[Purpose/significance]This paper aims to improve the translation quality of large language models and effectively alleviate the translation illusion problem, thereby enhancing cross-linguistic information retrieval capabilities. [Method/process]A translation generation method based on a knowledge enhancement framework is proposed. This framework optimizes the translation process from multiple dimensions, such as style, focus, and cultural adaptability, by combining external knowledge provided by the translation context building module and the knowledge base building and retrieval module, and then utilizing the guidance of the text attention module. [Result/conclusion]Experimental results show that the proposed method effectively enhances model performance. Specifically, on the WikiLingua, TED, and CCMatrix datasets, the model's BLEU scores improved by 5.29%, 5.94%, and 8.58%, respectively. In addition to traditional translation evaluation metrics, this paper also introduces a six-dimensional evaluation system based on large models and an evaluation experiment to address the translation illusion problem. Experimental results show that after applying the proposed framework to the large model, all metrics surpass those of existing mainstream translation tools. This research provides a new solution for text translation, which has significant practical implications for quickly and accurately understanding information from large amounts of foreign language data, and is helpful for intelligence gathering, analysis, and decision-making.

[Purpose/significance]Under the context of New Liberal Arts construction, the resolution of many major scientific and technological issues requires transcending traditional disciplinary boundaries. By identifying and integrating interdisciplinary knowledge, it becomes possible to grasp the direction of disciplinary development and predict emerging innovative trends. [Method/process]This study takes the full-text information of articles from six academic journals in China's library and information science (LIS) from 2017 to 2021 as its research object. Drawing on the kinetic energy theory from physics, it constructs a kinetic energy model for interdisciplinary knowledge integration from the dual dimensions of quoting scholars driving force and disciplinary attraction, and conducts empirical research.[Result/conclusion]The results indicate that the disciplinary gravitation coefficient can effectively quantify the gravitational effect of a disciplinary knowledge system on interdisciplinary knowledge, helping to characterize the expansion effect of the peripheral boundaries of the disciplinary knowledge system from the perspective of kinetic energy. The study also reveals that the development of library and information science exhibits significant technology-driven and social management-oriented characteristics. In terms of knowledge categories, the expansion of the library and information science knowledge system is primarily driven by thematic concepts and technological methods, while theoretical models are consistently sourced from sociology, management, and computer science. Indicator tools, however, face certain limitations due to their disciplinary attributes and adaptability factors.

[Purpose/significance]National defense science and technology intelligence serves as a powerful guarantee for promoting national defense science and techndogy innovation and development. Exploring optimization paths for defense science and technology intelligence service systems holds practical significance in developing new quality combat capabilities and safeguarding national security and stability.[Method/process] Through literature review and summarization, this study clarifies the impact of complex information environments on intelligent defense science and technology intelligence services, as well as the core tasks of intelligent intelligence services. Based on activity theory, the study deconstructs system elements and employs systems engineering principles to construct an intelligent defense science and technology intelligence service system within complex information environments. [Result/conclusion]The study identifies the system's constituent elements from three aspects: service subjects, service objects and service tools. Building on this foundation, it establishes an intelligent defense science and technology intelligence service system composed of demand comprehension layer, data mining layer, comprehensive analysis layer and service feedback layer. The construction of this system facilitates the transformation of defense science and technology intelligence services toward systematization and intelligence, providing theoretical guidance for the advancement of defense science and technology intelligence.

[Purpose/significance]To address the challenges in matching supply-demand information within the emergency material supply domain, this study constructs a knowledge service framework from three dimensions cognition, architecture, and operation to enhance the agility of emergency response and the resilience of supply chains. [Method/[process]A cognition-centered knowledge service framework is proposed for the emergency material supply field, encompassing five dimensions: scenario, approach, structure, perspective, and model. Subsequently, a system architecture is developed focusing on three aspects: emergency data integration, knowledge-driven supply, and emergency scenario perception. The operationalization of the proposed knowledge service is explored in terms of its logic, mechanisms, and patterns. [Result/[conclusion]The constructed knowledge service framework effectively supports the association and transmission of multi-stage cascading knowledge by refining data perception granularity, structuring complex knowledge networks, and achieving semantic matching of supply and demand. It provides methodological and platform-based support for addressing supply-demand imbalances, enhancing supply chain resilience, and balancing response agility with supply flexibility.

[Purpose/significance] Public data serves as a crucial resource for fostering and enhancing urban innovation resilience. Exploring the impact of government public data opening on urban innovation resilience is of great significance for advancing the high-quality economic development..[Method/process] This study treats the launch of government public data open platforms as a quasi-natural experiment, utilizing panel data from 246 cities between 2008 and 2023 to construct a multi-period difference-in-differences model. It investigates the impact mechanism of government public data opening on urban innovation resilience. [Result/conclusion]Research shows that government public data opening contributes to enhancing urban innovation resilience, primarily through two mechanisms: promoting technological innovation and stimulating entrepreneurial vitality; this positive effect is more pronounced in cities with weaker digital economy foundations, lower marketization levels, greater fiscal pressures, and those in western regions of china. The development level of the digital economy and fiscal pressure exhibit negative moderating effects, while marketization level demonstrates positive moderating effects. Therefore, recommendations include improving the system of public data opening, optimizing the urban innovation and entrepreneurship ecosystem, and implementing differentiated data opening strategies.

[Purpose/significance]Stimulating corporate innovation intention is a pivotal issue for promoting innovation. However, the academic discourse on its key drivers presents significantly divergent and even contradictory conclusions, leading to theoretical ambiguity and practical guidance challenges. This study aims to systematically and quantitatively integrate empirical research in this field to clarify the true effects of core driving factors and their operational boundaries. [Method/process]Adopting a meta-analysis approach, this study systematically retrieves and screens literature, ultimately analyzes 29 empirical studies and examine four potential moderating variables which include location, data type, industry, and culture. [Result/conclusion]The study finds that government support, internal relations, and laws and regulations constraints are the strongest drivers for stimulating innovation intention. Factors with contradictory conclusions in previous research, such as firm size and firm age, do not have a significant direct driving effect, with their roles being highly dependent on specific contexts. The driving mechanism exhibits significant context-dependency.

[Purpose/significance]Ensuring the data security of the public data opening platform is a prerequisite for releasing the value of data elements. Exploring the influencing factors of its data security guarantee level can provide a reference for the security governance of public data opening. [Method/process]Combining information ecology theory, and using NCA and fsQCA methods, this study takes 28 provincial government data open platforms as research objects for empirical analysis to reveal the key influencing factors and configuration paths of the data security assurance level of public data open platforms. [Result/conclusion]The findings indicate that seven antecedent variables influence the date security assurance level of public date open platforms. There are three configuration paths to enhance the level of data security guarantee. They are the subject and object-driven type under the dominance of technology, the government, data resource and economic level-driven type under the dominance of technology, and the multi-element collaborative-driven type under the dominance of technology.

[Purpose/significance]Finland is at the forefront of open science worldwide. Studying Finland's open science policies is of reference significance for promoting the formulation and implementation of open science policies in China. [Method/process]The article employs the qualitative analysis software Nvivo 12 Plus and the grounded theory approach to conduct a textual analysis of Finnish primary open science policies.Through a three-level coding analysis, the content of Finnish open data policy, open access policy, open education policy, open infrastructure policy, citizen science policy, open science evaluation policy, and open science regulatory policy are analyzed. [Result/conclusion]Finnish open science policy is characterized by strong support at the national strategic level, continuous strengthening of the scientific research integrity mechanism, ongoing development and refinement of evaluation and monitoring systems, and deep participation in international cooperation. China can draw on Finnish experience to carry out top-level design for open science, improve the monitoring and evaluation system for open science, optimize policies on research integrity, deepen international cooperation, and perfect the open science policy system to promote its high-quality development.

[Purpose/significance]The characteristics of academic journals have important value, and current evaluation systems lack assesment for this dimension. [Method/process]This paper proposes a variable weight method for factor reduction data, firstly uses factor analysis combined with manual classification to determine the characteristics of the journal, and uses Sigmoid function to standardize the public factors to determine the characteristic journals, and then uses the variable weight function to modify the original data for the characteristic indicators of the characteristic journals based on the data of forestry journals in CNKI. Three representative methods including linear weighted aggregation, weighted TOPSIS, and factor analysis, are used for conducting empirical research. [Result/conclusion]The characteristic evaluation of academic journals must be paid enough attention. The variable weight of factor reduction data provides a new idea from the perspective of basic data. The data variable weight linear weighting summary evaluation value has been reduced. The data variable weight TOPSIS evaluation value has been reduced. The explanatory power of factor analysis is improved after the data is weighted.

[Purpose/significance]Interdisciplinary research collaboration is an important way to promote knowledge innovation and solve complex social problems in the new era. Reviewing the research progress of interdisciplinary research collaboration at home and abroad in the past five years can help grasp the research frontiers in this field and provide direction for future research. [Method/process]This paper adopts a systematic review method, selecting literature closely related to the theme of interdisciplinary research collaboration from inWeb of Science and CNKI from 2020 to 2024, condensing the research topic, analyzing the main research viewpoints, and summarizing the changing characteristics, then proposing future research directions. [Result/conclusion]The existing studies mainly focuses on the operational models, measurement methods, influencing factors, and promotion strategies of interdisciplinary research collaboration. The field exhibits notable shifts in research scope, analytical perspectives, subject diversity, and methodological pluralism. Future research is supposed to focus on exploring more scientific interdisciplinary research collaboration evaluation system, building a cooperation operation guarantee system with the participation of multiple stakeholders, deepening the interdisciplinary research collaboration mode that meets  national strategic needs.

[Purpose/significance]This paper aims to review the research progress and applications of knowledge enhancement techniques in healthcare question answering systems, in response to the limitations of traditional systems in knowledge representation and reasoning, as well as challenges faced by current large language model-based systems, such as insufficient domain knowledge, privacy concerns, and hallucination. The review provides a systematic reference for improving the precision and knowledge reliability of such systems. [Process/method]Focusing on knowledge enhancement strategies, this paper firstly outlines their fundamental concepts and overall framework. The strategies are then categorized into explicit and implicit types, with an analysis of their characteristics, implementation methods, and applications in typical healthcare scenarios. Finally, future research directions are discussed. [Result/conclusion]The study indicates that knowledge enhancement techniques, which integrate external medical knowledge during the pre-training or inference stages of large language models, can effectively improve the accuracy, interpretability, and trustworthiness of the models. Explicit enhancement strategies emphasize the traceability and structured integration of knowledge, while implicit strategies focus on the semantic internalization and generative flexibility of knowledge. Their synergistic application provides crucial support for building more intelligent and reliable healthcare question answering systems.