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Objective To discuss the feasibility of directly processing paper-based ECG by using deep learning method, and to propose a method for identifying atrial fibrillation ( AF) from paper-based ECG rapidly and accurately. Methods The 12-lead ECG signals of 1 040 AF and 1 344 non-AF patients were selected from CPSC 2018 Challenge data, and were plotted as ECGs. These ECGs were printed out on papers, and rescanned into images. The paper-based ECGs with white background were obtained by a series of preprocessing. These paper-based ECGs were used to construct AF recognition dataset; the artificial intelligence algorithm (AIA) was trained, validated and tested using the real labels provided by the Challenge as the gold standard. Results The sensitivity, specificity and F1 score of the AIA based on paper-based ECGs are 0. 957, 0. 978 and 0. 969, respectively, and the area under receiver operating characteristic curve is 0. 994. The Grad-CAM based feature visualization results show that in paper-based ECGs, the position of P wave and F wave at the onset of AF provide main references for the algorithm to identify AF, which is consistent with the ECG diagnostic criteria of AF in clinical practice. Conclusion The AF recognition algorithm based on paper-based ECGs shows good performance. The visualization results verify the high efficiency and feasibility of directly analyzing paper-based ECGs by using AIA, which could provide guidance for clinical practice. [ABSTRACT FROM AUTHOR]

This paper takes smart cartography in the context of artificial intelligence as the starting point, analyzes the impact of space-time big data on cartography, points to three possible directions for the evolu‐ tion of the map, explains the connotation of smart cartography and its characteristics of crowd-sourced da‐ ta, user participation, model coupling and visual analysis, discusses in depth the explicit and implicit knowl‐ edge representation of smart cartography, proposes the idea of establishing a map knowledge graph, and outlines a platform development paradigm based on low-code technology. [ABSTRACT FROM AUTHOR]

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

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

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

The rise of Artificial Intelligence Generated Content (AIGC) exemplified by ChatGPT heralds the arrival of a new era of AI, which will inevitably reshape the way people interact with information. In this evolving landscape, information literacy education in colleges is set to experience a profound restructuring of its content frameworks and instructional models. This paper provides a review of research and teaching practices related to information literacy education in the context of AIGC both domestically and internationally. It delves into the advantages and limitations of AIGC and examines the opportunities and challenges which poses to the field of information literacy education. Building on this analysis, the paper proposes innovative ideas for the advancement of information literacy education, encompassing two dimensions: the enrichment of instructional content and the transformation of teaching methods. [ABSTRACT FROM AUTHOR]

Roll forming is a plastic processing technology that gradually bends metal strips and sheets laterally using sequentially arranged forming dies. Due to its low cost, high efficiency, and flexibility, it has become one of the important technologies for light weighting, energy saving, emission reduction, and safety improvement in various fields such as new energy, aerospace, and rail transportation in China. However, the complexity and discreteness of the process, uneven material thickness properties, and low level of equipment informatization and flexibility automation have made it similar to a “black box”, making it difficult to predict product quality, highly dependent on manual experience for production debugging, limited in formable cross sections, and unstable in yield. Therefore, this paper proposes a data-driven intelligent roll forming equipment (system) and introduces in detail its technical architecture and characteristics. By building a data architecture based on artificial intelligence, this system collects, screens, integrates, stores, and analyzes discrete data in traditional roll forming. Meanwhile, it integrates digital twins, artificial intelligence, contour detection technology, and multi-agent collaborative control to construct a self-correcting production mode that can replace manual experience. For the new energy vehicle industry, this paper provides a case study of using this system to solve springback control of structural components of roll-formed battery packs. Finally, suggestions and prospects for the development of this system are given. [ABSTRACT FROM AUTHOR]

Parkinson’s disease is a common neurodegenerative disorder characterized by the degeneration and death of dopaminergic neurons in the substantia nigra of the midbrain. PET/MR is a novel imaging technology that has been developed in recent years to enable the simultaneous acquisition of metabolic and structural images. This technology holds great value in the early detection of clinical markers for Parkinson’s disease. Artificial intelligence models have been extensively employed in clinical settings to assist physicians in diagnosis, yielding promising outcomes. In this paper, we aim to review the application of machine learning and PET/MR in the diagnosis of Parkinson’s disease, focusing on their potential to enhance diagnostic accuracy and facilitate early detection. [ABSTRACT FROM AUTHOR]

This paper expounds the working model of remote sensing from sensor, platform, and information interaction, reviews the development history of Chinese forestry in remote sensing application, analyzes the applications of remote sensing technology in forestry, and summarizes the methods, characteristics and technical difficulties of forestry remote sensing investigation. It is also suggested that forestry remote sensing should focus on image recognition and classification, quantitative remote sensing and inversion, scale effect and transformation, image change monitoring, so as to facilitate the high-quality development of forestry grassland in the new era. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology has developed rapidly in recent years. While improving the convenience of labor, it also has a certain impact on the current legal system. In particular, artificial intelligence products presently align with the external characteristics of objects protected under intellectual property laws. Consequently, numerous scholars argue that they warrant protection as intellectual property rights, sparking considerable debate. Taking into account the value pursuit of intellectual property law, the stability of the system, and the internal requirements of the objects it protects, this paper proposes denying intellectual property attributes of artificial intelligence products. Instead, it suggests affirming their legal status as fruits and establishing a civil protection framework under the concept of "fruits mode." [ABSTRACT FROM AUTHOR]

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

This paper builds a dance video generation system using a generative adversarial network, which can input a single image and a target dance video to make the people in the photo dance. The system mainly adopts human posture transfer technique and facial posture transfer technique to allow the computer to generate an image of a person that matches the target poses. Also, image inpainting technique repairs the holes in the picture caused by the characters being cut out from the background. In addition, the system uses a multi-scale region extractor to capture body features and incorporates region style loss into the loss function. For face pose transfer, a progressive face angle transformation framework is adopted and a mask discriminator is added to improve the quality of the generated image. Finally, for the color shift problem during the inpainting process, the system uses the perceptual color loss function based on the CIEDE2000 color difference formula to handle the problem, so that the inpainting results match human visual perception better. [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]

Federated learning is a revolutionary deep learning model, and it enables users to train the global model cooperatively without exposing their private data. However, some malicious clients can lead to the risk of single point of failure and privacy disclosure, this poses a serious threat to the security of federated learning. In response to the above issues, based on the existing research, this article proposed a blockchain empowered multi edge federated learning model. Firstly, this paper proposed to use blockchain instead of central server to enhance the stability and reliability of model training process; Secondly, this paper proposed a consensus mechanism based on edge computing to achieve a more efficient consensus process; In addition, incorporating reputation assessment into the federated learning training process can transparently measure the contribution value of each participant and standardize the behavior of work nodes. Finally, comparative experiments show that the scheme can maintain high accuracy in the malicious environment, and can resist higher malicious ratio compared with the traditional federated learning algorithm. [ABSTRACT FROM AUTHOR]

In the context of visual question answering (VQA) tasks, "explainability" refers to the various ways in which researchers can explain why a model works in a given task. The lack of interpretability of some existing VQA models has led to a lack of assurance that the models can be used safely in real-life applications, especially in fields such as autonomous driving and healthcare. This raises ethical and moral issues that hinder their implementation in industry. This paper introduces various implementations for enhancing interpretability in visual question answering tasks and categorizes them into four main categories: image interpretation, text interpretation, multi-modal interpretation, modular interpretation, and graph interpretation. The characteristics of each approach are discussed, and further subdivisions are presented for some of them. Furthermore, the paper presents several VQA datasets that aim to enhance interpretability. These datasets primarily focus on incorporating external knowledge bases and annotating image information to improve interpretability. In summary, this paper provides an overview of existing commonly used interpretable methods for VQA tasks and proposes future research directions based on the identified shortcomings of the current approaches. [ABSTRACT FROM AUTHOR]

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

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

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

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

Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency. It severely affects the quality of life of patients and may lead to death. Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment. In order to identify the risk of hepatic encephalopathy in patients in advance, many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible, so as to develop preventive and early management strategies. Most conventional hepatic encephalopathy risk prediction models currently assess the probability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators, however, their accuracy, sensitivity and positive predictive value are not high. The application of artificial intelligence to clinical predictive modelling is a very hot and promising area, which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis. To date, there have been few studies using AI techniques to predict hepatic encephalopathy. Therefore, this paper reviews the research progress of hepatic encephalopathy risk prediction models, and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models. It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models. [ABSTRACT FROM AUTHOR]

In the past 20 years, the development of artificial intelligence has made rapid progress, and it is increasingly applied in the medical field, including medical image-assisted diagnosis and treatment, health management, disease risk prediction and so on. In this paper, the application status of artificial intelligence-assisted detection and diagnosis system based on deep learning in anorectal diseases is summarized, and the new methods related to the diagnosis and treatment of anorectal diseases at home and abroad are summarized. It mainly reviews the research progress of artificial intelligence technology in the diagnosis and treatment of anal fistula, perianal abscess, hemorrhoids and other anorectal diseases. [ABSTRACT FROM AUTHOR]

In recent years, the combination of remote ECG monitoring and artificial intelligence ( AI ) technology has greatly assisted the diagnosis and treatment of cardiovascular diseases, and shows extremely broad development prospect. This paper summarizes the progress in the application of AI-based remote ECG monitoring technology in the intelligent diagnosis, hierarchical diagnosis and treatment, prognosis evaluation, and individualized management of acute coronary syndrome. It also introduces the active health intelligent management system of the heart which has been constructed and gradually improved in China on this basis. [ABSTRACT FROM AUTHOR]

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

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

In recent years, there have been advances in the fundamental research of acupuncture, yet the growing disconnect between this research and clinical practice has become increasingly evident. This paper revolves around the establishment of the discipline of Acupuncture Systems Engineering and explores the paradigm shift in acupuncture research from being science-driven to becoming technology-driven, with the aim of reshaping the innovative development path of acupuncture. Acupuncture Systems Engineering aims to investigate acupuncture techniques and their applications, while also seeking to establish research models and method that align with these pursuits, thereby providing new technological means and pathways for the study of acupuncture effects and achieving clinical translation. Under the guidance of Acupuncture Systems Engineering, acupuncture is no longer merely a traditional medical approach but has evolved into a multidisciplinary field of innovative traditional Chinese medicine, incorporating engineering, biology, information technology, artificial intelligence, and more. Through technology-driven research and application, Acupuncture Systems Engineering brings a revitalized and promising future to the field of acupuncture. [ABSTRACT FROM AUTHOR]

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

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

Constraint satisfaction problem is one of the most basic NP-complete problems in artificial intelligence. Over the years, with the in-depth study of constraint satisfaction problem, scholars at home and abroad have proposed a variety of case models. RB model is an example of growth domain constraint satisfaction problem with precise phase transition, which is very challenging to solve. In order to find a new efficient algorithm and promote the research of RB model algorithm for constraint satisfiability problem, firstly, this paper analyzed the model development and solving techniques of constraint satisfaction problem. Secondly, it sorted out various examples of RB model solving algorithms, and divided the solving algorithm literature into backtracking heuristic, information propagation and meta-heuristic related improved algorithms. This paper compared and summarized the algorithm principles, improved strategies, convergence and accuracy. Finally, it gave the research trend and development direction of solving RB model example algorithm. [ABSTRACT FROM AUTHOR]

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

After more than 300 years of industrialization, it's extremely necessary to reflect on the industrial culture, and at the time the impact of artificial intelligence must be taken into account. On the basis of previous studies, this paper further clarifies the connotation of industrial culture and reflects on the alienation of human and the relationship of control and anti-control between human and machine in industrial culture, and it analyzes their evolution in the era of artificial intelligence. Finally, the value of industrial culture is systematically summarized, and the promoting function of artificial intelligence for industrial culture is analyzed in terms of user locking, value restoration, value remodeling, value increment and value dissemination. [ABSTRACT FROM AUTHOR]

The rapid development of network technology, human-computer interaction and artificial intelligence has given birth to the metaverse and further promoted the digital transformation of all aspects of people's life. The concept of metaverse emerged in 2021, and has attracted extensive attention from industry, academia, media and the public. This paper attempts to deeply analyze the metaverse from the perspective of technology and application. First, the concept and connotation of the metaverse are elaborated from the definition, origin & development, characteristics and key technologies (network and computing, Internet of Things, human-computer interaction, electronic game, block chain, digital twin, etc.). Then, the enterprises and application examples involved in the metaverse are dis- cussed. Finally, the existing challenges and opportunities in the development of the metaverse are analyzed, and the future researches and applications are prospected. Through the meta-analysis of the current development status and research trend of the metaverse and the scientific evaluation of the potential application of the metaverse, we can provide useful reference for the researchers of the metaverse. [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]

With the advent of ChatGPT-4, the development and application of artificial intelligence (AI) technology have entered a new era, inevitably exerting a profound impact on the cultivation of modern scientific and technological talents. This paper begins by offering an overview of AI technology and its application in chemical research. It then discusses the updated criteria essential for fostering innovation ability of chemistry students in this new age of AI. Finally, the exploration and practice undertaken in the teaching reform for cheminformatics are presented. [ABSTRACT FROM AUTHOR]

On December 23-24, 2023, the 2023 Annual Conference on Information Behavior Research was held at Peking University, with more than 200 participants from over 70 universities across China. The theme of this year's conference is "Research on Information Behaviors from the AI Empowerment perspective". The agenda of the conference include keynote speeches, panel discussions, poster presentations, sub- forum oral presentation of full papers, and a workshop on eyetracking method, to conduct in-depth discussions on theoretical innovation, method application, situational expansion, humanistic care, social development, and other aspects of information behavior research in the new wave of artificial intelligence (AI) technology. The article summarizes the academic achievements and main viewpoints of the participants, providing theoretical enlightenment and research guidance for the exploration and innovation of information behavior research. [ABSTRACT FROM AUTHOR]

The trend discrimination of sudden change of typhoon intensity is always a difficult point in typhoon forecasting. Artificial intelligence(AI)is more and more used in the meteorological field because it can implicitly extract deep abstract and complex features from images by analyzing and learning a large number of samples by machines. In this paper, based on the deep residual network Resnet model in the field of artificial intelligence and the deep learning model LSTM based on time-space correlation, by labeling and learning the key information in the satellite cloud images of typhoons in the Northwest Pacific and South China Sea from 2005 to 2018, and introducing the life cycle indicator, an automatic and objective technology for judging the rapid strengthening trend of typhoons is proposed, which can effectively solve the problems of forecasting and judging the rapid strengthening trend of typhoons. Through the test and analysis of the operational typhoon cloud images from 2019 to 2021 and several rapid intensification (RI) typhoons in 2022, the results show that the typhoon RI trend discrimination technology based on AI is superior to the traditional subjective intensity forecasting method, and has certain business application value. [ABSTRACT FROM AUTHOR]

The 16th Creative Forum for Library Management and Service was held at the Shanghai Jiao Tong University Library from October 18th to 20th, 2023. The theme of the forum was "Technology, Talent, and Innovation: The Development of University Libraries in the Context of Transformative Education". Over 300 representatives from nearly 130 institutions and units across the country attended the forum. The forum included keynote speeches, the voice of youth, sharing of excellent papers. poster dis- plays, and visits to practice venues. Experts conducted in-depth discussions on information resource data services, library intelligent services, the application of artificial intelligence, the development of interdisciplinary information services, and the construction of future learning centers. This article summarizes the keynote thoughts of experts at the forum, providing theoretical references and practical experiences for promoting digital transformation. improving management efficiency, and innovating service models for libraries in the new era. [ABSTRACT FROM AUTHOR]

In view of AI model production and training, the traditional script based physical server or cluster mode has problems such as training inference separated, insufficient resource utilization, difficult migration of computing environment, and lengthy training process. This paper proposed a platform architecture for deep learning model training, the architecture divided into four layers: data platform layer, computing platform layer, training suite layer, and management platform layer. Firstly, it proposed an integrated framework masks differences in network structures and optimized the graphs. Secondly, it researched an adaptive resource matching mechanism based on GPU state reduced communication costs. At the same time, it improved re- source utilization by providing a heuristic algorithm based label matching scheduling algorithm. Moreover, the establishment of tenant management and disaster recovery mechanisms ensured the safety and reliability of the system platform. Finally, it established the validation of the usability, safety, reliability, and scalability through the simulation platform. Through the construction of deep learning platform, it will accelerate the implementation of AI production and promote the prosperity and development of AI technology and ecology. [ABSTRACT FROM AUTHOR]

Due to the lack of tailored security and privacy technology in many fields such as Internet of Things, artificial intelligence and big data, the system based on distributed ledger technology (DLT) can well meet their the security requirements as a decentralized data management method. Consequently, systems based on distributed ledger technology emerge in an endless stream. In order to help subsequent researchers understand the field of distributed ledger technology more comprehensively and quickly, this paper reviewed the research results based on distributed ledger technology in recent years. It analyzed the blockchain in depth from the perspectives of data structure, technical principle, consensus mechanism and performance, and compared and analyzed some recent advances in distributed ledger technology in detail based on the current research status, such as Hashgraph, Holochain and Truechain. Finally, this paper summarized the future development prospect of this field. [ABSTRACT FROM AUTHOR]

Based on the construction of a theoretical model of artificial intelligence (AI) affecting pollution emissions decisions, this paper empirically tested the impact and internal mechanisms of AI on pollution emissions by combining industrial robot data provided by the International Federation of Robotics and Chinese provincial panel data from 2006 to 2019. We found that: ① AI significantly reduced the pollution emission intensity, which was still valid after a series of robustness tests and using the instrumental variable method to overcome endogeneity. ② Our mechanism analyses showed that the pollution reduction effect of AI was mainly realized by promoting technological innovation, increasing investment in emission reduction equipment, and replacing low-skilled labor. Specifically, from the perspective of technological innovation, AI could effectively increase the number of patent applications; from the perspective of emission reduction investment, AI could significantly promote the investment in emission reduction equipment; and from the perspective of labor structure, AI could effectively replace low-skilled labor and optimize the labor input structure. ③ The heterogeneity analyses indicated that the environmental optimization effect of AI was more prominent in areas with stricter environmental performance assessments and in those with higher pollution emission intensity. ④ Further analyses showed that AI had the dual role of 'emission reduction and output enhancement,' meaning that it could promote the coordinated development of economic growth and environmental governance. Besides, based on firm-level microdata, we found that AI could significantly reduce corporate pollution emission intensity. Our findings provide empirical evidence and theoretical support for AI-empowered green transformation and high-quality development. [ABSTRACT FROM AUTHOR]

Due to the blockage of satellite signals by the buildings and other structures, the high-accuracy, reliability and real-time indoor location-based services is one of the challenges for BeiDou navigation satellite system. As one of the core technologies of the spatio-temporal digital foundation for future artificial intelligence and super-intelligence applications, high-precision indoor positioning has long been a research hotspot in industry and academia. This paper first reviews the development status of existing high-precision indoor positioning technologies, and compares their advantages, disadvantages and difficulties. Then it analyzes the development trend of related technologies under typical applications, and looks forward to the fu ture development direction and application scenarios of indoor positioning technologies. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting refers to the high-resolution prediction of precipitation in the short term, which is an important but difficult task. In the context of deep learning, it is viewed as a radar echo map-based spatiotemporal sequence prediction problem. Precipitation prediction is a complex self-supervised task. Since the motion always changes significantly in both spatial and temporal dimensions, it is difficult for ordinary models to cope with complex nonlinear spatiotemporal transformations, resulting in blurred predictions. Therefore, how to further improve the model prediction performance and reduce ambiguity is a key focus of research in this field. Currently, the research on precipitation nowcasting is still in the early stage, and there is a lack of systematic classification and discussion about the existing research work. Therefore, it is necessary to conduct a comprehensive investigation in this field. This paper comprehensively summarizes and analyzes the relevant knowledge in the field of precipitation nowcasting from different dimensions, and gives future research directions. The specific contents are as follows: (1) The significance of precipitation nowcasting, and the advantages and disadvantages of traditional forecasting models are clarified. (2) The mathematical definition of the nowcasting prob-lem is given. (3) Common predictive models are comprehensively summarized, analyzed. (4) Several open source radar datasets in different countries and regions are introduced, and download links are given. (5) The metrics used for prediction quality assessment are briefly introduced. (6) The different loss functions used in different models is discussed. (7) The research direction of precipitation nowcasting in the future is pointed out. [ABSTRACT FROM AUTHOR]

In an order of the abstract structure of the human body from inside to outside, this paper lists the state-of-the-art research advances of artificial intelligence (AI) especially deep neural network in ECG analysis from aspects of electrical conduction system and the structure of the heart ( including heart valves, myocardium, coronary arteries, etc. ), other systems out of the heart, ect. This paper introduces the application of AI-enabled ECG analysis technology in identifying arhythmias, electrolyte disturbances, valvular heart disease, coronary heart disease, and diseases of the circulation system and digestive system. This paper also covers its innovative application in the field of personal identification, and how to use AI technologies such as transfer learning in practical research. This paper is aimed to promote more cross-field cooperations, explore deep values of ECG, and help to realize the screening, diagnosis and treatment of multi-system diseases. [ABSTRACT FROM AUTHOR]

From the perspective of map cognition, this paper analyzes the important role of mental map in a successful event of family search. Firstly, several basic concepts of map cognition such as spatial cognition, mental map and cognition mapping are clarified, and the position and function of mental map in traditional cartographic triangle are expounded. A detailed empirical analysis is made on the cognitive process of both the seeker and the sought in this case. Three geospatial cognition problems in the interview are discussed: The adaptability difference of map tools to spatial cognition stage, the spatial scale and temporal characteris‐ tics of mental map matching, and the top-down influence of cognitive subjects' knowledge experience on cognitive results. Finally, this paper puts forward the development trend of geospatial cognition entering the new era under the promotion of brain science and artificial intelligence research. [ABSTRACT FROM AUTHOR]

This paper discusses the cognitive connotation of deep mapping. It attempts to consider deep mapping as an interdisciplinary research field, a combination of neuroscience and cartography, and a con‐ nection between brain mapping and geographic mapping. Recent developments in neuroscience, specifically the discovery of spatial cells such as place cells and grid cells, have provided a neuroscientific foundation for studying deep mapping. This paper proposes a conceptual model of deep mapping to establish the relation‐ ship between spatial cell properties and map functions (such as map scale, map measurement, map projec‐ tion, and navigation), and to elucidate the neural mechanism of maps by analyzing an abundance of neuro‐ science literature. This study of deep mapping aims to increase understanding of the nature of maps, enrich cartographic theories, encourage the integration of cartography and artificial intelligence, and expand their applications. [ABSTRACT FROM AUTHOR]

As one of the key technologies of artificial intelligence, image recognition has been widely used in various industries. The development of digital and intelligent water conservancy cannot be separated from the application of artificial intelligence image recognition technology. This paper firstly summarized the development and recognition process of image recognition technology, introduced the traditional image recognition technology, including statistical method, syntactic recognition method and geometric transformation method, and the image recognition technology with neural network algorithm, including depth confidence network, convolution neural network, cyclic neural network and graph convolution neural network and compared the advantages and disadvantages of the above image recognition technologies. This paper summarized the application of artificial intelligence image recognition in water conservancy industry from the four aspects of river velocity measurement, remote sensing water body recognition, water level monitoring and three-dimensional prediction. Finally, It was proposed that improving the adaptability, compatibility and recognition accuracy of technical scenarios would be the development direction of artificial intelligence image recognition in the water conservancy industry. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is a kind of bone disease related to aging. Its onset is obscure and development is gradual. Patients are without obvious clinical manifestations in the early stage. However, with the progress of the disease, bone loss and bone tissue microstructure destruction occur, and bone pain, spine deformation, and even osteoporotic fracture (OPF) and other serious complications may develop. Dual-energy X-ray absorption method is the current gold standard for clinical diagnosis of OP. Because its diagnostic accuracy is affected by the factors of body weight, lumbar degenerative changes, and aortic wall calcification, a false negative diagnosis is possible. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. At present, AI has been widely used in the research of OP. Its research in the field of OP screening, diagnosis, and prediction has become a new hot spot. This paper expounds the application status and advantages of AI in OP diagnosis and treatment from four aspects, i.e., OP early screening, medical imaging performance, clinical diagnosis and treatment data, and OPF risk prediction, so as to provide a new direction for the accurate diagnosis and treatment of OP. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to explore a new pharmacological science popularization (PSP) method to promote the correct knowledge of rational medication for children among the general public. The method integrates and summarizes existing pediatric PSP methods and experiences used at the National Children’ s Medical Center and proposes a new model of pediatric pharmacy that combines pervasive science and artificial intelligence (AI) to better serve pediatric pharmacy. The PSP method consists of nine parts: professional team, new media, talent training, community activities, platform operation, guideline publication, standard setting, popular science works and pharmaceutical service. The above nine parts can be divided into three levels: core business, support business and supplementary business. The core business level, which includes professional team, platform operation and pharmaceutical care, is critical to providing high-quality pharmaceutical care. The support business level, consisting of new media, talent training and community activities, provides support and services to ensure the smooth operation of the core business. The supplementary business level, consisting of guideline publication, standard setting, and popular science works, provides additional information and resources to enhance the quality and reliability of pharmaceutical care. Through the combination of these PSP levels, the organization can achieve its mission and goals and make a contribution to the pharmacy popular science industry. The new pediatric PSP method combined with AI can make an important contribution to drug safety and pharmaceutical care for children. On the one hand, the method can improve parents’ and children ’ s proper understanding and use of children ’ s medication and provide personalized pharmaceutical care according to patients’ needs; on the other hand, it can also stimulate the public’s curiosity and interest in the rational use of medication and promote popular science education focusing on AI technology, which has good prospects. [ABSTRACT FROM AUTHOR]

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

PointCloud is an important type of 3D data that is widely used in artificial intelligence applications such as autonomous driving, robotics, virtual and augmented reality. Point cloud semantic segmentation is a key task in point cloud processing, which aims to assign each point in the point cloud to a specific semantic category. This paper reviews the research progress of deep learning-based point cloud semantic segmentation in domestic and foreign studies. Firstly, commonly used open source datasets in point cloud semantic segmentation are summarized, and both indirect and direct deep learning processing methods based on point cloud are introduced along with their application progress. Additionally, the experimental results of these methods are presented and briefly compared. Finally, the problems in current point cloud semantic segmentation are discussed, and future research directions are proposed. [ABSTRACT FROM AUTHOR]

Since the outbreak of corona virus disease 2019, deep learning has been widely used in predicting epidemic trends, screening high-risk groups and making early diagnosis. Novel coronavirus infection mainly causes pulmonary diseases, and also affects cardiovascular system, resulting in electrocardiogram (ECG) abnormalities. ECG tracking image technology is a hot spot in the research of deep learning. Compared with traditional ECG judgment methods, its accuracy and sensitivity are both higher. It can be used in the effective diagnosis, clinical decision-making and prognosis evaluation of patients with novel coronavirus infection. This paper reviews on the ECG changes after novel coronavirus infection, and the advances of ECG tracking image technology applied in the auxiliary diagnosis of novel coronavirus infection, so as to provide references for clinical healthcare workers. [ABSTRACT FROM AUTHOR]