generator does make occasional mistakes, it admits these when challenged. The well-known disturbing tendency of large language models to hallucinate became evident when ChatGPT fabricated references. The interview provides a glimpse into the capabilities and limitations of ChatGPT and the future of AI-supported medical education. Due to the impact of this new technology on medical education, JMIR Medical Education is launching a call for papers for a new e-collection and theme issue. The initial draft of the call for papers was entirely machine generated by ChatGPT, but will be edited by the human guest editors of the theme issue. [ABSTRACT FROM AUTHOR]

This study explores the applications and implications of blockchain technology in the Australian higher education system, focusing on its integration with artificial intelligence (AI). By addressing critical challenges in credential verification, administrative efficiency, and academic integrity, this integration aims to enhance the global competitiveness of Australian higher education institutions. A comprehensive review of 25 recent research papers quantifies the benefits, challenges, and prospects of blockchain adoption in educational settings. Our findings reveal that 52% of the reviewed papers focus on systematic reviews, 28% focus on application-based studies, and 20% combine both approaches. The keyword analysis identified 287 total words, with "blockchain" and "education" as the most prominent themes. This study highlights blockchain's potential to improve credential management, academic integrity, administrative efficiency, and funding mechanisms in education. However, challenges such as technical implementation (24%), regulatory compliance (32%), environmental concerns (28%), and data security risks (40%) must be addressed to achieve widespread adoption. This study also discusses critical prerequisites for successful blockchain integration, including infrastructure development, staff training, regulatory harmonisation, and the incorporation of AI for personalised learning. Our research concludes that blockchain, when strategically implemented and combined with AI, has the potential to transform the Australian higher education system, significantly enhancing its integrity, efficiency, and global competitiveness. [ABSTRACT FROM AUTHOR]

We stand at the threshold of a revolution in science. It is possible that soon scientific writing as we know it will undergo a rapid change, all thanks to the ChatGPT 3.5 language model. The appearance of this tool on the technology market in November 2022 caused a massive stir among Internet users and the academic world. It turned out that chat has the potential to generate completely new and unique scientific texts. In the wake of ChatGPT, similar tools by technology giants came and are coming. Given the above, the scientific community has only a brief moment to try to answer the following questions: whether the texts generated in this way have real scientific value, whether it is ethical for researchers to use them, how to regulate copyright on the use of AI, what are the potential capabilities of artificial intelligence for writing scientific papers. The indicated themes are reflected in the presented text. [ABSTRACT FROM AUTHOR]

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI's potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into "Sustainability of AI" and "AI for Sustainability", revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field's maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations' Sustainable Development Goals (SDGs), and addressing stakeholders' influence. [ABSTRACT FROM AUTHOR]

There has been a rise in scepticism regarding the use of Artificial Intelligence (AI) in qualitative research tasks such as critical reviews, conceptualization, thematic and content analysis, and potentially theory development. Concerns have been raised over the possibility that researchers intentionally avoid discussing or even mentioning the use of AI in their studies for a variety of reasons, including the "fear" of criticism and rejection of their papers. The purpose of this paper, which is guided by critical perspective principles, is to examine the controversy surrounding the appropriate recognition of AI in theoretical discussions and qualitative research, including conceptual, critical reviews, empirical, and other types of studies of qualitative nature. Prior to a discussion of how to acknowledge the use of AI, the significance of notions of acknowledgment and academic integrity in the context of research are discussed. As the author of this paper, I acknowledge and document the use of both AI and the researcher's cognitive skills in the development of this theoretical critical perspective study through a four-phase process, while giving directions of when and how to acknowledge the use of AI in qualitative studies. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important source of competitive advantage as it enables task augmentation and automation. However, while AI can create significant value, it is important to note that AI investments are fraught with risks and uncertainties. Thus, managers are likely to carefully evaluate potential AI investments before committing to investing. However, we know little about how managers' appraisal of AI influences their investment choices. Drawing upon theorization in the areas of business value of AI, agentic information systems (IS) appraisal, and time-situated agency, we extend existing theory in two ways: (1) development of an AI classification (foundational typology) that proposes two dimensions (action autonomy and learning autonomy) for classifying AI by type and level of autonomy; and (2) development of propositions that leverage time-situated agency and the AI classification to explicate how managers' delegation preferences influence their AI investment appraisal. This paper contributes a foundational theoretical platform for furthering AI investment appraisal research. In addition, the paper sets an agenda for future research in this area. [ABSTRACT FROM AUTHOR]

This paper explores the emerging field of prompt engineering within generative AI, emphasizing its role as a critical intersection between art and science. Prompt engineering is identified as the key to unlocking the full potential of generative AI technologies by optimizing human-AI communication. Through a comprehensive analysis of the related literature, this study illustrates how prompt engineering transcends mere technical manipulation, requiring a blend of creativity, strategic thinking, and a deep understanding of generative AI capabilities. This paper provides various strategies for crafting effective prompts, from simple to sophisticated techniques, highlighting the importance of ethical considerations and the potential risks associated with prompt manipulation. By establishing a set of principles and guidelines, this paper aims to advance prompt engineering as a discipline essential for enhancing AI's functionality and reliability and, with this justification, introduces the 'Prompt Engineering for Gen[i]erative AI Framework'. After all, this paper calls for a multidisciplinary approach to prompt engineering, advocating for its recognition and development as a pivotal component of AI literacy and application. Through this exploration, this paper intends to contribute to the evolving dialogue on the integration of human creativity with generative AI capabilities, offering insights into the future of effective and ethical AI interaction. [ABSTRACT FROM AUTHOR]

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies. [ABSTRACT FROM AUTHOR]

As a help to researchers for organizing their thoughts and providing data-driven outcomes, Artificial Intelligence (AI) has the potential to develop scientific writing. AI-powered technologies have been created by businesses like Semantic Scholar and Paper Digest to scan scientific texts and extract pertinent data. By expediting the publishing process and enabling academics to concentrate more on their own work, AI-based writing tools like GPT-3 can produce high-quality papers that closely resemble those of well-known authors. These tools can help with idea organization, creating rough drafts, and enhancing the general caliber of scientific work. For instance, ChatGPT is a useful tool in research and publishing since it may help scientists with material arrangement, draft generation, and proofreading. The drawbacks of AI must be understood, as well as the difficulties posed by prejudice, ethical issues, and the requirement for human innovation. We can improve the scientific writing process and increase scientific research by utilizing AI's potential while adding human knowledge. But there is still room for development, and it is essential to guarantee openness, morality, and reliability in AI-driven technology for academic study. [ABSTRACT FROM AUTHOR]

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Purpose: Artificial intelligence (AI) has received much attention due to its promethean-like powers to transform the management and delivery of public sector services. Due to the proliferation of research articles in this context, research to date is fragmented into research streams based on different types of AI technologies or a specific government function of the public sector (e.g. health, education). The purpose of this study is to synthesize this literature, identify challenges and opportunities, and offer a research agenda that guides future inquiry. Design/methodology/approach: This paper aggregates this fragmented body of knowledge by conducting a systematic literature review of AI research in public sector organisations in the Chartered Association of Business Schools (CABS)-ranked journals between 2012 and 2023. Findings: The search strategy resulted in the retrieval of 2,870 papers, of which 61 were identified as primary papers relevant to this research. These primary papers are mapped to the ten classifications of the functions of government as classified by the Organisation for Economic Co-operation and Development (OECD), and the reported challenges and benefits aggregated. Originality/value: This study advances knowledge by providing a state-of-the-art of AI research based the OECD classifications of government functions, reporting of claimed benefits and challenges and providing a research agenda for future research. [ABSTRACT FROM AUTHOR]

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field. [ABSTRACT FROM AUTHOR]

This paper explores a paradigm shift in work culture in the contemporary art sector due to digital transition and the introduction of AI. New ways of working with AI and digital software are embedded and normalized in everyday Chinese artistic practices. This work includes new forms of creativity and efficiency, yet, simultaneously includes new types of digital labour. This paper conceptualizes this as "digital art work," which draws attention to the often-overlooked aspects of artists' work, particularly their everyday artistic practices that increasingly include digital software and AI. What is the role and position of the artist in an environment where digital software and AI are becoming more central in artistic creation? How do artists creatively (mis)use AI? What does this paradigm shift in work culture mean for the future of the artist's role and the future of the contemporary art sector? This paper draws on 48 semi-structured interviews with visual artists and arts professionals, including painters, sculptors, mixedmedia, and internet artists as well as contemporary art gallery owners, museum project directors, curators, and culture policymakers living and working in China during 2023. The findings show how Chinese artists are mastering AI and opening up new spaces for creativity and how the contemporary art sector in China has already transitioned to a new "digital way" in artistic creation. These findings can help to create policy around AI globally and provide solutions for the sustainability of the artist profession and the future of the contemporary art sector. [ABSTRACT FROM AUTHOR]

This editorial aims to present the papers accepted for the special issue (SI) 'Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management.' First, we provide a brief introduction considering the relationship between AI and operations and supply chain management (OSCM) by highlighting some companies already using and practical insights. In sequence, we introduce the papers selected for the SI. The last section gives some intriguing and challenging research directions for scholars and industry practitioners by highlighting potential topics, research opportunities, and possible benefits. Through this SI, we look forward to helping industry practitioners, policy-makers, scholars, and all interested in this field to gain more knowledge about AI applications and insights in relation to OSCM. [ABSTRACT FROM AUTHOR]

Chatbots have experienced significant growth over the past decade, with a proliferation of new applications across various domains. Previous studies also demonstrate the trend of new technologies, especially artificial intelligence, being adopted in libraries. The purpose of this study is to determine the current research priorities and findings in the field of chatbots in libraries. A systematic literature review was performed utilising the PRISMA checklist and the databases Scopus and Web of Science, identifying 5734 records. Upon conducting the first screening, abstract screening, full-text assessment, and quality assessments guided by the CASP appraisal checklist, 19 papers were deemed suitable for inclusion in the review. The results of the review indicate that the majority of the existing studies were empirical in nature (primarily adopting qualitative methods) and technology reviews with a focus on reviewing the implementation and maintenance, design, evaluation, characteristics, and application of chatbots. The chatbots of interest were mainly text-based and guided chatbots, with closed-source tools with access portals mostly built on library web pages or integrated with social software. The research findings primarily concerned the development models and necessary tools and technologies, the application of chatbots in libraries. Our systematic review also suggests that studies on chatbots in libraries are still in the early stages. [ABSTRACT FROM AUTHOR]

Gravitational wave research presents a range of intriguing challenges, each of which has driven significant progress in the field. Key research problems include glitch classification, glitch cancellation, gravitational wave denoising, binary black hole signal detection, gravitational wave bursts, and minor issues that contribute to the overall understanding of gravitational wave phenomena. This paper explores the applications of artificial intelligence, deep learning, and machine learning techniques in addressing these challenges. The main goal of the paper is to provide an effective view of AI and deep learning usage for gravitational wave analysis. Thanks to the advancements in artificial intelligence and machine learning techniques, aided by GPUs and specialized software frameworks, these techniques have played a key role over the last decade in the identification, classification, and cancellation of gravitational wave signals, as presented in our results. This paper provides a comprehensive exploration of the adoption rate of these techniques, with reference to the software and hardware involved, their effectiveness, and potential limitations, offering insights into the advancements in the analysis of gravitational wave data. [ABSTRACT FROM AUTHOR]

The rapid integration of advanced artificial intelligence technologies is revolutionizing various industries, with the Information Technology field being at the forefront of this transformation. This paper examines the influence of ChatGPT, a state-of-the-art language model developed by OpenAI, on the work environment of IT companies in Macedonia. ChatGPT's applications in automating routine tasks, enhancing customer support, assisting with coding and debugging, and managing knowledge significantly boost productivity and foster innovation. By providing real-time solutions and improving communication, ChatGPT supports a more efficient and collaborative work environment. To gain a deeper understanding of ChatGPT's impact, we conducted a comprehensive survey involving three IT companies in Macedonia. The survey explored the extent of ChatGPT's adoption, its perceived benefits, and the challenges encountered during its integration. The findings reveal that ChatGPT has been instrumental in automating repetitive tasks, thereby allowing employees to focus on strategic and creative endeavors. Furthermore, it has improved customer satisfaction by providing timely and accurate support. However, the deployment of ChatGPT also presents challenges, including ethical considerations, integration complexities, and the need for employee adaptation. Through detailed analysis and case studies of the surveyed companies, this paper highlights the practical benefits and potential obstacles of integrating ChatGPT into existing workflows. The results underscore ChatGPT's transformative potential in enhancing operational efficiency and driving innovation while emphasizing the importance of addressing ethical and practical implementation challenges. This study provides valuable insights for IT companies considering the adoption of AI technologies like ChatGPT in their work environments. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human–AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research. [ABSTRACT FROM AUTHOR]

A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm, detection of invoice pasting and repeating based on ultrasonic sensors, and tidal temporary storage of paper invoices, and so on, the automatic high-speed identification and inspection of bills in the supplier service hall are realized, and the efficiency and accuracy of bill processing in the supplier hall are improved. Experiments show that this research method reinforces ability of identification calibration and order correlation, and improves the efficiency of Invoice filing. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence (AI) systems into administrative procedures can revolutionize the way processes are conducted and fundamentally change established forms of action and organization in administrative law. However, implementing AI in administrative procedures requires a comprehensive evaluation of the capabilities and limitations of different systems, including considerations of transparency and data availability. Data are a crucial factor in the operation of AI systems and the validity of their predictions. It is essential to ensure that the data used to train AI algorithms are extensive, representative, and free of bias. Transparency is also an important aspect establishing trust and reliability in AI systems, particularly regarding the potential for transparent representation in rule-based and machine-learning AI systems. This paper examines the potential and challenges that arise from integrating AI into administrative procedures. In addition, the paper offers a nuanced perspective on current developments in artificial intelligence and provides a conceptual framework for its potential applications in administrative procedures. Beyond this, the paper highlights essential framework conditions that require continuous monitoring to ensure optimal results in practice. [ABSTRACT FROM AUTHOR]

Since its release in November 2022, ChatGPT and the related AI technology have disrupted multiple fields of society where people anticipate its pathways with a mixture of hope and fear. Among the affected fields, education, in particular, may incur one of the largest impacts in the future partly due to its nature of learning and teaching knowledge, an element that is more or less questioned by the rise of these technologies. As education can be seen as a component that determines the future of every other field of society, tools such as ChatGPT must be optimally regulated to enhance its gain or mitigate its loss. To contribute to this goal, this paper approaches the state of ChatGPT and its expected impacts on higher education through the lens of two major educational theories—John Dewey's Reflective-Thought-and-Action model and revised Bloom's taxonomy—aiming to propose possible evaluative criteria for the optimal usage of ChatGPT in academia. As ChatGPT is a relatively new topic of research yet a topic that requires an immediate focus due to its capabilities, this paper also aims to provide these criteria as one of the concrete starting points of future research in this field. [ABSTRACT FROM AUTHOR]

This paper explores the impact of using Artificial Intelligence (AI) in the photographic design process. In the context of rapidly evolving technology, AI applications have become increasingly prominent in various fields, including the photographic industry. This study examines how advanced machine learning and image processing algorithms can improve and simplify creative processes in photography. The main issues addressed include the use of AI in automatic image selection and editing, automatic identification of relevant subjects and generation of artistic effects. These technologies bring not only efficiency but also an increase in the quality of the final results. The paper also highlights the ethical issues associated with the use of AI in photographic design and the importance of human control in these automated processes. The study draws on current research in AI and photography, examining practical applications of existing technologies and exploring future prospects. The results indicate that the intelligent integration of AI into the photographic design process can bring significant benefits, but it is essential to consider ethical issues and maintain a balance between human creativity and automation. [ABSTRACT FROM AUTHOR]

In the paper titled "Exploring the Top 5 Drones for Land Measurements", we conduct a comprehensive analysis of the latest drone technology applied in the field of geospatial surveying. The study evaluates five leading drone models based on their accuracy, efficiency, and cost-effectiveness in land measurement tasks. Through a series of field tests and data comparisons, the paper highlights the strengths and limitations of each drone, providing insights into their suitability for various surveying contexts. The findings aim to guide professionals in selecting the most appropriate drone technology for their specific land measurement needs, ultimately contributing to more informed decision-making in the industry. This abstract summarizes the paper's intent to bridge the gap between drone technology and practical application in land surveying. [ABSTRACT FROM AUTHOR]