[Purpose/Significance] This study aims to explore the challenges that artificially intelligent (AI) scientists may bring to scientific epistemology. [Method/Process] Scientific discovery has long been of interest to AI researchers. The next big step in AI is the development of AI scientists. AI scientists should be able to independently motivate, make, understand, and communicate discoveries. Although the current robot scientists are still just a form of AI-driven automated experimental apparatus, and the best AI systems today cannot define their own hypothesis space and experimental design. At best, they can be considered to be a primitive form of AI scientists. Clearly, the specific path of AI-driven scientific research or the transition to AI scientists will inevitably be influenced by the frontier development of AI. Current AI systems must overcome the following major technical challenges: 1) making strategic choices in their research goals; 2) developing the ability to generate exciting and novel hypotheses in areas that push boundaries; 3) designing innovative approaches and experiments to test hypotheses that go beyond the use of prototype experiments; 4) focusing on and describing important discoveries in a way that can be understood by human scientists. The highly autonomous AI scientists can either make discoveries on their own or collaborate with other human and machine scientists to make Nobel-level discoveries. After reviewing the relevant AI applications in scientific research, this study illustrates the main characteristics of AI scientists and the two disruptive changes they bring about at the epistemological level: a leap in AI capabilities and AI for Science as the 5th paradigm of scientific research. [Results/Conclusions] The implications of AI for Science are revolutionary, but recent AI-driven explorations in scientific research increasingly support the possibility of its realization. In this situation, discussions on the epistemological issues of relevant sciences need to go beyond general philosophical debates and instead explore epistemological strategies for the coming scientific revolution in AI. In view of the coming scientific revolution in AI, this study proposes four strategies. First, we should pay more attention to the problems and solutions in the process of developing AI scientists. Second, the key to advancing the scientific revolution in AI is to find ways to eliminate factors that may lead to failure. Then, we use different strategies to achieve the scientific revolution of AI. Finally, we take advantage of metaphorical methods to help us develop AI scientists.

[Purpose/Significance] This article is aimed to explore the impact of artificial intelligence generation technologies such as large language models (LLMs) on users' information retrieval behavior and to suggest ideas for information retrieval systems and information resource construction. In this way, it provides insights into and references for the future establishment of the artificial intelligence generated content (AIGC) information platform with Chinese characteristics as well as the information literacy education system. [Method/Process] In the field of library intelligence, with the wide application of AI technology in information service work, LLMs represented by ChatGPT have also become a hot topic of discussion. Taking the booming development of LLMs such as ChatGPT as background, we analyzed the impact of the increasing popularity of this technology on information retrieval systems and user retrieval behavior from the perspective of user information behavior by combining the technical features of LLMs with the characteristics of existing products. Literature survey and empirical analysis were used. [Results/Conclusions] The use of LLMs as information retrieval systems has unparalleled advantages over traditional products. These advantages include the ability to understand and process natural language queries, generate relevant and context-specific responses, and interact with users in a more human-like way. The application of LLMs in information retrieval systems has the potential to transform the way users search for information, influence the underlying logic, action priorities, and retrieval expectations of user information retrieval behavior. However, the existing shortcomings of LLMs in terms of reliability and accuracy still make it difficult for them to replace traditional information retrieval methods immediately. Language models may not always provide accurate and reliable answers, especially when dealing with complex or domain-specific queries. Additionally, LLMs may struggle to understand and process contextual information effectively, leading to limitations in their ability to extract relevant and context-aware insights. It is recommended to pay attention to this technology in the construction of information retrieval systems and information resources, and to explore the combination of LLMs and information services in order to cope with the changes in future user information needs and to further make full use of the value of existing information resources. Limited by the lack of expertise in the field of AI and the fact that LLMs are not yet widely used in practice in China, the research findings are only a reflection and exploration of the impact of LLMs on users' information behavior.

[Purpose/Significance] With the advent of the algorithmic era, libraries' information delivery channels have shifted from offline physical entities to online digital platforms. This transformation has brought about significant changes in the way users access and utilize scientific data. The frequency of use of scientific data has increased exponentially, as more and more users rely on data to support their research, education, and innovation activities. The huge demand and application of use of data poses challenges to the development of libraries, among which the service guarantee of intellectual property rights (IPR) for scientific data is becoming a key factor affecting the development of digital libraries. IPR is a legal concept that protects the ownership and control of data creators and providers over their data. It also regulates the rights and obligations of data users and re-users. Therefore, this study aims to explore the influencing factors and optimization strategies of libraries' IPR service quality for scientific data. [Method/Process] To achieve this goal, this study used a questionnaire analysis method to collect data from a sample of 252 individuals belonging to a highly knowledgeable group, such as researchers, academics, and students. These individuals are the main users and producers of scientific data, and their perceptions and expectations of the quality of IPR services by libraries are crucial for improving the service. The questionnaire consists of four parts: demographic information, IPR awareness, IPR satisfaction, and IPR improvement suggestions. The reliability of the questionnaire factors is between 0.724 and 0.913, indicating a high level of internal consistency. The validity of the questionnaire is verified by confirmatory factor analysis, which shows a good fit between the data and the model. Based on the data, this study conducts a path analysis to test the hypotheses and obtain the results. [Results/Conclusions] The results show that the following factors have a significant positive impact on the quality of libraries' IPR services for scientific data: the implementation efficiency of policies and regulations (beta=0.326, p<0.001), talent team building (beta=0.274, p<0.001), the data management technology (beta=0.211, p<0.001), the diversification of service models (beta=0.358, p<0.001), and the number of data IPR sharing agreements (beta=0.329, p<0.001). These factors reflect the importance of improving the legal, human, technical, and organizational aspects of libraries' scientific data IPR services. Based on the findings, this study proposes five optimization strategies for libraries in scientific data IPR service: strengthening the implementation of policies and regulations, improving the training and motivation of talent teams, upgrading the data management technology, innovating the service model, and increasing the number of data IPR sharing agreements. These strategies can help libraries to improve the quality of their scientific data IPR services and meet the needs of the users in the algorithmic era.

[Purpose/Significance] Today, the methods of global technology cooperation have become diversified, while the patterns of knowledge and resource flows have become networked. The way knowledge flows between science and technology and promotes innovation is a hot topic in informatics research. Under such circumstances, the transfer and flow of technical knowledge has become increasingly important for the creation of new technologies and the generation of new knowledge. It can provide potential solutions for solving technical problems and generating technological competitive advantages. This study reveals the structural characteristics of the knowledge flow network between science and technology from a micro perspective, and provides new ideas for discovering new knowledge flow and communication channels, understanding the links between science and technology, and exploring innovation breakthroughs. [Method/Process] This paper adopts a multi-perspective approach and exames technical knowledge citation, scientific knowledge citation, and the intersection of scientific knowledge. Specifically, the study focuses on patent-patent citation, patent-paper citation, and paper-discipline co-occurrence, and uses a multi-layer network method to develop a science-technology (S&T) knowledge flow micro-network analysis framework. Through this framework, knowledge flow patterns between science and technology can be comprehensively explored. An empirical study was conducted in the field of genetically modified corn to investigate the knowledge flow patterns between science and technology. Using a multi-layered network model, this study aims to explore the relationship between science and technology from three different perspectives. [Results/Conclusions] The results of the study suggest that technology nodes that absorb multidisciplinary knowledge are more likely to engage in knowledge diffusion or absorption. Additionally, knowledge flow is more likely to occur between the technology nodes corresponding to two interdisciplinary subjects. The analytical framework proposed in our study can reveal the characteristics of knowledge flow from a micro perspective, and can identify two types of knowledge flow structures in the empirical case of genetically modified corn, which extends and enriches existing research on knowledge flow. In the future, the S&T knowledge flow research framework proposed in this study has the potential to be further extended and widely applied by incorporating different types of knowledge subjects, enriching knowledge attribute factors, and expanding knowledge flow paths. For instance, the research framework can be extended to broader fields to explore the relationship between knowledge flow patterns and the characteristics of these fields. It can also be used to explore knowledge flow patterns and potential technological breakthroughs at different flow levels and innovation themes. Such research can serve as a basis for innovation policy and provide theoretical support for the development of strategies and technology research directions.

[Purpose/Significance] There is no shortage of discussions in the library industry on how the value of academic libraries can be reflected by affecting students' learning outcome. There are also excellent cases such as the "Value of Academic Libraries' Initiative", which have a great impact on the theoretical and practical research in the field, and have initially formed a research system. Due to the differences in the higher education systems of different countries and the focus of higher education talent cultivation goals in different eras, it is necessary to consider the actual situation and based on the construction background of "double first-class" university construction to explore the paths and countermeasures that are in line with the development strategy of higher education in the new era of Chinese academic libraries. Scientific measurement and evaluation of academic library services from the student-oriented perspective is an important way to clarify the key path of library value enhancement, which is helpful for libraries to optimize their services, improve their development framework and enhance their own value. [Process/Method] In view of the above background and related research deficiencies, this research focuses on how academic libraries can improve their own value in the new era to better serve the strategic development of universities, and become the core of value for various stakeholders to discuss. Meanwhile, the methods used in related research lack the in-depth analysis of the process of library influence. In order to systematically sort out the internal logic of library impact on students' learning outcome, it is essential to explore the role of process factors in it, and it is not enough to use only regression analysis to conduct data statistics. This study analyzed 985 questionnaires by using the structural equation model analysis method, built a student-oriented value impact model of academic libraries with "situation-effect-outcome" as the main line, and explored the key influencing factors. [Results/Conclusions] It is suggested that the academic libraries should enhance their value influence in the following four aspects: to strengthen the policy guidance to improve the service utilization rate of academic libraries, attach importance to the contribution and value of academic libraries in the construction of "double first-class" university service, study and build a service system that meets the needs of users, and strengthen the service support for "double first-class" university construction. In this study, the research area is set in the University of Chinese Academy of Sciences and the interviewees are set at the postgraduate education stage. Although it is representative to a certain extent, the sample scope is still limited. The applicability of the conclusions obtained from the empirical research needs to be further verified to improve the reliability of the conclusions. In the future, it is necessary to investigate the influence of more types of university libraries on students' academic performance and the influence of university libraries on students' academic performance at different stages of education. To verify whether school factors and the training level of students have an impact on the correlation between university libraries and students' academic performance, and whether the causal relationship between the cognition and evaluation of students promoted by university libraries and students' academic performance is applicable to all universities and students.

[Purpose/Significance] With the development of the Internet and the popularization of smart devices, the scale of social media users around the world is increasing, and social media has become the center of people's communication, but it also produces a series of negative problems. Digital stress refers to the painful subjective experience of users caused by different stressors in the process of heavy use of social media. At present, most studies on social media focus on users' social media behaviors. In recent years, digital stress has gradually attracted the attention of researchers. However, most studies discuss the impact of single variables on results from the perspective of linear regression, and few studies focus on the complex mechanism behind digital stress. This study aims to explore the influencing factors and mechanism of social media users' digital stress in order to further understand the path of digital stress and provide reference for relieving users' digital stress. [Method/Process] This paper adopted a mixed research method combining grounded theory and qualitative comparative analysis. First, 21 users were selected as interview objects, interview data were coded, and theoretical models were constructed. Second, a questionnaire was designed according to the theoretical model and 262 questionnaires were collected. The fuzzy set qualitative comparative analysis (fsQCA) method was used to analyze the questionnaire data and explore the formation path of social media users' digital stress. [Results/Conclusions] There were 9 influencing factors of social media users' digital stress: overuse, technology intrusion, information overload, communication load, fear of missing out, approval anxiety, social expectation, digital coping skills and self-control. They were classified into four dimensions: technology, social, environment and personal characteristics. There are six paths that trigger digital stress, among which overuse and fear of missing out are the key factors leading to high-level digital stress, technology intrusion and information overload are the important conditions for digital stress, and digital coping skills and self-control have no significant effects. At the same time, this paper has some limitations. First, the end point and result variable of this paper is the digital pressure of social media users, focusing on the factors that affect the generation of digital pressure and how the combination of factors and variables ultimately leads to the generation of results. Less attention is paid to user behavior. Although some attention is paid in the interview, antecedent variables are emphasized in the analysis. Future studies can take users' social media usage behavior into consideration to explore the whole process formation mechanism of influencing factors-digital pressure-user behavior. In addition, most of the research samples are young users of social media, and users of other age groups are less involved. In the future, the research samples should be further expanded to improve the universality of the research conclusions.

[Purpose/Significance] The birth of the Internet has brought revolutionary impact on bibliometrics, giving rise to a number of online download indicators for academic literature. The most representative basic indicator among them is the download frequency, but it also includes the annual download rate, the total download volume, the download half-life, and the Google Scholar Index. The proposal of these indicators provides a new method and means of measuring scholarly dissemination and impact, which is a significant development of traditional bibliometrics and an important component of alternative metrics. Given the lack of indicators that comprehensively characterize the dissemination, impact, knowledge and information volume of academic journals, this paper proposes the download factor indicator to address this problem. [Method/Process] First, according to the changes of download frequency and citation frequency over the years, based on the citation data of CSSCI journals of library information and bibliology on CNKI, a panel data model was used to establish a prediction model of download frequency and citation frequency, and the optimal lag period for designing the download factor was determined. The indicator of download factor was proposed, that is, the average number of downloads per hundred times of each paper after 2 years of publication. This paper further used ridge regression to analyze the relationship between the download factor and the impact factor, h-index, and the number of articles. [Results/Conclusions] The download frequency with a lag of 1 year and 2 years determines 80% of the citation frequency. This article innovatively adopts a panel data model and comprehensively evaluates the impact of download frequency on citation frequency in both current and lagged periods, thereby greatly improving the prediction accuracy. The download factor can better measure the knowledge information volume, dissemination level, influence and academic quality of the journal. The timeline for downloading factor indicators is synchronized with the influencing factors, both within 2 years after the publication of journal articles, focusing on the evaluation of academic communication level. The download factor has the highest correlation with the main indicator of the impact of journal quality, the h-index, and has a high correlation with the impact factor and publication volume. It has good statistical indicator properties and is a comprehensive indicator for evaluating journals; the download factor index needs to be more inspection of application in disciplines and use of data. This article is based on the conclusions drawn from the research of 19 CSSCI journals in library and information science literature. The relationship between download frequency and citation frequency in other disciplines, as well as the construction of download factors, require further research in conjunction with the latest data.

[Purpose/Significance] With the popularity and development of social media, Weibo has become an important information transmission platform. Understanding the law of emergency information transmission based on the characteristics of Weibo users is of great significance for grasping the development trend of events, evaluating the influence of information, and formulating effective response strategies. [Methods/Process] Selecting typical cases of four types of emergencies (natural disasters, accidents, public health events, and social security events) as the research objects, this paper crawled the user data of the information transmission on the Weibo platform and analyzed the distributions of user gender, user geographical distribution, user attention and fan number and user type of the information transmission for these events, in order to reveal the differences and similarities of the user characteristics and transmission laws for the four types of emergencies. [Results/Conclusions] As for the similarities of the user characteristics and transmission laws, first, the audiences of the four types of emergencies have obvious regional characteristics, and the users in economically developed provinces generally have a higher attention rate. Second, the transmission users of all types of emergencies are generally concentrated on active users and grassroot users with strong and weak propagation power respectively, accompanied by a small number of authoritative users with strong influence. However, there are differences in the broadness and gender distribution of the audiences for different types of emergencies. First, natural disasters have a wider spread of the audiences and relatively higher attentiveness; accidents and public health events tend to be local events with relatively low attentiveness, whose audiences may be more concentrated in and around the affected areas; the audience spread of social security events is scattered depending on the nature of the event, the scope of influence as well as the communication channels. Second, female users pay more attention to natural disasters, public health events and social security events, while male users pay more attention to accidents, which can be attributed to the different emotional orientations and psychological characteristics associated with users of different genders. These results provide insights for the formulation of targeted guidance strategies for information dissemination. In future studies, we will collect data on other social media platforms, obtain more information on user characteristics through different channels, and introduce more in-depth analysis methods and indicators to comprehensively reveal the dynamic mechanisms of emergency information dissemination, thus improving the accuracy and effectiveness of the research.