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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]

With the global outbreak of coronavirus disease (COVID-19) all over the world, artificial intelligence (AI) technology is widely used in COVID-19 and has become a hot topic. In recent 2 years, the application of AI technology in COVID-19 has developed rapidly, and more than 100 relevant papers are published every month. In this paper, we combined with the bibliometric and visual knowledge map analysis, used the WOS database as the sample data source, and applied VOSviewer and CiteSpace analysis tools to carry out multi-dimensional statistical analysis and visual analysis about 1903 pieces of literature of recent 2 years (by the end of July this year). The data is analyzed by several terms with the main annual article and citation count, major publication sources, institutions and countries, their contribution and collaboration, etc. Since last year, the research on the COVID-19 has sharply increased; especially the corresponding research fields combined with the AI technology are expanding, such as medicine, management, economics, and informatics. The China and USA are the most prolific countries in AI applied in COVID-19, which have made a significant contribution to AI applied in COVID-19, as the high-level international collaboration of countries and institutions is increasing and more impactful. Moreover, we widely studied the issues: detection, surveillance, risk prediction, therapeutic research, virus modeling, and analysis of COVID-19. Finally, we put forward perspective challenges and limits to the application of AI in the COVID-19 for researchers and practitioners to facilitate future research on AI applied in COVID-19. [ABSTRACT FROM AUTHOR]

Trust is an essential factor in online and offline transactions. However, the role of customer trust has received limited attention in the homesharing economy. Drawing on the revised stimulus organism response model and trust transfer theory, this paper examines how customer trust in home-sharing hosts and platforms affects customer relationships, manifested in customer engagement and loyalty. As artificial intelligence (AI) is extensively utilized within home-sharing platforms to facilitate business operations and enhance the customer experience, this study also examines the influence of AI on customer trust and other related outcomes. The research was undertaken in China, with respondents who had used home-sharing platforms. Results from structural equation modeling show that customer trust had a significant positive relationship with customer engagement and loyalty. Customer engagement mediates the relationship between trust and loyalty, while AI may have a negative moderating effect between host trust and customer engagement and customer engagement and loyalty. The paper contributes to marketing, sharing economy and AI research. The work has implications for practitioners offering suggestions to develop marketing strategies for business growth and sustainability. [ABSTRACT FROM AUTHOR]

COVID-19 occurred in China in December 2019 and spread to the world. Untact culture becoming commonplace due to the spread of COVID-19, is accelerating digital transformation across society. The government is increasing government fund to overcome national crisis situations such as COVID-19. In order to efficiently execute these government funds, the government and policy financial institutions need to come up with two measures. One is to establish a transparent government fund execution process, and the other is to secure the objectivity of the review process. In this paper, to solve this problem, we designed the execution process of government funds from the government to end users with Consortium Blockchain, and designed a machine learning algorithm for internal review of policy financial institutions. The research data proposed in this paper will be helpful in setting the future government policy direction for government fund execution. [ABSTRACT FROM AUTHOR]

Summary: Energy internet (EI) is a complex coupled multienergy system; it is essential to investigate its multienergy dispatching optimization issues. To this end, this paper first proposes a novel conception of smart dispatching for EI with a complex cyber‐physical‐social system (CPSS) network from the perspective of parallel dispatch, called parallel dispatching robot (PDR), and investigates the implementations of PDR based on smart artificial society (SAS) modeling. First, we introduce EI and describe the dispatching issues of EI. Second, we discuss several important concepts supporting the parallel dispatch conception of EI, including knowledge automation (KA), CPSS, and parallel machine learning (PML). On the basis of these, we elaborate the concept of parallel dispatch. Moreover, we construct a large closed‐loop feedback control framework of parallel dispatch for EI integrating a CPSS network based on KA and PML. Third, we establish an experimental platform for PDR research based on the proposed parallel dispatch framework. Fourth, we develop the PML‐based SAS models of a single PDR in centralized dispatching modes and group PDRs in decentralized dispatching modes to achieve crowd wisdom emergence and performance improvement in current cyber‐physical system frameworks of EI. Moreover, we design an external global closed loop for PDR to evaluate its operation stability. Lastly, we conduct a detailed discussion on PDR and offer some prospects for its engineering implementations. The biggest innovation of this paper lies in systematically proposing the smart dispatching concept and framework for complex CPSS‐based EI from the perspective of parallel dispatch and thoroughly investigating how to use SAS modeling to implement parallel dispatching and control for EI considering human and social factors, which is a major extension and theoretical improvement to existing single smart wide area robot concept and a preliminary attempt in investigating a shift from Energy 4.0 to Energy 5.0 in China. [ABSTRACT FROM AUTHOR]

As we know, AI is deeply integrated with all walks of life. While upgrading traditional products and transforming traditional industries, a large number of new products, new industries and new formats have emerged in large numbers, and new industrial space has been further expanded. The potential for steady economic growth remains huge. However, the influence of merelygrowing capital asset and labor force dimension in terms of boosting economic progress has been waning globally. Considering the China's data panel data outlying areas during 1978–2017 as the research sample, possible economic growing rate of Chinahas been calculated in detail according to the labor-intensive structural time-varying elastic model. It shows a U-shaped development track during 2018–2027, with an average growth rate between 5.00%and 6.00%. There are also great variances in the probable economy growing rates in different regions in China. It shows that relying solely on traditional factors of production to drive potential economic growth, China has been unable to maintain the prosperity of stable development over the past few decades. Based on the experience of typical representative countries, this paper also puts forward some relevant countermeasures and suggestions including focusing on a new round of technological revolutions such as big data, new generation AI, 5 G, IOT, cloud computing, robotics, and blockchain to progress economical growing rate of China in the future. [ABSTRACT FROM AUTHOR]

Since World War II, the United States and its allies have overseen a global order built on trade liberalization and the development of a Western model of global integration. That order now appears to be winding down. Beyond the era of "Western hegemony", Asia is returning to the patterns of commerce and cultural exchange that thrived long before European colonialism and American predominance (Khanna, 2019). Underwriting Asia's resurgence is the return of China. This paper charts China's rise and Chinese globalization focusing on China as a global technology leader, detailing AI and Chinese human capital development, before turning to China's global higher education to provide an interpretation of Chinese Globalization. [ABSTRACT FROM AUTHOR]

With the advent of the Internet, we have entered into the information age, which makes it possible and easier to obtain a large number of representatives data. On the other hand, with the rapid improvement of computer hardware, and software especially the computers speed, and the ability of the computer has been greatly improved. Data-driven and algorithm applications of modern artificial intelligence models have been widely used in various fields. Solving problems through machine learning has become a way of thinking that all industries are willing to study to reduce labor costs and improve processing efficiency. Beijing Formax Co., Ltd. takes advantage of its own data processing jobs to apply machine learning technology to some projects, and has achieved good results. This paper focuses on the application of artificial intelligence and Beijing Formax's knowledge service for the publishing industry in the era of big data including several typical cases. [ABSTRACT FROM AUTHOR]

Jia, Y., 2020. The accounting information disclosure quality of listed companies in marine fishery. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 168-170. Coconut Creek (Florida), ISSN 0749-0208. Improving the mechanism of accounting information disclosure (hereinafter referred to as AID) is the basic condition of listed companies, which is the basic way to protect the legitimate rights and interests of Internet users. AID of listed companies is the only information that most small and medium-sized Internet users pay attention to, which will directly affect the investment of small and medium-sized users. Therefore, the quality of AID has become an important role in promoting the industry development. High standard and high quality information disclosure will give investors a real sense. However, there are many problems in AID of most marine fishery listing formulas, which will lead to many problems, such as the increase of investment risk, the decrease of investment amount and the decline of competitiveness. Low quality AID will affect the adjustment and optimization of China's fishery industry structure. Therefore, this paper constructs the entropy model of AID quality of listed fishery companies, which will comprehensively consider the influencing factors. Finally, this paper puts forward some policies to enhance the disclosure of accounting information (hereinafter referred to as AI) in the fishery industry. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience. [ABSTRACT FROM AUTHOR]

As with other jurisdictions, China has introduced a range of newer technologies into its justice sector to promote greater access to justice, improve judicial transparency and to promote just outcomes for disputing parties with legal issues. Chinese courts' practice of embracing technology is unique as an overarching approach has been adopted by the central government to build the 'smart court' system across the entirety of its courts. Arguably, the smart court system, which relies on computer technologies that enable big data use, blockchain formation and advisory and determinative forms of artificial legal intelligence, has, to a certain degree, promoted easier access to justice, enabled faster dispute resolution, saved costs by moving judicial process online and ensured that judgments can be enforced. On the other hand, however, there are concerns relating to the use of some technologies that include the use of automated judgments, digital divide issues, judicial independence, as well as issues linked to privacy and data protection. This article concludes that some caution should be exercised in developing the 'smart court' system, primarily in relation to the oversight and introduction of more disruptive technologies to ensure that cheap and quick dispute resolution can be achieved without detrimental impacts on justice. [ABSTRACT FROM AUTHOR]

The article responds to the recent evolution in AI judiciary, especially as presented in China. The authors compare the basic methodological background of Western and Eastern legal systems, concluding that the West is rather inclined to post-positivist methodology in law, which seems incompatible with the full use of AI in legal decision-making. In China, with its more pragmatic approach, the actual process of decision-making might be closer to a new form of technological legal positivism, distinct from Western trends in jurisprudence. Finally, various forms of applicability of AI in law and judiciary are pondered upon, so as to use at least some positive aspects of AI in this sector--albeit with reservations regarding the decision-making proper. [ABSTRACT FROM AUTHOR]

This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT. Highlights: A systematic review examining AIEd in higher education (HE) up to the end of 2022. Unique findings in the switch from US to China in the most studies published. A two to threefold increase in studies published in 2021 and 2022 to prior years. AIEd was used for: Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring System, and Managing Student Learning. [ABSTRACT FROM AUTHOR]

In order to make AI tools for spam review detection to be more trustworthy, two types of explanation were designed, the Text-AI tool and the Behaviour-AI tool. The Text-AI tool establishes detection criteria based on the textual features of reviews, while the Behaviour-AI tool establishes detection criteria based on the behavioural features of reviewers. The trust of younger (20–26 years) and older (50–78 years) adults in AI tools and the changes in credibility judgments for reviews and overall attitude toward the product made by them based on AI tools' detection results were measured. We mainly found that: (i) Almost all the older participants reported that they trust AI tools' prediction, but 48.7% of them would abandon AI when AI's prediction was different from their own judgment. (ii) Younger adults showed a higher trust in the Behaviour-AI tool than in the Text-AI tool, especially when the AI tool detected more spam reviews than themselves. (iii) Regardless of age and types of explanation, participants perceived AI tools as more competent and benevolent when AI tools outperformed participants by detecting unexpected spam reviews. [ABSTRACT FROM AUTHOR]

China is chasing dominance in emerging artificial intelligence (AI) technologies in both the private and military sectors, as a central part of its effort to be the leading global cyber power. The rise of AI – a subset of cyber as are machine learning, quantum computing, and other new technologies – does not herald a new arms race equivalent to that of the Cold War. Rather, the concern should be on the profound disruption to the existing westernized global order. This piece reviews how the 1990s westernized national creation called cyberspace created so many ubiquitous, embedded vulnerabilities whose easy exploitation directly accelerated the rise of an otherwise impoverished authoritarian and aggressive China. Today no single democracy has the scale and sufficient resources to alone match the foreknowledge and strategic coherence of the newly confident and assertive China. To change current global trends, the small group of consolidated democratic civil societies needs a collective approach to counter China's growing dominance across all fields of cyberspace. The piece ends describing the Cyber Operational Resilience Alliance (CORA) to provide the public and private scale and collective strategic coherence required to ensure the future wellbeing and security of democracy in an overwhelmingly authoritarian, post-western, cybered world. [ABSTRACT FROM AUTHOR]

Background: Deep learning-assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggested the prerequisites for health care service providers and residents willing to use it.Objective: The aim of this paper is to reveal the preferences of health care service providers and residents for using artificial intelligence (AI) in community-based eye disease screening, particularly their preference for accuracy.Methods: Discrete choice experiments for health care providers and residents were conducted in Shanghai, China. In total, 34 medical institutions with adequate AI-assisted screening experience participated. A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences.Results: Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90% (odds ratio [OR]=0.61 for 10% overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents' preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors' supervision (OR=0.24; P<.001).Conclusions: Deep learning model under doctors' supervision is strongly recommended, and the specificity of the model should be more than 90%. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents. [ABSTRACT FROM AUTHOR]

Digital technologies, such as artificial intelligence (AI), big data, and the Internet of Things, are becoming increasingly mature. They have become the driving force behind a new wave of innovation and entrepreneurship activities worldwide. AI is the core of the contemporary digital technology industry. New high technologies bring greater opportunities for entrepreneurs to carry out entrepreneurial activities and bring unprecedented challenges to traditional entrepreneurial activities and theories. The existing literature on the impact of AI on entrepreneurial activities is still in the exploration stage. Based on this, this study aims to explore the entrepreneurial process of AI start‐ups, combining with existing literature, select AI unicorn start‐ups ByteDance for a case study, and apply the grounded theory method to analyse and summarise the entrepreneurial process of Internet start‐ups with AI. Thus, it can provide insights for many AI start‐ups and contribute to the existing entrepreneurial research. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) based on deep learning, convolutional neural networks and big data has been increasingly effective in the diagnosis and treatment of multiple primary pulmonary nodules. In comparison to previous imaging systems, AI measures more objective parameters such as three‐dimensional (3D) volume, probability of malignant nodules, and possible pathological patterns, making the access to the properties of nodules more objective. In our retrospective study, a total of 53 patients with synchronous and metachronous multiple pulmonary nodules were enrolled of which 33 patients were confirmed by pathological tests to have primary binodules, and nine to have primary trinodules. A total of 15 patients had only one focus removed. The statistical results showed that the agreement in the AI diagnosis and postoperative pathological tests was 88.8% in identifying benign or malignant lesions. In addition, the probability of malignancy of benign lesions, preinvasive lesions (AAH, AIS) and invasive lesions (MIA, IA) was totally different (49.40±38.41% vs 80.22±13.55% vs 88.17±17.31%). The purpose of our study was to provide references for the future application of AI in the diagnosis and follow‐up of multiple pulmonary nodules. AI may represent a relevant diagnostic aid that shows more accurate and objective results in the diagnosis of multiple pulmonary nodules, reducing the time required for interpretation of results by directly displaying visual information to doctors and patients and together with the clinical conditions of MPLC patients, offering plans for follow‐up and treatment that may be more beneficial and reasonable for patients. Despite the great application potential in pneumosurgery, further research is needed to verify the accuracy and range of the application of AI. [ABSTRACT FROM AUTHOR]