[Purpose/Significance] In the context of the booming development of the digital economy, public data, as a fundamental strategic resource of the country, plays an important role in promoting high-quality economic development, enhancing government governance capabilities, and stimulating social innovation vitality through its development and utilization. The "Opinions on Accelerating the Development and Utilization of Public Data Resources" (hereinafter referred to as the Opinions), is the first top-level design document for the development and utilization of public data resources at the central level. It directly affects the success or failure of the market-oriented allocation reform of data elements in terms of policy effectiveness. Therefore, a comprehensive and systematic evaluation of the Opinions not only helps identify the strengths and weaknesses of policy design, but also provides scientific basis for the continuous optimization of policies, thereby ensuring the efficient development and utilization of public data resources and providing strong support for high-quality economic and social development. [Method/Process] This study introduces an innovative evaluation framework called S-CAD (Consistency Dependency Sufficiency) evaluation framework, which analyzes policy texts in depth through three dimensions: consistency, sufficiency, and dependency. 1) Consistency analysis focuses on the logical coherence between policy positions, goals, means, and expected outcomes. 2) Necessary and sufficient analysis evaluates the necessity and adequacy of policy measures for achieving goals. 3) Dependency analysis identifies key chains and stakeholders' interests and demands in policy implementation to evaluate the feasibility and acceptability of the policy. In terms of specific applications, this study first clarifies the dominant viewpoint of the policy (policy makers) and related viewpoints of policy implementers, participants, influencers. Subsequently, four typical elements of stance, objectives, means, and expected outcomes were identified from the policy text, and an analysis chart of the content of the Opinion was constructed. Inviting scholars from the field of information resource management to participate ensured the evaluation's scientificity and accuracy. Consistency analysis shows that the policy stance, objectives, means, and expected outcomes of the Opinion are logically closely related. The objectives revolve around accelerating the development and utilization of public data resources, and the means and objectives support each other. The expected outcomes are highly consistent with the means, reflecting the systematic and rational design of the policy. The analysis of necessity and sufficiency shows that policy measures play an important role in achieving goals, such as deepening the reform of data element allocation and regulating the authorized operation of public data, all of which provide strong guarantees for achieving policy goals. A dependency analysis reveals potential challenges in policy implementation. These challenges include difficulties in coordinating departmental interests, unclear details of data authorization operations, insufficient data quality and availability, and public concerns about privacy protection. In response to these issues, this study proposes suggestions such as strengthening departmental collaboration, clarifying data authorization operation processes, improving data quality and availability, strengthening data security management and privacy protection publicity. [Results/Conclusions] The issuance of the Opinions provides an important policy framework and guidance for the development and utilization of public data resources, but there is still room for improvement in areas such as departmental collaboration and privacy protection. To enhance public trust and support for policies, future, policy measures should be further refined, data authorization operation mechanisms should be optimized, data quality and utilization efficiency should be improved, and data security management and privacy protection should be strengthened. By continuously monitoring the development trends of the data industry and adjusting policies in a timely manner, we ensure the efficient and orderly promotion of the development and utilization of public data resources. This approach injects strong impetus into the high-quality development of the economy and society.

[Purpose/Significance] Open government data (OGD) has increasingly adopted storytelling elements to improve public engagement and enhance user comprehension. Although this narrative approach enhances data accessibility and cognitive resonance, it raises significant privacy concerns. Specifically, storytelling may activate users' cognitive schemas, enabling them to infer sensitive personal information even from anonymized datasets. This dual effect between data usefulness and privacy risk is becoming an increasing challenge for data providers and policymakers. In this study, we aim to explore how storytelling in OGD affects users' cognitive reasoning processes and leads to privacy risks. Our work innovatively combines cognitive psychology, information science, and privacy risk assessment. This interdisciplinary approach offers a new perspective on how data narratives shape inference behavior. Distinct from existing research, this paper focuses on how cognitive mechanisms driven by storytelling influence users' perception and extraction of private information. This research holds practical significance for designing privacy-aware data disclosure strategies that strike a balance between openness and protection. [Method/Process] In order to analyze the cognitive mechanisms underlying privacy risk, we adopted a mixed-methods research design grounded in relevance theory, schema theory, and the S-O-R model. We first constructed a user cognitive connection model that conceptualized how narrative stimuli activated cognitive processing and led to privacy-related inferences. Based on this model, we developed a privacy risk assessment index comprising three primary dimensions: data association and reasoning, data processing and decoding, and implicit suggestion and implication. We then conducted a controlled experiment involving 236 participants, who were randomly divided into a storytelling group and a non-storytelling group. To analyze the collected data, we used the CRITIC method to assign objective weights to evaluation indicators and applied a fuzzy comprehensive evaluation method to quantify and compare privacy risks across groups. [Results/Conclusions] Our results demonstrated that storytelling significantly heightened users' ability to infer sensitive personal information. The average inference score in the storytelling group was significantly higher than that in the non-storytelling group (p<0.05), and the comprehensive privacy risk level was rated as "medium risk" compared to the non-storytelling group's "low risk." Across all three risk dimensions, the storytelling group consistently exhibited greater cognitive engagement and higher potential for privacy exposure. These findings suggested that while storytelling enhanced user understanding, it also increased the risk of privacy violations. As such, we recommended that government data platforms adopt non-storytelling or partially abstracted data presentation strategies to reduce risk while preserving clarity. From a policy perspective, we advocated for the integration of intelligent narrative-generation algorithms and privacy-by-design principles to protect users' information. Although limited by sample size and data diversity, this study offered a foundation for future research into the cognitive underpinnings of privacy risk. Further work may explore other forms of storytelling, demographic influences on inference behavior.

[Purpose/Significance] Online medical communities (OMCs) have become an indispensable infrastructure that allows patients to access professional knowledge and enables physicians to expand their service, However, persistent complaints about information quality, physician disengagement and patient attrition reveal that the involved parties often fall into a non-cooperative trap. Prior literature has dominantly examined either the static motivational profiles of individual physicians or patients, or the dyadic interactions between one knowledge contributor and one knowledge seeker. Our understanding of how value co-creation unfolds in a service-ecosystem setting, where platforms, physicians, and patients simultaneously adjust their strategies, is hampered by the absence of a holistic, dynamic, and multi-actor perspective. Therefore, this paper shifts the analytical lens from isolated behavior or bilateral exchange to an evolutionary game among three interdependent stakeholders - the platform, the physician and the patient - within the knowledge service ecosystem. By embedding regulatory cost-benefit logic, trust mechanism and perceived-value-loss arguments into an evolutionary framework, the study unpacks the conditions under which collective knowledge sharing can be sustained and identifies the critical levers that can nudge the system towards a virtuous equilibrium. The findings will advance service-dominant logic and knowledge-sharing theory by revealing how micro-level strategic adaptations aggregate to create macro-level ecosystem viability. The findings will provide actionable insights for platform governance aimed at mitigating the real-world crises such as physician burnout and patient dissatisfaction. [Method/Process] Drawing on evolutionary game theory, we constructed a tripartite model in which the platform chooses between active regulation and passive regulation, the physician between active contribution (including both knowledge transfer and affective/extra-role support) and passive contribution, and the patient between active participation (information search, feedback and self-management behavior) and passive participation. Utility functions were specified to capture the net payoffs of each actor under eight possible strategy combinations, incorporating extra benefits, additional costs, perceived value losses, platform incentives and trust-based moderators. Using replicator dynamics, we derived the evolutionary stable strategies (ESS) for each actor and the joint ESS for the system. MATLAB simulations were then employed to trace the trajectory of strategy adjustment under varying parameter values, with sensitivity analyses performed for regulatory cost, physician reward, community trust and patient effort cost. Parameter ranges were anchored in prior empirical evidence and refined to ensure convergence to feasible equilibria. [Results/Conclusions] The analytical and simulation results converge on three main insights. First, the system possesses a unique pareto-dominant equilibrium-the triad (active regulation, active contribution, and active participation)-that emerges when the product of each actor's trust-adjusted net benefit exceeds the corresponding threshold. Second, the transition path is highly sensitive to the relative magnitude of marginal benefits and costs: lowering the platform's regulatory expenditures or increasing its incremental revenue will accelerate convergence to active regulation; enhancing physicians' reputational and intrinsic rewards or reducing their affective labor cost will markedly elevate cooperative contribution; and compressing patients' cognitive and privacy cost while enlarging their health outcome gain will propel active participation. Third, community trust operates as a critical moderator: when trust is high, physicians are willing to contribute even if the perceived value loss from non-contribution is modest, whereas low trust neutralizes the effect of potential gains and locks the system into a low-effort trap. From a managerial perspective, the study recommends that platforms deploy AI-assisted tools to relieve physicians of repetitive tasks, calibrate incentive budgets to prevent overspending, and establish fairness-enhancing governance practices to foster trust. However, limitations include the omission of knowledge flows between physicians and between patients as well as the reliance on stylized parameters. Future research could extend the model to a multi-layer network incorporating professional sub-communities and patient peer groups, and calibrate the payoff structure with field data could enhance the model's external validity.

[Purpose/Significance] With the rapid advancement of global science and technology, emerging technologies are constantly evolving, placing higher demands on national strategic planning and resource allocation. Artificial intelligence (AI), as a core driver of the current technological revolution, requires close attention to its internal technical topic evolution to anticipate disruptive changes and guide the direction innovation. Although existing research primarily focuses on identifying technical topics through bibliometric or patent analysis, there is still insufficient quantitative forecasting of their future development. To address this gap, this study proposes an integrated analytical framework that combines BERTopic-based topic modeling with an IWOA-optimized BiLSTM neural network, achieving a unified approach to both topic identification and trend forecasting. Unlike traditional LDA models or expert-based subjective judgment, this method demonstrates significant advancements in semantic representation, model optimization, and prediction accuracy. It expands the theoretical boundaries of emerging technology forecasting and offers valuable quantitative support for science and technology policy-making. [Method/Process] This study utilized 22,243 AI-related patent records collected from 2015 to 2024. BERTopic was applied to extract representative technology topics from patent abstracts. A multi-dimensional evaluation system was constructed using three indicators: novelty, impact, and growth rate, capturing different aspects of emerging technologies. The CRITIC method was employed to objectively assign weights to each dimension, enhancing the robustness and balance of the composite index. BERTopic, which integrates BERT-based semantic embeddings with HDBSCAN density-based clustering, improves both the coherence and granularity of topic extraction. For trend prediction, an Improved Whale Optimization Algorithm (IWOA) was introduced to fine-tune BiLSTM's learning rate, epoch count, and hidden layer size. IWOA enhances global optimization through Gaussian chaos initialization, Levy flight strategy, nonlinear control factors, and elite reverse learning. [Results/Conclusions] Experimental results show that BERTopic achieves superior topic coherence compared to baseline models and successfully identifies five emerging technical areas, including Intelligent Models and Algorithms, Information Processing, Deep Neural Networks, Smart Robotics, and Numerical Control Systems. The IWOA-BiLSTM model outperforms conventional LSTM and BiLSTM models in error metrics (MAPE, RMSE, and MAE), confirming its predictive advantage. Forecast results indicate that these emerging topics will experience sustained growth over the next five years, reflecting strong application potential and industrial value. This study confirms the feasibility and effectiveness of the integrated "identification–prediction" framework, providing a data-driven tool for strategic decision-making in science and technology development. Limitations include dependence on data quality and a current focus on the field of AI. Future research should expand the framework to other strategic areas, such as renewable energy, biomedicine, and intelligent manufacturing, to further validate its generalizability.

[Purpose/Significance] This study focuses on the participation behavior of users prone to depression who participate in user-generated content (UGC) platforms, aiming to explore their behavioral heterogeneity and the underlying influencing mechanisms. The research aims to expand the theoretical scope of studies on user behavior while providing UGC platforms with practical guidance on building differentiated user care models and refining operational strategies. By utilizing authentic user-generated content as the data foundation, this study addresses the representational limitations commonly associated with traditional small-sample approaches, such as surveys and interviews. It introduces a data-driven perspective and methodological innovation to the field of information behavior research. Furthermore, this study enhances the understanding of varying psychological and behavioral needs among different types of depression-prone users. The findings can assist platforms in optimizing user experience, improving emotional support systems within online communities, and informing the development of more targeted and responsive intervention strategies. [Method/Process] First, web scraping techniques were used to collect a large volume of depression-related posts from the Xiaohongshu platform as the primary data source. Second, representative keywords were extracted through Word2Vec and K-means clustering algorithms. A keyword co-occurrence network was then constructed using the Leiden clustering algorithm to identify semantic relationships. By integrating user attribute information, the study achieved a fine-grained classification of heterogeneous depression-prone user groups. Third, drawing on self-determination theory (SDT) and the technology acceptance model (TAM), and leveraging BERTopic for advanced topic modeling, the study constructed a comprehensive factor model to examine the mechanisms influencing user participation behavior in depth. [Results/Conclusions] The research identifies three distinct types of depression-prone users: adolescent depression expression, help-seeking expression, and emotional breakdown expression. Results indicate that posting and commenting behaviors across these groups are primarily driven by emotional needs and environmental factors. Emotional needs are the dominant motivator for active participation, while environmental influences significantly contribute to triggering interaction, especially within comment sections. Additionally, adolescent depression expression and emotional breakdown expression show stronger tendencies toward self-related needs, reflecting deeper emotional and identity concerns. In contrast, help-seeking expression exhibit more evident competence-related needs, focusing on practical advice and problem-solving. Although competence and technical factors account for a smaller proportion, they still play a meaningful supporting role in shaping the structure and substance of user participation behavior on UGC platforms.

[Purpose/Significance] As the knowledge economy grows and global technological competition intensifies, universities have become essential drivers of innovation within national innovation systems. Not only do high‑level research universities generate original scientific discoveries, they also serve as catalysts for technological innovation and drivers of industrial upgrading. Their roles span from conceiving breakthrough ideas to shepherding technologies through product development and into marketable applications. Nevertheless, the multifaceted nature of these contributions remains insufficiently characterized, making it difficult for policymakers and university leaders to fine‑tune strategies that maximize impact. A comprehensive understanding of how universities contribute at each stage of the innovation continuum is therefore vital for optimizing their functions, informing targeted policy interventions, and reinforcing the synergetic linkages between academia, industry, and government in both national and global contexts. [Method/Process] To clarify universities' distinct contributions at each stage of innovation, this study presents an innovation value chain model and corresponding analytical framework that systematically maps their core functions - serving as knowledge innovators during basic research, technology developers in applied research, transfer agents in product development, and academic entrepreneurs in commercialization. Based on this model, we constructed an analytical framework comprising qualitative and quantitative indicators tailored to capture university activities at each stage. During the basic research phase, metrics such as publication volume, citation impact, and basic science funding shed light on the roles of universities as innovators of knowledge. During applied research, patent filings, joint industry‑university project counts, and collaborative R&D expenditure serve as proxies for technology development capacity. The product development phase assessment centers on technology licensing volume, spin‑off formation rate, and prototype demonstration projects to gauge technology transfer effectiveness. Finally, commercialization was examined via start‑up success rates, venture funding attracted, and market penetration of university‑originated products. Empirical analysis was conducted on representative samples drawn from China's C9 League universities and the U.S. Ivy League universities, leveraging bibliometric databases, patent offices, and institutional reports to ensure data robustness. [Results/Conclusions] The findings demonstrate that universities in China and the U.S. play distinct yet complementary roles at different innovation stages. Chinese universities exhibit rapidly growing research outputs and increasing basic research capability, signaling a powerful catching‑up momentum in building technological reserves. Their strengths lie primarily in knowledge generation and early‑stage technology development, supported by substantial increases in R&D investment and talent cultivation. In contrast, U.S. universities maintain leadership in original innovation quality and commercialization efficiency, underpinned by high‑impact publications, a mature ecosystem of technology transfer offices, and established venture funding networks. They excel at translating research breakthroughs into market‑ready products and ventures, achieving higher license income per patent and greater market penetration. This comparative analysis underscores the necessity of diverse, stage‑specific university roles and highlights opportunities for cross‑border learning. In the future, Chinese higher education institutions (HEIs) can enhance their commercialization performance by adopting proven U.S. strategies, such as streamlined intellectual property policies, incentive programs for faculty entrepreneurship, and extensive industry partnerships, while adapting these practices to local contexts. By doing so, they can improve the quality and market depth of their knowledge and technology outputs, and optimize the university technology transfer system, thereby providing robust support for achieving sustainable, high‑quality economic development.