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Background: Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students., Methods: A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students' experience in using ChatGPT., Results: After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research., Conclusion: The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language., (© 2024. The Author(s).)

The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing innovative talent cultivation capability. First, by CIPP model (Context, Input, Process and Product Evaluation), an assessment indicator system is built, consisting of 89 indicators in 21 categories. Then, based on indicator characteristics, this paper uses public data statistics, database retrieving, student survey, teacher survey, support personnel and expert investigation, to collect indicator values. After this, by a powerful machine learning algorithm, gradient Boosting regression tree, a capability assessment model is established. And based on collected data, established model is compared with several regression models in innovative talent cultivation capability assessing. Results confirm the performance superiority of our solution.

In recent years, with the economic development of various countries, education has also made great leaps, and piano teaching has been popularized and promoted in various universities. Pianos are also put into use in universities through normal channels. According to the survey, there are certain drawbacks in the teaching process of piano, which hinder the teaching effect to a great extent. In order to better adapt to the interactive teaching mode, this paper takes the flipped classroom in piano teaching as an example to conduct a systematic study based on the method of artificial intelligence. This paper introduces three typical artificial intelligence technologies and analyzes the three evaluative indicators corresponding to the effect of piano interactive teaching. The analysis results show that the prediction effect based on fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by artificial intelligence shows a strong consistency with the actual evaluation effect.

The importance of using innovative technologies in modern education is continuously growing. This paper examines the influence of intelligent painting systems (IPS) on student self-efficacy and involvement. The conducted study involved 234 students from the Guangzhou Academy of Fine Arts. An experimental approach was used to ensure scientific validity of the study. The experimental group trained using the IPS (based on deep learning and uses a fine-tuned ResNet-50 model), whereas the control group studied according to traditional methods. The results obtained upon completion of the training confirmed the positive influence of the implemented system. The Multivariate analysis of variance (MANOVA) also showed statistically significant differences between the groups. The practical significance of the study lies in the fact that it justifies the introduction of intelligent systems into art education to increase its effectiveness as well as student involvement. The findings of the study have important implications for the development of educational and artistic policy. Furthermore, they contribute to the improvement of teaching methods and the quality of education in this field. The study can also serve as an impetus for further research on the use of technology in education and its impact on pedagogical practice.

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized guidance to the students in all aspects. The paper has established an automatic early warning system to monitor the students' psychological state at any time period. The data is collected from 650 respondents from four different public universities in China. The data analysis has been done with the help of powerful SPSS software and the methodology which we used for determining sample size is, Random sampling. The study involves a qualitative assessment to identify participants' characteristics and categorize them to appropriate clusters. The findings of the research showed that the most obvious differences in mental health between students who used automatic warning and those who did not use automatic warning were: depression, anxiety, hostility, terror, and psychosis. The proportion of students who use early warning was less than those who did not use early warning. Research contributes to policymakers to emphasize the importance of incorporating mental health support and resources into educational policies. The novelty of the study seeks to provide a deeper understanding of how AI and big data can optimize mental health education for English students. With the support of AI and Big data there is a constant monitoring and improvement effect on English education students' mental health.

Aural decoding skill is an important contributor to successful EFL listening comprehension. This paper first described a preliminary study involving a 12-week undergraduate flipped decoding course, based on the flipped SEF-ARCS decoding model. Although the decoding model (N = 44) was significantly more effective in supporting students' decoding performance than a conventional decoding course (N = 36), two main challenges were reported: teacher's excessive workload, and high requirement for the individual teacher's decoding skills. To address these challenges, we developed a chatbot based on the self-determination theory and social presence theory to serve as a 24/7 conversational agent, and adapted the flipped decoding course to a fully online chatbot-supported learning course to reduce the dependence on the teacher. Although results revealed that the chatbot-supported fully online group (N = 46) and the flipped group (N = 43) performed equally well in decoding test, the chatbot-supported fully online approach was more effective in supporting students' behavioral and emotional engagement than the flipped learning approach. Students' perceptions of the chatbot-supported decoding activities were also explored. This study provides a useful pedagogical model involving the innovative use of chatbot to develop undergraduate EFL aural decoding skills in a fully online environment.

Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice. Design/methodology/approach: A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively. Findings: The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain. Research limitations/implications: If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation. Practical implications: While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks. Social implications: As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students. Originality/value: The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

After nearly 30 years after the reform and opening up the rapid development of economy, China also had a "industrial pollution" and "poverty", mainly because of the low level of productivity, population pressure overload caused by environmental degradation, and between population pressure and environmental degradation A vicious cycle, is essentially a syndrome caused by poverty. We've been paying a lot of attention to the former for a long time At present, the environmental awareness education in colleges and universities has made great progress compared with the past. College students basically have certain environmental knowledge and awareness, and have high enthusiasm for environmental protection and environmental protection cause, and pay great attention to it. However, when problems arise or involve their own interests, their environmental awareness and behavior lag significantly. In this paper, system dynamics, neural network model and computer simulation are used to study environmental protection in ecologically fragile areas.

Intangible cultural heritage is an important part of Chinese excellent traditional culture, and college dance teaching is paid more attention by researchers of physical education and computer technology. In order to help the inheritance and development of non-legacy culture in college dance teaching, this paper analyzes the influencing factors of wireless communication network in college dance teaching, constructs an interactive platform for non-legacy digital dance teaching based on wireless communication network technology, and applies it in the dance teaching process of a university in Henan province to highlight the role of wireless communication technology in dance teaching. The results show that the interactive platform can effectively reduce teachers' physical energy consumption and has certain reliability and practicality. Through artificial intelligence algorithm, the traditional dance teaching method has been changed, and a new exploration of non-legacy dance teaching has been realized.

The update of artificial intelligence (AI) is triggering the innovation of education concepts, education methods, and education means. This paper takes the impact of contemporary visual communication design on design methods, design means, and presentation methods brought by AI as the starting point; combines the possibilities brought by the characteristics of AI to the teaching mode; integrates and deepens the development of educating mode; and makes AI play a benefit in the educating of visual correspondence design, so as to transform and upgrade to the intelligent teaching of visual communication design.

Following a preliminary study that examined the potential effectiveness of a computer-assisted consecutive interpreting (CACI) mode, this paper presents a further trial of the CACI workflow. The workflow involves respeaking using speech recognition (SR) in phase I and production assisted by the SR text and its machine translation (MT) output in phase II. This study introduces a training framework for CACI that encompasses respeaking, sight translation, and post-editing. Additionally, it seeks to evaluate the CACI workflow with a group of trained students. Comparative analyses were conducted between conventional consecutive interpreting (CI) and CACI. Most of the findings from the preliminary study were successfully replicated in this study. The investigation revealed that CACI outperformed conventional CI in overall interpreting quality and accuracy in both directions of interpreting. Moreover, CACI exhibited higher fluency and lower cognitive load compared to conventional CI, albeit only in the L1-L2 direction. The quality of respeaking was found to be positively correlated with interpreting quality in both directions, underscoring the critical role of respeaking within the CACI workflow.

Information technology is a significant action to promote education informatization. Currently, there are numerous literature studies on information technology education, but existing research lacks a comparative summary of the research status, research hot spots, and research trend of information technology in China and abroad from an overall perspective. Therefore, this paper selected the information technology education literature collected by China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WoS) from 2000 to 2021 as the research object and used bibliometrics to analyze the literature characteristics, core forces, research hot spot, and frontier trend of information technology education in China and abroad. The results showed that: (1) The discipline literature of information technology education showed a fluctuating growth trend, in which the growth rate of foreign literature was faster, and the scale of Chinese literature was slightly smaller; Both Chinese and foreign literature involved pedagogy, computer, and other disciplines, with the characteristics of multi-disciplinary integration. (2) The United States was the leading country in this field, followed by China, with the total literature accounting for more than 50%, ranking the leading position in the world; China Audio-Visual Education and Computers & Education were representative journals in China and abroad. (3) Foreign research focused on information technology, education, science, attitude, model, perception, experience, self-efficacy, Internet, etc. China's hotspots included information literacy, computational thinking, curriculum standards, application contexts, teaching models, subject integration, etc. Foreign research focused on both theory and practice, while China focused on the theoretical nature of the subject, but the degree of practice was insufficient. (4) Student participation, technology integration, teaching innovation, Internet +, core literacy, and artificial intelligence (AI) have been popular until now, constituting the discipline frontier in information technology education.

Recently, artificial intelligence (AI)-programmed automated writing evaluation (AWE) has attracted increasing attention in language research. Using a small data set arising from an analysis of five Chinese university-level English as a foreign language (EFL) students' submissions, this paper examined in detail how EFL students interacted with the feedback of Pigai, the largest AI-programmed AWE in China. The analysis started with the intention of capturing the machine feedback on the five students' submissions and the exchanges between the participants and Pigai over repeated submissions, ranging from 3 to 12 submissions. The analysis showed that the learners' interactions with Pigai focused on error corrective feedback in the initial two submissions. In the case of one student who had 12 submissions, the non-error corrective feedback increased gradually over time, providing rich linguistic resources but without examples and contextual information. The students' take-up rates of feedback with linguistic resources were much lower than that of error corrective and general feedback. A terrain model to map the stages and nature of student responses showed a more complete dynamic process, in which students' responses changed from the initial mechanical responses at the discrete language level to more considered approaches in response to machine feedback. The findings of this study have implications for both language pedagogy and the future design and development of AWE for second or foreign language learning.

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]

Objective: To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field. Methods: Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword "metaverse". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China. Results: Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence. Conclusion: The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges. [ABSTRACT FROM AUTHOR]

This paper focuses on how regulatory bodies respond to artificial intelligence (AI)-enabled medical devices. To achieve this, we present a comparative overview of the United States (USA), European Union (EU), and China. Our search in the governmental database identified 59 AI medical devices approved in China as of July 2023. In comparison to the rules-based regulatory approach in China, the approaches in the USA and EU are more standards-oriented. [ABSTRACT FROM AUTHOR]

This paper aims to develop a new model of Project-Based Instructional Taxonomy (PBIT) that provides a tool of course design that facilitates Computational Thinking (CT) development as creative action in solving real-life problems. Theoretically, PBIT is built on an integrative framework bringing together with studies on CT education, creativity, Bloom's Taxonomy, and Project-Based Instruction (PBI). This guides the course design to make alignment between diverse elements including education objectives, categories of CT, levels of learning ability, process of project facilitation, and methods of grading. A case will be discussed that focuses on a course Deep Learning and Technologies in AI bachelor program at Northeastern University (NEU) in China. It also shows how PBIT is applied in teaching practice that benefits students' CT development. As the conclusion indicates, this paper has contributions to future research on creativity, PBI, CT, and AI education. [ABSTRACT FROM AUTHOR]

In this paper, we aim to document and analyze the long arc of connectivity between China and Latin America. First, using analytical international trade and economic tools, we provide a new economic perspective of the First Globalization–-the Ming and early Qing China's economic and business links with Latin America and Europe. Second, due to U.S. government trade policies toward China in the 1990s, the Second China's Globalization was ushered in with its entry to the World Trade Organization (WTO). During this second phase, China exchanged manufactured goods with minerals and resources from Latin America. Finally, the tough technology and trade stance by the Trump and Biden Administrations prompted China to intensify the third phase of China interaction with Latin America. For the 2020s and beyond, one important pillar of the Third China-Latin America-Europe Globalization is heightened efforts for China to increase its digital-AI, telecommunication and platform business presence and access into the Latin American markets. To enrich our analysis, we will provide Chinese-style digital-AI business and economic models to help illustrate our discussions. Throughout our paper, we want to point out that government policies were instrumental in bringing forth these Three Globalizations that involve China-Latin America-Europe. [ABSTRACT FROM AUTHOR]

In the context of rapid advancement in automation and increasing global warming, understanding the impact of artificial intelligence (AI) on carbon emissions (CES) is a cutting-edge research topic. However, there is limited focus in existing research on the nonlinear carbon reduction effect (CRE) of AI. This paper first theoretically elaborates the dual impact mechanisms of AI on CES and illuminates the nonlinear carbon reduction mechanisms of AI. Then, this study employs panel data encompassing 30 Chinese provinces between 1997 and 2019 to empirically test the net effect of AI on CES and the nonlinear carbon reduction effect of AI through econometric models. The results are as follows: first, although AI can both reduce and increase CES, AI primarily helps decrease CES. This conclusion holds true even after considering robustness, endogeneity, and spatial heterogeneity. Secondly, relative to the central and western regions, AI has significant achievement in reducing carbon intensity and per capita CES in the eastern region. However, there is still room for improvement in terms of reducing the total CES in the eastern region. Thirdly, improving the AI development level (AIDL) can magnify the marginal CRE of AI and lead to a nonlinear CRE of AI. Lastly, even if the AIDL remains constant, improving the level of marketization, human capital, digital infrastructure, economic development, openness, and government intervention can also amplify the marginal CRE of AI and lead to a nonlinear CRE of AI. To fully harness the potential of AI for green development, concerted efforts should be directed towards enhancing the innovation and application of AI technologies with carbon reduction potential. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR2 = 0.01~0.27, ΔRMSE = 1.88~13.77 m3·hm−2, ΔMAE = 1.17~13.57 m3·hm−2). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R2 is greater than 0.91, while the fitting R2 of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the "airborne LiDAR + satellite remote sensing" data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, the assessment method based on machine learning, especially the ensemble learning method, has attracted more and more attention in the field of credit assessment. However, most of the ensemble assessment models are complex in structure and costly in time for parameter tuning, few of them break through the limitations of lightweight, universal and efficient. This paper present a new ensemble model for personal credit assessment. First, considering the conflicts and differences among multiple sources of information, a new method is proposed to correct the category prior information by using the difference measure. Then, the revised prior information is fused with the current sample information with the help of Bayesian data fusion theory. The model can integrate the advantages of multiple benchmark classifiers to reduce the interference of uncertain information. To verify the effectiveness of the proposed model, several typical ensemble classification models are selected and empirically studied using real customer credit data from a commercial bank in China, and the results show that among various assessment criteria: the proposed model not only effectively improves the multi-class classification performance, but also outperforms other advanced multi-class classification credit assessment models in terms of parameter tuning and generalizability. This paper supports commercial banks and other financial institutions examination and approval work. [ABSTRACT FROM AUTHOR]

Large datasets are required to develop Artificial Intelligence (AI) models in AI powered smart farming for reducing farmers' routine workload, this paper contributes the first large lion-head goose dataset GooseDetect lion , which consists of 2,660 images and 98,111 bounding box annotations. The dataset was collected with 6 cameras deployed in a goose farm in Chenghai district of Shantou city, Guangdong province, China. Images sampled from videos collected during July 9 -10 in 2022 were fully annotated by a team of fifty volunteers. Compared with another 6 well known animal datasets in literature, our dataset has higher capacity and density, which provides a challenging detection benchmark for main stream object detectors. Six state-of-the-art object detectors have been selected to be evaluated on the GooseDetect lion , which includes one two-stage anchor-based detector, three one-stage anchor-based detectors, as well as two one-stage anchor-free detectors. The results suggest that the one-stage anchor-based detector You Only Look Once version 5 (YOLO v5) achieves the best overall performance in terms of detection precision, model size and inference efficiency., (© 2024. The Author(s).)

Oracle bone script, one of the earliest known forms of ancient Chinese writing, presents invaluable research materials for scholars studying the humanities and geography of the Shang Dynasty, dating back 3,000 years. The immense historical and cultural significance of these writings cannot be overstated. However, the passage of time has obscured much of their meaning, presenting a significant challenge in deciphering these ancient texts. With the advent of Artificial Intelligence (AI), employing AI to assist in deciphering Oracle Bone Characters (OBCs) has become a feasible option. Yet, progress in this area has been hindered by a lack of high-quality datasets. To address this issue, this paper details the creation of the HUST-OBC dataset. This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources. The hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs. All the codes and datasets are available at https://github.com/Pengjie-W/HUST-OBC ., (© 2024. The Author(s).)

This article discusses the application of artificially intelligent robots within eldercare and explores a series of ethical considerations, including the challenges that AI (Artificial Intelligence) technology poses to traditional Chinese Confucian filial piety. From the perspective of Confucian ethics, the paper argues that robots cannot adequately fulfill duties of care. Due to their detachment from personal relationships and interactions, the "emotions" of AI robots are merely performative reactions in different situations, rather than actual emotional abilities. No matter how "humanized" robots become, it is difficult to establish genuine empathy and a meaningful relationship with them for this reason. Even so, we acknowledge that AI robots are a significant tool in managing the demands of elder care and the growth of care poverty, and as such, we attempt to outline some parameters within which care robotics could be acceptable within a Confucian ethical system. Finally, the paper discusses the social impact and ethical considerations brought on by the interaction between humans and machines. It is observed that the relationship between humans and technology has always had both utopian and dystopian aspects, and robotic elder care is no exception. AI caregiver robots will likely become a part of elder care, and the transformation of these robots from "service providers" to "companions" seems inevitable. In light of this, the application of AI-augmented robotic elder care will also eventually change our understanding of interpersonal relationships and traditional requirements of filial piety., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research. [ABSTRACT FROM AUTHOR]

With rapid economic development, the gradual deterioration of the natural environment has posed unprecedented challenges to human social civilization. The marine economy, as an important part of economic development, is the breakthrough of economic transformation for many coastal countries. Additionally, green development and environmental impact assessment have become the focus of research in these countries. This study employs remote sensing technology, an efficient observational method, to significantly enhance the efficiency of ocean information observation. It investigates ocean carbon emissions within the framework of carbon neutrality. First, we identified the ships along the coastline based on marine remote sensing information through the YOLO (you only look once) framework. Second, we applied the LSTM (long short-term memory) method to combine the target identification results and the historical data of carbon emissions to complete the corresponding carbon emission data fitting. Finally, carbon emission data from the past three years in the offshore area of Dalian were used to make accurate predictions. The results suggested that the recognition rate of the proposed target detection method could reach 88%, and the LSTM method has shown the best performance in terms of absolute error for the subsequent short-term carbon emission prediction. This framework not only provides essential technical support for analyzing remote sensing information within the context of carbon neutrality but also introduces innovative insights for carbon emission prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China., Competing Interests: The author reports no conflicts of interest in this work., (© 2024 Yang.)

With the continuous development of informatization, digitalization and artificial intelligence technology, the working mode of the pathology department has gradually changed from the traditional manual check, paper circulation and physical carrier storage to the informatization process and digital storage. The traditional pathology discipline has ushered in unprecedented opportunities and challenges. Digital pathology department also emerge as the times require. Simultaneously, with the full integration of artificial intelligence technology in pathology department, the concept of "department of digital and intelligentialized pathology" was proposed. Based on information and digital technology, the digital intelligent pathology department integrates intelligent management system, optimizes the previous cumbersome management and workflow of the pathology department, develops advanced technologies such as intelligent material extraction, unmanned organization processing, artificial intelligence quality control, artificial intelligence diagnosis, and promotes the intelligent construction of the pathology department.

Background: The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics., Methods: The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5425 documents were identified and analyzed., Results: The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology., Conclusions: The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field., Competing Interests: Declaration of competing Interest The authors alone are responsible for the content and writing of the paper. This paper has not received any financial support, endorsement, or oversight from any commercial entity. Drs. Fülöp and Soliman are current employees of the United States Veterans Health Administration. However, the opinions and views expressed in this paper are the Authors’ own and do not represent the official views or policies of the United States Veteran Health Administrations., (Published by Elsevier Inc.)

AbstractThe rise of artificial intelligence (AI), represented by ChatGPT, has triggered a new wave of technological competition between nations. Major powers like China and the United States, backed by abundant resources, dominate the discourse and hold a significant advantage in the AI field. However, as the technological rivalry between these two countries intensifies, AI represents a classic ‘double-edged sword’ for many small and medium-sized countries. Therefore, the core argument of this paper is that the AI policies of small and medium-sized countries are not simply about bandwagoning or depending on major powers. At their core, these policies represent a complex form of technological hedging. External security concerns drive small and medium-sized countries to adopt hedging as a primary strategy, while internal interest preferences influence the varying intensities of this technological hedging. The findings of this paper indicate that small countries’ technological policies can be categorized into three models: strong hedging, medium hedging, and weak hedging. Different paths of technological hedging reflect the rational balance these nations make between security and economic interests in different contexts. The case of AI development in ASEAN also demonstrates the significant imbalance between member states. Singapore is classified as a technological frontrunner, while Malaysia, Indonesia, and Vietnam are categorized as followers. Laos, Cambodia, Myanmar, Thailand, Brunei, and the Philippines are considered latecomers in the field of AI. In terms of specific AI policies, Singapore and Vietnam adopt a strong technological hedging strategy, seeking more cooperation and interaction with the U.S. or other countries due to caution and concerns about China’s technological security. Malaysia, Indonesia, Thailand, and the Philippines follow a medium hedging strategy, while Cambodia, Laos, Myanmar, and Brunei adopt a weak hedging approach. The differences in hedging intensity are primarily due to varying internal legitimacy pathways and the strength of backup resources. ASEAN member states pursue different technological paths based on their own circumstances to promote AI development. [ABSTRACT FROM AUTHOR]

This study delves into Human–Computer Intelligent Interaction (HCII), a burgeoning interdisciplinary field that builds upon traditional Human–Computer Interaction (HCI) by integrating advanced technologies like Natural Language Processing (NLP) and Machine Learning (ML). In this paper, we scrutinize 5,781 HCII papers published between 2000 and 2023, narrowing our focus to 803 most relevant articles to construct co-citation and interdisciplinary networks based on the CiteSpace Software. Our findings reveal that the publications of the United States and China are relatively high with 558 and 616 publications respectively. Furthermore, we found that machine learning and deep learning have emerged as the prevalent methodologies in HCII, which currently emphasizes multimodal emotion recognition, facial expression recognition, and NLP. We predict that HCII will be integrated into advanced applications such as neural-based interactive games and multi-sensory environments. In sum, our analysis underscores HCII's role in advancing artificial intelligence, facilitating more intuitive and efficient human–computer interactions, and its prospective societal impact. We hope that our review and analysis may guide the efforts of researchers aiming to contribute to HCII and develop more powerful and intelligent methods, tools, and applications. [ABSTRACT FROM AUTHOR]

The challenge of elderly care presents a formidable task, demanding the collective attention of governmental bodies and diverse sectors of society. The integration of Artificial Intelligence (AI) into the research and development of Social Elderly Care Service (ECS) has emerged as a dominant trend, holding substantial importance in the establishment of an efficient ECS system. This study aims to serve as a comprehensive reference for the advancement of China's ECS system, achieved through the harmonious integration of a social ECS system with AI capabilities. This paper introduces the fundamental theory of AI, delving into the intricacies of the greyscale model of AI. Furthermore, it provides an overview of the current landscape of elderly care and elder care institutions, offering scientific data and insights to propel further research on AI development and system construction. Through an analysis of the existing research status, the study identifies prevalent issues within the AI-ECS integration, emphasizing pivotal factors influencing the construction of a robust social ECS system. To address these concerns, the study puts forth specific and viable policy recommendations. Notably, the questionnaire's statistics underscore that 83% of the elderly populace would opt for AI-driven solutions in selecting intelligent products, thereby underscoring the pivotal role of AI within the social ECS system. The challenges facing elderly care systems, including demographic shifts, resource constraints, and evolving societal norms, demand innovative solutions for providing efficient and effective care. This study addresses these challenges by exploring the integration of Artificial Intelligence (AI) into Social Elderly Care Services (ECS) in China. By delving into the theory of AI and assessing the existing research status, the study identifies key issues in AI-ECS integration and proposes viable policy recommendations. Insights from stakeholder surveys further highlight the importance of AI-driven solutions in meeting the needs of the elderly population., (© 2024. The Author(s).)

Research on artificial intelligence for brain injury is currently a prominent area of scientific research. A significant amount of related literature has been accumulated in this field. This study aims to identify hotspots and clarify research resources by conducting literature metrology visualization analysis, providing valuable ideas and references for related fields. The research object of this paper consists of 3000 articles cited in the core database of Web of Science from 1998 to 2023. These articles are visualized and analyzed using VOSviewer and CiteSpace. The bibliometric analysis reveals a continuous increase in the number of articles published on this topic, particularly since 2016, indicating significant growth. The United States stands out as the leading country in artificial intelligence for brain injury, followed by China, which tends to catch up. The core research institutions are primarily universities in developed countries, but there is a lack of cooperation and communication between research groups. With the development of computer technology, the research in this field has shown strong wave characteristics, experiencing the early stage of applied research based on expert systems, the middle stage of prediction research based on machine learning, and the current phase of diversified research focused on deep learning. Artificial intelligence has innovative development prospects in brain injury, providing a new orientation for the treatment and auxiliary diagnosis in this field., Competing Interests: Declaration of Competing Interest All authors declare that they have no conflicts of interest., (Copyright © 2024. Published by Elsevier Inc.)

Background: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest., Objective: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs., Methods: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset "Chinese Stroke Clinical Records" (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined "CliRoberta" through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity., Results: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds., Conclusion: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Cancer has become an important public health problem affecting the health of Chinese residents, as well as residents all over the world. With the improvement of cancer prevention and treatment, the growth of the mortality rate of cancers has slowed down gradually, but the incidence rate is still increasing rapidly, and cancers still impose heavy disease and economic burdens. Cancer screening and early cancer diagnosis and treatment are important ways to reduce the burden of cancer-related diseases. At present, various projects for early cancer diagnosis and treatment have been implemented in China. With the expansion of the coverage of these projects, the problems related to project implementation, operation, and management have emerged gradually. In recent years, emerging information technologies have been applied in the field of health and have facilitated health management and clinical decision-making. Meanwhile, China announced multiple policies to encourage and promote the application of information technologies in the field of health. Therefore, combined with the analysis of major problems in cancer prevention and control projects, this paper probes into how to apply information technologies such as biological information mining, artificial intelligence, and electronic information collection technology to various stages of cancer prevention and control. Information technologies realize the integrated management of prevention and control processes, for example, mobilization and preliminary identification, high-risk assessment, clinical screening, clinical diagnosis and treatment, tracking and follow-up, and biological sample management of high-risk groups, and promote the efficient implementation of cancer prevention and control projects in China., (©Pei Dong, Ayan Mao, Wuqi Qiu, Guanglin Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.02.2024.)

Ulcerative colitis (UC) has seen a significant increase over the past 3 decades. However, our understanding of its etiology, pathogenesis, and pharmacological treatment remains limited. This comprehensive review aims to address these gaps by analyzing trends, evaluating previous research, and providing insights for future investigations. We conducted a bibliometric analysis of UC-related papers indexed in the Web of Science from 1993 to 2022. The author, organization, country, and keyword networks in the field of UC were visualized. A total of 36,483 papers were included, showing a continuous upward trend. Most research on UC was conducted in universities, with hospitals leading in high-quality studies. The United States emerged as the primary contributor, followed by China and the United Kingdom. The overall quality of UC-related publications improved, indicating sustained interest in the field. The keywords related to UC was classified into 9 clusters. Keywords detection revealed that UC research focused mainly on the discovery of its etiology and exploration of treatment methods, with research directions evolving from initial treatment of UC and related diseases to clinical trials of UC and subsequently incorporating genomics and bioinformatics techniques to study UC and explore new therapeutic methods and drugs, including recent advances in gut microbiota. Our study identified gaps in understanding the etiology, pathogenesis, and treatment of UC. Future research in UC should focus on genomics, personalized treatment, microbial therapy and leveraging machine learning and artificial intelligence. These areas hold the potential for improving UC diagnosis, treatment, and management., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Evapotranspiration (ET) is a key link between atmospheric processes and land surface hydrological processes. With the impact of global warming and human activities, research on ET has become a hot topic. Supported by a total of 1,222 Chinese and English literatures from China National Knowledge Infrastructure and the Web of Science Core Collection from 2013 to 2022, this paper adopts the bibliometric visualization method to review the current research progress and future trend of ET with respect to the time of publication, countries, institutions, journals, and research hotspots. The results show that the number of related research articles is increasing rapidly and the journals with high citations are Journal of Hydrology, Agricultural and Forest Meteorology and Agricultural Water Management. The research hotspots have been focused on prototype observation, remote sensing inversion, mechanism equation, model simulation, spatial-temporal analysis, and attribution identification. In the future, there is an urgent need to integrate algorithms such as machine learning and artificial intelligence, to develop higher resolution remote sensing products, to improve the mechanism equations based on precise observations, and to clarify the impact of synergistic effects on ET among the driving factors.

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

Previous research in security studies contends that information warfare (IW) is becoming a critical element in states' overall security strategies. Additionally, many researchers posit that artificial intelligence (AI) is quickly emerging as an important component of digital communications and states' military applications worldwide. However, less is known regarding how states are incorporating AI in their information warfare and influence operations (IWIO). Thus, given the growing importance of AI and IW in global security, this paper examines how the United States, China, and Russia are incorporating AI in their IWIO strategies and tactics. We find that the US, China, and Russia are utilizing AI in their IWIO approaches in significant ways depending on each state's overall IW strategy, with important implications for international security. [ABSTRACT FROM AUTHOR]

The Communist Party of China's 19th National Congress underlined the necessity of speeding the development of a manufacturing powerhouse and advanced manufacturing sector by supporting the deep integration of the Internet, big data, artificial intelligence, and the real economy. This study employed principal component analysis to extract the prominent risk factors from questionnaire data in order to manage the risks connected with the Internet strategic transformation of manufacturing firms. To confirm the major risk factors, a structural equation modeling was created using Amos-24 software. The findings revealed that risk factors of Internet strategic transformation in manufacturing businesses are mostly expressed in equipment flexibility risks, organizational versatility risks, smart technology risks, Internet technology risks, flexible management risks, and financing management risks. The paper offers useful theoretical and practical insights into the risks of China's manufacturing businesses' Internet strategic transformation. The findings can assist manufacturing firms in better identifying and managing these risks, supporting their smooth transition to the Internet economy. [ABSTRACT FROM AUTHOR]

China communicates strategic narratives about artificial intelligence (AI) in digital media productions to create a shared meaning about its actions and its image in the global race to develop AI. Building upon the literature in emotions and strategic narratives, this study seeks to clarify which emotions are discursively used in China's international AI narratives, and their function and significance. Specifically, the study investigates emotion discourses in AI-focused videos disseminated on China's international broadcasting (CGTN YouTube channel). The analysis reveals that pride, hope and fear discourses have multiple functions in China's international AI narratives on CGTN. Hope is used to represent China as a key competitor in the global AI race, who is catching up to the US. China uses pride to showcase its advances in AI applications, to highlight its transformation of traditional industries, and to identity itself as equal to the US. Fear is used to frame US's perception of China's AI developments, to suggest their crisis of confidence, but also to refute the "China threat". Additionally, the fear discourse is used to deliberate anticipated risks, particularly on job loss and data privacy. These emotion discourses forge China's identity as a future global AI power. The paper then discusses how these findings contribute to key debates about Chinese discourse and media strategy. [ABSTRACT FROM AUTHOR]

This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research's status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance. [ABSTRACT FROM AUTHOR]

Purpose: As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP. Design/methodology/approach: This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis. Findings: The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP. Practical implications: The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company's collaboration and for effective management to improve NPP in the face of global competition. Originality/value: This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC. [ABSTRACT FROM AUTHOR]