Professor Mao Taitian and colleagues argues elucidates the adaptation logic, practical pathways, and prerequisites of intelligent agents to empower the high-quality development of scientific information. Professor Bao Yulai and colleagues advocate that integrating the perceptual elasticity of domain-specific large models with the cognitive rigidity of ontology can establish a new paradigm for intelligence services in complex scenarios. The synergy between the two can not only expand the theoretical boundaries of information science and serve national strategies, but also advance intelligence services from assisted analysis to intelligent decision-making. Professor Wei Jianxiang and colleagues point out that generative artificial intelligence has triggered problems such as synthetic data anomie, difficulties in auditing information pollution, and elevated risks of intelligence leakage. By constructing a human AI collaboration model, relying on intelligence thinking for deviation correction, implementing full-process verification, and enhancing personnel's intelligence literacy, such measures can ensure the secure and controllable utilization of intelligence resources. Professor Wu Peng and colleagues propose that scientific information empowers the integration of technological innovation and industrial innovation, and that artificial intelligence reshapes intelligence research capabilities and thus can assist in tackling key technological bottlenecks and fostering industries. It is necessary to consolidate the digital-intelligent foundation, promote intelligent upgrading, and make forward-looking strategic arrangements. Professor Yu Chuanming reveals that artificial intelligence mirrors the essence of information science, driving knowledge organization through a three-stage evolution of alignment, integration, and augmentation, ultimately transforming knowledge organization into a fully connected disciplinary network characterized by value emergence.

Purpose/significance Grounded in the strategic height of national security, this paper explores the connotation reconstruction and strategic transformation of national defense science and technology intelligence in the new era. It aims to respond to the evolution of international competition and the security landscape, and to promote the adaptation of the defense technology intelligence system to the requirements of the modernization of national security governance. Method/process By critically examining the limitations of its traditional connotations, this study systematically reconstructs the new connotations of defense technology intelligence across five dimensions: strategic orientation, content expansion, methods and technologies, system integration, and functional extension. Based on this reconstruction, it delves into the practical logic and implementation mechanisms of its strategic transformation along five primary pathways: strategic guidance and forward-looking warning, comprehensive domain awareness and ecosystem monitoring, intelligent empowerment and digital enablement, system integration and collaborative sharing, and cognitive shaping and discourse power construction. Result/conclusion The research indicates that defense science and technology intelligence must undergo a fundamental transformation from supporting operations to shaping security. By deepening civil-military integration, strengthening intelligent empowerment, and promoting system coordination, an independent, controllable, forward-looking, and leading national technology intelligence system can be constructed, which could more effectively serve the need of national security and development.

Purpose/significance Technology introduction re-innovation is the key path for enterprises to quickly acquire advanced technologies and transform them into independent innovation advantages,which is of great significance in promoting the upgrading of the national technology innovation system and enhancing industrial competitiveness. Method/process This article proposes a specific approach for analyzing the technology introduction and re-innovation model of enterprises based on cros-border patent transfer data, and takes strategic emerging industries in Shandong Province as an example for empirical research, then proposes the selection strategies for enterprises technology introduction re-innovation models. Result/conclusion Research shows that strategic emerging industry enterprises in Shandong Province mainly adopt a one-ime and blank introduction model in technology introduction, with flexible strategies but a low starting point for technology accumulation; in the process of technological innovation, the low yield innovation mode is the main mode, and there are significant differences in the digestion, absorption, and innovation capabilities among enterprises. The proportion of rapid innovation mode is relatively high, and the innovation speed gap between enterprises is obvious. Overall, the technology introduction strategy in Shandong Province is relatively flexible, but the ability of enterprises to digest, absorb and innovate is insufficient, and the overall efficiency and speed of re-innovation output need to be improved. The characteristics of this model are influenced by factors such as industrial development stage, enterprise basic condition, and regional policy environment. Therefore, when choosing a technology introduction re-innovation model, enterprises should comprehensively consider market competition, technological foundation, resource endownments, and strategic goals to achieve the best adaptation between the model and internal and external environment.

Purpose/significance Future industries are important carriers of new quality productive forces which can play a leading role in the economic and social development. Quantitative analysis and evaluation of China's future industry policies can provide support and reference for the formulation, optimization and adjustment of subsequent policies. Method/process Taking a total of 84 policy texts at the central, provincial, municipal and county levels in China as the research objects, a three-dimensional analysis framework of "theme-tool-effectiveness" was constructed. By comprehensively applying LDA thematic analysis, content analysis and PMC index model, theme distribution, content characteristics and comprehensive effectiveness of policy texts were deeply analyzed. Result/conclusion The study found that policy themes are diverse and rich, highlighting both innovation and coordination. Policy tools are reasonably allocated, though their combined effectiveness requires further enhancement. Overall policy efficacy is acceptable, yet regional development disparities remain pronounced. Recommendations included tailoring policy themes to local conditions, refining implementation details, balancing policy tool allocation, strengthening synergistic effects, improving monitoring, evaluation, and feedback mechanisms, and promoting the diffusion of high-quality policies.

Purpose/significance To overcome the limitations of traditional, label-dependent evaluation indicators in identifying journal acceptance preferences, this article aims to construct a novel label-free quantitative detection paradigm to more comprehensively and objectively reveal potential publishing stickiness between authors and journals. Method/process By introducing disproportionality measurement methods from the field of pharmacovigilance, we proposed the Author-Journal Balance Ratio index, applied it to 86 law journals to measure the potential publishing stickiness between authors and journals, and analyzed the 5 060 identified potential publishing stickiness signals. Result/conclusion The results show that the Author-Journal Balance Ratio index is effective at identifying relationships driven by traditional factors like institutional affiliation and editorial board membership. More importantly, it demonstrates a distinct advantage in revealing more subtle, deeper potential publishing stickiness that goes beyond such a priori labels. The Author-Journal Balance Ratio index provides a novel tool and perspective for understanding and improving the academic publishing environment. Future research will explore its applicability in other disciplines and comprehensively evaluate the academic impact of author-journal stickiness.

Purpose/significance By constructing an SBERT deep learning model to quantitatively evaluate research results in the Yellow River Basin, this study aims to more scientifically and comprehensively reveal research hotspots and evolutionary trends in the Yellow River Basin, providing reference for regional resource development and policy optimization. Method/process Taking 5 131 literature from CNKI, VIP, and Wanfang Chinese databases in the past 15 years as the research object, based on semantic vectorization, dimensionality reduction, and topic clustering, incorporating temporal sorting and evolutionary context, normalizing calculation to determine hot and cold topics, and exploring future development trends from multiple perspectives. Result/conclusion 24 core themes were identified and classified into 5 directions: education and teaching, ecological governance, cultural tourism integration, intangible cultural heritage protection, and spiritual and cultural identity.Dynamic evolution indicates that the research focus is expanding in depth towards refinement and intersection under the drive of policies. In the future, attention can be paid to the evolution of the Yellow River spirit in different regions, differentiated paths for the protection of intangible cultural heritage and the integration of cultural tourism, practices of ecological governance and technology application, and the quantitative correlation between policies and academic achievements.

Purpose/significance The wave of deglobalization has impacted the global development trajectory of Open Science, with U.S. Open Science policies undergoing a drastic contraction. It is necessary to understand this reconstructive trend of Open Science and identify the development opportunities therein. Method/process By introducing a "deglobalization" perspective and drawing on the policy tracking audit method, this study examines samples of U.S. Open Science policies and conducts an in-depth analysis of the underlying motivations and impacts. Result/conclusion During the era of deglobalization, the United States has significantly reduced the implementation and on-the-ground effectiveness of its Open Science policies through executive orders, funding freezes, leaving key positions vacant, and deregulation, thereby exerting a far-reaching impact on the global Open Science landscape. China can seize the opportunity presented by the comprehensive reshaping of the global Open Science to explore new models of sovereignty-controllable data openness and build a polycentric global Open Science ecosystem.

Purpose/significance In line with the national requirements for building a carbon emission early warning mechanism, conducting the urban carbon emission early warning research is of great significance for achieving the “dual-carbon” goals. Method/process This paper selected 16 prefecture-level cities in Anhui Province as research samples. A carbon emission early warning indicator system was constructed based on the DPSIR framework. By using data from urban statistical yearbooks, the LSTM model was employed with parameter optimization via genetic algorithms to forecast various early warning indicators for 2024-2025.On this basis, a combined subjective-objective weighting method was then applied to calculate the urban carbon emission warning index and classify the warning levels of the cities. Result/conclusion During the study period, no cities in Anhui Province fell into the categories of extremely high risk or zero risk. Overall carbonemission risk showed a declining trend, with the number of higher risk cities decreasing and the number of lower risk cities increasing. Significant differences were observed in earlywarning levels and index structures across cities, with central regions generally exhibiting higher risk levels than the northern and southern areas. By analyzing the dynamic evolution and structural characteristics of the five dimensions of the earlywarning index, and considering differences in urban endowments, differentiated low-carbon development pathways were proposed, providing decision support for targeted regional carbon reduction strategies.

Purpose/significance The shaping of online public opinion security situational awareness is a key approach to governance. In digital and intelligent era, integrating digital intelligence empowerment with intelligence-driven theory holds significant theoretical and practical value for enhancing the capability and effectiveness of online public opinion security situational awareness. Method/process This study, based on a thorough explanation of the core concepts of digital intelligence empowerment and intelligence-driven theory, systematically analyzes the internal logic of shaping network public opinion security situation awareness through digital intelligence empowerment and intelligence-driven approaches, then constructs and interprets a model for shaping network public opinion security situation awareness under these frameworks. Result/conclusion The model constructed in this study integrates three sub-models: network public opinion security situation awareness, network public opinion security situation intelligence generation, and network public opinion security situation shaping. These three components work in concert to achieve comprehensive perception, in-depth understanding, precise prediction, and proactive shaping of network public opinion security situations, ultimately aiming to effectively maintain and systematically shape the security landscape of online public opinion.

Purpose/significance From the perspective of system vulnerability, this study aims to construct a dynamic monitoring and early-warning indicator system that systematically reflects science and technology security risks, so as to enhance the foresight and accuracy of risk perception and early warning. Method/process Introducing the system vulnerability theory and based on the "exposure-sensitivity-adaptive capacity" analytical framework, science and technology security risks are categorized into three dimensions: technological entity, actor behavior, and institutional constraints. Risk factors within each dimension were identified, leading to the selection of 9 secondary indicators. Innovatively, these indicators are classified into two types: continuous and threshold-triggered. A "one-vote veto" mechanism is introduced, which constructs an indicator system capable of hierarchically monitoring both gradual "quantitative change" risks and sudden "qualitative change" risks. Result/conclusion The research shows that this framework not only provides a systematic theoretical perspective for comprehensively understanding science and technology security risks, but also clarifies the data sources and specific measurement content of each indicator at the application level, ultimately forming an operable monitoring tool.

Purpose/significance "Open Competition Mechanism for Selecting Best Candidates"is a new research management model proposed by China to break through key core technologies, which is of great significance for addressing "bottleneck" technical problems. Methods/process Based on information asymmetry theory, complexity science theory, and adaptive governance theory, this paper adopts theoretical interpretation, case study, and comparative analysis. It systematically diagnoses information asymmetry issues in the project management of this mechanism; constructs a three-dimensional adaptive governance framework of"Perception—Diagnosis,Response—Design, Accumulation—Learning";designs adaptive governance tools by drawing on the experience of the U.S. Defense Advanced Research Projects Agency(DARPA)model; and conducts empirical verification with data from Shaanxi Province's"Open Competition Mechanism for Selecting Best Candidates"projects. Result/conclusion The study finds that information asymmetry exists throughout the three main stages of project implementation. Accordingly, it proposes embedding uncertainty profiling at the front end, integrating flexible governance tools during project execution, and establishing a knowledge feedback and optimization mechanism across the whole process.The findings provide practical guidance for overcoming the limitations of the traditional static research project management model.

Purpose/significance Addressing the challenges posed by the complex semantic characteristics of intangible cultural heritage brocade imagery, the difficulty in extracting their profound connotations, and the inadequate utilisation of multi-scale features by traditional deep learning models, this paper aims to explore a method for analysing the characteristics of intangible cultural heritage brocade images that integrates multi-scale visual features. Method/process This paper constructs a multi-scale feature analysis framework for intangible cultural heritage brocade images (ICH_BC), integrating convolutional neural networks with Transformer architectures. The framework employs ResNet to extract local texture and detail features from brocade images, utilises VIT to capture global structural and stylistic information, and dynamically weight-combines features across scales through self-attention mechanisms to achieve collaborative modelling of multi-scale visual features. Building upon this foundation, the framework employs UMAP dimensionality reduction for visualisation alongside Class Activation Maps (CAM) to analyse the high-dimensional feature distributions and regions of interest learned by the model. This facilitates understanding of the model's discriminative principles and explores pathways for translating digital features into cultural characteristics. Result/conclusion The experiment validated the framework using eight categories of nationally designated intangible cultural heritage brocade images. Results demonstrated that the ICH_BC framework outperformed single-scale feature models and static feature fusion methods in classification performance, confirming the efficacy of multi-scale visual feature fusion. Feature visualisation revealed distinct spatial distributions across different brocade categories, indicating varying model emphasis on local patterns versus overall compositional features. This approach provides an effective and generalizable technical pathway and analytical paradigm for understanding and analysing cultural heritage images with complex visual characteristics. It holds theoretical and practical value for advancing the digital research, conservation, and dissemination of cultural heritage.