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[Purpose/Significance] Argument mining, a research hotspot in the field of computational linguistics, provides machine processable structured data for computational models of argument. Argument mining tasks are closely related to artificial intelligence (AI) technologies, such as natural language processing and knowledge representation. There are numerous systematic studies in academia and a clear technical realization route has come into being. New research results continue to emerge as a result of rich resources and rapid development and iteration of deep learning, large language models (LLMs), and other technologies. This study, which reviews the research status and progress of argument mining, can serve as a resource for future research and application development. [Method/Process] Through literature review, this paper systematically reviews the relevant research basis (including foundational techniques and semantic representation models), summarizes the related technical system in terms of task framework, influencing factors of technological complexity, and method classification, and then introduces the argument mining practice and application cases for specific fields and research objectives and makes a comparative analysis. Most importantly, the overall development and characteristics of this research field are summarized, with a focus on tracking the progress of multimedia argument mining in the context of the new AI environment. [Results/Conclusions] Relevant research has experienced the development of "machine learning - deep learning" and "text only - multimodal", and the levels of development and application of various fields vary much. Future research may focus on how to achieve multigranularity and multimodal content generalization, as well as how to promote its application and implementation in practice. Possible research directions include: 1) the use of LLMs in argument mining, because they exhibit significant benefits in downstream applications such as natural language processing and multimodal learning, and can also provide certain technical conditions for the generation of argument content; 2) the use of domain knowledge organization systems such as vocabulary, knowledge base and knowledge graph: with these systems, researchers can combine domain-specific argument mining models with rich knowledge structure, to strengthen semantic representation and organization improve the systematization and dig deeper into argument mining model research in the domain; 3) promoting the application research and practice of argument mining in more fields or across disciplines, and improving the retrieval and visualization of argument information, such as combining information retrieval methods with argument mining to build the next generation of argument search engines.

[Purpose/Significance] In recent years, challenges such as pandemics, wars, and natural disasters have posed numerous threats to China's food security. As the core of future agricultural productivity improvement, the importance of the seed industry has been continuously emphasized by the government. To facilitate preservation and utilization, scholars have integrated and digitized a vast amount of germplasm resources. However, the current platforms of crop germplasm resource knowledge services still suffer from issues such as diverse and fragmented large-scale heterogeneous data sources, lack of interconnection among data, and insufficient exploration of the data, thereby falling short of achieving intelligent and semantic research on germplasm resources. Therefore, this article aims to propose an effective method for knowledge organization and semantic association to meet the growing demand for intelligent knowledge services from users. The proposed method is to provide insights into the development of germplasm resource knowledge service platforms tailored for computational breeding. [Method/Process] This paper conducted a comparative analysis by examining the description and organization of germplasm resource data domestically and internationally. Four mainstream international platforms of germplasm resource knowledge services were selected for comparison from five perspectives: general overview, resource quantity, the types of knowledge, retrieval methods, and results. The deficiencies of these platforms in intelligent services such as text mining, semantic retrieval, and knowledge computation were summarized. In general, these platforms still rely on keyword-based retrieval as the primary means of searching, lacking systematic modeling of germplasm resource knowledge and the ability to achieve semantic retrieval in an intelligent environment. However, with the development of information technology in the era of big data, there is a growing demand in China to promote the development of computational breeding and provide more accurate, faster, and more intelligent knowledge resources to researchers and ordinary farmers through AI-based germplasm resource knowledge services. Therefore, in response to these new demands, the article proposes the construction of a panoramic crop germplasm resource knowledge graph and the development of a knowledge graph-driven germplasm resource knowledge service platform. [Results/Conclusions] The knowledge graph provides a more efficient and intelligent form of knowledge organization, and a knowledge service platform based on the knowledge graph contributes to improved efficiency and accuracy of knowledge services. In the next step, this research will focus on building a large-scale germplasm resource knowledge graph based on germplasm resource data and expanding it with other data, such as genotype, phenotype, environmental, and literature information. The application exploration will be conducted in scenarios such as intelligent question answering and knowledge-based computational breeding.

[Purpose/Significance] Knowledge Graph has become a major research hotspot in the era of artificial intelligence due to its ability to provide a new means of organization and representation of knowledge. As the field continues to evolve, numerous scholars have proposed advanced algorithms and technologies for each core stage of constructing a knowledge graph, and many large domestic and foreign enterprises have also developed their independent knowledge graph management systems. However, the majority of these graph tools developed are designed for commercial use and are often too expensive and difficult to deploy locally for small and medium-sized research teams. This presents a challenge for information organizations such as research libraries with massive resources, which require a more adaptable, universal, and efficient tool to build and manage knowledge graphs. To meet this need, it is important to develop an open-source, user-friendly, and customizable knowledge graph management system that can be easily deployed by small and medium-sized research teams. [Method/Process] In summary, this article offers a thorough and informative analysis of six mainstream knowledge graph management systems, both domestically and internationally. It delves into the unique characteristics of each system within the business process and provides an in-depth comparative analysis based on several important factors, including system functionality, technology selection, open-source availability, and application domains. The article refers to the standard construction process of knowledge graphs and highlights the platform characteristics of each system during the construction process while also examining their limitations based on current data characteristics. In response to practical needs, the article focuses on multi-path, multi-engine, distributed, and collaborative construction, integrating advanced graph algorithms and considering a well-developed underlying graph storage strategy. [Results/Conclusions] As a result,the article presents an in-depth analysis of the construction model for a collaborative development and management system of an integrated knowledge graph. It not only investigates the current state of knowledge graph management systems but also proposes novel optimization ideas. These ideas include distributed collaborative construction, which allows for simultaneous contributions from multiple sources, and parallel management of multiple graphs, enabling efficient organization and retrieval. Additionally, some suggestions are put forward: developing multi-path knowledge extraction techniques to enhance the knowledge acquisition process, and using specialized multi-graph storage engines for optimized storage and retrieval. Last, the article emphasizes the importance of incorporating cross-media and multimodal knowledge into the graph for a comprehensive representation of information.

[Purpose/Significance] From the perspectives of strategic planning and implementation, this paper explores the development characteristics, development trend and practice path of agricultural knowledge service, and provides reference for the digitalization and intelligent development of agricultural knowledge service under the background of data-driven intelligence, and serves to support major national strategies such as rural revitalization and digital countryside construction. [Method/Process] In order to analyze the characteristics of agricultural knowledge service, this paper analyzes and summarizes the service from the perspectives of industry and discipline. In terms of industry, it focuses on the analysis of industry positioning of agricultural knowledge service, while in terms of discipline, the subject characteristics of agricultural knowledge service are analyzed. In order to grasp the development trend of agricultural knowledge service, this paper analyzes the service development course from the historical perspective and reveals the development trend. Finally, based on the above conclusions, combined with the needs of national strategy for agricultural knowledge service in supporting strategic development and serving rural construction, the development suggestions of agricultural knowledge service were put forward, including two levels of strategy and implementation. [Results/Conclusions] About the characteristics of agricultural knowledge service, under the perspective of industry, agricultural knowledge service takes knowledge service as the core; from the perspective of discipline, agricultural knowledge service is characterized with the service based on the agricultural field. From the development stage, agricultural knowledge service has gone through three stages. The first stage is the exploratory period of interwoven development with agricultural information service. The second stage is characterized with the explosive development of platformization. It is now entering the third stage - the stage of data-driven intelligence. From the perspective of the development trend, this stage is characterized with precision, intelligence, digitalization and knowledge. In order to promote the innovative development of agricultural knowledge service and better serve and support the national strategy, this paper puts forward development strategies, including the strategic level and the implementation level. At the strategic level, agricultural knowledge service should be guided by national strategies and policies, optimize the distribution of knowledge service, maximize the benefit of resource and service allocation, focus on the key points of national strategic development, and give priority to meet the key needs of national strategic development. At the implementation level, agricultural knowledge service should implement intra-industry and extra-industry cooperation, integrate superior resources, and promote the coordination between the service supply and the service demand, so as to build a harmonious knowledge service ecology.

[Purpose/Significance] This study is focused on the current popular "chatbot" ChatGPT to deepen users' overall cognition of ChatGPT, and provide reference and inspiration for the development of intelligent knowledge services in libraries by combining the power of new artificial intelligence (AI) technologies.[Method/Process] The article comprehensively analyzes ChatGPT, including its development history, technical features, common application scenarios, and integrated application program paths. In addition, it compares ChatGPT with similar AI technologies developed domestically and internationally (such as Google's Brad and Meta's BlenderBot 3), intuitively reflecting that new AI technologies such as pre-training models and cognitive intelligence have become the research and development focus of major technology institutions. The article also analyzes the technical limitations and existing security risks of ChatGPT, pointing out the optimization direction for secondary development and indicating its potential hazards for other researchers. Furthermore, the potential impact of ChatGPT on the Chinese libraries and information institutions are explored. By studying the application of ChatGPT in libraries and information service institutions, this article attempts to provide an in-depth understanding of how to use this technology to improve information retrieval, knowledge management, and user engagement. Finally, a comprehensive overview of ChatGPT and its potential impact on the Chinese information environment is provided. [Results/Conclusions] The integration of new technologies such as big data and AI has great potential for China's library and information service institutions to provide better and more intelligent knowledge services. With the development of modern technologies, libraries and information service institutions have been faced with new challenges and opportunities at the same time. The challenges come from the overwhelming amount of information, the diversification of information resources, and the increasing demands of users for personalized services. The opportunities arise from the availability of advanced technologies such as big data and AI that can help libraries and information service institutions to address these challenges. By fully integrating big data and AI into libraries and information service institutions, these institutions can leverage the power of these advanced technologies to provide more intelligent knowledge services. High-quality scientific and technological resources and knowledge organization systems can play a vital role in ensuring that these institutions are equipped with the necessary infrastructure and expertise to successfully implement these technologies. In conclusion, the integration of big data and AI represents a significant opportunity for China's libraries and information service institutions to provide better and more intelligent knowledge services. By relying on high-quality scientific and technological resources and knowledge organization systems, these institutions can comprehensively improve their level of intelligent knowledge services, and better meet the needs and demands of their users in the digital age.

The wide application of advanced information technologies such as big data, Internet of Things and artificial intelligence in agriculture has promoted the modernization of agriculture in rural areas and the development of smart agriculture. This trend has also led to the boost of demands for technology and knowledge from a large amount of agricultural business entities. Faced with problems such as dispersiveness of knowledges, hysteric knowledge update, inadequate agricultural information service and prominent contradiction between supply and demand of knowledge, the agricultural knowledge service has become an important engine for the transformation, upgrading and high-quality development of agriculture. To better facilitate the agriculture modernization in China, the research and application perspectives of agricultural knowledge services were summarized and analyzed. According to the whole life cycle of agricultural data, based on the whole agricultural industry chain, a systematic framework for the construction of agricultural intelligent knowledge service systems towards the requirement of agricultural business entities was proposed. Three layers of techniques in necessity were designed, ranging from AIoT-based agricultural situation perception to big data aggregation and governance, and from agricultural knowledge organization to computation/mining based on knowledge graph and then to multi-scenario-based agricultural intelligent knowledge service. A wide range of key technologies with comprehensive discussion on their applications in agricultural intelligent knowledge service were summarized, including the aerial and ground integrated Artificial Intelligence & Internet-of-Things (AIoT) full-dimensional of agricultural condition perception, multi-source heterogeneous agricultural big data aggregation/governance, knowledge modeling, knowledge extraction, knowledge fusion, knowledge reasoning, cross-media retrieval, intelligent question answering, personalized recommendation, decision support. At the end, the future development trends and countermeasures were discussed, from the aspects of agricultural data acquisition, model construction, knowledge organization, intelligent knowledge service technology and application promotion. It can be concluded that the agricultural intelligent knowledge service is the key to resolve the contradiction between supply and demand of agricultural knowledge service, can provide support in the realization of the advance from agricultural cross-media data analytics to knowledge reasoning, and promote the upgrade of agricultural knowledge service to be more personalized, more precise and more intelligent. Agricultural knowledge service is also an important support for agricultural science and technologies to be more self-reliance, modernized, and facilitates substantial development and upgrading of them in a more effective manner.

Background China is currently making efforts to transform the current hospital-centric service delivery system to people-centred primary health care (PHC)-based delivery system, with service delivery organized around the health needs and expectations of people. To help direct China's PHC reform efforts, a profile of high-quality PHC from the public's perspective is required. Objectives To profile high-quality PHC from the perspective of the Chinese public. Methods Semistructured interviews were conducted in 6 provinces (Henan, Shandong, Zhejiang, Shaanxi, Shanxi, and Heilongjiang) in China. In total, 58 interviewees completed the recorded interview. For transcription, trained research assistant listened to the recording of the interviews, summarizing each 30-s segment in English. Next, thematic analysis was performed on the narrative summaries to identify thematic families. Results Seven themes and 16 subthemes were generated from the analysis of our interview data. In order of their frequency, the interviewees expressed a high expectation for interpersonal communication and technical quality; followed by access, comprehensive care, cost, continuity, and coordination. Conclusions Using qualitative data from 6 provinces in China, knowledge was generated to reveal the public's views and expectations for high-quality PHC. Our results confirm the urgent need for quality improvement efforts to improve patient experience and technical quality. The government also needs to further improve the delivery system and medical training programme to better meet public expectation in these areas, especially in establishing an innovative integrated primary care model, and strengthening interpersonal and clinical competency training for family doctors. [ABSTRACT FROM AUTHOR]

Telomere length is closely linked to biological aging, oxidative stress, and the development of cardiovascular diseases. This study aimed to assess the association between dietary selenium intake and telomere length in individuals with hypertension. Data on dietary selenium intake were captured through the National Health and Nutrition Examination Survey (NHANES) computer‐assisted dietary interview system (CADI). Telomere length determination entailed selecting blood samples from all participants in the NHANES database. The analysis was performed using Analysis System software, with Empower stats utilized for data analysis. Results showed that there was a significant association between dietary selenium intake and telomere length in hypertension, particularly within the female group. In female hypertension cases, a 1 mcg increase in dietary selenium intake corresponded to a telomere length increase of 1.19 bp, even after adjusting for age, race, BMI, marital status, physical activity, energy intake, and stroke history. The relationship between dietary selenium intake and telomere length exhibited a linear pattern in female hypertension patients. This study identified a positive association between dietary selenium intake and telomere length in hypertension, particularly within the female group. [ABSTRACT FROM AUTHOR]

The interdisciplinary nature and rapid development of the Semantic Web led to the mass publication of RDF data in a large number of widely accepted serialization formats, thus developing out the necessity for RDF data processing with specific purposes. The paper reports on an assessment of chief RDF data endpoint challenges and introduces the RDF Adaptor, a set of plugins for RDF data processing which covers the whole life-cycle with high efficiency.

Subtitle translation, in comparison with other forms of translation, is more closely related to visual symbols. This article examines the correlation between images and translated subtitles in movies viewed from the information focus and the information content. Through a detailed exploration of translation cases from the animated film Chang An, this study discusses in which way the interplay of images and translated subtitles optimize the expressiveness of the movie, based on which, it is suggested that by applying specific translation techniques, the translation of subtitle text should be tailored to harmonize with visual elements, so as to achieve better information dissemination in visual-text mediums. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] To solve the problem of unbalanced supply and demand of agricultural Sci-Tech knowledge services in the big data environment, this paper proposed a "data + demand" driven knowledge service supply model. [Method/Process] The proposed model contains a knowledge service system model for different application scenarios and a technical supporting system with convergence of multi-source heterogeneous agricultural science and technology big data, knowledge computing and intelligent services as links. [Results/Conclusions] We developed a multi-scenario agricultural knowledge service system characterized as "Inclusive + Specialized + Customized", by addressing the key technologies of agricultural Sci-Tech big data integration and knowledge service, and thus provided differentiated, accurate and in-depth knowledge services for users of multiple types, levels and disciplines. The agricultural knowledge service system helps address the contradiction between supply and demand of knowledge services, and meet the need of inclusive & personalized knowledge service simultaneously to a certain extent.

[Purpose/Significance] New quality productive forces is the latest pattern of productivity development under the background of data-driven intelligence. To explore the mechanism and practical way of bidirectional empowerment of knowledge service and new quality productive forces has important theoretical and practical significance for giving full play to the effectiveness of new quality of data elements, new power of digital intelligence technology and new application of intelligent service, so as to promote the cultivation of new quality productive forces and improve knowledge service. [Method/Process] Based on the summary of the development process of knowledge service and the new interpretation from the perspective of new quality productive forces, this study analyzes the internal relationship and external manifestations of the bidirectional empowerment of knowledge services and new quality productive forces, constructs the bidirectional empowerment mechanism of knowledge services and new quality productive forces, and proposes the bidirectional empowerment path. [Results/Conclusions] "Factor reorganization, scientific and technological innovation, and quality service" has become the internal relationship between knowledge service and new quality productive forces . Data factor as the core production factor, digital intelligence technology as the key production technology, and intelligence-driven new business forms and new models as the common carrier, which is an important basis for intelligent knowledge service and new quality productive forces bidirectional empowerment mechanism. Intelligent knowledge service cultivates the new quality productive forces through three important ways: accelerating scientific and technological innovation, opening up new ways and creating new business forms. New quality productive forces drives the development of intelligent knowledge service and improves the service by solving the resource dilemma, technology dilemma and talent dilemma, and creating new competitive advantages. This is an important manifestation of the bidirectional empowerment mechanism of new quality productive forces and intelligent knowledge service. After clarifying the empowerment basis and the performance of the bidirectional empowerment mechanism, this study proposes that the two kinds of empowerment should be realized from the following four aspects: a) Taking the market demand as the guide by mining new value and responding to new demand, and grasping the new quality growth engine; b) Taking data elements as the center, the compacting intelligent driving base is realized by building the data element empowerment system and improving the standardization of element utilization; c) Taking technological innovation as the key way by releasing the potential of key elements and creating fusion application scenarios to consolidate the foundation of fusion application; d) Starting from the application scenario as the starting point, the bidirectional empowerment mechanism is put into practice in various fields. [ABSTRACT FROM AUTHOR]

Gallium nitride high-electron-mobility transistor (GaN HEMT) power devices are favored in various scenarios due to their high-power density and efficiency. However, with the significant increase in the heat flux density, the junction temperature of GaN HEMT has become a crucial factor in device reliability. Since the junction temperature monitoring technology for GaN HEMT based on temperature-sensitive electrical parameters (TSEPs) is still in the exploratory stage, the TSEPs' characteristics of GaN HEMT have not been definitively established. In this paper, for the common steady-state TSEPs of GaN HEMT, the variation rules of the saturation voltage with low current injection, threshold voltage, and body-like diode voltage drop with temperature are investigated. The influences on the three TSEPs' characteristics are considered, and their stability is discussed. Through experimental comparison, it is found that the saturation voltage with low current injection retains favorable temperature-sensitive characteristics, which has potential application value in junction temperature measurement. However, the threshold voltage as a TSEP for certain GaN HEMT is not ideal in terms of linearity and stability. [ABSTRACT FROM AUTHOR]