[Purpose/Significance] Frontiers in scientific research are an important direction in the field of technology strategic intelligence.This article reviews the book"Identification of Frontiers in Future Emerging Scientific Research"to provide reference for research and practice in the field of technology strategic intelligence, such as exploration and evolution of scientific research frontiers, identification of key core technologies, and prediction of disruptive technologies. [Method/Process] The paper evaluated the core concepts and viewpoints of the book from the perspectives of multi-source data fusion, text mining methods, innovation in multidimensional indicator systems, application of visual analysis tools, and empirical research in the field of carbon nanotubes. [Result/Conclusion] The publication of this book plays an important guiding and leading role in the research of scientific and technological intelligence, as well as in the assessment of future research frontiers by scientific and technological management departments.

[Purpose/Significance] Public data authorization operation is an important means to promote data transaction circulation and explore data value.From the perspective of policy tools, entering into public data authorization operation policies can help explore the attention allocation of existing policies and provide reference for the future introduction of public data authorization operation policies or documents by the country or other provinces and cities. [Method/Process] Public data authorization operation is an important means to promote data transaction circulation and explore data value.From the perspective of policy tools, entering into public data authorization operation policies can help explore the attention allocation of existing policies and provide reference for the future introduction of public data authorization operation policies or documents by the country or other provinces and cities. [Result/Conclusion] Currently, the operation of public data authorization is still in the exploratory stage, both in theory and practice.After analyzing policy samples, it was found that there are imbalances in the proportion of supply oriented policy tools, abuse of environmental policy tools, and urgent need to expand demand oriented policy tools in the use of policy tools.It is still necessary to coordinate the proportion of supply oriented policy tools Strengthen the balance of environmental policy tools and expand and improve demand based policy tools to optimize the policy system of public data authorization and operation in China.

[Purpose/Significance] Named entity recognition of folk literature text data helps deepen the description and presentation of folk literature materials, laying a solid foundation for building a complete knowledge system of Changbai Mountain's intangible heritage. [Method/Process] This study proposed a Changbai Mountain intangible heritage folk literature entity extraction model based on BERT-BiGRU-MHA-CRF.Bidirectional Gated Recurrent Unit (BiGRU)was introduced to better handle the long sequence dependence of entities in sentences and solve the gradient vanishing problem.Then, the Multi-head Attention (MHA)mechanism was added to enhance the attention weight allocation for key entities, thus obtaining better entity recognition results. [Result/Conclusion] Compared with the mainstream multi-task joint learning benchmark models BERT-CRF and BERT-BiLSTM-CRF, the proposed model achieves the highest accuracy in named entity recognition of folk literature, with a precision rate of 86.76%.This study preliminarily realizes accurate entity recognition of folk literature text, which is conducive to in-depth analysis and knowledge mining of folk literature materials and helps protect and inherit the cultural memory of Changbai Mountain.

[Purpose/Significance] Relational extraction is a core component of medical record processing, which is crucial for improving the accuracy and efficiency of electronic medical record processing.In order to solve the problems of entity redundancy, entity word nesting and entity overlapping in the relational extraction of Chinese electronic medical records, and to improve the efficiency of medical information extraction, a novel relational extraction model for Chinese medical records is proposed. [Method/Process] The relationship extraction task was decomposed into three parts: relationship prioritization decoder, global entity extraction and subject-object alignment.Firstly, the relationship was predicted and filtered by the decoder, and the entity extraction was restricted based on the predicted relationships.Secondly, the relationship-specific attention mechanism and the global pointer network were adopted to effectively deal with the problem of information overlapping and subject/object nesting.Finally, the entity correspondence matrix was introduced to align the subject, object, and their relationships into a ternary group. [Result/Conclusion] Comprehensive experiments are conducted on CMeIE Chinese medical record dataset and DiaKG real diabetes Chinese dataset respectively and compared with six models, and it is found that the F1 values of this paper's model on the datasets CMeIE and DiaKG are improved by 6.6% and 5.8% compared with the mainstream model CasRel, respectively.The results show that the model in this paper can effectively solve the problems of entity nesting and entity overlapping caused by the complexity of Chinese medical records, which is valuable for medical information extraction and data processing processes.

[Purpose/Significance] Artificial Intelligence (AI)technology has produced algorithmic alienation, innovation, and development.This study takes the alienation phenomenon brought by algorithmic progress as the entry point, introduces the concept of ambivalent attitudes, and studies the mechanism of the formation of academic users' willingness to use Artificial Intelligence Generated Content (AIGC)technology to provide reference and ideas for facilitating the rational use of AIGC technology by academic users, improving the function of platforms by technology service providers, and algorithmic governance by relevant departments. [Methodology/Process] Based on the ABC attitude model and self-regulation theory, the paper constructed a theoretical model of the influence of algorithm alienation on the use of AIGC technology by academic users from the perspectives of algorithm appreciation and algorithm aversion and empirically analyzed 425 questionnaire data by using Structural Equation Modeling (SEM)and Fuzzy Set Qualitative Comparative Analysis (fsQCA)methods. [Findings/Conclusions] SEM results confirmed that ambivalent attitudes significantly negatively affect academic users' willingness to use AIGC.Algorithm appreciation(information quality, functional quality)negatively affects ambivalent attitudes, algorithm aversion(information alienation, governance lag)positively affects ambivalent attitudes, and ambivalent attitudes mediate between algorithm appreciation, algorithm aversion, and willingness to use.Meanwhile, algorithmic literacy and social support mediate ambivalent attitudes and willingness to use AIGC technology.fsQCA results further show that quality-oriented(S1), self-efficacy(S2), and group-driven(S3)form a high willingness to use, whereas risk-averse(NS1)and norm-deficient(NS2)trigger a non-high willingness to use.

[Purpose/Significance] To discuss the influencing factors of social media users' participation in algorithmic risk governance will help to improve the effect of algorithmic risk governance in our country and improve the algorithmic governance system of multi-governance. [Method/Process] Based on Stimulus-Organism-Response (SOR)model framework, social cognitive theory and perceived value theory, the paper conducted an empirical study using survey data from 2313 questionnaires. [Result/Conclusion] The results show that: In the total effect model, algorithmic literacy, government regulation and platform algorithmic responsibility positively and observably affect users' willingness to participate in governance, and government regulation is the most influential stimulus.After adding the mediating variables of perceived benefit and perceived risk, algorithmic literacy and platform algorithmic responsibility have indirect effects on users' willingness to participate in governance through the partially mediating role of perceived benefit and perceived risk.Government regulation indirectly influences users' willingness to participate in governance through the completely mediating effect between perceived benefit and perceived risk; Through calculation, the mediating effect of perceived benefit is more significant than that of perceived risk.The research conclusions provide a theoretical reference for improving China's algorithmic risk governance system, and has a positive practical guiding significance for motivating users to participate in the realization of multi-governance of algorithmic risks.

[Purpose/Significance] Transferring from search engine search to conversational search has become the trend of users' information search, exploring the influencing factors of users' search transfer behaviors, constructing a comprehensive behavioral transfer analysis framework, and improving the success rate of users' information search transfer behaviors, so as to provide theoretical foundations for optimization of information retrieval systems and conversational search platforms. [Method/Process] Based on the capability, opportunity, motivation-behaviour (COM-B)model, the paper constructed a model of factors influencing users' information search transfer behaviour, used structural equation modelling to analyse the data of 413 cross-sectional questionnaires, and discussed the barriers and facilitators influencing information search transfer behaviour. [Result/Conclusion] Mental ability versus physical ability, physical opportunity versus social opportunity, autonomous motivation versus reflective motivation are positive factors influencing information-seeking transfer behaviours, while insufficient policy support and availability of dialogical search systems are impediments to users' information-seeking transfer, while ability and opportunity can positively influence information-seeking transfer motivation.

[Purpose/Significance] The Current laws, regulations and industry standards propose graded protection for human resources and social security data, but all of them lack quantitative basis.This paper aims to solve the problem by quantitative measurement of human resources and social security privacy value. [Method/Process] The study surveyed human resources and social security related literature, established its privacy text base; built a framework for privacy vocabulary in human resources and social security, established a sensitive vocabulary in human resources and social security, and identified the sensitivity of human resources and social security data; design a privacy measurement model to measure the privacy values of human resources and social security data. [Result/Conclusion] According to the size of communication privacy value, the sorting results of personal human resources and social security data are: salary and insurance benefits data, professional technical data, administrative work data, personal basic data, and onboarding mobility data.This paper explores objectively existing human resources and social security privacy texts, and the measurement results are objective, providing a more scientific basis for the classification of human resources and social security data.

[Purpose/Significance] The performance of generative artificial intelligence models depends on the security of training data, and frequent security risks of training data have become an obstacle to the development of artificial intelligence technology.Ensuring the security of training data is of great importance for the healthy development of technology. [Method/Process] Through literature, experience and comparative analysis, this paper revealed the security risks of generative artificial intelligence training data, and put forward countermeasures based on the experience of EU governance and China's practice. [Result/Conclusion] The study found that the current training data has opaque data sources, non-standard labeling, unsafe content and hidden dangers of leakage.The EU has established a regulatory system with the core of ensuring data sources, labeling, content and leakage prevention and control.In the future, China should strengthen data source management, unify labeling standards, improve content security rules, and strengthen data protection technology to ensure the safety of training data and promote the healthy development of technology.

[Purpose/Significance] Applying configuration thinking to clarify the dissemination paths of public opinion in emergency events can help the government grasp the direction of public opinion and formulate more targeted response measures. [Method/Process] Firstly, the paper drew from information ecology theory and identified six elements that influenced the dissemination paths of public opinion in emergency events(event type, social environment, government, netizen, online media, and opinion leader).Meanwhile, the paper selected forty cases of emergency events as research samples and collected data in stages over time.Secondly, through multi-stage fsQCA analysis, the paper explored the principles that governed the effects of elements and their combinations on the dissemination of network public opinion in emergency events.Finally, the paper applied the PSM method to evaluate the impact of different configuration paths on the rise of network public opinion heat in emergency events. [Result/Conclusion] The dissemination paths of network public opinion in emergency events are influenced by multiple elements.Event type and social environment serve as the initiating factors of network public opinion, netizen and event type act as the outbreak factors, and the government functions as a pacifying factor.During the initial, outbreak, and declining periods, high/non-high heat dissemination paths in network public opinion form ten types, and significant differences exist in the impact of different configuration types on the heat of network public opinion in emergency events.

[Purpose/Significance] This study deconstructs the online public opinion related to enterprises from multiple dimensions, including emotion, scale and influence, and then establishes a quantitative index model for each dimension to explore how online public opinion affects enterprise value, with the aim of providing a path reference for enterprises, investors, regulators and other subjects to effectively perceive the development trend of their own online public opinion. [Method/Process] A multi-dimensional enterprise online public opinion measurement model was constructed using various technologies and methods, such as machine learning, natural language processing, and sentiment lexicon.Enterprise online public opinion data was captured from the Weibo platform, and financial data of listed companies in the entertainment industry were captured from the Wind financial database.Regression analysis was used to explore the specific impact of multi-dimensional enterprise online public opinion on enterprise value. [Result/Conclusion] The results indicate that the positive and negative sentiment polarity of online public opinion related to enterprises significantly affects the increase or decrease of enterprise value, while the intensity affects the magnitude of the increase or decrease; The value of enterprises is significantly influenced by the scale of online public opinion, with a positive correlation; The impact of online public opinion on enterprise value is not significant.The multidimensional enterprise network public opinion measurement model can effectively quantify the relevant dimensional indicators of network public opinion, and the research results provide more detailed references for enterprises and regulatory agencies, which helps to accurately resolve network public opinion risks.

[Purpose/Significance] This study sorts out and defines the concepts of citation diffusion depth and breadth, and explores the effects of citation diffusion depth and breadth characteristics in the earlier stages of a paper s publication on the citation diffusion intensity in the later stages, in order to enrich the explanation of the mechanism of correlating the characteristics of a paper s earlier citations with its later citations. [Method/Process] This study collected the bibliographic data of scientific papers published in 3 journals in 2013 from each of 50 subject categories, as well as the bibliographic data of their earlier citing literatures, to construct earlier ego citation networks for the target papers.The paper defined the citation diffusion depth as the average cascade depth of citations and the citation diffusion breadth based on Brillouin s Index from three dimensions: discipline breadth, regional breadth, and author breadth.And the paper used a negative binomial regression model and Double/De-biased Machine Learning method (DDML)to analyze the relationship between the depth and breadth of earlier citation diffusion and the later diffusion intensity of papers. [Result/Conclusion] Earlier citation diffusion depth shows a significant negative effect on the later diffusion intensity under both 2-year and 3-year citation windows, while earlier citation diffusion discipline breadth shows a positive effect on later diffusion intensity under both citation windows.Regional breadth shows a positive effect on later diffusion intensity of papers under the 2-year citation window, but doesn t show significance under the 3-year citation window.Author breadth shows a significant positive effect on papers in low-cited group, but has a negative impact on papers in high-cited group.

[Purpose/Significance] Clarifying the growth pattern of core scholars in the Chinese humanities is conducive to providing practical references for the formulation or adjustment of Chinese policies on cultivating and rewarding talents in this field, and promoting the construction of Chinese new liberal arts from the perspective of talent development. [Method/Process] In order to excavate the potentially common growth patterns of core scholars in the Chinese humanities, this study constructed a record of the temporal changes in their research productivity, academic influence, and comprehensive competence from a retrospective perspective, and demonstrated their generalized academic growth patterns in three aspects through Dynamic Time Warping and trajectory clustering algorithms. [Results/Conclusion] The results indicate that the growth patterns of research productivity among core scholars in Chinese humanities field are diverse.The academic influence and comprehensive capabilities of core scholars in philosophy and literature tend to develop in the later stages, but the discipline of history tends to concentrate in the early stages.The growth of academic capabilities in these three aspects typically shows a synchronicity effect.The phased characteristics, disciplinary differences, and combination rules of scholars' growth patterns provide guidance for the formulation of evaluation methods, evaluation cycles, and evaluation mechanisms for talents in this field.

[Purpose/Significance] Aiming at health management scenarios, taking real-world individual health and medical data as the research object, we construct a multi-dimensional individual health portrait conceptual model to improve the practicality of individual health portraits. [Method/Process] Based on the review of existing research on individual health portraits, aiming at health management application scenarios, by analyzing real-world individual and group health data, a label system for multi-dimensional health portraits that conformed to real-world data conditions was proposed, and a conceptual model of health portraits including four dimensions: demographic characteristics, health literacy, behavioral habits, and health status was constructed.A health portrait was established for 500000 patients with fatty liver disease in the UK biobank, and a predictive study was conducted based on the health portrait. [Result/Conclusion] A four-dimensional health portrait is constructed based on real-world data.The multiple labels included in the portrait cover the patient's basic indicators, objective indicators and controllable behaviors, reflecting the individual's health status.Through accurate health status identification and long-term outcome assessment, the ROC curves of three models at different time points from single dimension to multi-dimensional were demonstrated.The results showed that with the gradual inclusion of information from the four dimensions, the AUC value gradually increased from 0.58 to 0.64, verifying the effectiveness of multi-dimensional health portraits under the"4P"model in predicting long-term health outcomes, and proving that individual health portraits can help doctors and individuals better understand their health status and promote the implementation of precision health management.

[Purpose/Significance] Doctors' proactive knowledge contribution through the publication of health science articles plays an important role in enhancing the public health knowledge level within online health communities.Studying the impact of this behavior on patients' doctor selection can help alleviate information asymmetry between doctors and patients, while also enhancing doctors' economic benefits and social image. [Method/Process] The study, based on the Information Systems Success Model (ISSM), constructed a model to analyze the impact of proactive knowledge contribution on patients' doctor selection from three dimensions: usage, information quality and service quality.By collecting panel data from doctors on the Haodf platform, the Propensity Score Matching-Difference-in-Differences (PSM-DID)method was used to quantify the effects of doctors' article publication behavior, quantity, and types on the increase in consulting patients, and to analyze the moderating roles of doctors' titles and online reputation. [Result/Conclusion] The results show that doctors' proactive knowledge contribution significantly promotes patient selection behavior.Reposted, curated, paid, and video articles have notably higher conversion rates, highlighting the importance of information quality and media richness.Additionally, doctors with higher titles show diminishing marginal returns, while high online reputation has a positive moderating effect.Furthermore, the effect of proactive knowledge contribution in the chronic disease field is significantly stronger than in other medical fields.This study not only expands theoretical research on doctors' proactive knowledge contribution behavior but also provides practical insights for optimizing operational strategies in online health communities.