Search

BACKGROUND: The development of artificial intelligence in the medical field is rapidly advancing, with increasing research on its applications in the field of bone trauma. Through bibliometric analysis, this paper analyzed the research hotspots of artificial intelligence in the field of bone trauma in recent years, and predicted the future research trend. OBJECTIVE: To summarize the development history, research status, hot spots, and future development trends of artificial intelligence technology in the field of bone trauma to provide new insights for future research. METHODS: This study selected relevant literature from the Web of Science core database, covering the period from the inception to August 2023, and retrieved 420 articles related to the application of artificial intelligence, machine learning, and deep learning in the field of bone trauma. After manual screening, 202 articles related to this article were exported, and Citespace software was used for visual analysis of cooperation of countries, institutions, cited journals, citation analysis, keyword co-occurrence, and other aspects. RESULTS AND CONCLUSION: (1) The overall number of publications from the 202 selected articles showed an upward trend, indicating significant research potential for future studies. The country with the highest centrality and the highest publication volume was the United States. The University of California (USA) was the most prolific research institution. (2) The top five most commonly used keywords in bone trauma research using artificial intelligence were deep learning, artificial intelligence, bone density, machine learning, and diagnosis. The keyword with the highest centrality was bone density, and the keyword with the highest frequency was deep learning. (3) The top 10 most cited reference papers provided comprehensive insights into the feasibility of applying artificial intelligence techniques to the diagnosis of bone trauma from various perspectives. Among them, eight papers focused on bone and joint injuries and deep convolutional neural networks. One paper discussed the use of deep learning in detecting osteoporosis in CT scans to prevent fragility fractures, while another paper explored the correlation between the application of artificial intelligence in identifying changes in skin texture and the recognition of bone characteristics. (4) In the future, the research hotspots of artificial intelligence will mainly focus on the specific study of fractures caused by bone and joint trauma and osteoporosis. The research trend mainly focuses on improving the performance of artificial intelligence algorithms, using new artificial intelligence technologies to accurately classify and quickly and efficiently diagnose bone injuries, especially for the diagnosis of complex and hidden fractures. By establishing finite element analysis models, more standardized evaluations of bone injuries can be achieved. [ABSTRACT FROM AUTHOR]

BACKGROUND: The sports medicine community has widely called for the use of machine learning technology to efficiently process the huge and complicated sports data resources, and construct intelligent sports injury prediction models, enabling accurate early warning of sports injuries. It is of great significance to comprehensively summarize and review such research results so as to grasp the direction of early warning model improvement and to guide the construction of sports injury prediction models in China. OBJECTIVE: To systematically review and analyze relevant research on sports injury prediction models based on machine learning technology, thereby providing references for the development of sports injury prediction models in China. METHODS: Literature search was conducted on CNKI, Web of Science and EBSCO databases, which mainly searched for literature related to machine learning techniques and sports injuries. Finally, 61 articles related to sports injury prediction models were included for analysis. RESULTS AND CONCLUSION: (1) In terms of external risk feature indicators, there is a lack of competition scenario indicators, and the inclusion of related feature indicators needs to be further improved to further enrich the dimensions of the dataset for model training. In addition, the inclusion feature weighting methods of the sports injury prediction model are mainly based on filtering methods and the use of embedding and wrapping weighting methods needs to be strengthened in order to enhance the analysis of the interaction effects of multiple risk factors. (2) In terms of model body training, supervised learning algorithms become the mainstream choice. Such algorithms have higher requirements for the completeness of sample labeling information, and the application scenarios are easily limited. Therefore, the application of unsupervised and semi-supervised algorithms can be increased in the later stage. (3) In terms of model performance evaluation and optimization, the current studies mainly adopt two verification methods: HoldOut crossover and k-crossover. The range of AUC values is (0.76±0.12), the range of sensitivity is (75.92±11.03)%, the range of specificity is (0.03±4.54)%, the range of F1 score is (80.60±10.63)%, the range of accuracy is (69.96±13.10)%, and the range of precision is (70±14.71)%. Data augmentation and feature optimization are the most common model optimization operations. The accuracy and precision of the current sports injury prediction model are about 70%, and the early warning effect is good. However, the model optimization operation is relatively single, and data augmentation methods are often used to improve model performance. Further adjustments to the model algorithm and hyperparameters are needed to further improve model performance. (4) In terms of model feature extraction, most of the internal risk profile indicators included are mainly based on anthropometrics, training load, years of training, and injury history, but there is a lack of sports recovery and physical function indicators. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University 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 Biology Teaching is the property of East China Normal University 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 e-Education Research is the property of Northwest Normal University 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 e-Education Research is the property of Northwest Normal University 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 Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics 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 Continue Education Research is the property of Harbin Normal University 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 Continue Education Research is the property of Harbin Normal University 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.)

Objective To develop an artificial intelligence (AI) system based on deep algorithms for diagnosing metacarpophalangeal (MCP) joint lesions in gouty arthritis using high-frequency musculoskeletal ultrasound and to validate its diagnostic performance. Methods High-frequency musculoskeletal ultrasound images of the hands of 410 patients with gouty arthritis who underwent ultrasonography in our hospital from February 2017 to December 2023 were retrospectively collected and divided into a training set (n=287) and a validation set (n=123) in a 7:3 ratio. An Al system for diagnosing MCP joint lesions in gouty arthritis was developed using deep algorithms and trained with the training set. With positive polarized light examination of MCP joint synovial fluid as the diagnostic criterion, the diagnostic performance of junior and senior ultrasound physicians and the Al system for MCP joint lesions in gouty arthritis in the validation set was compared. Results Among the 410 patients with gouty arthritis, 343 had MCP joint lesions, with an incidence rate of 83.66%. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the Al system for diagnosing MCP joint lesions in the validation set were 85.29%, 85.71%, 85.37%,and 0.855, respectively. The sensitivity and accuracy of the senior ultrasound physicians in diagnosing MCP joint lesions in the validation set were higher than those of the junior ultrasound physicians, with statistically significant differences (P<0.05). The sensitivity and accuracy of the Al system in diagnosing MCP joint lesions in the validation set were higher than those of the junior ultrasound physicians but lower than those of the senior ultrasound physicians, with statistically significant differences (P<0.05). However, there was no statistically significant difference in sensitivity between the Al system and the senior ultrasound physicians (P>0.05). Conclusion The Al system developed using deep algorithms has good diagnostic performance for MCP joint lesions in high-frequency musculoskeletal ultrasound images of patients with gouty arthritis and provides higher diagnostic value compared to junior ultrasound physicians. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

不当的电离辐射暴露可能会导致染色体畸变等生物学效应, 对人类健康造成潜在的威胁。作为染色体畸变的 主要诊断依据, 染色体图像的处理至关重要。随着科学技术的不断进步, 深度学习相关算法显著地提升了图像分割 与图像分类的精度。通过全面回顾人工智能（artificial intelligence, AI）, 尤其是深度学习技术在染色体畸变图像分析 中的应用进展, 比较分析了国内外的研究现状, 探讨了AI 在染色体图像预处理、体征提取、图像分割和分类等关键 环节的应用, 并讨论了基于人工智能的染色体畸变分析面临的挑战及未来的发展方向, 以期为进一步提高染色体畸 变分析的准确性和效率提供参考. [ABSTRACT FROM AUTHOR]

First-order logic automated theorem proving is an important branch in artificial intelligence. In order to improve the deduction efficiency of unit resulting resolution, a new unit resulting deduction method is proposed based on multi-clause, dynamic and synergized deduction, named contradiction separation unit resulting deduction method. Its definition, deduction method, deduction advantage analysis and algorithm implementation are given in detail. The proposed deduction method allows two or more clauses involved in each deduction steps, and allows multiple non-unit clauses to participate in one unit resulting deduction, and it can better handle the long clauses. The proposed deduction algorithm can select optimal clause under the strategy and dynamically set the variable unification complexity, and optimize the deduction search path by backtracking mechanism. Taking the last two years international prover competition problems (The total number is 500 respectively) and the most difficult problems with a rating of 1 from the TPTP benchmark database as the test object, the Eprover with the proposed contradiction separation unit resulting deduction algorithm can solve 10 theorems and 10 theorems more than the original Eprover respectively. It can solve 17 theorems and 13 theorems respectively that original Eprover cannot solve, and can solve 9 theorems with a rating of 1 that cannot be solved by all other provers. The experimental results show that the proposed contradiction separation unit resulting deduc-tion method can be effectively applied to the first-order logic automated theorem proving. [ABSTRACT FROM AUTHOR]

The incidence and mortality of lung cancer are extremely high, and the 5-year survival rate of patients after diagnosis is less than 20%; as such, early screening and intervention are important. However, doctors are overwhelmed with a large workload of CT scan review in a short period of time and should ensure that the rate of missed diagnosis and misdiagnosis is low. Artificial intelligence can assist doctors in early diagnosis and precise treatment of lung cancer, but this technology has problems. This paper reviews the application of artificial intelligence in the prediction of benign and malignant lung nodules, pathology, gene mutation, intervention scheme, treatment response, and prognosis and presents the corresponding challenges. [ABSTRACT FROM AUTHOR]

Aiming at the intelligent design of combat systems, we propose a conceptual framework and application methods of Agent based on artificial intelligence technology. First, we analyze the concept of Agent and discuss the important signifi- cance of studying Agent in the combat system. Then, we introduce the research framework of artificial intelligence based on Agent, and multiple application methods of Agent in combat systems. Finally, we analyze the development trends of Agent technology and the risks and challenges that its combat applications may face. [ABSTRACT FROM AUTHOR]

The traditional garment industry is confronted with numerous challenges including escalating labor costs inadequate design innovation and suboptimal supply chain management. These issues primarily arise from the laborintensive characteristics of the garment sector and its significant reliance on human resources. As globalization continues to influence market dynamics companies are increasingly pressured to maintain competitive pricing while ensuring highquality products that meet consumer demands for sustainability and ethical production practices. Artificial intelligence AI a technology that emulates human cognitive functions and can substantially replace manual operations has been extensively adopted in the apparel industry-particularly in enhancing efficiency across clothing design production processes sales strategies and managing manufacturing costs. AI applications range from predictive analytics that forecast fashion trends based on consumer behavior data to automated cutting machines that optimize fabric usage during production. Furthermore machine learning algorithms can analyze vast datasets to identify patterns in customer preferences or inventory levels enabling businesses to make informed decisions regarding stock management. Nevertheless a comprehensive examination of AI's application within the clothing domain remains lacking. While some studies have explored specific use cases of AI technologies such as computer vision for quality control or chatbots for customer service enhancement there is still a gap in understanding how these innovations interconnect throughout various stages of product development. Apparel product lifecycle management PLM serves as an integrated management solution that encompasses design manufacturing processes distribution channels and sales strategies to assist companies in tracking and overseeing their apparel product lifecycle effectively. By integrating PLM systems with AI capabilities like real-time data analysis and automation tools organizations can streamline workflows significantly—from initial concept development through final retail delivery. Consequently this paper aims to thoroughly investigate the specific applications of artificial intelligence at each stage of apparel product lifecycles through two lenses application scenarios and algorithms it will analyze both the current state and limitations of existing research with the intention of providing insights for scholars in related fields while fostering transformation and advancement within the garment industry to enhance its competitiveness. This exploration will not only highlight successful case studies but also address potential barriers such as technological adoption rates among small enterprises or concerns regarding data privacy when utilizing consumer information for algorithm training purposes. [ABSTRACT FROM AUTHOR]

Teacher subjectivity refers to the traits of autonomy, self-awareness, and initiative exhibited by teachers in their professional development and educational practice. In the context of fostering new quality of talent in the digital era, teacher subjectivity has become an issue to be further discussed. From the perspectives of self, others, and society, this paper analyzes the challenges that new technologies, particularly artificial intelligence or AI, bring to teacher subjectivity in the educational field. These challenges include the erosion of teachers' subject consciousness due to technological dependency, destabilization of teachers' subject status in human-machine interactions, and infringement of teachers' rights by technological oversight. The reshaping of teacher subjectivity in the digital era is crucial to individual teacher's growth, teacher-student relationships, and educational development. To address this, efforts should be made to enhance teachers' initiative in the use of digital technology, create an educational environment where technological rationality and humanistic care coexist, and safeguard teachers' rights to data privacy and intelligent teaching. [ABSTRACT FROM AUTHOR]

Copyright of Interdisciplinary Studies of Literature is the property of Interdisciplinary Studies of Literature 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 Interdisciplinary Studies of Literature is the property of Interdisciplinary Studies of Literature 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 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.)

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.)

Copyright of Journal of Technology Economics is the property of Chinese Society of Technology Economics 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 Technology Economics is the property of Chinese Society of Technology Economics 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 Social Work (1672-4828) is the property of Journal of Social Work 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 Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University 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 Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University 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 e-Education Research is the property of Northwest Normal University 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 e-Education Research is the property of Northwest Normal University 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 Education Research (1680-6360) is the property of Angle Publishing 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 Planning Education & Research is the property of Sage Publications Inc. 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 artificial intelligence has presented new opportunities and challenges for language education. Using artificial intelligence techniques such as automatic speech recognition and generative artificial intelligence, conversational chatbots have been integrated into language learning and teaching. However, findings of the impact of chatbots on second and/or foreign language learning (L2) are mixed. Therefore, this research conducted a meta‐analysis to synthesize the existing research. Thirty‐one studies with 41 effect sizes were included in this meta‐analysis. It was found that chatbots have a medium effect on L2 learning (g = 0.608, Z = 6.77, p < 0.001). Moderating analysis further showed that the effects were moderated by the mobility of access to chatbot, modality of the chatbot, whether or not generative AI technology was involved, and the nature of comparison groups in the research design. The findings suggest that chatbots in general have positive effects on L2 learning. The results suggest that to enhance the advantages of chatbots, their accessibility, modality and adaptability need to be considered. Furthermore, a more refined perspective is needed when discussing the benefits of the use of chatbots in language learning process. [ABSTRACT FROM AUTHOR]

儿童口腔行为管理可提高患儿对口腔疾病诊疗的积极态度, 使患儿在配合的情况下进行安全高效的口腔 疾病诊治。近年来, 人工智能 (AI) 技术在儿童口腔行为管理中得以广泛应用并获得良好效果。虚拟现实 (VR) 技术 通过交互式视景仿真和信息交流, 可提供患儿视、触、听等多感官刺激以减轻患儿的恐惧情绪, 缓解儿童牙科焦虑。计算机控制的单颗牙齿局部麻醉 (STA) 技术可减轻局部麻醉注射疼痛, 大大提高了儿童口腔疾病治疗的配合度。 AI驱动的游戏系统可通过整合游戏元素如点数、徽章和关卡, 对患儿进行正面奖励, 让儿童口腔行为管理变得更容 易。AI聊天机器人可以通过对话互动来为儿童提供情感支持, 有利于患儿对口腔疾病诊疗过程中的恐惧事件进行 记忆重构。AI技术可以有效监测患儿的异常行为, 在患儿就诊前及时发现潜在行为危险, 为后续的口腔疾病治疗 奠定基础。 [ABSTRACT FROM AUTHOR]

Copyright of New Media Research is the property of New Media Research 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 Aesthetic Education / Meiyu Xuekan is the property of Hangzhou Normal University 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 New Media Research is the property of New Media Research 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 New Media Research is the property of New Media Research 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.)

Participatory ethical norms emphasize the involvement of diverse stakeholders, aiming to construct a more comprehensive and balanced ethical governance framework. The rapid development of artificial intelligence (AI) technology is leading society through unprecedented transformations, significantly impacting ethical perspectives, social governance models, and the symbiotic relationship between humans and technology. The participatory ethical norms, characterized by multi-stakeholder participation, interactivity, and openness, represent a crucial pathway for addressing the challenges posed by the rapid development of AI technology. Constructing an AI governance framework based on participatory ethical norms provides solutions for the sustainable, fair, and transparent development of AI from multiple aspects including principles, boundaries, and pathways, addressing ethical risks and challenges across technological, cultural, economic, and social dimensions. As an emerging governance framework, participatory ethical norms reflect human-centric values, enhance governance efficiency and fairness, strengthen the balance of rights and shared responsibilities among diverse stakeholders, and promote the synergistic advancement of ethical standards and technological innovation, fostering harmonious coexistence between AI and human society. Based on this, the study proposes recommendations for legislation, dynamic adjustment of AI ethical assessment systems, enhanced education and training with active international cooperation, and the introduction of a "sandbox mechanism" for regulatory pilots, thereby appropriately addressing future risks in the governance of intelligent society. [ABSTRACT FROM AUTHOR]

Autonomous machine computing, an innovative blend of algorithms, software, and cutting-edge computing hardware, is poised to be the next major paradigm shift in the global economy, following personal, mobile, and cloud computing. This study delves into the research and commercialization of the robotics industry, underscoring the critical importance of establishing a comprehensive autonomous machine computing ecosystem. This study argues that autonomous machine computing necessitates a complete ecosystem that encompasses applications, programming languages, and the foundational hardware architectures, and presents a comprehensive review of significant research contributions across these areas. Moreover, the study explores the synergy between autonomous machine computing and advancements in semiconductor technology, highlighting how such technological strides will significantly broaden the capabilities of robots, particularly in terms of sensing, computing, and communication. Looking ahead, the authors predict that within the next decade, autonomous machine computing will deeply integrate into all societal sectors, profoundly influencing the global economy. From enhancing smart homes to revolutionizing healthcare, agriculture, and beyond, robotics computing is set to be the linchpin of innovation, opening up unprecedented business opportunities while substantially elevating the quality of life. Consequently, promptly defining and advancing the architecture of autonomous machine computing is crucial for positioning our nation at the forefront of the global robotics industry's major breakthroughs and developments over the coming decade. [ABSTRACT FROM AUTHOR]

The application ecology of artificial intelligence foundation model is rapidly expanding. The environment, society, and governance are facing new challenges and opportunities. Exploring the construction of a governance framework for the development risks of foundation model has important theoretical value and practical significance for promoting the healthy and sustainable development of artificial intelligence. Based on the theories of ESG and artificial intelligence governance, this study analyzes the development benefits and typical risks of foundation model from the perspective of ESG and then constructs a risk governance framework and implementation strategies for artificial intelligence foundation models. This study shows that a dual approach of technological governance and institutional innovation is necessary to address the development risks of foundation models. Following the logic of "entry perspective → risk identification → governance mechanism → governance strategy", the study exploringly proposes the risk governance framework of foundation model. Based on the impact cycle and scope of the foundation model risk, this study proposes four differentiated governance strategies, including "release, control, pilot, and iteration". [ABSTRACT FROM AUTHOR]

A subset of patients with non-alcoholic fatty liver disease （NAFLD） can progress to nonalcoholic steatohepatitis （NASH）. When NASH reaches a fibrosis degree of F≥2 and a NAS score of≥4， this stage of NASH is referred to as fibrotic NASH， which is a key focus in clinical drug trials. Currently， liver biopsy is the gold standard for assessing the histological changes of the liver， but its clinical application is limited by its invasiveness， and therefore， it is of particular importance to develop noninvasive detection methods for fibrotic NASH. This article summarizes the recent research achievements in novel noninvasive diagnostic methods for fibrotic NASH and elaborates on these new diagnostic methods for predicting fibrotic NASH in terms of current status， challenges faced， and prospects for future development. [ABSTRACT FROM AUTHOR]

With the advancement of surgical techniques and perioperative management, liver transplantation has become an effective method to save the lives of patients with end-stage liver disease. However, the imbalance between organ supply and demand remains a significant challenge, and the decision-making process for liver transplantation still faces many difficulties. Artificial intelligence can efficiently extract features and establish connections from complex factors, benefiting clinical decision-making in liver transplantation through data-driven approaches. These include optimizing pre-transplant candidate eligibility decisions and donor-recipient matching, predicting the risk factors for disease recurrence and other related complications after transplantation, and guiding the management of liver transplant recipients. Therefore, this article reviews the application of artificial intelligence in organ allocation, donor liver evaluation, post-operative recurrence of hepatocellular carcinoma, and prediction of post-operative complications in liver transplantation, aiming to provide a reference for the research and development of artificial intelligence in the field of liver transplantation. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in kidney transplantation research, but long-term prognosis still faces many challenges, such as rejection and infection. Artificial intelligence, which simulates and executes algorithms and systems similar to human intelligence, it shows great potential in handling complex disease treatments and prognosis predictions. With the continuous advancements in artificial intelligence technologies like machine learning and deep learning, researchers have started to apply these technologies to predict and optimize long-term outcomes of kidney transplantation, achieving certain results. Therefore, this article reviews the application of artificial intelligence technologies in predicting kidney transplant outcomes, monitoring drug concentrations, and managing long-term complications. It explores the progress of artificial intelligence applications in long-term kidney transplant recipients, discusses potential limitations and solutions, and aims to provide new ideas for promoting the practical application and dissemination of artificial intelligence in the field of kidney transplantation. [ABSTRACT FROM AUTHOR]

The advent of artificial intelligence (AI) has expanded the scope and meaning of digital labor, revealing certain positive factors that could potentially lead to labor liberation and the comprehensive development of human freedom. However, at its core, the positive aspects of digital labor in the AI era are ultimately geared towards the inherent nature of capital self-expansion. It still relies on the appropriation of human creativity as a prerequisite and reinforces alienated labor by separating human intelligence from the individual. In view of Karl Marx, the alienation of labor stems from the capitalist application of the machinery, while on the contrary the application of machinery is separated from their capitalist use within the machine system. Therefore, in the AI era, because the digital labor contributes to the comprehensive development of human freedom by reducing necessary labor time and transforming human-machine relations, it is essential to harness capital and establish ethical legislation for AI technology. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Distance Education (1672-0008) is the property of Zhejiang Open University 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 Distance Education (1672-0008) is the property of Zhejiang Open University 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 Digital Education is the property of Haiyan Publishing 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 Railway Investigation & Surveying is the property of Railway Investigation & Surveying 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.)