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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.)

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.)

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.)

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 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.)

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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.)

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 the evolution of the Internet from Web 1.0 to Web 3.0, algorithms have played an irreplaceable role, enabling information technology to functionally realize the transmission of one-way content to decentralized interaction. Algorithms have a subtle impact on people's lives, work, and learning, and this impact is so great that it has produced the idea of algorithmic society and algorithmic era. Algorithms belong to the basic category of digital law and are an important tool for digital rule of law. In existing research, scholars are more concerned about how to regulate algorithms, and less concerned about the legal nature of algorithms. There has been a brief discussion on this issue. However, how the legal nature of algorithms should be defined is still inconclusive. This article aims to answer this question. To this end, this paper sorts out the existing theoretical schemes of the legal nature of algorithms and their interrelationships, including speech, due process, patents, and trade secrets, and analyzes the sources and arguments of these theoretical schemes. There are internal and external reasons why the legal nature of algorithms has formed many disputes. These reasons are also difficult to define the legal nature of algorithms, including: the algorithm ontology and carrier are easy to confuse, the algorithm and the program are indiscriminate, the algorithm and data, the result mix, and the limitation of governance rules. When seeking justification for various theoretical claims, one logical premise is easily overlooked, that is, the criteria for determining the legal nature of algorithms. This paper puts forward these standards, namely the principle of legality, the principle of respecting the fruits of labor, the principle of maintaining public safety, the principle of promoting innovation, and the principle of maximizing social benefits. On this basis, this paper examines various theoretical schemes, and finally proves that the legal nature of the algorithm is a trade secret, not something else. In terms of methodology, this paper uses the method of identification analysis to discuss various scenarios one by one. Compared with previous literature, this paper makes contributions in the following aspects: first, this paper sorts out the existing debates on the legal nature of algorithms in detail, grasps the arguments of these theoretical schemes, and specifically examines them. Second, this paper analyzes the reasons for many disputes over the legal nature of algorithms. The widespread use of algorithms has also caused heated debates about algorithms. Algorithms have been in the field of legal science for a long time, but there are frequent misunderstandings about how their legal nature should be defined, and the reasons behind them deserve careful analysis. Third, this paper clarifies the criteria for defining the legal nature of algorithms, and argues that algorithms are trade secrets on this basis. Algorithms cannot be called speech, nor are they due process. The argument that algorithms are patents is also untenable, and although algorithms have the possibility of patentability in the current legal system, algorithm patenting is not a good solution. [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 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 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.)

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 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]

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 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.)

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Copyright of Publishing Journal is the property of Wuhan University, School of Information 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.)

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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.)

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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 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 Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency 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.)

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]

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]

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.)

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.

The production mode of livestock breeding has changed from extensive to intensive, and the production level is im‐ proved. However, low labor productivity and labor shortage have seriously restricted the rapid development of China's livestock breeding industry. As a new intelligent agricultural machinery equipment, agricultural robot integrates advanced technologies, such as intelligent monitoring, automatic control, image recognition technology, environmental modeling algorithm, sensors, flexible execution, etc. Using modern information and artificial intelligence technology, developing livestock feeding and push‐ ing robots, realizing digital and intelligent livestock breeding, improving livestock breeding productivity are the main ways to solve the above contradiction. In order to deeply analyze the research status of robot technology in livestock breeding, products and literature were collected worldwide. This paper mainly introduced the research progress of livestock feeding robot from three aspects: Rail feeding robot, self-propelled feeding robot and pushing robot, and analyzed the technical characteristics and practical application of feeding robot.The rail feeding robot runs automatically along the fixed track, identifies the target animal, positions itself, and accurately completes feed delivery through preset programs to achieve accurate feeding of livestock. The self-propelled feeding robot can walk freely in the farm and has automatic navigation and positioning functions. The system takes single chip microcomputer as the control core and realizes automatic walking by sensor and communication module. Com‐ pared with the rail feeding robot, the feeding process is more flexible, convenient and technical, which is more conducive to the promotion and application of livestock farms. The pushing robot will automatically push the feed to the feeding area, promote the increase of feed intake of livestock, and effectively reduce the labor demand of the farm. By comparing the feeding robots of developed countries and China from two aspects of technology and application, it is found that China has achieved some innova‐ tion in technology, while developted countries do better in product application. The development of livestock robot was pros‐ pected. In terms of strategic planning, it is necessary to keep up with the international situation and the trend of technological de‐ velopment, and formulate the agricultural robot development strategic planning in line with China's national conditions. In terms of the development of core technologies, more attention should be paid to the integration of information perception, intel‐ ligent sensors and deep learning algorithms to realize human-computer interaction. In terms of future development trends, it is urgent to strengthen innovation, improve the friendliness and intelligence of the robot, and improve the learning ability of the ro‐ bot. To sum up, intelligent livestock feeding and pushing machine operation has become a cutting-edge technology in the field of intelligent agriculture, which will surely lead to a new round of agricultural production technology reform, promote the trans‐ formation and upgrading of China's livestock industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Forensic Medicine / Fayixue Zazhi is the property of Journal of Forensic Medicine 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 Telecommunications Science 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.)