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ChatGPT and Bard (now known as Gemini) are becoming indispensable resources for researchers, academicians and diverse stakeholders within the academic landscape. At the same time, traditional digital tools such as scholarly databases continue to be widely used. Web of Science and Scopus are the most extensive academic databases and are generally regarded as consistently reliable scholarly research resources. With the increasing acceptance of artificial intelligence (AI) in academic writing, this study focuses on understanding the reliability of the new AI models compared to Scopus and Web of Science. The study includes a bibliometric analysis of green, sustainable and ecological buying behaviour, covering the period from 1 January 2011 to 21 May 2023. These results are used to compare the results from the AI and the traditional scholarly databases on several parameters. Overall, the findings suggest that AI models like ChatGPT and Bard are not yet reliable for academic writing tasks. It appears to be too early to depend on AI for such tasks.

This paper critically examines the significant strides China has made toward sustainable urban development, emphasizing the evolution of green building practices since 2015. Through a detailed analysis of green building policies under China's 13th and 14th Five-Year Plans, as well as the General Code for Building Energy Conservation and Renewable Energy Utilization, this study highlights the progressive integration of sustainability in construction. Notable advancements include the implementation of stringent energy efficiency standards and the incorporation of renewable energy technologies, which have collectively shifted the paradigm of architectural practices in urban China. Moreover, the paper draws upon several case studies, such as the Shanghai Chenghuaxinyuan Project and Mini Sky City, to illustrate the tangible outcomes and the effectiveness of these policies in real-world settings. These examples not only demonstrate significant reductions in energy consumption and carbon emissions but also reflect the scalability and replicability of such initiatives in other urban contexts. Key findings suggest that China's proactive policy landscape has catalyzed a national movement towards environmentally responsible construction, setting a global benchmark for sustainable urban development. The paper argues that these policy-driven initiatives have important implications for practitioners, who are encouraged to adopt innovative construction technologies and sustainable practices. For policymakers, the study underscores the necessity of continuous support for research and development in green technology, coupled with robust regulatory frameworks to sustain momentum in the green building sector. Finally, the paper offers comprehensive recommendations aimed at enhancing the efficacy and adoption of green building practices, proposing that future policy directions should focus on integrating smart city technologies to further optimize energy use and urban sustainability.

Abstract In response to the urgent environmental and social challenges posed by mining operations, this paper introduces the Mining Area Sustainability Index (MASI), a novel framework aimed at transforming the mining sector towards sustainable practices. Mining activities have historically led to significant environmental degradation, including water contamination and habitat destruction, contributing to climate change and biodiversity loss. These activities also have profound social implications, such as displacing communities, endangering health, and distributing economic benefits inequitably, often leaving local communities in developing countries marginalized. Recognizing these challenges, this paper outlines the consolidation and standardization in sustainability reporting within the mining sector as a pivotal development. The introduction of international standards by the International Sustainability Standards Board (ISSB) and the European Union's European Sustainability Reporting Standards (ESRS) marks significant strides towards enhancing transparency, accountability, and sustainability across mining operations. Furthermore, the concept of double materiality, assessing both financial and social impacts of mining, represents an advancement in comprehending the broader societal impacts of mining alongside its environmental and economic effects. MASI emerges as a comprehensive tool designed to assess the sustainability of mining areas, offering a nuanced understanding of mining activities' impacts on local environments, societies, and economies. By focusing on localized, community-centric evaluations, MASI aims to fill existing gaps in sustainability assessment and provide a reference for local residents to gauge the sustainability of their surroundings. This framework advocates for a multidimensional approach to sustainability, encompassing ecological preservation, social welfare, and economic viability, urging a reimagined, sustainable future for mining communities.

Abstract This “Policy Brief” explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom’s air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution’s impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs.

Artikkelen analyserer fremveksten av personalfeltet i årene etter andre verdenskrig og ser det i sammenheng med behovet for å utvikle en personalforvaltning som kunne stimulere arbeidsviljen og produktiviteten. Selv om praksisen kunne variere, var det mange av de første personalsjefene som inntok en mer nøytral stilling i forholdet mellom arbeidsgiver og arbeidstaker. Det ble imidlertid rettet kritikk mot at personalfunksjonen la for sterk vekt på det sosiale velferdsarbeidet og personalsjefenes innflytelse kunne også være begrenset. Gjennom 1970-årene kom personalfeltet tettere på kjernevirksomheten og ble mer orientert mot bedriftens produktivitet fremfor ansattes velferd og trivsel. Vi drøfter også den personalfaglige yrkesrollen som i mange år led under mangelen på en samlende interesseorganisasjon som kunne koordinere feltet. Yrkesrollen var praktisk rettet med oppgaver som gjorde det faglige grunnlaget for yrkesutøvelsen sammensatt. Til tross for mange initiativer, viste det seg å være utfordrende å etablere en samlende personalfaglig utdanning som kunne styrke yrkesgruppens faglige grunnlag og anseelse.

Ambiguity has always been a pain in the neck of Natural Language Processing (NLP). Despite enormous AI tools for human language processing, it remains a key concern for Language Technology Researchers to develop a linguistically intelligent tool that could effectively understand linguistic ambiguity and creativity possessed by human language. In this regard, the newly designed AI tool ChatGPT has dramatically attracted human attention due to its remarkable ability to answer human questions from a wide range of domains, which needs a reality check. This article scrutinises ChatGPT’s ability to answer and interpret neologisms, codemixing, and linguistically ambiguous sentences. For this, we have tested lexically, syntactically, and semantically ambiguous expressions, codemixed words, as well as a few language game instances. The findings show that ChatGPT still fails to understand linguistically complex sentences, specifically those common in everyday discourse or not part of any standard textbook. More specifically, semantically ambiguous sentences and language games remain an uphill task for ChatGPT to understand. This has implications for further improving the output of ChatGPT.

AbstractIn this study, we aim to investigate the potential advantages of integrating the new generative artificial intelligence (AI) technology, ChatGPT, into higher education, specifically within the field of medical education. The focus is on exploring ChatGPT’s applications in personalized learning, assessment, and content creation while also addressing the management of its limitations and ethical considerations. Furthermore, we explore the use of ChatGPT as a medical instructor in a medical classroom context. We seek to elucidate responses to a preset of questions in two categories separated based on targeted remediation, pedagogical knowledge, teacher ethics, query detail, practicality, and communication pattern. These responses are analyzed based on two rubrics designed on the basics of pedagogical prerequisites, and findings are reached with a thorough comparative analysis. We hope that this research will improve the effective implementation of ChatGPT as a tool for enhancing learning and skill development while maintaining ethical awareness for medical professionals.

AbstractScientific workflows are a common and critical part of scientific computing, involving complex computations and oversized and distributed computing resources. Efficient workflow execution requires scheduling algorithms considering task dependencies, resource requirements, and deadlines. Cloud computing provides an innovative architecture for extensive heterogeneous computing services. However, scheduling hybrid cloud resources with deadline restrictions while observing QoS standards is an NP-complete task. Mapping workflow tasks to virtual machines and determining the optimal schedule order is a challenging aspect of cloud computing. By executing task requests on the most advantageous virtual machine in the resource pool, energy consumption, overall execution time, and computing costs can be reduced. This research aims to identify the best location to process applications using user’s demand and priority. A multi-objective genetic algorithm is proposed to achieve this objective, which considers conflicting objectives such as time, energy, cost, and deadline. The algorithm initializes an efficient ranking heuristic approach and predicts the earliest finish time (PEFT) using the Bayesian approach to improve the Pareto fronts. This approach enhances the VM migration of cloud-based tasks and optimizes the search space for conflicting objectives. Experimental findings show that the proposed approach reduces cost by 5–6% and time delay by 8% compared to existing approaches. The proposed approach offers an effective solution for scheduling scientific workflows on cloud computing resources while considering various QoS standards. The results demonstrate the potential of multi-objective genetic algorithms for optimizing workflow scheduling in cloud computing environments.

IntroductionEnvironmental degradation poses a significant threat, making the comprehension of sustainable behaviour imperative for both environmental and business reasons. Embracing sustainable practices and reducing the unnecessary consumption of resources is essential in our current times. One can be sustainable by focusing on either buying sustainable or green products or by using fewer resources, i.e., by adopting curtailment behaviour. The purpose of this study is to determine various factors and their impact on the curtailment behaviour of youths.MethodsWe used an exploratory and descriptive research design. Responses were generated from 513 young respondents using a structured questionnaire developed based on the extant literature.ResultsThe structural model findings showed that social norms, eco-concern, and religiosity significantly and positively affect curtailment behaviour. However, we observed no significant influence of financial concern or government policy on curtailment behaviour. We also tested the moderation impact of government policy on the relationship between economic concern and curtailment behaviour. The findings suggest that the relationship between eco-concern and curtailment behaviour is stronger for consumers exhibiting higher commitment to government policy.DiscussionIt is imperative that politicians exercise critical thought and devise strategies to encourage more sustainable consumer behaviour. With the strains that our world is under now and in the future, we need to unite around a common goal: ensuring that our planet is sustainable for coming generations. The study findings are useful for academicians, marketers, and policymakers.

The integration of digitalization and sustainability principles, encapsulated within the Twin Transformation (TT) approach, has emerged as a transformative paradigm within sport management. However, there is a critical gap in understanding how these two transformative forces can be synergistically harnessed within the field of sport management. This paper explores the conceptual underpinnings of TT and its implications for enhancing organizational performance and addressing contemporary challenges in the sports industry. Drawing upon interdisciplinary perspectives, the paper examines how TT fosters innovation, enhances fan engagement, and promotes environmental responsibility within sport management. Through a synthesis of theoretical insights and practical examples, the paper highlights the potential of TT to drive positive change across various facets of sport management, from operational practices to stakeholder engagement. Furthermore, the paper underscores the importance of ongoing research and scholarly inquiry in advancing our understanding of TT and its implications for theory and practice in sport management. Overall, this paper provides a comprehensive overview of TT in sport management, offering valuable insights for researchers, practitioners, and stakeholders seeking to navigate the dynamic landscape of the sports industry.

The paper outlines the urgency and strategies for transitioning from fossil fuels to renewable energy discussed at the 2023 Climate Change Conference (COP28). It emphasizes the Paris Agreement's role and highlights the environmental harm of fossil fuels, advocating for sustainable alternatives like solar, wind, hydro, and geothermal power. This transition is crucial for reducing greenhouse gas emissions and promoting economic and health benefits. Key findings reveal advancements in renewable technologies, resulting in job creation, energy independence, and improved health due to reduced pollution. However, the transition faces challenges such as high initial costs and the need for advanced infrastructure, particularly in developing countries. The paper underscores the importance of sustainable mining for essential materials like lithium and cobalt, and references António Guterres's “five-point energy plan” as a strategic approach to address these issues. In conclusion, the paper stresses the necessity of global collaboration among governments, businesses, and civil societies. It asserts that the path to sustainable energy is rich with opportunities for innovation, growth, and equitable progress, providing a comprehensive roadmap for a feasible and just energy transition for all nations.

Machine learning (ML) can enhance agricultural yields by combating plant diseases and climate change. However, traditional image processing techniques for disease detection have limitations in robustness and generalizability. In this study, we investigate deep transfer learning for fine-grained disease classification in maize plants, which is a challenging task due to the subtle and nuanced disease patterns. We use four tailored deep learning frameworks: VGGNET, Inception V3, ResNet50, and InceptionResNetV2. ResNet50 achieves the highest validation accuracy of 87.51%, precision of 90.33%, and recall of 99.80%, demonstrating the efficacy and superiority of our approach. Our study offers an innovative solution for accurate disease classification in maize plants.

Digital fabrication technologies, such as 3D printing and CNC (Computer Numerical Control) machining, are spearheading a transformative shift in urban development across China. This movement is underpinned by robust governmental support and attractive financial incentives, which are pivotal in fostering innovation within the realms of construction and architectural design. The integration of these cutting-edge technologies is poised to redefine conventional construction methodologies, promising unprecedented levels of efficiency and precision. This evolution extends beyond mere construction efficiency; it is instrumental in the emergence of smart cities, enhancing urban services and infrastructures and advancing sustainability goals. The deployment of digital fabrication techniques signifies a pivotal moment in China's urbanization journey, ushering in a new epoch where the dynamics of urban construction and design are fundamentally altered. This paradigm shift is anticipated to have profound implications for the future of urban development, offering exciting opportunities for innovation and sustainability. With digital fabrication at the helm, China is navigating towards a future where urban environments are not only more efficiently constructed but are also more responsive to the evolving needs and aspirations of their inhabitants. The result is a promising horizon for urban development, where the fusion of technology, sustainability, and smart urban planning paves the way for cities that are not only more livable but also emblematic of future-forward thinking and environmental stewardship.

In this study we test ChatGPT-4’s ability to provide accurate information about the origins and evolution of SWOT analysis, perhaps the most widely used strategy tool in practice worldwide. ChatGPT-4 is tested for historical accuracy and hallucinations. The API is prompted using a Python script with a series of structured questions from an Excel file and the results are recorded in another Excel file and rated on a binary scale. Our findings present a nuanced view of ChatGPT-4’s capabilities. We observe that while ChatGPT-4 demonstrates a high level of proficiency in describing and outlining the general concept of SWOT analysis, there are notable discrepancies when it comes to detailing its origins and evolution. These inaccuracies range from minor factual errors to more serious hallucinations that deviate from evidence in scholarly publications. However, we also find that ChatGPT-4 comes up with spontaneous historically accurate facts. Our interpretation of the result is that ChatGPT is largely trained on easily available websites and to a very limited extent has been trained on scholarly publications on SWOT analysis, especially when these are behind a paywall. We conclude with four propositions for future research.

Introduction: The simplicity of integration and co-type features of microstrip antennas make them intriguing for a broad variety of applications, particularly with the growing usage of mmWave bands in wireless communications and the constant rise in data transfer in communication situations.Method: This paper proposes a novel design of micrstrip patch antenna for mmWave B5G communication. The main idea is to realize four-mode antenna the operates in four different frequencies. The geometry is rectangular patch whose resonance frequency is adjusted by varying the walls and pins of the structure.Results: Simulation results show that the proposed antenna design has improved fractional bandwidth and performance as compared with existing antennas.Discussion: The observed curve indicates that, in agreement with the modeling findings, there are four resonance spots in the operational frequency region of 2.5–3.4 GHz: 2.68 GHz, 2.9 GHz, 3.05 GHz, and 3.3 GHz, which correspond to TM1/2,0, TM3/2,0, and TMRS, respectively, and TM1/2,2 four resonant modes, within the frequency range, the observed antenna gain peak is around 9 dBi, which is consistent with the measured results.

PurposeThe objective of this study is to explore the impact of artificial intelligence (AI) development on the sports industry labor market, the ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles.MethodologyIt refers to the inductive approach in the spirit technological determinism theory. It is based on the literature review and written qualitative, semi-structured interviews (N = 14) with sports human resources, management, and technology professionals (purposive sampling). Analysis involved inductive coding and line-by-line analytics of the data.FindingsThe labor market implications of AI in the sports industry are multifaceted. New job roles are likely to emerge, demanding a blend of AI expertise, data-analysis skills, and sports domain knowledge. Professionals in roles such as sports data analysts and marketing experts may find increasing opportunities. However, certain jobs undergo transformation as AI automates routine tasks. It requires individuals to upskill or transition to roles that require a deeper understanding of AI. This necessitates the creation of responsibilities focused on ethical AI governance and oversight.OriginalityIt is important to research the impact of AI dissemination on the sports industry labor market in a holistic manner because the effects of AI are complex and far-reaching. While there are potential benefits to the implementation of AI, there are also potential risks and challenges that need to be addressed, the implementation of AI in the sports industry could have broader social and ethical implications that need to be considered.

IntroductionThis study empirically investigates the attitude of tobacco and alcohol consumers towards health insurance purchase in India. The study aims to determine the factors which plays a significant role in determining the purchase intention of health insurance among tobacco and alcohol consumers.MethodsWe propose an extended theory of planned behavior (TPB) model comprising factors like attitude, subjective norms, perceived behavior control, perceived usefulness, perceived product risk, and intention to purchase. We collected responses from 420 tobacco and alcohol consumers through a Google Form link shared via different social media platforms. SPSS has been used to perform exploratory factor analysis, whereas AMOS has been used to validate the constructs, confirm the relationships among the variables, and analyze the data.ResultsThe analysis outcomes demonstrate that subjective norms, perceived product risk, and perceived behavioral control are the factors that have a positive and significant effect on health insurance purchase intention among consumers.DiscussionThis research offers valuable insights to the insurance sector, government officials, policymakers, and academicians. Insurance companies may consider the criteria analysed when creating policies to promote the expansion of the health insurance sector.

In a groundbreaking advance for sustainable energy, a recent study unveils a transformative approach to hydrogen production, signaling a major shift in renewable energy. This study integrates one-dimensional covalent organic frameworks (COFs) with the innovative concept of harvesting atmospheric water, marking a significant stride in the quest for cleaner energy sources. It stands as the first successful integration of Atmospheric Water Harvesting (AWH) with photocatalysis, as well as the inaugural application of COFs in the photocatalytic evolution of hydrogen from water vapor. It envisions a future where our atmosphere becomes a vital, untapped reservoir for clean energy, merging cutting-edge science with a profound commitment to environmental sustainability. This breakthrough challenges conventional energy paradigms and opens up exciting possibilities for a world striving towards ecological balance and energy innovation.

The mineral processing industry is pivotal in natural resource extraction and historically contributes to environmental damages like land degradation and groundwater contamination. In the face of resource scarcity and environmental pollution, sustainable solutions are increasingly vital. The circular economy focuses on reusing, recycling, and reducing waste, which turns unwanted by-products into valuable assets. Sustainable chemical processes offer innovative solutions in waste material reclamation and integration into the production chain. These practices not only mitigate waste but also enable resource recovery, turning waste management costs into profits. Repurposing mineral waste reduces mining's environmental impact, lessens new mining needs, and yields economic advantages.

BackgroundLarge language models (LLMs) have revolutionized natural language processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 (OpenAI), and Bard (Google LLC), find applications beyond natural language processing, attracting interest from academia and industry. Students are actively leveraging LLMs to enhance learning experiences and prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India. ObjectiveThis comparative analysis aims to evaluate the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. MethodsIn this paper, we evaluated the performance of the 3 mainstream LLMs, namely GPT-3.5, GPT-4, and Google Bard, in answering questions related to the NEET-2023 exam. The questions of the NEET were provided to these artificial intelligence models, and the responses were recorded and compared against the correct answers from the official answer key. Consensus was used to evaluate the performance of all 3 models. ResultsIt was evident that GPT-4 passed the entrance test with flying colors (300/700, 42.9%), showcasing exceptional performance. On the other hand, GPT-3.5 managed to meet the qualifying criteria, but with a substantially lower score (145/700, 20.7%). However, Bard (115/700, 16.4%) failed to meet the qualifying criteria and did not pass the test. GPT-4 demonstrated consistent superiority over Bard and GPT-3.5 in all 3 subjects. Specifically, GPT-4 achieved accuracy rates of 73% (29/40) in physics, 44% (16/36) in chemistry, and 51% (50/99) in biology. Conversely, GPT-3.5 attained an accuracy rate of 45% (18/40) in physics, 33% (13/26) in chemistry, and 34% (34/99) in biology. The accuracy consensus metric showed that the matching responses between GPT-4 and Bard, as well as GPT-4 and GPT-3.5, had higher incidences of being correct, at 0.56 and 0.57, respectively, compared to the matching responses between Bard and GPT-3.5, which stood at 0.42. When all 3 models were considered together, their matching responses reached the highest accuracy consensus of 0.59. ConclusionsThe study’s findings provide valuable insights into the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. GPT-4 emerged as the most accurate model, highlighting its potential for educational applications. Cross-checking responses across models may result in confusion as the compared models (as duos or a trio) tend to agree on only a little over half of the correct responses. Using GPT-4 as one of the compared models will result in higher accuracy consensus. The results underscore the suitability of LLMs for high-stakes exams and their positive impact on education. Additionally, the study establishes a benchmark for evaluating and enhancing LLMs’ performance in educational tasks, promoting responsible and informed use of these models in diverse learning environments.

The effectiveness of implementing intelligent reflecting surface (IRS) for millimeter-wave (mmWave)-non-orthogonal multiple-access (NOMA) systems has allowed for significant sum-rate improvements. The majority of recent research has not discussed how well the IRS-mmWave-NOMA combination performs. Therefore, a new technique for resource optimization in IRS-mmWave-NOMA B5G wireless networks is proposed in this research. The key concept is to use an iterative algorithm to solve the optimization issue while incorporating many crucial constraints like the selection of the IRS beam, transmit power distribution, and decoding order, among others. Simulation results show that the proposed approach outperforms existing state-of-the-art algorithms in terms of computation delay, sum rate and NMSE. The computational complexity also validated the simplicity and hardware-friendly feature of the proposed algorithm.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was launched in November 2022 to early June 2023. It aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By retrieving data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's early footprint in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations, and innovations to enhance ChatGPT's capabilities and impact across domains.

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions.

AbstractIn this study, we present a brief narrative review focused on ChatGPT, a state-of-the-art conversational agent developed using OpenAI’s Generative Pretrained Transformer (GPT) framework. Distinctive for its ability to generate text of high quality in real-time, ChatGPT has emerged as a leader among artificial intelligence chatbots, garnering interest from both commercial and scholarly circles. Our review explores the technological underpinnings of ChatGPT, examines its inherent features that support its performance, and analyzes existing research on its applications and impacts across several domains. Through this assessment, we delineate ChatGPT’s strengths and limitations, offering informed recommendations for future investigations in this burgeoning research field.

AbstractThe introduction of the AI-powered chatbot ChatGPT by OpenAI has sparked much interest and debate among academic researchers. Commentators from different scientific disciplines have raised many concerns and issues, especially related to the ethics of using these tools in scientific writing and publications. In addition, there has been discussions about whether ChatGPT is trustworthy, effective, and useful in increasing researchers’ productivity. Therefore, in this paper, we evaluate ChatGPT’s performance on tasks related to bibliometric analysis, by comparing the output provided by the chatbot with a recently conducted bibliometric study on the same topic. The findings show that there are large discrepancies and ChatGPT’s trustworthiness is low in this particular area. Therefore, researchers should exercise caution when using ChatGPT as a tool in bibliometric studies.

AbstractIn this paper the virus theory is applied to gain insight into the intra-organizational diffusion and evolution of the Balanced Scorecard. The study draws on longitudinal data about the use of the Balanced Scorecard in a Norwegian bank, by interviews with managers and rich internal documents. The findings suggest that virus theory offers a diverse set of theoretical mechanisms that can describe a multitude of organizational processes in the evolution of a management idea. Specifically, it is found that the case organization was infected directly through top management´s contact with the idea and indirectly through an external institution. Interestingly, there was an incubation period and the idea was lingering in the organization without implementation. However, organizational immunity to the idea was averted by management, and new routines and structures led to replication. Furthermore, the idea mutated to resemble more a measurement than a strategical tool. The concept was eventually deactivated in the organization but may still lie dormant. The paper concludes that virus theory is well-suited for analysing the intra-organizational diffusion and evolution of management ideas. In this way, virus theory can fill in some of the blind spots of management fashion theory.

Introduction: Metamaterials consist of periodic arrangements of artificial subwavelength units that possess electromagnetic properties not present in natural media. It has attracted more interest due to its ability to alter electromagnetic radiation in a flexible manner, which has resulted in the development of multiple radio frequency devices based on metamaterials. Metamaterials with the required frequency band for electric or magnetic resonance can be made using unit cell structure. The incident electromagnetic wave will enter the metamaterials and be kept there in the absence of reflection.Methods: This paper proposes a novel broadband THz absorber filter based on graphene for emerging applications. The proposed structure comprised of three parts. The top layer consists of graphene, the middle layer consists of dielectric and the bottom layer is made up of gold.Results: The proposed structure is experimentally designed and validated using the COMSOL simulator.Discussion: Simulation results show that the proposed absorber has better performance as compared with existing methods.

The Internet-of-Things (IoT)-based healthcare systems are comprised of a large number of networked medical devices, wearables, and sensors that collect and transmit data to improve patient care. However, the enormous number of networked devices renders these systems vulnerable to assaults. To address these challenges, researchers advocated reducing execution time, leveraging cryptographic protocols to improve security and avoid assaults, and utilizing energy-efficient algorithms to minimize energy consumption during computation. Nonetheless, these systems still struggle with long execution times, assaults, excessive energy usage, and inadequate security. We present a novel whale-based attribute encryption scheme (WbAES) that empowers the transmitter and receiver to encrypt and decrypt data using asymmetric master key encryption. The proposed WbAES employs attribute-based encryption (ABE) using whale optimization algorithm behaviour, which transforms plain data to ciphertexts and adjusts the whale fitness to generate a suitable master public and secret key, ensuring security against unauthorized access and manipulation. The proposed WbAES is evaluated using patient health record (PHR) datasets collected by IoT-based sensors, and various attack scenarios are established using Python libraries to validate the suggested framework. The simulation outcomes of the proposed system are compared to cutting-edge security algorithms and achieved finest performance in terms of reduced 11 s of execution time for 20 sensors, 0.121 mJ of energy consumption, 850 Kbps of throughput, 99.85 % of accuracy, and 0.19 ms of computational cost.

COVID-19 has brought significant changes to our political, social, and technological landscape. This paper explores the emergence and global spread of the disease and focuses on the role of Artificial Intelligence (AI) in containing its transmission. To the best of our knowledge, there has been no scientific presentation of the early pictorial representation of the disease's spread. Additionally, we outline various domains where AI has made a significant impact during the pandemic. Our methodology involves searching relevant articles on COVID-19 and AI in leading databases such as PubMed and Scopus to identify the ways AI has addressed pandemic-related challenges and its potential for further assistance. While research suggests that AI has not fully realized its potential against COVID-19, likely due to data quality and diversity limitations, we review and identify key areas where AI has been crucial in preparing the fight against any sudden outbreak of the pandemic. We also propose ways to maximize the utilization of AI's capabilities in this regard.

Waste management is a complex research domain. While the domain is challenging in terms of content, it is also a diverse and cross-disciplinary research subject. One of its important components includes efficient decision-making at various levels and stages. Therefore, Multi-criteria decision-making (MCDM) techniques have found decent applications in this domain. The field of MCDM techniques-based waste management has been examined using bibliometric analysis in this paper in order to report a systematic overview of the trends and advancements in this area of study. The Scopus database provided 216 publications on the aforementioned subject written between 1992 and 2022. The 216 articles include 56 countries, 158 institutions, and 160 authors. Furthermore, Asian countries, including India, Iran, and China, dominate this field of study. The geographical disparity in the output of publications is visible. Journal of cleaner production, Waste Management and Waste Management and Research are the major journals publishing on MCDM techniques-based waste management research. Given that majority of the articles include multiple authors, it can be said that there is a lot of collaborative research in this area. Overall, the current study provides a thorough analysis of the development in the domain of waste management using MCDM techniques. The trend suggests that it will continue to be a focus of research for academicians, environmentalists and policymakers in the future.

In this research, the design and use of combined systems for the simultaneous production of water, heat, and energy have been proposed, and, to fulfill the water, electricity, and heat demands of a hotel, modeling of the multi-effect evaporative desalination (MED) and combined heat and power (CHP) generation system was done. Then, the design of these two systems was administered in a combined way. This design was applied in order to evaluate the economy of the combined system compared to separate systems. The performed scenario was executed every 24 h during the two seasons of the year. The genetic algorithm was used to optimize this system, and it was considered the objective function to minimize the annual costs. The results showed that the nominal capacity of the gas turbine and backup boiler in the CHP + MED + thermal energy storage (TES) system was (14%) larger and (8.2%) smaller, respectively, compared to the CHP+ MED system. In addition, by using the energy storage tank in the combined CHP + MED system, 5.1% of the annual costs were reduced.

Terahertz (THz) radiation, which has applications in the imaging of objects, non-destructive testing, satellite communication, medical diagnostics, and biosensing, has generated a great deal of attention due to its remarkable properties. This paper proposes a novel broadband filter for THz applications. The main idea is to overcome the insertion loss and bandwidth issues by modeling a frequency-domain finite difference method and guided-mode resonance (GMR). The optimal design scheme of the wideband pass filter based on the circular resonant ring is discussed by comparing the transmission parameters under various parameters. This scheme overcomes the restriction of the narrow passband bandwidth of the prior THz filters and achieves approximately 3 dB bandwidth of 0.54 THz. The proposed THz filter paper also has the advantages of a straightforward structure, low processing costs, and ease of conformal with other structures, and it can be used for stealth fighters, new communication technology, and precise instruments. In addition, when compared to existing models, the suggested filter offers higher 3 dB BW operation, increased transmittance, low insertion loss, and stable performance at various oblique angles.

PurposeIndian higher education institutions are diverse in nature; there are institutions with good infrastructure and resources as well as institutes that have little in terms of resources and infrastructure. Keeping in mind the relevance of knowledge sharing in academic institutions, the researchers in the present study have tried to find factors determining the knowledge sharing behavior of the academicians of different institutes in India.DesignThe researchers in the present work have expanded on extant research by demarcating factors that affect the knowledge sharing behavior of academicians. A structured questionnaire was shared through e-mail and social media groups, and a snowball approach was used to reach out to the maximum number of respondents.FindingsThe present study offers an integrated and extended theory of planned behavior (TPB) theoretical model, augmenting it with constructs such as motivation and the opportunity to share knowledge adapted from related studies. The findings of this research provide theoretical as well as practical suggestions in determining and explaining the knowledge sharing behavior of academicians.OriginalityThe researchers in the present study have tried to present a shorter and more reliable scale that can be used to assess the behavioral intentions of academicians to share knowledge.

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review of over 100 Scopus-indexed publications on ChatGPT, aiming to provide a taxonomy of ChatGPT research and explore its applications. We critically analyze the existing literature, identifying common approaches employed in the studies. Additionally, we investigate diverse application areas where ChatGPT has found utility, such as healthcare, marketing and financial services, software engineering, academic and scientific writing, research and education, environmental science, and natural language processing. Through examining these applications, we gain valuable insights into the potential of ChatGPT in addressing real-world challenges. We also discuss crucial issues related to ChatGPT, including biases and trustworthiness, emphasizing the need for further research and development in these areas. Furthermore, we identify potential future directions for ChatGPT research, proposing solutions to current challenges and speculating on expected advancements. By fully leveraging the capabilities of ChatGPT, we can unlock its potential across various domains, leading to advancements in conversational AI and transformative impacts in society.

With the rapid development and integration of AI in various domains, understanding the nuances of AI research has become critical for policymakers, researchers, and practitioners. However, the results are vast and diverse and even can be contradictory or ambivalent, presenting a significant challenge for individuals seeking to grasp and synthesize the findings. This perspective paper discusses the ambivalent and contradictory research findings in the literature on artificial intelligence (AI) and explores whether ChatGPT can be used to navigate and make sense of the AI literature.

HACCP (Hazard Analysis and Critical Control Points) and modern quality management systems have a significant impact on public health in the food industry. These systems ensure that food products are safe for consumption by identifying and managing potential hazards at every stage of the production process. To stimulate ongoing studies in both developing and underexplored areas of inquiry, this research synthesizes and organizes the contributions made in this field. It examines more than 40 years of studies from Scopus data base on HACCP and modern quality management systems in the food industry using the VOSviewer software version 1.6.18 (Leiden University, The Netherlands) and bibliometrix R-package. This represents, to the authors’ knowledge, the first bibliometric analysis undergone in this direction. The graphical framework demonstrates the highest developments in research and the literature review investigates barriers and opportunities of implementing HACCP in food industry organizations. Findings indicate that until the beginning of the 1990s, there was not a large number of scientific production in the field of HACCP and modern quality management systems in the food industry. The USA were the most prolific affiliation terms of scientific production until 2012, when studies from Italy, the United Kingdom, China and Greece intensified. Currently, the most prolific country in terms of publications is Italy. In terms of global cooperation, the United Kingdom, The United States and The Netherlands represent most active nations on this topic Motor themes that reflect the main interest of the researchers include food diseases, quality control, hazards or food supply. The study also provides future research directions regarding food quality and safety management. These should be focused on improving the safety, quality, and sustainability of food products, while also adapting to changing consumer demands, emerging risks, and regulatory requirements.

Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.

The emergence of Industry 5.0 took place in the mid-2010s, presenting a novel vision for the future of an industry that places emphasis on human involvement in the production process. Following the outbreak of the COVID-19 pandemic, there has been a substantial surge in the popularity of this concept, gaining traction not only in the business realm but also within academic circles. This increased attention can be attributed to a heightened focus on crucial aspects such as sustainability and resilience. The objective of this study is to present an updated overview of key bibliometric trends in Industry 5.0 research. The findings indicate a remarkable expansion of research activities in the field of Industry 5.0, as evidenced by a substantial increase in the number of publications and citations. Concurrently, the growth of Industry 5.0 research has led to the emergence of diverse perspectives and the exploration of related research themes such as artificial intelligence, big data, and human factors. In summary, this study enhances our understanding of the Industry 5.0 concept by providing an updated overview of the current state of research in this area and suggesting potential avenues for future investigations.

The old economic and social growth model, characterized by centralized fossil energy consumption, is progressively shifting, and the third industrial revolution, represented by new energy and Internet technology, is gaining traction. Energy Internet, as a core technology of the third industrial revolution, aims to combine renewable energy and Internet technology to promote the large-scale use and sharing of distributed renewable energy as well as the integration of multiple complex network systems, such as electricity, transportation, and natural gas. This novel technology enables power networks to save energy. However, multienergy synchronization optimization poses a significant problem. As a solution, this study proposed an optimized approach based on the concept of layered control–collaborate optimization. The proposed method allows the distributed device to plan the heat, cold, gas, and electricity in the regional system in the most efficient way possible. Moreover, the proposed optimization model is simulated using a real-number genetic algorithm. It improved the optimal scheduling between different regions and the independence of distributed equipment with minimal cost. Furthermore, the inverse system and energy and cost saving rate of the proposed method are better than those of existing methods, which prove its effectiveness.

In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.

The concept of Industry 4.0 has received enormous levels of attention since it was introduced in 2011 and continues to be a dominant management trend. However, during the last few years, Industry 5.0 has emerged as an alternative visionary management concept. Although Industry 5.0 builds on and is related to its predecessor, it provides a different vision of the future of industry. Therefore, the aim of this paper is to carry out an in-depth examination of these two related management concepts, using management fashion theory as an interpretive framework and lens. Viewing Industry 4.0 and 5.0 from this vantage point involves analyzing and tracing the involvement activities of the suppliers of the two concepts as well as gauging the concepts’ demand-side impact. Generally, the comparative analysis of the cases of Industry 4.0 and 5.0 indicates that, while the growth and popularity of Industry 4.0 appear to have plateaued, it remains at a high level. Although Industry 5.0 is growing in popularity and is gaining followers and supporters, it is still a much more marginal concept and movement. This study contributes to the management fashion literature by showing how two highly related but competing management concept movements coevolve. Furthermore, the two cases provide novel insights into the trajectories and lifecycles of fashionable management concepts, in particular, the early phase where actors are actively attempting to generate credibility and legitimacy and drum up support and enthusiasm for their preferred ideas.

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor’s judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.

In this study, the thermodynamic performance of a combined gas turbine system equipped with a tubular solid oxide fuel cell and hydrogen fuel was investigated. All components of the system were separately modeled using thermodynamic relations. The simulation results showed that the efficiency of the combined system decreased with an increase in the turbine inlet temperature, whereas the power of the system increased. In addition, increasing the temperature entering the turbine and increasing the pressure ratio increased the production entropy and, as a result, increased the irreversibility of the system. The results of the research at the design point showed that 65% of the irreversibility of the system was caused by the combustion chamber and fuel cell (35% of the amount of entropy produced, the contribution of the combustion chamber, and 30% of the contribution of the solid oxide fuel cell) and 19% was due to the contribution of the heat exchanger. In addition, the combined system has an efficiency of 9.81%, while the system without a fuel cell has an efficiency of 33.4%, which shows the extraordinary performance of the combined system.

BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals.

IoT-enabled healthcare apps are providing significant value to society by offering cost-effective patient monitoring solutions in IoT-enabled buildings. However, with a large number of users and sensitive personal information readily available in today’s fast-paced, internet, and cloud-based environment, the security of these healthcare systems must be a top priority. The idea of safely storing a patient’s health data in an electronic format raises issues regarding patient data privacy and security. Furthermore, with traditional classifiers, processing large amounts of data is a difficult challenge. Several computational intelligence approaches are useful for effectively categorizing massive quantities of data for this goal. For many of these reasons, a novel healthcare monitoring system that tracks disease processes and forecasts diseases based on the available data obtained from patients in distant communities is proposed in this study. The proposed framework consists of three major stages, namely data collection, secured storage, and disease detection. The data are collected using IoT sensor devices. After that, the homomorphic encryption (HE) model is used for secured data storage. Finally, the disease detection framework is designed with the help of Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm. The experiment is conducted on a Python-based cloud tool. The proposed system outperforms current e-healthcare solutions, according to the findings of the experiments. The accuracy, precision, F1-measure, and recall of our suggested technique are 96.87%, 97.45%, 97.78%, and 98.57%, respectively, according to the proposed method.

Background: Curcumin has been widely used to treat a variety of diseases and disorders since ancient times, most notably for the purpose of healing wounds. Despite the large number of available reviews on this topic, a bibliometric tool-based meta-analysis is missing in the literature. Scope and approach: To evaluate the influence and significance of the countries, journals, organizations and authors that have contributed the most to this topic, the popular bibliometric markers, including article count, citation count, and Hirsch index (H-index), are taken into account. Their collaborative networks and keyword co-occurrence along with the trend analysis are also sketched out using the VOSviewer software. To the best of our knowledge, this is the first bibliometric review on the topic and hence it is envisaged that it will attract researchers to explore future research dimensions in the related field. Key findings and conclusions: India provided the most articles, making up more than 27.49 percent of the entire corpus. The International Journal of Biological Macromolecules published the most articles (44), and it also received the most citations (2012). The Journal of Ethnopharmacology (28 articles) and Current Pharmaceutical Design (20 articles) were the next most prolific journals with 1231 and 812 citations, respectively. The results indicate a significant increase in both research and publications on the wound-healing properties of curcumin. Recent studies have concentrated on creating novel medicine-delivery systems that use nano-curcumin to boost the effect of the curcumin molecule in therapeutic targeting. It has also been observed that genetic engineering and biotechnology have recently been employed to address the commercial implications of curcumin.

Google Books Ngram Viewer (GNV) is an analytical tool that uses quantitative methods to analyze digitized text. This paper looks at the possibilities and limitations of using GNV in management fashion research, an area of management research that examines the lifecycle and evolution of management concepts and ideas. GNV provides a historical big picture of the lifecycle and popularity of specific terms and phrases in books. It is argued that this tool could have a natural application in the study of management fashions, since books are a medium through which popular management concepts and ideas have traditionally been diffused. The paper provides several illustrative examples of how GNV can be applied to study management fashions and identifies the tool’s main possibilities and limitations. Although GNV has obvious advantages such as accessibility and user-friendliness, researchers should exercise caution, as it only provides a partial picture of the impact of management fashions.

Communication between teachers and parents is an important research topic in the field of special education. Although this type of communication is fraught with challenges such as conflict and lack of trust, there are also some success factors such as collaborative two-way communication and the use of appropriate technologies to facilitate communication. In this paper, we set out to explore this topic in more depth, by examining the experiences of parents and teachers of special-needs students when it comes to communication. A small exploratory qualitative study of social media posts from Reddit is performed. The aim is to shed light on the experiences of parents and teachers. The findings show that both parts of this dyadic relationship have mixed experiences, but the general sentiment is skewed towards the negative. These findings are discussed in relation to the literature on parent-teacher communication.