Traditional Automated Guided Vehicles (AGVs) typically rely on predefined paths to complele sorting and delivery tasks, lacking flexibility. In recent years, the explosion of ecommerce, sustained growth in order volume, and the demand for flexible manufacturing with high-mix, low-volume production have posed new challenges to the logistics industry. Autonomous Mobile Robots (AMRs), with their capability for flexible route planning and intelligent environmental interaction, are gradually replacing AGVs as a focal point in smart logistics. This paper reviews the technological evolution from AGVs to AMRs, encompassing both intrinsic and extrinsic driving factors. By conparing performance differences in various scenarios, it proposes optimization solutions for key challenges such as path planning and multi-robot collaboration. In the future, further iterations of AMRs can be achieved through integration with 5G/6G, high-precision navigation, and human-robot interaction technologies.

Intelligent manufacturing has become the core direction of the transformation in the manufacturing industry. The in-depth integration of artificial intelligence(AI)technology and mechanical and electronic engineering has driven a fundamental transformation in traditional manufacturing models. Focusing on the practical application of AI in the mechanical and electronic industry, this paper systematically analyzes the application paths of AI technology in the perception, control,optimization and other links of mechanical and electronic equipment, and deeply explores the breakthroughs and industrial value of AI-driven electromechanical systems in precision improvement, energy consumption reduction, fault early warning and other aspects. Research shows that with the empowerment of AI technologies such as machine learning and computer vision, mechanical and electronic equipment can achieve autonomous decision-making and adaptive adjustment, which significantly improves production efficiency and product quality, providing practical references and data support for the intelligent upgrading of the manufacturing industry. 

With the popularization of AIGC technology and the explosive growth of the short-video industry, secondary creation in short videos has become a core issue involving both cultural communication value and copyright disputes. Its creative forms have evolved from traditional editing and montage to diversified modes such as AI “remix” and template-based generation, leading to blurred copyright boundaries and difficulties in infringement determination. Based on the current framework of the Copyright Law and judicial practice cases, this paper focuses on the core controversies over copyright boundaries of secondary creation in short videos, and systematically analyzes key issues including the identification of fair use, the localized application of transformative use, and the division of platform responsibilities. It also sorts out the divergences in the judicial application of the “three-step test” and the “four-factor test”. Finally, it proposes an infringement determination path and systematic improvement suggestions featuring “typed definition— hierarchical determination— collaborative governance”, so as to provide legal support for balancing original incentive and creative freedom and regulating the development of the industry.

The 20th CPC Central Committee's fourth plenum called for seizing tech revolution opportunities to develop new productive forces. While AI and robots boost efficiency, they also disrupt employment, erode tax bases, and widen income gaps, necessitating robot taxation for fairness. Key obstacles include unclear legal status of robots, ambiguous taxpayer identity and tax nature, and lack of collection systems. This paper suggests optimizing the current tax system by clarifying taxpayer status and tax nature, and improving collection mechanisms to integrate robot-related activities, balancing tech progress with public interest.

Through patent search and analysis, this study investigated the application trends, geographical distribution of patent applications, global and Chinese applicant rankings, annual trends in patent applications by major applicants, and technological R&D hotspots in the field of battery thermal management technology for new energy vehicles. The results indicate that the number of related patent applications has maintained rapid growth. China has become the primary region for patent applications, with Chinese companies CATL and BYD ranking among the top globally in terms of patent application volume. South Korean company LG Energy Solution has emerged as the primary competitor to Chinese firms. Technologically, battery cooling has become the predominant research direction, while multi-circuit thermal management systems have become a key research hotspot . This research aims to provide references for the R&D and patent layout of battery thermal management technology for new energy vehicles in China.

Against the backdrop of increasingly fierce global competition in technological innovation, patents, as a key carrier of a company's core competitiveness, urgently need to shift their layout strategy from quantity oriented to emphasizing both quality and strategy. This article systematically explores five typical models of enterprise patent layout and deeply analyzes the three optimization paths of patent layout: firstly, using patent map tools to identify “blank areas” and “dense areas”, accurately grasping layout opportunities and risks; The second is to implement a phased layout plan, locking in technical concepts in the early stage of research and development, supplementing and improving patents in the middle stage, and fully covering product structure and functions in the commercialization stage to achieve full cycle protection; The third is to build a combination of multiple types of patents, forming a three dimensional protection system that integrates technology, structure, and vision.

Drawing on the latest data from the Web of Science Core Collection, Medline, Nature Index, Highly Cited Researchers list, as well as reports published by WHO, FDA, NMPA, and IQVIA, this study systematically examines the evolution of global medical science and technology strategies, the shifting landscape of basic research, and current pharmaceutical R&D dynamics, with a particular focus on China–US comparative innovation performance. Findings reveal that global medical science and technology have transitioned into a strategic system central to national security, bioeconomic development, and supply chain resilience, with pathogen surveillance, vaccine platforms, and biomanufacturing emerging as critical priorities. Basic research has entered a high-plateau stabilization phase, where the United States continues to dominate in research scale, leading institutions, and talent concentration; China, however, has surpassed the US in Nature Index high-quality output within biological sciences, though it remains comparatively weaker in health sciences and clinical research. In pharmaceutical R&D, investment and M&A activities increasingly converge on ADCs, cell and gene therapies, GLP-1 agonists, and AI-enabled drug discovery, with emerging biotech firms becoming key generators of first-in-class therapeutics. Both China and the United States remain global leaders in new drug approvals, yet the US retains clear advantages in first-in-class innovation and rare disease therapeutic ecosystems. Overall, China is evolving into a pivotal force in global medical innovation, but further progress requires strengthening health sciences, clinical translation capacity, research platforms, and systemic innovation infrastructure.

The advent of AIGC technology is profoundly transforming the audio-visual cultural and creative industry, giving rise to novel talent demands. However, the current education system’s lag in curriculum, competency development, and industry alignment has led to a severe mismatch between talent supply and market demand. To address this challenge, this paper analyzes the current state and root causes of this talent supply-demand issue and, based on the CDIO-CBE philosophy, constructs a talent cultivation adaptation model that encompasses objectives, curriculum, pedagogy, and evaluation. This model aims to achieve a dynamic coupling between educational supply and industrial demand, providing an innovative solution for the high-quality cultivation of talent and precise industry alignment in the AIGC era.

Cultivating highly skilled talents for the low-altitude economy is a necessary move for higher vocational colleges to align with the national emerging industry strategy and achieve high-quality development of their own. Currently, higher vocational colleges face several challenges in talent cultivation: insufficient adaptability of program layout to the low-altitude industrial chain, weakened technical and practical attributes of curriculum systems, inadequate integration of theory and practice in teaching activities, and discrepancies between talent cultivation quality and enterprise needs. To address these issues, higher vocational colleges can enhance talent cultivation efficiency and achieve precise supply of highly skilled talents for the low-altitude economy through the following approaches: dynamically optimizing program layout guided by industrial chain demands, constructing a low-altitude economy-featured curriculum system based on modular courses, realizing in-depth integration of theory and practice through scenario-based practice, and building a demand feedback loop supported by school-enterprise collaboration.

Double Machine Learning (DML) is a latest breakthrough in causal inference. Traditional methods struggle with high-dimensional data and complex nonlinear causal relationships, but DML combines machine learning flexibility and statistical rigor, using orthogonalization and cross-fitting to solve these issues.DML handles high-dimensional data, estimates nonlinear causal effects, identifies heterogeneous treatment effects, and reduces estimation bias. It is now widely used in policy evaluation and economic analysis.In the future, DML can be combined with other methods or integrated with big data and AI, to better adapt to complex data environments and deliver more accurate causal inference results.

Against the backdrop of the manufacturing industry’s intelligent transformation, the mechanical and electronic sector faces challenges including insufficient synergy across value chain segments and delayed value extraction. By establishing AI-driven cross-segment data integration systems, prioritizing high-value segments, and embedding green development principles, the industry achieves coordinated value chain upgrades and deeper value realization. This transformation significantly shortens product development cycles, enhances quality control, reduces energy consumption, and facilitates the shift from “manufacturing” to “smart manufacturing,” ultimately building an efficient, low-carbon value chain ecosystem. 

CNS journals—Cell, Nature, and Science—serve as key indicators of the global landscape of basic research, with their publication patterns reflecting shifts in international scientific strength and frontier directions. Drawing on data from the Web of Science and InCites platforms, this study analyzes Article publications in CNS journals from 2015 to 2024 across multiple dimensions, including country and city contributions, disciplinary structure, research hotspots, international collaboration, and author characteristics. The results show that the United States maintains its leading position, though its share is declining; China exhibits rapid growth, with strong performance in materials science and physics, but continues to face structural weaknesses in life sciences and health-related fields. Emerging research hotspots are increasingly concentrated in artificial intelligence, virology, nucleic acid chemistry, and two dimensional materials, alongside expanding international collaboration networks. Based on these findings, the study proposes policy recommendations to strengthen original research capabilities, optimize support mechanisms for early-career researchers, and enhance cross-border and cross-sector collaboration.

Amidst pressures from climate change, frequent disasters, and urban-rural disparity, rural system vulnerability is escalating. Enhancing rural resilience is thus a critical path to sustainable development. This study investigates the mechanism through which digital village construction, via “digital empowerment,” strengthens rural resilience in less-developed regions. It proposes a theoretical framework encompassing digital infrastructure, application, literacy, and governance to analyze the multi-dimensional (economic, social, ecological, governance) enhancement of rural resilience by digital technologies. Findings reveal that digital empowerment significantly fortifies rural adaptability, recoverability, and transformability by optimizing resource allocation, enhancing social connectivity, and improving governance efficacy. However, structural challenges persist, including a deepening digital divide, technology-supply mismatch, and high project dependency. Accordingly, this paper proposes optimization pathways: inclusive policies centered on enhancing “capabilities,” participatory technology adaptation mechanisms, and resilient governance innovation. These aim to elevate digital empowerment from “incremental” to “transformative” resilience, offering theoretical and practical insights for comprehensive rural revitalization.

With the rapid development of science and technology, Generative Artificial Intelligence has become a significant force driving educational transformation. In the governance practices of higher vocational colleges, Generative AI can significantly enhance administrative efficiency and promote the comprehensive improvement of education quality. Based on the characteristics of higher vocational colleges, this paper explores the specific advantages that Generative AI can bring to various areas such as teaching management, student services, and resource allocation, and analyzes the potential problems and challenges associated with the introduction of this new technology.

Cryo-electron microscopy is a pivotal technique for resolving biological ultrastructure, where high-quality sample preparation, particularly cryo-thinning for thick samples, is essential. Traditional cryo-thinning techniques, such as cryo-ultramicrotomy, face challenges including sample compression artifacts, operational complexity, low efficiency, and insufficient positioning accuracy, which constrain in situ structural biology research. Driven by the increasing demand for high-fidelity, high-precision in situ observation, correlative light and electron microscopy has become the core development direction. The Chinese Academy of Sciences Scientific Instruments High-End Instrument Research Institute（Beijing） and the Institute of Biophysics，Chinese Academy of Sciences team have successfully commercialized an innovative achievement—the ELI-Triscope System. This system realizes groundbreaking “three beam co-focusing,” integrating an inverted fluorescence imaging system. By monitoring the actual fluorescence signals of target molecules, it achieves “cutting while seeing,” resolving the traditional “blind cutting” dilemma. With positioning accuracy better than 200 nanometers, it enhances the capability for targeted thinning of subcellular structures. Simultaneously, by eliminating ice contamination risks associated with sample transfer, it drastically reduces sample preparation time to approximately 45 minutes per sample. This provides robust support from a domestically developed tool for the precise analysis of organelle-scale in situ three-dimensional structures.

In recent years, the rapid development of AIGC technology has posed unprecedented challenges to the academic integrity system of scientific journals. Starting from the application of artificial intelligence generation technology in the field of scientific journal publishing, this paper analyzes the impact of AIGC on paradigms of academic content production, academic quality review, data security, copyright disputes, and other aspects. In response to the impact of AIGC technology on the academic integrity of scientific journals, this paper proposes governance pathways, including improving academic publishing standards and ethical frameworks, enhancing technical detection and review capabilities, strengthening academic integrity education, establishing multi stakeholder collaborative governance mechanisms, and refining relevant laws and regulatory systems.

The scientific and systematic nature of local government carbon emission policy systems plays a crucial role in achieving the carbon peak and carbon neutrality goals. This study constructs a policy evaluation system based on the PMC index model to quantitatively assess carbon emission policies in Hunan Province before and after the introduction of the “dual carbon” goals. The findings indicate that the average PMC index for Hunan Province’s carbon emission policies is 6.81, reflecting an overall good level. After the introduction of the “dual carbon” goals, policy quality improved significantly, with the average PMC index rising from 6.12 to 7.50. The policies perform well in terms of timeliness, functionality, and transparency, but there is room for improvement in incentive mechanisms, implementation levels, and public participation.

E-commerce platforms face challenges like fragmented user behavior and inefficient marketing resource allocation in new media. A multi-source data fusion architecture covering transactions, social platforms, and ads was built. Data from 237,000 users over 180 days was analyzed, identifying 5 user groups via K-Means and predicting conversion with 83.7% accuracy using XGBoost. Precision targeting boosted conversion by 37.3%, user lifetime value by 28.3%, ad ROI from 1:2.8 to 1:4.5, and reduced customer acquisition costs by 32.5%.

This paper studies the application of big data technology in current smart anti-smuggling, covering an overview of big data technology, the connotation of smart anti-smuggling, and its significance. It analyzes the application status in the new era, takes X Coastal and H Border Customs Anti-Smuggling Bureaus as examples to demonstrate practical effects, identifies existing problems (low data integration efficiency, inadequate interconnection, shortage of professionals, and imperfect institutional mechanisms), and puts forward relevant suggestions to promote the sustainable development of big data technology in smart anti-smuggling construction and application.

Medical college is an important source of original innovation in biomedicine, but the database analysis shows that the industrialization rate and transformation ratio of achievements are low. The results of medicine are characterized by high originality, strong practicability and interdisciplinary. From the perspective of supply side, this paper constructs the cultivation mechanism of talents for achievement transformation from two dimensions of continuing education and training of scientific and technological workers and the cultivation of technical brokers on campus, so as to explore the path to improve the efficiency of achievement transformation.

中国科学院杭州医学研究所（以下简称杭州医学所）成立于2019年，是中国科学院与浙江省深化合作的国家级科研院所，旨在抢占生命健康领域科技制高点。医学人工智能中心作为医学所的核心布局之一，旨在依托杭州医学所的肿瘤大数据、生物样本资源及人才优势，解决智慧医疗大数据安全共享、深度挖掘等关键共性问题，推动前沿人工智能技术与生物医药领域的突破性进展。

Guided by the “dual carbon” goals, this paper conducts research on the green transformation paths of railway diesel locomotives. By comprehensively applying literature research, case analysis, and cost-benefit analysis methods, it systematically reviews the current transformation status, deeply analyzes the core challenges in the technical, economic, and policy dimensions, and proposes three transformation paths: technological innovation, power transformation, and industrial collaboration. Through quantitative analysis, the transformation costs and benefits are clarified. The feasibility is verified by the power transformation project of the Dongfeng 4D diesel locomotive in Tangshan Locomotive Depot of Beijing Bureau Group. Finally, suggestions are provided from the policy, technology, and infrastructure levels, offering theoretical and practical references for the low-carbon transformation of the depot and the railway industry. 

在位于浙江杭州的中国医药港核心区，一座总占地面积200余亩的科研园区正成为生命健康领域的创新高地，这便是2019年成立的中国科学院杭州医学研究所（以下简称“杭州医学所”）。 杭州医学所由中国科学院与浙江省人民政府合作共建，是中国科学院首个以医学命名的直属研究机构，也是浙江省重点引进和打造的生命健康科创高地主平台。2024年1月25日，杭州医学所正式挂牌，进入高质量发展新阶段。

The research systematically expounds the application mechanism of full life cycle data perception and fusion technology, as well as artificial intelligence-based power generation efficiency prediction and optimization models in the field of new energy power generation. It reveals the functional mechanism of digital intelligence technology in enhancing the accuracy of renewable energy resource assessment, achieving predictive maintenance of equipment, promoting the coordinated optimization of source-grid-load-storage, effectively mitigating the volatility of power generation, and improving the level of power consumption.

As global climate change intensifies and China advances its “dual carbon” strategy, the synergy between building “zero-carbon islands” and low-carbon logistics transformation has emerged as a key driver for regional ecological and economic progress. Ningde City, home to 643 islands, issued a special declaration in 2023-2024 setting a 2027 target to establish a “zero-carbon island” demonstration project. This study examines the logistics sector in Ningde, analyzing current challenges including high-carbon energy structures and inadequate infrastructure. The paper proposes a low-carbon transition framework encompassing energy substitution, facility upgrades, technological innovation, and policy support. Key initiatives include promoting multi-energy complementary systems and developing smart logistics dispatch platforms. These efforts aim to provide the “Ningde Model” for green logistics in coastal island cities, offering both theoretical and practical references for implementing China’s “dual carbon” goals in island regions.

To address new energy vehicles (NEVs) promotion bottlenecks in Chongqing’ s hilly regions and support “dual carbon” goals and rural revitalization, research shows Chongqing had 706,500 NEVs by the end of 2024 with leading charging infrastructure. Challenges: uneven charging station distribution, inefficient maintenance, insufficient government-enterprise collaboration. Countermeasures: govt-enterprise-community mechanism, optimized “30-minute charging circle”, digital maintenance & value-added services.Expected effects: fill rural charging gaps, improve user satisfaction, and provide references for similar areas.

Due to the particularity of generative artificial intelligence infringement, accurately determining the type of infringement liability in the current attribution principles requires first determining its legal positioning. The legal positioning of generative artificial intelligence as a service form rather than a product form exists because it does not meet the definition of “product” in traditional product liability, and the damage caused by its output information usually does not directly threaten personal safety. Therefore, strict product liability rules should not be applied. This definition is beneficial for encouraging technological innovation and avoiding the unfair phenomenon of users intentionally inducing infringement and demanding developers to take responsibility. To achieve fairness and scientificity in the determination of responsibility, it is recommended to adopt a mechanism of multiple differentiation of responsibility subjects, and allocate responsibility based on the specific roles of technology developers, operators, and users in the infringement chain. Especially for the determination of the fault of service providers, an objective standard of duty of care should be adopted. By applying the responsibility framework of network service providers through analogy and introducing dynamic attribution principles and risk diversification mechanisms, it can provide institutional space for the healthy development of the generative artificial intelligence industry while safeguarding the rights and interests of victims.

As the most dynamic and promising economic entities and innovation carriers in Xuchang’s market economy, technology-based small and medium-sized enterprises (SMEs) have long faced the real challenges of “difficult, expensive, and slow financing,” which severely hinders their high-quality development. This paper analyzes the development overview and financing environment of technology-based SMEs in Xuchang, summarizes the f inancing constraints faced by these enterprises, and proposes strategies from the government, financial institutions, and the enterprises themselves to promote the healthy development of technology-based SMEs in Xuchang.

Against the backdrop of the overlap between the dual circulation pattern and the Belt and Road Initiative, the Xinjiang Pilot Free Trade Zone, as a hub for opening up to the west, needs to achieve a technological breakthrough to connect emerging industries with markets along the routes. This paper points out three major issues: insufficient R&D investment, lack of core technological reserves among R&D entities, and unsmooth channels for scientific and technological achievement transformation, which hinder the development of emerging industries such as new energy equipment. It further proposes three practical paths: constructing a diversified funding system, strengthening technological reserves of R&D entities, and building a full-chain achievement transformation platform. This provides a reference for the Xinjiang Pilot Free Trade Zone to realize internal and external linkage development through technological empowerment, helping it serve the national dual circulation strategy and the Belt and Road Initiative.

As a vital source of economic vitality and resilience in the national economy, small and medium sized enterprises (SMEs) have garnered significant attention from the Party Central Committee and the State Council. In recent years, a strategic direction has been proposed for SMEs to pursue specialized, refined, distinctive, and innovative development. At present, local governments across China have prioritized cultivating specialized and refined SMEs as a key driver for high-quality development, improving industrial systems, and building a new development paradigm. This paper reviews the conceptualization and evolution of the “specialized, refined, distinctive, and innovative” development philosophy, analyzes the current state of SMEs in Puyang City, identifies existing gaps and challenges, and offers recommendations such as emphasizing planning guidance, strengthening policy support, and optimizing enterprise assistance services. These measures aim to elevate the cultivation of specialized and refined SMEs in Puyang City to a new level and contribute to high-quality economic and social development.

Human resource management serves as the core backbone of enterprise development, playing a pivotal role in enhancing organizational effectiveness. This paper aims to delve into the intrinsic correlation and optimization pathways between performance evaluation and incentive mechanisms within enterprise human resource management. By analyzing the consistency of their objectives, dynamic feedback loops, and strategic transmission effects, it dissects prevalent issues such as indicator disconnection and feedback deficiency. Subsequently, it proposes optimization measures, including anchoring strategic decoding indicators and constructing a dynamic feedback mechanism. The study reveals that the synergistic operation of the two can achieve resonance between enterprise and employee development, leading to the conclusion that enhancing their effectiveness requires strategic anchoring, dynamic feedback, equitable alignment, and digital empowerment.

Using the CNKI database as the source of literature statistics,CiteSpace visualization analysis software was used to conduct a graph analysis of 628 literature on cultural tourism research in Fujian Province from 1999 to 2024. The research status and development trends in the field of cultural tourism in Fujian Province were analyzed. The results indicate that the number of publications from 1999 to 2024 showed a fluctuating and increasing trend; most of the institutions conducting relevant research are universities in the eastern and southern regions of Fujian; the number of publications by each institution is generally small; and academic communities need to be formed among institutions. Tourism culture, tourism development, cultural tourism, and cultural tourism integration are the main research hotspots , among which cultural tourism integration is the main line and key direction of future research.

In 2025, a questionnaire survey of 80 intellectual property service institutions in Guizhou Province was conducted, analyzing aspects such as institutional structure, service capacity, policy environment, and development challenges. Findings reveal that these institutions are predominantly small and micro private enterprises, with services highly concentrated in traditional agency work and weak capabilities in emerging sectors. Common challenges include vicious competition, poor policy implementation, talent shortages, and inefficient supply-demand alignment. This study proposes systematic measures—enhancing industry regulation, optimizing policy support, establishing talent development systems, and building service platforms—to foster professional and high-end sector growth.

Under the background of high-quality economic development and the Dual-Carbon goals, corporate Environmental, Social, and Governance (ESG) has gradually become a hot topic in the market. With the rapid development of internet technology, social media, as an emerging platform for public opinion supervision, has become an important force affecting ESG information disclosure. This paper reviews the literature from the past five years on media attention, social media, and ESG. It summarizes the impact of social media on the quality of corporate ESG disclosure and its influencing mechanisms. We find that social media mainly promotes ESG information disclosure through three mechanisms: supervision and constraint, information transmission, and technological innovation. This paper also summarizes the limitations of existing research and provides an outlook for future studies.

Based on the concept of green competitiveness, this paper elaborates on the significance of enhancing Guizhou's industrial parks’ green competitiveness and analyzes current challenges in energy consumption statistics and clean energy utilization. Considering the development status of Guizhou’s industrial parks and aligning with the policy orientation of “quota-based applications and provincial-level selection,” the study proposes strategic recommendations including optimizing green industrial layouts (such as the “park-in-park” model) and strengthening green technology applications (like carbon capture and resource recycling) to promote the coordinated integration of economic development and low-carbon goals in Guizhou’s industrial parks.

今年6月，中国科学院杭州医学研究所附属肿瘤医院（浙江省肿瘤医院）联合阿里巴巴达摩院召开新闻发布会，正式发布全球首个胃癌影像筛查AI模型DAMO GRAPE。该模型通过深度学习技术实现腹部平扫CT的智能分析，对胃癌高危人群的初筛检出率达24.5%，且检出病例中约40%为无症状胃癌患者，相关成果登上国际权威期刊《自然·医学》（Nature Medicine），标志着我国胃癌防治迈入“AI影像筛查”新时代。

When digital technology is deeply integrated with public governance, artificial intelligence has become a key driving force for the transformation of public hospital governance and the upgrade of intelligent services. This article takes public value creation and sustainability as the two main lines, focusing on the practical logic of AI empowering intelligent services in public hospitals, analyzing its paths of public value creation in enhancing the accessibility of medical services, optimizing the allocation of public medical resources, improving service quality and patient experience, and dissecting the core institutional obstacles such as budget investment, personnel adaptation, and data barriers faced by AI empowerment. It proposes corresponding sustainable optimization paths. The main research focus of this article is to solve the institutional predicaments related to the release of public value in intelligent services, release the public value of intelligent services, and assist public hospitals in achieving high-quality development.

The industrialization of wind turbine cluster optimization technology is constrained by structural factors including the absence of trust mechanisms, immature business models, data barriers, and talent discontinuities. Based on industrialization logic and typical case analysis, this paper reveals that multi-level verification systems, ecological collaborative networks, performance-oriented mechanisms, and standardization construction constitute the core pathways for industrial implementation. Through institutionalized trust reconstruction and value distribution innovation, the technology transforms from localized verification to large-scale application, providing systematic solutions for high quality development of the wind power industry and achievement of carbon neutrality strategic objectives.

When generative artificial intelligence uses a vast amount of copyrighted works without the copyright holders’ permission during the training process, it is liable for infringement under China's existing legal framework. Copyright compliance has thus emerged as a crucial challenge that industry players cannot afford to ignore. However, the conventional compliance model of “authorization before utilization” encounters significant obstacles in the artificial intelligence domain. Moreover, the fair use doctrine fails to exempt profit - making model training, rendering the exploration of novel compliance approaches both urgent and indispensable. By delving into conclusive judicial decisions, we can discern the current judicial stance on copyright issues in artificial intelligence training in China. From this, we can reverse-engineer practical and actionable compliance strategies, thereby offering valuable guidance for the development of the artificial intelligence industry. 

This study employs Multi-Agent System technology to simulate knowledge services that support subject based self-directed learning in university library Future Learning Centers. By analyzing learner needs and designing collaborative agent pathways, a multi-agent service framework is constructed with functions including learning path design, resource recommendation, intelligent Q&A, and feedback evaluation. A simulation focusing on the "Economic History" discipline demonstrates that the system effectively integrates library resources, improves service automation, and enhances personalized support. The approach offers a practical and replicable solution for libraries with limited resources, providing insights for the development of Future Learning Centers.