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Artificial intelligence (AI) is reconstructing the educational space and activity process, profoundly transforming the learning, living and thinking modes of teachers and students in the field of higher education. Higher education bears the mission of cultivating high-end talents for the country and society, and the Al-driven transformation of higher education is a global trend. The role and impact of artificial intelligence on higher education have attracted much attention. However, higher education institutions are still in the early stages of applying Al in education at this stage. and existing literature lacks a comprehensive analysis of how Al empowers higher education and its application scenarios. This paper explores the sustainable development and promotion path of higher education in the intelligent era through a holistic and coherent analysis of Al empowering higher education. First of all, based on the empowerment theory, along the line of "goal-means-agent-scenario", it clarifies the logical path of Al empowering higher education, and discusses trustworthy Al as a key engine empowering higher education, interacting continuously with educational scenarios to support the construction of a powerful country in higher education. Secondly, from the perspective of "students-teachers-campuses", it analyzes the typical scenarios of Al empowering higher education. Empowering students is reflected in learning support, life support and career support. Empowering teachers focuses on academic research, instruction and professional development. Empowering campuses mainly focuses on discipline construction. education management and environment improvement. Finally, it explores the practical approaches of Al empowering higher education from the perspectives of value orientation, digital competence, scenario-driven, social experiments, etc., in order to promote the standardized and orderly integration of Al into higher education ecology. This paper expands on three aspects compared to previous literature. Firstly, it combs out the basic logic of Al empowering higher education based on the empowerment theory, and argues that the core essence of Al empowering higher education lies in the continuous interaction and evolution of technology and corresponding educational scenarios. clarifying the pre-requisite questions such as "why empower". "with what to empower", "for whom to empower", and "how to empower". Secondly, it combines rich practical cases to analyze typical scenarios where Al empowers students, teachers, and campuses. Lastly, in response to challenges encountered during the process of integrating Al into specific educational scenarios, it proposes practical directions such as clarifying value orientations. enhancing digital competence, strengthening scenario-driven approaches and conducting social experiments. This paper, to a certain extent reveals the internal logic and practical direction of how artificial intelligence empowers higher education. Based on the empowerment theory, it thoroughly explores the goals. means, agents. and scenarios of Al empowering higher education, and proposes corresponding practical strategies based on potential challenges. It can provide valuable guidance and reference for facilitating the standardized and orderly integration of Al into the higher education ecology. [ABSTRACT FROM AUTHOR]

Teaching students in accordance with their aptitude is a millennium dream of education and personalized cultivation of college students is an important content and foundation to achieve this goal. Data-driven personalized educational evaluation is an effective tool to measure the quality of talent training, and it is an inevitable requirement to adapt to the development of the artificial intelligence era. Traditional student evaluation mainly relies on the manpower of teachers and experts, which may cause problems like difficulties in data collection, too many links in the evaluation process, coarse-grained indicator data and low real-time current situation effect. And it is difficult to cope with the large number of students, process, all-round. and personalized development evaluation. Starting from the perspective of artificial intelligence empowering higher education and based on reviewing the current status of personalized educational evaluation for college students, this paper proposes a human-machine collaborative personalized educational evaluation method for college students. The core is to give full play to the advantages of the new generation of artificial intelligence technology. The interaction and collaboration between humans and artificial intelligence can not only effectively combine the advantages of human experts in high-order thinking such as abstraction and reasoning with the capabilities of machines in data computing, storage, processing and search but also enable comprehensive collection of multi-level. fine-grained and process-oriented data. Furthermore, it allows for the establishment of a human-guided. data-driven and human-machine collaborative evaluation method. achieving rapid. efficient comprehensive, and accurate personalized educational evaluation for large-scale student groups. Then it expounds the practical path of the human-machine collaborative personalized educational evaluation method for college students, which is specifically manifested in clarifying the evaluation standards through the human-machine co-construction of the educational evaluation index system and then using the human-machine collaboration model to implement the evaluation. Finally, from the perspectives of data collection. technology application index construction and human-machine collaboration four typical application cases in the context of personalized educational evaluation for college students were presented. These include personalized educational evaluation for college students online learning based on convolutional neural network technology. personalized knowledge evaluation for college students based on an ensemble knowledge tracking framework. personalized cognitive and emotional evaluation for college students based on multimodal perception technology, and personalized evaluation of college students' daily behavioral habits based on graph convolutional network technology.Compared with the existing researches, this paper expands in the following two aspects; first, it proposes a method and path for human-machine collaborative personalized educational evaluation, clarifies the relationship between humans and intelligent machines, the construction method of the evaluation index system, the modes of human- machine collaboration, and the development pathways, providing a reference for personalized educational evaluation of college students in the new era. Second, combined with the analysis of the four typical cases of personalized educational evaluation for college students in the higher education field, this paper validates the effectiveness of the human-machine collaborative personalized educational evaluation method by using convolutional neural networks. integrated knowledge tracking, multimodal emotion perception, and graph convolutional neural network technologies. The research in this paper, to some extent, points out the direction for the realization of data-driven personalized educational evaluation in the era of artificial intelligence. The educational evaluation index system co-constructed by humans and machines is the primary content of personalized educational evaluation, and the implementation evaluation of human-machine collaboration is a key part of personalized educational evaluation. The personalized educational evaluation of human-machine collaboration in education possesses data-driven, objectivity, and comprehensive standards, intelligent means all-weather, and extended service capabilities, which can significantly enhance the efficiency and quality of evaluation. It is a crucial means for the success of educational evaluation reform in the new era and an effective approach to realizing the modernization of educational evaluation. [ABSTRACT FROM AUTHOR]

At present, the Ukraine crisis has triggered the accelerated evolution of the world geopolitical pattern. Trade frictions and the impact of the COVID-19 epidemic have increased the risk of rupture of the global industrial chain. In this context, countries have shifted from pursuing “efficiency” of the industrial chain to emphasizing both “efficiency and security”, and the issue of industrial chain resilience and security has been raised to unprecedented new heights. Improving the resilience of the industrial chain is a key component in promoting high-quality development and maintaining national security. Based on the multi-regional input-output table(ADB-MRIO)compiled by the Asian Development Bank, this paper calculates the resilience of the industry chain, then combines it with industrial robots data provided by the International Federation of Robotics(IFR)to deeply explore the impact and mechanism of artificial intelligence on the resilience of the industry chain. The study shows that:(1)overall, artificial intelligence can significantly improve the resilience of the industry chain. This result is still significantly valid under robustness tests such as dealing with endogenous problems, changing variables, changing sample range, and changing estimation methods.(2)The mechanism test results show that the effects of technological innovation and governance system optimization are the internal mechanisms for AI to enhance the resilience of the industrial chain.(3)The heterogeneity analysis shows that at the national level, artificial intelligence has a more significant impact on enhancing the resilience of industrial chain in developed countries and countries with higher levels of digital infrastructure penetration. At the industry level, compared to low tech manufacturing, artificial intelligence plays a more prominent role in enhancing the resilience of the industrial chain in mid to high tech manufacturing.(4)When considering the impact of the 2008 financial crisis, we also find that the financial crisis has weakened the positive effect of artificial intelligence on the resilience of the industrial chain. This paper contributes to the existing literature in three aspects. First, in terms of research issues, this paper provides a possible path to effectively improve the resilience of the industrial chain from the perspective of artificial intelligence, and further enriches the research on the determinants of industrial chain resilience. At the same time, we also put forward the internal mechanism of artificial intelligence to improve the resilience of the industrial chain, that is, artificial intelligence will affect the resilience of the industrial chain through two possible channels: technological innovation effect and governance system optimization effect. Second, in terms of index construction, based on the analysis of industrial chain resilience in the International Monetary Fund's World Economic Outlook Report, this paper uses the input-output method to construct the index of industrial chain resilience at the country-industry level from the perspective of intermediate input diversification, providing empirical evidence for accurately identifying the industrial chain resilience in various industries and countries. Third, in terms of research data, this paper manually links the multi-regional input-output table(ADB-MRIO)and the International Federation of Robotics(IFR)to form the dataset needed for this research. In addition, the sample interval of this study is from 2007 to 2019. Compared with previous studies using the WIOD input-output table up to 2014, it can better reflect the current new pattern of global economic and trade adjustment and has higher timeliness. The research in this paper helps to explore the possible path to ensure the resilience and safety of the industrial chain, and then provides beneficial theoretical support for achieving high-quality development under the new round of scientific and technological revolution. [ABSTRACT FROM AUTHOR]

The development of emerging technologies such as mobile internet. artificial intelligence, and big data has had a profound impact on China's education industry. The deep integration of technology and education has continuously improved the level and quality of digital and intellectual development in education. Education and technology, as the two pillars of revitalizing the country through science and technology, provide mutual feedback and strategic support. However, existing literature only focuses on the penetration and impact of high-tech developments such as artificial intelligence on the field of higher education, and fails to recognize its empowerment of higher education and the overall transformation and institutional construction of higher education. In view of this fact, based on the national macro strategic positioning and theoretical analysis, this paper systematically discusses the strategic enabling value of Al for high-quality higher education and the path of institutional construction of higher education. On the one hand, in the era of digital intelligence, technological self- reliance and self-reliance are important strategic support for national development. Artificial intelligence, as a new focus of international competition, is leading a new generation of technological and industrial revolutions. The strategic features of Al empowering high-quality higher education are to achieve the integration of science and education through high-tech leadership in basic research. to force innovation in higher education through originality, and to promote the integration of industry and education through organization. On the other hand, in order to adapt to the changing requirements of artificial intelligence for high-quality higher education, higher education should also rely on the integration of its basic functions of teaching, scientific research and social services as an important entry point, further play its leading role in cultivating top-notch innovative talents, building a foundation for basic research, and developing interdisciplinary research, and promote the overall transformation of higher education in the new era. By deeply integrating industry and education in the field of artificial intelligence, we will vigorously promote the growth and success of innovative talents with top-notch foundations. Compared to previous literature, this article expands on the following two aspects: firstly, based on a high position and high-level situation guided by macro policies such as the report of the 20th National Congress of the Communist Party of China, the 14th Five Year Plan and the 2035 Long Range Goals, with the two-way empowerment of education and technology as the logical main line, fully recognizing the timeliness of artificial intelligence strategic technology and its key strategic support role in building an educational and technological power. There is an important supplement to the previous narrow view on the advantages and disadvantages of artificial intelligence in education. Secondly, unlike previous views that only focused on the infiltration and impact of artificial intelligence on the field of higher education, this article believes that higher education will actively adapt and undergo comprehensive reforms. High-quality higher education, relying on the structural integration of teaching, scientific research. and social services. can fully play its leading role in education. With the mission of cultivating top-notch innovative talents as the fundamental mission, it will strongly promote the development of an educational and technological powerhouse, and promote the construction of a strong talent country. The research in this article, to some extent, reveals the internal logical relationship and strategic value positioning of the dual empowerment of education and technology which has important theoretical reference and practical guidance for the current implementation of the strategy of revitalizing the country through science and education and strengthening the country through talent. [ABSTRACT FROM AUTHOR]

Voice conversion is a research topic in the fields of speech and artificial intelligence. The goal of voice conversion is to change the timbre of speech while preserving the content of the source speech, making it sounds like spoken by the target speaker. It is essential to ensure both the quality and naturalness of the converted speech. Voice conversion based on nonparallel data gains much attention currently, where models are trained using non-parallel multilingual speaker datasets, enabling many-to-many and any-to-any voice conversions. This paper provides a comprehensive summary and analysis of recent developments in non-parallel voice conversion. Firstly, we outline the early voice conversion techniques based on parallel corpus and their limitations. Then, we introduce and compare various approaches to voice conversion based on nonparallel data, providing a thorough analysis. Finally, a summary and outlook on voice conversion technology is provided. [ABSTRACT FROM AUTHOR]

In recent years, applications such as 360 video, augmented reality, virtual reality, etc. have been rapidly developed and gradually formed a new mode of immersive metaverse experience. These new services and applications have common features such as high fidelity and immersive interaction. As a new paradigm, edge computing is increasingly ready to support these features. This paper firstly elucidates that the key to releasing the power of edge computing to support immersive metaverse experience is the processing of AI models and data for content generation. Then, this paper presents a generic framework to realize adaptive deep learning model deployment and data flow to support metaverse services and applications. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence continues to heat up. Large language models have attracted much attention and set off a wave of enthusiasm around the world. The success of artificial intelligence is not essentially a "miracle" of large computing power, but a change in computing models. Firstly, this paper affirms the fundamental role of data in AI, and points out that synthetic data will be the main source of data in the future. Then, this paper reviews the development of computing models, highlights the historic competition between neural network models and Turing models. We points out that the important hallmark of large language models is the emergence of intelligence in machines, emphasizes that the essence of large language models is "compression", and analyzes the reasons for the "illusion" of large language models. Finally, we call on the scientific community to attach importance to large scientific models in "AI for research(AI4R)". [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Reading promotion has been a basic and normalized activity in libraries, and has been carried out for many years. Although it has achieved remarkable results in promoting the dissemination of knowledge and culture, it has reached a developmental bottleneck that urgently requires innovative breakthroughs. The introduction and application of artificial intelligence generated content (AIGC) technology can innovate the content and form of the library work, and promote the connotative development of library services. Currently, there is limited research on the application of AIGC in library reading promotion, with most studies focusing on theoretical discussions of work scenarios rather than practical applications. The aim of this article is to explore new technology applications in new scenarios to help solve common problems in the field. [Method/Process] Through literature research and case analysis, based on the characteristics of AIGC content, we look forward to AIGC empowering library reading promotion scenarios, including inspiring activity creativity, and enriching work forms; creating reading IP? and forming work brands; promoting in-depth and professional reading, and optimizing work content; strengthening service management, and promoting the construction of work evaluation standards. Taking the "New Book Recommendatioii" activity as an example, we analyze work application elements such as planning, promotion, organization, and evaluation, and construct a work model suitable for the scenario. [Results/Conclusions] Although AIGC has great potential and broad prospects for application in library reading promotion, it faces many application challenges, such as the need for a change in librarians' work mindset, issues of data privacy and ethics, and the quality and accuracy of generated content This paper discusses the application paths of AIGC in library reading promotion, including strengthening skills training to comprehensively enhance librarians' new literacy in the AI era; exploiting the role of "AI pTompter" to deepen reading services; building a rich database of event data corpora to ensure digital content generation; and improving rules for content review and quality control. Compared to other information resource management tasks, such as reference consulting and academic services, the impact of AIGC is relatively mild. AIGC is applied as a tool for reading promotion, with librarians remaining the primary actors, and the implementation of work elements is still carried out within the existing work framework. The discussion of AIGC applications in library reading promotion practice in this paper offers methodological insights for library reading promotion, with scenario elaboration, model construction, and path generation facilitating the realization and broad application of these related services. The limitation of this study, however, is the lack of concrete data on the effectiveness of the work discussed, which requires further research. [ABSTRACT FROM AUTHOR]

Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psychological disorders. This exerts a negative impact on individuals, families and society. Timely diagnosis and intervention are crucial to prevent permanent damage to vision and stereopsis. Presently, strabismus diagnosis is reliant on the ophthalmologists′ evaluations which results in a lack of efficiency and coverage. However, routine school screening proves inadequate in assessing strabismus degree with low accuracy. Therefore, how to improve the efficiency of strabismus screening is an issue of great importance. This paper delves into the present landscape of strabismus diagnosis and treatment, considering both local and global research advancements. It focuses on the evolution of artificial intelligence technology, illuminating the utilization of artificial intelligence models and algorithms in strabismus. By pinpointing and exploring their strengths and limitations, it offers valuable insights, paving the way for future investigations into artificial intelligence-assisted strabismus diagnosis and treatment. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] In recent years, the rapid development of artificial intelligence (AI) technology has become an international research hotspot. Embedding ChatGPT-like AI system in libraries will lead to a new direction for the development of their intelligent services, which is a necessary choice to improve the library service level, optimize service efficiency and promote service model innovation. It is also an important measure to adapt to the development trend of the information age, which brings new opportunities and challenges to the information and digital construction of libraries. This article aims to explore the realization path of ChatGPT to help libraries provide intelligent services, provide theoretical reference and practical basis for the intelligent service by libraries, and promote the construction of library intelligent service system. [Method/Process] By reviewing the relevant literature at home and abroad, on the basis of summarizing the core technology of ChatGPT, and studying the practical application cases of some domestic libraries, this article analyzes the application scenarios and directions in which ChatGPT can support library services, explains the current limitations of this technology and the threats and challenges it poses to libraries and librarians, and thus proposes corresponding strategies. [Results/Conclusions] ChatGPT technology will further optimize the knowledge service system of libraries and improve the intelligent service capability of libraries. It brings vitality to the libraries, but also brings threats and challenges. Librarians should grasp the integration capabilities of humans and AI in time, and make full use of their unique advantages. Libraries should strengthen the capacity building of their smart services oriented to ChatGPT, innovate the mode and mechanism of their smart services, and rely on ChatGPT to build a smart service platform, provide smart service resources, build a sound service system, build a service team, and improve the level of their smart services. Continuous efforts should be made to promote the in-depth development of smart library services, so as to meet the diversified and personalized information needs of library readers. To this end, this paper makes a preliminary exploration on the opportunities, challenges and development strategies of enabling smart services in libraries through ChatGPT. Due to the limited conditions, only some practices of domestic libraries are explored, and no cases of international libraries are introduced, which has certain limitations and needs to be improved in future studies. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Talent data have become the most important production factor and strategic resource. Building a scientific and technological (S&T) talent database has become an inevitable way to narrow the digital divide and accelerate the digital and intelligent transformation of talent work. Therefore, this study builds an S&T talent database to promote scientific decision-making for talent development, precision in attracting new quality technical talent, reform in evaluating S&T talents, and building talent system for new quality productive forces. [Method/Process] By analyzing the practical requirements and significance of building an S&T talent database, this study first explores and analyzes the intrinsic logic of promoting the development of new quality productive forces through an S&T talent database. It then summarizes the challenges facing the current construction of a S&T talent database, including the scattering and concealment of S&T talent data, the lack of policies and standardized systems for S&T talent data, the inadequate exploration of value-added S&T talent data, the need to expand the application of digital technology in talent work, and the security risks of S&T talent data. In response to these challenges, this paper finally proposes the idea of building an S&T talents database, and introduces the research exploration and application practice on it, including the construction of big data database for S&T talent aimed at the development of new quality productive forces, the development of AI-powered talent data computing engine, research into the system for profiling new quality technical talent, the analysis of talent growth paths for the training of new quality technical talent, the identification method of new quality talented professionals based on big data, the development of an efficient digital platform for talent management, and the development of a strategic analysis platform for technical talent. [Results/Conclusions] The construction of S&T talent database is an objective requirement for the development of the digital era and an inevitable requirement for the formation of new quality productive forces. Building big data for S&T talent, empowering talent workflow with big data and artificial intelligence technology can help empower talent workflow, release the enormous energy contained in digitalization, effectively activate the internal momentum of talented professionals, institutions, society, and government, and then continuously improve the efficiency of talent resource allocation, the operational efficiency of talent work, the overall effectiveness of talent development governance, and promote the development of new quality productive forces. [ABSTRACT FROM AUTHOR]

This paper analyzes and combs the application and research status of artificial intelligence technology in the field. of military intelligence, in order to provide reference for subsequent military intelligence research. This paper summarizes the development and application of artificial intelligence in military intelligence work from the aspects of intelligence analysis and military command decision-making. Based on the intelligence workflow, the military intelligence service model under artificial intelligence technology is analyzed. This paper systematically combs the research and development status of typical projects of intelligent intelligence systems in the United States, and analyzes the key development trends and technical difficulties of artificial intelligence in the military intelligence field. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] This study aims to complement the shortcomings existing in the current research, such as the limited research on the micro-level of enabling rural revitalization and the unclear impact of knowledge services on rural revitalization, in order to strengthen the understanding of the mechanism and path of enabling rural revitalization by agricultural intelligent knowledge services, and to provide theoretical reference and practical solutions for better enabling rural revitalization by the knowledge services and better developing agricultural intelligent knowledge services. [Method/Process] This paper first reviews the literature to understand the current research status of enabling rural revitalization and knowledge services affecting rural revitalization, and then determines the research focus. Based on the three perspectives of data, application and service, this paper explores the enabling basis of agricultural intelligent knowledge services, analyzes the enabling mechanism from the three aspects of agricultural production, farmer life and rural governance, clarifies the necessity and possibility of agricultural intelligent knowledge services enabling rural revitalization, and constructs the internal mechanism of the knowledge services enabling rural revitalization. Moreover, the predicament of agricultural intelligent knowledge services enabling rural revitalization is analyzed from the internal and external aspects, and the performance and impact of these dilemmas are analyzed. Finally, according to the analysis results, the optimization path is proposed from the three dimensions of enabling demand, scene and effect to alleviate the problem. [Results/Conclusions] Agricultural knowledge services have entered the intelligent stage, presenting the characteristics of intelligence, personalization and precision, and have established a good foundation for enabling rural revitalization in terms of data, application and service. By enabling the development of new production modes, the transformation of farmers' multiple identities, and the equal participation of multiple subjects in governance, digital intelligence momentum will be injected into the comprehensive promotion of rural revitalization. However, from the internal perspective of agricultural intelligent knowledge services, there are some shortcomings such as obvious user group bias and the lack of service content. From the external point of view of the knowledge services, there are the challenges of unbalanced infrastructure and low user literacy. These deficiencies and challenges limit the enabling role of agricultural intelligent knowledge services in rural revitalization, which will limit the maximum play of the enabling effects. Accordingly, this paper provides the corresponding solution. First, in-depth research could be carried out on rural revitalization stakeholders and strategic content to clarify the empowerment needs. Second, integrated service platform could be built to integrate and empower scenarios. Third, the promotion model could be constructed to enhance the enabling effect to alleviate the empowerment dilemma. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] The new generation of generative artificial intelligence technology and its applications have entered a new stage of development. Models such as ChatGPT are leading the way in model-driven content generation, ushering in a new era and attracting discussions and attention from various sectors. Currently, there is a wealth of research on artificial intelligence from the perspectives of technological advancements, machine language, algorithm models, application scenarios and so on. However, there are relatively fewer articles specifically focusing on the application of AI generated content (AIGC) in GLAM fields, such as galleries, libraries, art museums. AI in the GLAM field is currently undergoing a transition from digitization to intelligentized level. Therefore, this paper explores the multi-scenario innovative applications of AI in the GLAM field with the theme of "Shaping the Future: AIGC and GLAM Innovative Development", which aims to contribute to the innovation research of AI and cultural, development and provide valuable insights in this field. [Method/Process] The structure of this paper consists of three parts: the evolution and current status of AIGC, the exploration of AIGC's applications in various scenarios within the GLAM field, the risks and challenges faced by AIGC and GLAM in terms of innovative development. Firstly the article provides a brief introduction to the development history of artificial intelligence, the concept of AIGC, algorithm models, typical features, and its current development status both domestically and internationally. Secondly, it presents a review and summary of existing application cases of AIGC in the GLAM field, including the use of virtual scenarios, virtual IP idols, ancient book OCR technology, VR/AR experiences in the fields of image analysis, digital humanities research, and new formats of digital cultural industries. The article analyzes how AIGC enriches the content production, innovation, and user interaction modes, as well as resource management models within GLAM. It also highlights the potential significant advantages in specific areas such as digital individuals, digital collections, and digital media. Lastly, the paper addresses the potential risks and challenges that may arise during the process of AIGC and GLAM's innovative development. These challenges encompass technical aspects, algorithmic biases, network information security, ethical and moral considerations, academic research, publishing and legal regulations. [Results/Conclusions] AIGC possesses advantages such as high efficiency, intelligence, and immersive experiences. It not only brings about transformative changes in content within the GLAM field but also breaks free from traditional interactive modes characterized by singularity and flatness in user engagement. By leveraging technologies such as VR/AR, virtual digital characters, and virtual spaces, AIGC enables diverse and three-dimensional interaction models with customs, thereby significantly expanding the applications in the GLAM field. If we appropriately and fully utilize the valuable opportunities presented by AIGC, it can play a positive role in inheriting and promoting the Chinese excellent civilization and cultural heritage. Furthermore, it will also play a crucial role in accelerating the digital transformation and upgrading of the cultural industry. [ABSTRACT FROM AUTHOR]

Morphological analysis of unruptured intracranial aneurysms is widely used in risk assessment and surgical design. Compared with the manual measurement in the two-dimensional perspective, the three-dimensional perspective can reflect the morphology changes of aneurysms more accurately. But the manual measurement in the three-dimensional perspective has poor accuracy and repeatability. Therefore, the measurement of artificial intelligence in the three-dimensional perspective has greater advantages than that of manual measurement. However, the method of aneurysm morphological measurement in the three-dimensional perspective based on artificial intelligence has not been proposed. The standardized measurement in the three-dimensional perspective is of great significance to the clinical and research application of morphological parameters. In this paper, a three-dimensional measurement method for unruptured intracranial aneurysms based on artificial intelligence is presented, in order to promote the homogenization of aneurysm research and the standardization of clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The production and sales of high-quality fruits are inseparable from fruit quality testing, while traditional fruit quality testing methods have low precision, high cost, poor timeliness, and strong destructiveness. With the continuous advancement of science and technology in recent years, low-cost and high-efficiency non-destructive testing technology for fruit quality has been developed rapidly. Among them, hyperspectral imaging technology has gradually become a research hotspot. This paper summarizes the technical principle, application and development status of this technology in the field of fruit quality, and discusses its performance potential, existing problems, development trend and application prospects in the field of non-destructive testing of fruit quality. On the whole, hyperspectral imaging technology can non-destructively and efficiently realize the effective detection of various fruits and various quality indicators, such as maturity, sugar content, acidity, red index, etc.; Limited by hardware technology, its development focuses on data mining, that is, in the case of limited hardware development, accurate analysis results can be obtained through continuously updated and optimized targeted algorithms; on the other hand, expensive equipment, complex data processing, and poor model universality are the main problems that this technology needs to be further optimized and improved; Its future development will be based on efficient data processing of cloud computing and artificial intelligence, research and development of hyperspectral detection equipment for fruit quality with a wider range of applications, and multi-source comprehensive non-destructive testing. With the continuous development of technology, hyperspectral imaging technology has broad application prospects in non-destructive testing of fruit quality, and will become one of the important means of fruit quality testing in the future. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Taking the popular "chat robot" ChatGPT and the recently launched similar product "iFLYTEK Spark" as the research object, this paper explores their applications in the identification of online health information, and discusses their advantages and disadvantages, in order to provide reference for the large-scale pre-training language model in the field of health information identification. Based on the review of relevant literature on online health information authentication, deep learning models have been widely applied in the task of online health information authentication in recent years. With the rapid development of large pre-training language models such as ChatGPT, it is a novel idea to explore their discriminating ability in online health information. [Method/Process] Researchers selected health-related information from the most authoritative rumor-refuting websites in China, used "ChatGPT" and "iFLYTEK Spark" to verify the authenticity of the online health information, evaluated their performance, and compared their identification results with the expert identification results. The identification accuracy of ChatGPT and iFLYTEK Spark language model was 93.9% and 92.9%, respectively, and the F1 value was 0.951 and 0.946, respectively, which had a good application effect. The generated explanatory texts were more detailed and the language was relatively smooth. In terms of the length and dispersion of the explanatory text, ChatGPT is closer to that of medical experts, while iFLYTEK Spark's explanatory text is relatively long and less discrete. In terms of semantic similarity, ChatGPT and iFLYTEK Spark were almost equal in performance, and their understanding of health information was close to that of human experts to some extent. Through the analysis of typical samples, it can be seen that an AI large model cannot accurately identify news or emergency information for the time being, and the understanding of individual health propositions with complex semantics will occasionally be biased. [Results/Conclusions] The experimental results show that ChatGPT and iFLYTEK Spark have good discriminative effect in the field of online health information identification, but there are shortcomings, and manual intervention is needed to ensure the accuracy and reliability of the results. In the future, in the field of AI large model research, researchers are suggested to attach importance to the construction and application of high-quality corpora in vertical fields. In the field of online health information identification, practitioners can use models such as ChatGPT as tools to help identify and refine health information. There are also limitations in this article. For example, the amount of data involved in the test is not large enough, ChatGPT uses GPT3.5 model, and the online application time of iFLYTEK Spark language model is relatively short. In future studies, the amount of online health information can be further increased, and the updated version of an AI large model can be tested and evaluated. [ABSTRACT FROM AUTHOR]

Agriculture is a basic industry deeply related to the national economy and people's livelihood, while it is also a weak industry. There are some problems with traditional agricultural risk management research methods, such as insufficient mining of nonlinear information, low accuracy and poor robustness. Artificial intelligence(AI) has powerful functions such as strong nonlinear fitting, end-to-end modeling, feature self-learning based on big data, which can solve the above problems well. The research progress of artificial intelligence technology in agricultural vulnerability assessment, agricultural risk prediction and agricultural damage assessment were first analyzed in this paper, and the following conclusions were obtained: 1. The feature importance assessment of AI in agricultural vulnerability assessment lacks scientific and effective verification indicators, and the application method makes it impossible to compare the advantages and disadvantages of multiple AI models. Therefore, it is suggested to use subjective and objective methods for evaluation; 2. In risk prediction, it is found that with the increase of prediction time, the prediction ability of machine learning model tends to decline. Overfitting is a common problem in risk prediction, and there are few researches on the mining of spatial information of graph data; 3. Complex agricultural production environment and varied application scenarios are important factors affecting the accuracy of damage assessment. Improving the feature extraction ability and robustness of deep learning models is a key and difficult issue to be overcome in future technological development. Then, in view of the performance improvement problem and small sample problem existing in the application process of AI technology, corresponding solutions were put forward. For the performance improvement problem, according to the user's familiarity with artificial intelligence, a variety of model comparison method, model group method and neural network structure optimization method can be used respectively to improve the performance of the model; For the problem of small samples, data augmentation, GAN (Generative Adversarial Network) and transfer learning can often be combined to increase the amount of input data of the model, enhance the robustness of the model, accelerate the training speed of the model and improve the accuracy of model recognition. Finally, the applications of AI in agricultural risk management were prospected: In the future, AI algorithm could be considered in the construction of agricultural vulnerability curve; In view of the relationship between upstream and downstream of agricultural industry chain and agriculture-related industries, the graph neural network can be used more in the future to further study the agricultural price risk prediction; In the modeling process of future damage assessment, more professional knowledge related to the assessment target can be introduced to enhance the feature learning of the target, and expanding the small sample data is also the key subject of future research. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] The paper aims to identify and evaluate the top experts of international cooperation of artificial intelligence (AI) in China, so as to provide references for further international cooperation on AI in China. [Method/Process] The top AI experts whose H index is greater than 40 are selected as the research object. Based on Aminer platform, this paper identifies top AI experts for international cooperation in China, and uses Topsis method to evaluate academic level of these experts. [Results/Conclusions] The results show that: (1) The cooperation of top experts between China and the United States is more frequently than that between China and other countries combined. (2) Top experts are mainly male, and the proportion of women is extremely low. (3) The institutions of top experts are distributed in 22 countries (regions) around the world, and the distribution of institutions is generally consistent with the situation of science & technology and economic strength of each region. (4) Some of top experts have the dual identities of university researchers and enterprise scientists. (5) Chinese and American top experts play a leading role in international cooperation in the field of academic papers. International migration of AI scientists reflects in Chinese scholars studying or working abroad, while the main international expert recruits in China are American experts. Meanwhile袁although international AI cooperation has been fully carried out in China, it still has not established effective interflow and direct cooperation with some top experts, which include some leading AI experts. [ABSTRACT FROM AUTHOR]

Labor is the basic condition of all human history, and it is also an important entry point for sociology to understand social development. In the era of artificial intelligence, the labor mode required for AI product production is different from the physical labor in the industrial era and the emotional labor in the era of service industry. In the production with “human-computer interaction” as the core demand, the dominant labor form is cognitive labor related to human abilities such as recognition, judgment and creation. Workers need to give human cognition to machines through the process of “transformation of cognitive system”. However, from the perspective of labor division, AI production enterprises still organize production under the logic of “separation of concept and executive” and the principle of “deskilling”, and many workers are also degraded to machine assistance. As a result, workers have become sensory organs that help machines receive information from the limbs that execute machine instructions. In addition, the intensification of market competition and the increase of work pressure have made cognitive workers fall into a difficult situation similar to those of previous workers. Therefore, from the perspective of trade unions and the government, this paper puts forward policy suggestions to better endow workers with production subjectivity, promote workers’ career development and enhance the protection of workers’ rights and interests. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke (AIS) is characterized by a high rate of disability and mortality, and rapid and accurate diagnosis and appropriate treatment are the keys. The severity of AIS patients depends on the location of early ischemic changes (EIC) in the brain tissue and the size of the lesion, so it is necessary to evaluate the patients' conditions by combining with imaging. The Alberta stroke program early CT score (ASPECT score) is a 10-point imaging score system that evaluates the EIC in the middle cerebral artery supplying region of the stroke patients in an accurate and simple way by quantifying the severity in terms of the imaging to help clinicians make medical decisions. ASPECT score is not only widely used to predict the prognosis of stroke patients, but also to identify the population benefiting from the endovascular therapy and evaluate the risk of endovascular therapy for patients. ASPECT score combined with multimodal imaging has also been used to predict ischemic core volumes and the prognosis. In recent years, with the development of artificial intelligence, automatic ASPECT scoring methods based on machine learning have emerged. This paper reviews ASPECT scoring methods, its value in the treatment and the assessment of prognosis in AIS, multimodal ASPECT scoring, and the application of automated ASPECT scoring combined with artificial intelligence. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] This paper summarizes reports of the 395th Youth Scientist Forum of China Association for Science and Technology and 2020 National Library and Information Youth Academic Forum held on November 5-6, 2020 with the theme of "AI enables knowledge service and opens up the future of intelligent digital agriculture". [Method/Process] The expert reports are classified and summarized from three aspects: 1) AI promoting the research method innovation of library and information science (LIS) and upgrading knowledge service infrastructure, 2) AI promoting technological innovation in agricultural and 3) the problem of information poverty in the era of AI. [Results/Conclusions] AI is accelerating the transformation of ways, means and facilities of knowledge service in the field of library and information; meanwhile, the convergence of AI and S&T fields such as agriculture puts forward high-level requirements for the field of library and information, which requires practitioners to organically integrate library and information services, AI technology and S&T innovation scenes; in addition, the "information poverty" and other social problems caused by the accelerated promotion of AI need the attention of library and information researchers. [ABSTRACT FROM AUTHOR]

To boost the exploitation of energy resources in no manpower(little manpower) and to guarantee the demand of national energy and strategies, a complete integration of information technology and the theories of mineral resource exploitation is the direction for the revolution of energy resources development in the new era. A new round of information technology revolution occurs in the areas, such as big data, cloud computing, artificial intelligence, internet of things, regional chain and internet, and intelligent unmanned exploitation in coal, uranium, and oil and gas. In considering the integration of information technology, the scientific conception of precise mining of coal and its associated resources was innovatively developed in this paper. The innovative geospatial physical science, multi-phase and multi-field coupling theory and intelligent control system are proposed as a core guarantee system. supported by the "trinity"scientific research means including internet+on-site monitoring, physical/numerical simulation and basic experiments. The holographic real-time display of coal and associated resource endowment, original/disturbed geological disaster and the working condition of mining equipment is required. Also, the intelligent and flexible decision on mine planning, operation management and the ecological restoration of closed mines, the intelligent depth perception, accurate delineation and efficient resolution of the crisis for potential disaster, the self-repair of the damage space, the resilience recovery of control system, the adaptive adjustment of the equipment are all need to be achieved. Precise mining of coal and associated resources can effectively solve all these major problems subjected to exploration and monitoring, collaborative development, disaster prevention and control, and negative environmental externalities, to provide new ideas for the future development of coal and associated resources. In this paper, five key scientific issues and seven main research directions for the precision mining of coal and associated resources are summarized, and a phased plan is proposed for the safe, efficient and green mining of resources in the new era. [ABSTRACT FROM AUTHOR]

Musculoskeletal diseases are diverse. Medical imaging examination is the primary means of disease diagnosis. Reasonable selection of image post-processing methods can provide reliable evaluation basis for disease diagnosis, surgery and prognosis. As a new stage of the development of computer science, artificial intelligence technology can efficiently and accurately preprocess and analyze images, and assist clinicians in diagnosis and treatment. It will bring new opportunities for the development of musculoskeletal imaging. This paper reviews the current situation and challenges of artificial intelligence in the field of imaging diagnosis of musculoskeletal system diseases, in order to provide some reference for the relevant research scholars. [ABSTRACT FROM AUTHOR]

This paper probed into the whole height bench cast blasting process and described the influence factors from 3 major perspectives; natural geological, blasting scheming and factitious ones, and selected the throwing rate which was generally accepted in the cast blasting field to assess the blasting performance. Then a novel GA-SVM model was constructed to analyze the real collected explosion data from open pit mining, and verified in a certain open-pit. Also the MIV method was employed to analyze the influence factor at each input factor. The study indicate that; (T) the presented GA-SVM model performs more robust and accurate than other artificial intelligence models such as BP, RBF, GRNN and GA-BP, which has a more stable prediction accuracy of 83.75%. Moreover, due to the ubiquitous paradigm of the presented approach, it provides a single, unified approach to evaluating other blasting performance factors such as the longest thrown distance and loose coefficient etc; (2) for this certain open pit which maintains a steady lithological character and design parameters, the bench height, explosive specific charge possess a positive correlation coefficient with the throwing rate, while line of least resistance, the slope angle and the profile width perform the opposite. [ABSTRACT FROM AUTHOR]