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.

With the breakthroughs and development of technology, the application scenarios of artificial intelligence (AI) have been continuously expanding, and the application levels have been increasingly deepened, bringing new opportunities and challenges to nursing education in the new era. This study systematically reviews the specific applications of AI in multi-modal content generation, virtual simulation technology, personalized teaching, and teaching model innovation. It also analyzes the technical limitations, ethical dilemmas, and cost constraints existing in practical applications, and proposes relevant countermeasures such as macro planning, cross-disciplinary collaboration, systematic training, and the reconstruction of the teaching evaluation system. The aim is to promote the integration of AI and nursing education and provide references for cultivating high-quality nursing talents that can adapt to the development of the new era.

With the rapid advancement of artificial intelligence (AI) technology, generative AI has been increasingly integrated into office environments, significantly transforming traditional work practices. This study examines the application of Deepseek in Microsoft Word as a case study, systematically exploring the advantages, practical challenges, and typical use cases of generative AI in office scenarios. Through in-depth analysis of real-world implementations in text generation, precise information extraction, and human-machine collaboration, the research proposes optimization strategies for future human-machine synergy development in office settings. These insights aim to provide theoretical support and practical references for implementing generative AI in workplace applications.

The social security system faces challenges in resource allocation, coverage, and efficiency. This study explores data integration methods, including behavior monitoring, fund tracking, and group identification, and proposes strategies such as classification, dynamic allocation, and policy adjustment. It also examines automated benefit calculation, service platform integration, and regional matching. The results improve data support and enhance system adaptability and efficiency.

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. 

Guizhou Province, as a western region with diverse ethnic populations, demonstrates significant advantages in the integrated development of culture and tourism. In the first three quarters of 2024, total tourist arrivals increased by 11.0% and total tourism revenue grew by 14.8%, showing remarkable results. However, issues such as insufficient integration depth still exist. In the future, high-quality development transformation of culture-tourism integration should be promoted through measures such as strengthening industrial chain integration, innovating development models, and improving brand building.

Under the background of “double carbon” strategy and transportation power, the intelligentization of new energy vehicles has become the core fulcrum of industrial transition. In this paper, the importance, main content, development status, innovation and future trend of intelligent technology for new energy vehicles are deeply discussed, and how intelligent technology for new energy vehicles can promote industrial transition and social sustainable development is comprehensively analyzed from multiple dimensions.

During the 15th Five-Year Plan period, when digital-intelligence technologies permeate every domain, such libraries must take the triadic integration of smart resources, smart services, and smart spaces as their core mission, providing replicable institutional and technological models for digital-intelligence transformation. Research shows that, against this backdrop, the principal technological paradigms for building a future-oriented, vocation-type learning center are the graph-based organization of resources, personalized service delivery, and intelligent spatial interaction.

Information retrieval is one of the hot research topics in the field of computer science, and it has a wide range of application scenarios. However, it is mainly limited to specific domains or medium, so it’s unable meet the interaction needs of different user groups with different media. Aiming at the task of cross-domain information retrieval, this paper proposes a method based on a multimodal generative large model. Firstly, it analyzes the cross domain information retrieval technology in detail, summarizes the characteristics of information retrieval, and elaborates on the retrieval methods and processes. Then, a cross-domain information retrieval model based on multimodal generation is proposed. Finally, the effectiveness and reliability of the algorithm model are verified through experimental results. The results show that the proposed method achieves high accuracy in multimodal large models and achieves the best performance in cross-domain information retrieval tasks.

在近日举行的第十七届中国生物产业大会上，中国科学院天津工业生物技术研究所原所长、天津市人大常委会副主任马延和做了题为《生物制造与二氧化碳利用》的报告，以下为报告整理。

9 月4日，第十七届中国生物产业大会在武汉开幕。本届大会以“生物科技赋能未来·生物制造重塑世界”为主题，举办生物制造高层论坛等26场活动，17位中外院士和700余家生物产业领域重点企业参加。南京师范大学副校长黄和在生物制造高层论坛做了题为《合成生物驱动生物制造产业革新》的报告。他指出，以合成生物学为基础的生物合成与生物制造，正在引发全方位的产业变革。

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.

This research solves the problems of information island and insufficient data analysis in traditional policing work by constructing a new intelligent policing model based on big data and knowledge graph as the core framework. The research expounds in detail how big data provides rich and comprehensive data sources for public security work, and how knowledge graph uses its powerful semantic correlation ability to realize deep integration, efficient retrieval and intelligent reasoning of these data. At the practical application level, the paper analyzes the practical cases and applications of big data and knowledge atlas fusion in case detection, crime prediction, social security prevention and control, resource optimization allocation and other police scenarios. On this basis, it looks forward to the future challenges and development trends of intelligent policing, and provides valuable thinking and suggestions for the digital transformation and intelligent upgrading of public security field.

With the advancement of low-altitude airspace management reform and the rapid development of the low-altitude economy industry, the construction of a standard system plays a vital role in promoting the industry’s standardization, systematization, and high-quality development. Based on data from the National Public Service Platform for Standards Information, this paper systematically reviews and analyzes national and industry standards related to the low-altitude economy across China in three main areas: low-altitude manufacturing, flight support, and operational services. The study classifies and counts the number of standards in each subfield, identifies key drafting organizations, and highlights critical technical directions covered by the standards as well as existing gaps. The results of this study provide decision-making references and guidance paths for government departments in policy formulation, for enterprises in technological research and development planning, and for the industry in improving its standard system.

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.

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.

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.

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.

The rapid development of generative artificial intelligence (GAI) has enhanced societal efficiency while simultaneously triggering novel risks such as data security breaches, algorithmic biases, and infringement of personal rights, thereby posing challenges in determining liable entities for tort liability. Clarifying accountability subjects is pivotal to constructing a risk governance framework. Given that generative AI lacks independent intent expression and liability-bearing capabilities, coupled with potential societal risks arising from granting it legal personhood, it is currently inappropriate to confer legal subject status upon GAI. In cases where generative AI causes infringement, program designers, service providers, and users should assume corresponding responsibilities based on their respective actions and fault, thereby safeguarding the legitimate rights and interests of the infringed parties.

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.

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.

This paper focuses on the application potential and challenges of AI technology in ice-snow tourism and cultural communication in Jilin Province. It analyzes the current situation and characteristics of local ice snow tourism and culture, explores the application advantages and cases of AI, examines the challenges and puts forward strategies, aiming to promote high-quality development of the province’s ice-snow tourism and extensive dissemination of ice-snow culture through AI empowerment.

With the accelerated advancement of digital transformation, artificial intelligence (AI) is fundamentally reshaping corporate management paradigms. The maturation of technologies such as machine learning, natural language processing, and intelligent decision-making systems enables businesses to optimize operational workflows, enhance decision-making efficiency, and strengthen market competitiveness. However, the widespread adoption of AI also presents multifaceted challenges including data security concerns, ethical compliance issues, and implementation barriers. This paper systematically examines the core characteristics of AI in enterprise management, analyzes current major challenges, and illustrates its practical applications through case studies in supply chain optimization, customer relationship management, and human resource management.

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.

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. 

The digital economy profoundly reshapes the traditional pattern of factor allocation and has become a key driving force in the formation of new-quality productivity. Centering on factor allocation efficiency, this paper analyzes the impact pathways of data factors, platform mechanisms, and digital technologies on the allocation of labor, capital, and technological resources, and constructs an analytical framework covering basic impact mechanisms, changes in key areas, real-world obstacles, and optimization pathways. Empirical methods are used to verify that the digital economy has a direct effect, mediating effect, and threshold effect, which significantly promote new-quality productivity. Optimizing digital infrastructure and data market mechanisms will effectively enhance factor mobility and collaborative efficiency, thereby driving high-quality development.

With the accelerated advancement of educational digital transformation, the construction of the “Three Full Education” (education by all, for all, and in all aspects) coordination mechanism in colleges and universities is of great significance. This study points out that currently, colleges and universities face problems such as insufficient coordination awareness and capabilities among education providers, low efficiency in resource integration, and imperfect coordination management mechanisms in the educational process. Through approaches like promoting the integration of ideological and political education in colleges and universities with digital technologies, strengthening coordination among internal departments, and building digital bridges for home-school co-education, the construction of the “Three Full Education”coordination mechanism can be advanced. This will enhance the quality of education, promote the high quality development of higher education, and provide a solid guarantee for cultivating talents for the new era.

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.

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.

Since 2022, facing the challenges from declining R&D capability index, escalating geopolitical tensions, and the disruptive leap of emerging technologies, Japan gradually reforms its S&T systems under the blueprint “crisis driven, security bound, application oriented, capability rooted”. At the national level, the creation of the “Key and Advanced Technology R&D through Cross Community Collaboration Program” (K-program) and the AI Safety Institute marks the first time innovation policy has been tightly fused with comprehensive national security, while other important reforms—front-loading commercialisation, rebuilding the innovation ecosystem, and optimizing the research environment—bridge the traditional science-to-economy gap. At the institutional level, RIKEN and AIST serve as flagship models, by adopting matrix-style research organisations, “data + AI” paradigm, full-chain industry–university–institute start-up connect, and rigid research-security governance to strengthen their ability to deliver governmental missions. The reform measures on one hand address the current crises over R&D investment, talent, and security, while on the other hand seeking to secure future frontiers, revitalize industries, and sharpen competitiveness.

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.

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%.

The thriving rise of artificial intelligence (AI) technology not only expands the dimensions of knowledge presentation but also ignites students’ passion for exploring the mysteries of life, bringing new opportunities for talent cultivation in the field of biology. This study comprehensively analyzes the application status and challenges of AI in the cultivation of biological talents, and explores the direction of its future development in combination with the latest technological trends. AI technology promotes the professional development of scientific research talents, teachers, and interdisciplinary technical talents through personalized learning, intelligent resource integration, and cross-disciplinary practice. This study provides insights for promoting the deep integration of biological teaching with AI technology, fostering high-quality development of talents in the field of biology.

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.

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.

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。

AI technology has profoundly transformed the broadcasting and hosting industry. However, human AI collaboration has also given rise to numerous challenges. To address these issues, this paper proposes five developmental pathways: promoting the parallel development of intelligent algorithms and artistic expression; constructing a symbiotic ecosystem between virtual spaces and real-world scenarios; exploring the golden ratio of human-machine collaborative skills; achieving an equilibrium between algorithmic compensation and humanistic care; and establishing hard constraints for technical boundaries and industry standards. To provide a reference path for promoting sustainable cooperation between humans and AI in the broadcasting and hosting industry.

Design an AI-driven intelligent scheduling system for the logistics industry to address issues such as slow response and unreasonable resource allocation in traditional logistics scheduling. Construct a data-driven decision model using machine learning and deep learning algorithms, integrating four functional modules: transportation optimization, warehouse management, distribution network, and monitoring platform. The system achieves core functions including dynamic path planning, intelligent inventory prediction, and precise distribution scheduling, significantly improving logistics efficiency.

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.