Search

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an effective representation of knowledge, knowledge graph network can be used to represent rich factual information between different categories and become an effective knowledge management tool. It has achieved great results in the application and research of knowledge engineering and artificial intelligence. Knowledge graph is usually expressed as a complex network structure. Its unstructured characteristics make the application of graph neural network to the analysis and research of knowledge graph become a research hotspot in academia. The purpose of this paper is to provide extensive research on knowledge graph construction technology based on graph neural network to solve two types of knowledge graph construction tasks, including knowledge extraction (entity, relationship and attribute extraction) and knowledge merging and processing (link prediction, entity alignment and knowledge reasoning, etc.). Through these tasks, the structure of knowledge graph can be further improved and new knowledge and reasoning relationships can be discovered. This paper also studies the advanced graph neural network method for knowledge graph related applications, such as recommendation system, question answering system and computer vision. Finally, the future research directions of knowledge graph application based on graph neural network are proposed. [ABSTRACT FROM AUTHOR]

The development of digital technology has accelerated the transformation of financial online payment methods, bringing convenience to payment but also increasing the hidden dangers of fraudulent transactions. Antifraud research is particularly essential to protect users' property and prevent financial crises. With the advancement of data governance and sharing technology, digital payment transaction data present new characteristics of massive, multi-source and heterogeneous. Integrating data intelligence technology based on big data and artificial intelligence into anti-fraud research has important theoretical research significance. The digital credit payment model formed by the full combination of credit card payment and digital payment has the most mature data accumulation and theoretical basis at present, providing the most ideal data resources and theoretical support for the research of anti-fraud models. Starting from the concept, this paper firstly introduces the definition, research difficulties, and data framework of the digital credit anti-fraud research problem in combination with the actual business scenarios in China. Secondly, based on the modeling strategy, the frontier progress of digital credit transaction anti-fraud research is reviewed from two aspects of data balance and model optimization. This paper focuses on the theoretical basis, applicable scenarios, and latest achievements of various machine learning algorithms and deep learning algorithms in anti-fraud research, and based on the above content, a comprehensive evaluation is made. Finally, combined with the research status and from the perspective of demand, this paper summarizes the three major hotspots including the generalization and interpretability of anti-fraud research, and the sensitivity to new fraudulent transaction models, and concludes with an outlook on future research directions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Shanghai Jiaotong University (Medical Science) is the property of Journal of Shanghai Jiaotong University (Medical Science) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[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 studies the mechanism and function of artificial intelligence (AI) technology in public art creation, display and dissemination. Focusing on the three characteristics of public art, public and local, this paper puts forward some suggestions on the breakthrough and development of artificial intelligence in public art by exploring new directions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In recent years, artificial intelligence (AI), such as image recognition and fixed-point and digital scanning, has been increasingly applied in dental orthodontics. ChatGPT is an emerging artificial intelligence technology that has attracted a considerable amount of attention from people of all walks of life since its introduction. This paper discusses the application potential of ChatGPT in the field of orthodontics from two perspectives—education and clinical practice—and analyzes its limitations. Current reports show that ChatGPT can improve the efficiency of teaching in orthodontic education as well as assist users in completing medical mock exams training, writing papers and conducting academic research, etc. In the clinic, ChatGPT can be used to engage patients in medical dialog, simplify clinical procedures and improve diagnostic and treatment efficiency. However, because ChatGPT is still in the early stages of research and application, there are problems such as insufficient data, inaccurate answers, and poor academic ethics and privacy. In the future, additional studies, training and optimization are needed to ensure the safety, ethics and benefit of AI, laying the foundation for improving the use of AI in the field of orthodontics.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Agricultural Sciences is the property of South China Agricultural University, Guangdong Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the "14th Five-Year Plan" era, the digitization of the financial industry has entered a phase of deepening and high-quality development. Strengthening Financial Data Security Governance (FDSG) and protecting financial data security have become an objective need and essential requirements for developing national economic security. FDSG utilizes data governance measures as a fundamental tool with a focus on sensitive data to ensure the comprehensive security of the entire lifecycle of financial data. It aims to promote data circulation among financial institutions, activate data value, and facilitate market-oriented allocation of financial data elements. FDSG is increasingly intertwined with big data, artificial intelligence, cloud computing, and blockchain technologies, transforming traditional data security governance into intelligent governance and accelerating the evolution of FDSG towards automation, intelligence, efficiency, and precision. FDSG's essence, scope, and governance framework were introduced, and the core concepts and critical supporting technologies for the intellectual development of FDSG were elaborated, outlining the roadmap for intelligent governance. With consideration of FDSG's demands and characteristics of intelligent technologies, vital directions for the practical application of intelligent technologies in FDSG were outlined, including data classification, data traceability, content control, privacy protection, and data twinning. Examples of current intelligent governance practices in banking, securities, and insurance industries were provided. Policy recommendations were proposed in the paper to promote the standardized and intelligent development of FDSG in China's new era, aiming to foster the sustainable development of financial institutions and industries and ensure a healthy and secure environment for 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]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional neural networks have the disadvantages of over- reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power. The application development of artificial intelligence technology is limited to a certain extent. However, with the advent of the technological age, artificial intelligence, which is affected by user requirements, urgently needs to be able to successfully perform operations such as computer vision applications on portable devices. For this reason, this paper takes the convolution of popular lightweight neural networks in recent years as the research object. Firstly, by introducing the concept of lightweight neural network, the development status of lightweight neural networks and the problems faced by convolution in the network are introduced. Secondly, the convolution is divided into three aspects: lightweight of convolution structure, lightweight of convolution module and lightweight of convolution operation, specifically through the study of the convolution design in various lightweight neural network models, the lightweight effects of different convolutions are demonstrated, and the advantages and disadvantages of the optimization methods are explained. Finally, the main ideas and usage methods of all lightweight model convolutional design in this paper are summarized and analyzed, and their possible future development is prospected. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Library & Information Science in Agriculture is the property of Editorial Board of Journal of Library & Information Science in Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The deep integration of optical fiber communications and high-capacity high-frequency wireless communications is the core technology base of future The 6th Generation Networks(6G). It holds significant importance in building typical 6G scenarios such as "immersive communications, ubiquitous connectivity, and communications Artificial Intelligence (AI) integration". This paper introduces mainstream technologies and their implementation for optimizing the architecture and improving the spectral efficiency of fiber-wireless integrated transmission systems. We summarize some of the progress made by our team in these areas. First of all, facing the large-capacity demand for a new generation of immersive communications, we propose a novel architecture for seamless "fiber-wireless-fiber" integrated transmission system with the use of commercial Digital Coherent Optics(DCO). We firstly realize the photonics-assisted 100/200/400 GbE real-time THz wireless transmission, achieving a maximum line rate of 2X240. 558 Gbit/s. Secondly, for application scenarios with wide coverage and flexible deployment, Digital Sub Carrier Multiplexing (DSCM) technology was introduced into the optical fiber wireless converged access system. Our team designs and constructs a coherent Passive Optical Networks (PON) that supports up to 32 channel of fixed broadband access and 32 channel of W-band millimeter-wave wireless access. This coherent PON can reach the rate of 100 Gbit/s and is capable of flexible rate adjustments, facilitating subsequent iterative upgrades. Finally, in response to the demand for communication AI integration, our team proposes a likelihood-aware Vector-Quantization (VQ) Variational Auto Encoder (VAE) based on AI technology for end-to-end optimization of fiber-wireless integrated communication systems. Without the need for tera-hertz power amplifiers, we successfully demonstrate the wireless transmission of Dual-Polarization (DP) terahertz signal at a net rate of 366. 4 Gbit/s over 6.5 m 2 X 2 Multiple Input Multiple Output (MIMO) wireless link and 20 km Standard Single Mode Fiber (SSMF) link. The aforementioned technologies exhibit tremendous potential in typical future 6G scenarios. Besides, we briefly introduce our work in these areas, and provide an outlook on beyond 100 Gbit/s fiber-wireless integrated transmission technology from the perspectives of high capacity, long distance, integration and intelligentization. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of artificial intelligence, the selection of algorithms that meet application requirements from feasible algorithms has become a critical problem to be solved urgently in various fields, that is, the algorithm selection problem. The approach based on meta-learning is an important way to solve the algorithm selection problem, which is widely applied in algorithm selection research and achieves good results. The approach selects appropriate algorithms by constructing the mapping model from problem features to candidate algorithms performance, mainly including the steps of extracting meta-features, calculating candidate algorithms performance, constructing meta-dataset and training meta-model, etc. Firstly, this paper expounds the concept and framework of algorithm selection based on meta-learning, and reviews related surveys. Secondly, it summarizes the research progress from three aspects: meta-features, meta-learners and meta-model performance measures, introduces typical methods and compares the advantages, disadvantages and application scope of different types of methods. Then, it outlines the application of algorithm selection based on meta-learning in different learning tasks. Next, it utilizes 140 classification datasets, 9 candidate classification algorithms and 5 performance indicators to conduct algorithm selection experiments to compare the performance of different algorithm selection methods. Finally, it analyzes the current challenges and problems, and discusses future development directions. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI) technology, especially the application of deep learning (DL), the detection and quantitative evaluation of typical imaging markers of small cerebral vascular disease (CSVD) has been accelerated and the accuracy has been improved. In recent years, it has attracted much attention in the field of medical imaging. This paper intends to summarize the research progress and problems of deep learning in the imaging markers of CSVD such as cerebral microbleeds (CMBs), white matter hyperintensities (WMH), enlarged perivascular space (EPVS), lacunes, recent small subcortical infarcts (RSSI) and cerebral atrophy, so as to provide support for the precise treatment of CSVD. [ABSTRACT FROM AUTHOR]