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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.)

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]

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]

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]

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]

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]

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.)

The Manned-Unmanned Teaming technology can fully leverage the low cost and expendable advantages of unmanned platforms to expand the operational space of manned platforms, thereby enhancing their situational awareness, penetration capability, and survivability. This paper provides a research overview on the development of MUM-T in the United States, Britain, France, Australia, South Korea, and other countries. It provides several application examples of MUM-T in foreign militaries on marine, land, and air space. It also analyzes some technical challenges and key technologies of MUM-T in wireless communication and networking, remote measurement and control, autonomous decision-making, and human-machine interaction. Finally, the future development trend of MUM-T is prospected, which can serve as a reference for the study of collaborative combat styles and related technological research in future joint operational systems. [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]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering 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.)

心脑血管疾病是全球主要的致死和致残病因之一。尽管医疗技术不断进步，心脑血管疾病的诊治仍存在许多亟待解决的问题，人工智能与机器学习的出现为应对这些问题提供了新途径。美国心脏学会2024年发布的科学声明从影像分析、心电图判读、床旁监护、可穿戴设备、基因组学及电子健康记录等方面介绍了人工智能与机器学习在临床中的应用及面临的挑战。本文对该指南进行介绍，并分析人工智能与机器学习在脑血管病领域中应用的优势，为我国未来在这一领域的研究提供参考。 Abstract: Cardiovascular and cerebrovascular diseases are among the leading causes of death and disability worldwide. Despite continuous advancements in medical technology, the diagnosis and treatment of these diseases still face many unresolved challenges. The emergence of artificial intelligence and machine learning provides new approaches to addressing these issues. The American Heart Association’s 2024 scientific statement highlights the applications and challenges of artificial intelligence and machine learning in clinical scenarios, including imaging analysis, electrocardiogram interpretation, bedside monitoring, wearable devices, genomics, and electronic health records. This paper aims to introduce these aspects and analyze the advantages of artificial intelligence and machine learning in the field of cerebrovascular diseases, providing a reference for the future development of artificial intelligence and machine learning in this area in China.

未破裂颅内动脉瘤形态学分析被广泛用于动脉瘤破裂的风险评估和术前规划。三维视角较既往的二维视角更能精确体现动脉瘤的形态变化，但人工测量在三维视角下准确度较低且可重复性差，而人工智能在三维视角下的测量更有优势。但是，目前尚缺乏人工智能在三维视角下的动脉瘤形态测量方法。考虑到三维视角下的规范化测量对形态学参数的临床和科研运用具有重要意义，本文提出了一套三维视角下基于人工智能的未破裂颅内动脉瘤形态学指标测量方法，以期推动动脉瘤研究的同质化和临床诊疗的规范化发展。 Abstract: 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.