Journal of science and technology exploration
Journal of Science and Technology Exploration (ISSN 3105-5915, eISSN 3106-6313) is an academic journal dedicated to advancing scientific research and technological innovation. Committed to serving researchers, engineering and technical professionals, as well as university faculty and students, the journal focuses on breakthroughs at the frontiers of science and the transformation of technological applications, with particular attention to advances in basic science, innovations in engineering and technology, the development of intelligent and information technologies, and the industrialization of scientific and technological achievements. It aims to provide a high-level platform for academic exchange and the presentation of research outcomes in scientific research and technological innovation.
Quantum Machine Learning for Accelerating Large Model Training
Aiming at the practical bottlenecks in current large model training, such as high computing cost, low efficiency, exponential growth of long-sequence computing complexity, and increasing pressure of hardware energy consumption and carbon emissions, based on the hardware conditions and technical boundaries in the era of Noisy Intermediate-Scale Quantum (NISQ) computing, this paper proposes an original Cascaded Quantum-Assisted Computing Paradigm, and systematically explores the acceleration path and implementation mechanism of the deep integration of quantum machine learning technology and classical large model training process. By embedding the two core technologies independently proposed, Quantum Amplitude Attention Pooling and Quantum Random Circuit Embedding, into the core modules of Transformer architecture with the highest computing load and most prominent complexity, this study constructs a quantum-classical hybrid training framework with stable structure, strong compatibility and direct deployability on existing quantum cloud platforms. On the premise of not destroying the overall structure and training logic of classical models, it realizes dynamic offloading and collaborative computing of high-complexity subtasks to quantum processors. Experimental results show that on the current mainstream NISQ quantum computing devices, the proposed framework can achieve a stable improvement of 18%—25% in overall training efficiency in public text classification standard tasks, while the model accuracy loss is controlled within 0.5 percentage points, with strong engineering application value. This paper further systematically analyzes the practical constraints such as quantum hardware noise, quantum-classical data interaction overhead, and algorithm-hardware adaptability. Combined with the future evolution trend of fault-tolerant quantum computing, it comprehensively prospects the long-term development path, technological breakthrough direction and industrial application scenarios of quantum machine learning in the field of large model training, providing new theoretical ideas and practical references for breaking through the computing power ceiling of large model training under the classical computing system.
Quantum machine learning; Large model training; cascaded quantum-assisted computing paradigm; Quantum amplitude attention pooling; NISQ devices
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Research on Innovation and Entrepreneurship Mode of Precision Agricultural Planting Supported by Big Data Technology
With the continuous progress of agricultural modernization, big data technology has provided critical support for precision planting. This paper focuses on the development path of precision agricultural planting promoted by big data, and explores the operation modes and mechanisms of this planting mode in the field of agricultural innovation and entrepreneurship. By sorting out and analyzing the application frameworks of core technologies such as the Internet of Things, remote sensing observation and cloud computing, this paper constructs a massive data support system required for precision planting, and summarizes four different innovation and entrepreneurship modes: platform-based data service mode, multi-party collaborative operation mode, public co-creation incubation mode and comprehensive solution supply mode. Research shows that big data has significantly improved the scientificity of agricultural production decision-making and enhanced resource utilization efficiency, and spawned many new technical service industries. However, there are also many problems, such as difficulties in data sharing, low participation of small farmers, and single profit mode. This paper puts forward improvement suggestions in terms of policy guidance, platform construction, personnel training and ecological cooperation, so as to provide theoretical support and application reference for the sustainable development of smart agriculture.
Big data; Precision planting; Smart agriculture; Innovation and entrepreneurship mode; Digital transformation
The Integration of AR and AI Drives the Construction of an Innovative Ecosystem for Strategic Emerging Industries: From the Perspective of the Entire Industrial Chain
In the context of the deepening digital economy, the integration of AR (Augmented Reality) and AI (Artificial Intelligence) has become a key lever for the restructuring of the innovation ecosystem of strategic emerging industries. Based on the theories of technological integration, innovation ecosystem, and industrial chain governance, this paper adopts a three-step approach of "concept deconstruction - theoretical mapping - framework derivation" to construct an analytical framework consisting of the "driving layer (AR-AI functional coupling) - path layer (full-chain empowerment of the industrial chain) - goal layer (construction of innovation ecosystem)". Focusing on the positioning of AR glasses as the core carrier for the implementation of the AI large model industry, this paper takes Hangzhou Lingban Technology Rokid, Sany Heavy Industry, and Lianying Medical as case studies. Using literature research, semi-structured interviews, and multi-case comparison methods, it systematically analyzes the internal mechanisms of AR-AI integration in upstream Rokid collaboration, midstream manufacturing optimization, and downstream scenario iteration. It identifies three major bottlenecks: insufficient technological collaboration, dependence on core components, and inefficient cross-subject governance. In combination with policy requirements such as the "14th Five-Year Plan for the Development of Strategic Emerging Industries," this paper proposes optimization paths from four dimensions: technological breakthroughs, subject collaboration, policy guarantees, and ecological governance. It provides both theoretical depth and practical operability guidance for the high-quality development of the innovation ecosystem of strategic emerging industries in the digital economy era.
Digital economy; Integration of AR and AI; Strategic emerging industries; Innovation ecosystem; Industrial chain governance; AR glasses
Research on the Intent-Driven, Dynamic Pedigree Database Collation Method
As the global data volume surges into the Zettabyte era, traditional database organization methods based on predefined schemas encounter significant bottlenecks, such as semantic decoupling, obscure lineage, and prohibitive governance costs when faced with complex business intents and high-frequency data evolution. This paper proposes an innovative paradigm for database organization characterized by “Intent-Driven and Dynamic Pedigree” (IDDP). Firstly, a high-precision intent recognition model is constructed by incorporating an improved Transformer architecture and contrastive learning algorithms, enabling the accurate mapping from natural language intents to logical operational operators. Secondly, a five-tuple dynamic pedigree model comprising Entities, Relations, Time, Versions, and Context is defined to address the challenges of lineage tracking and consistency maintenance during long-cycle data evolution. At the system implementation level, an “Intent-Pedigree-Data” (IPD) three-layer mapping engine and an event-driven incremental update mechanism are designed. Experimental validations conducted on the TPC-DS standard dataset and a real-world e-commerce dataset with tens of millions of records demonstrate that the proposed method achieves an intent parsing accuracy of 89.4% under complex relational queries, representing an improvement of approximately 34% over traditional methods. Furthermore, the dynamic adaptation latency is reduced by 95%, while the pedigree storage overhead is maintained at only 2.4% of the original data volume. This research demonstrates that the IDDP method significantly enhances the autonomous organization capabilities of databases, providing a novel theoretical foundation and engineering pathway for value extraction from large-scale heterogeneous data assets.
Intent-driven; Dynamic pedigree; Database organization; Data lineage; Deep learning; Autonomous database
Application and Efficacy Analysis of Artificial Intelligence in Property Management
With the advancement of urbanization, the property management industry is confronted with numerous challenges. Artificial intelligence technology underpins its transformation and plays a vital role in equipment management, service optimization, social value and other dimensions. Domestic and foreign researches have respective emphases, while domestic practice still has deficiencies. Adopting multiple research methods, this study constructs an analytical framework and develops relevant system platforms. Empirical tests verify the prominent advantages of intelligent technologies. It also identifies multi-level implementation challenges, proposes targeted countermeasures, and prospects future research directions.
Artificial intelligence; Estate management; Intelligent security
Research on the Impact of New Agricultural Productivity on Agricultural Export Trade
The new quality agricultural productive forces are the core driving force for promoting agricultural modernization and high-quality development. Based on the panel data of 31 provinces in China from 2012 to 2022, this paper empirically examines the impact of new quality agricultural productive forces on agricultural product export trade and its mechanism of action. The research findings are as follows: (1) The new quality agricultural productive forces significantly promote the growth of agricultural product export trade, and this conclusion remains valid after robustness checks; (2) The mechanism analysis indicates that agricultural technical efficiency plays a partial mediating role in the promotion of exports by new quality agricultural productive forces; (3) Agricultural insurance has a significant positive moderating effect, which can enhance the promoting effect of new quality agricultural productive forces on exports. This study provides empirical evidence for understanding the relationship between agricultural technological innovation and trade competitiveness, and offers directions for policy formulation: it is necessary to continue to increase support for agricultural science and technology, improve the agricultural insurance system, and further unleash the export-promoting effect of new quality agricultural productive forces by enhancing technical efficiency.
New agricultural productivity; Agricultural export trade; Quality and efficiency improvement; Sustainable development
A Comparative Study on the Development Paths of Large-Scale Architectural Technologies in Ancient Eastern and Western Civilizations
Taking the period from 4000 BC to the Renaissance as its temporal span, this paper systematically compares the differences in the development paths of large-scale architectural technologies between the Eastern architectural system represented by the Sinic cultural sphere and the Western architectural system represented by the circum-Mediterranean cultural sphere. The study finds that both Eastern and Western architectural technologies originated in rammed earth and simple stone-brick structures in their early stages. However, due to differences in geographical environment, climatic conditions, and social demands, they subsequently evolved along distinctly different trajectories. Western architecture followed a technological path of “stone and brick–column orders–arches–domes–pendentives,” placing emphasis on breakthroughs in spatial span and vertical height. In contrast, Eastern architecture followed an evolutionary route of “rammed earth–timber frame–dougong brackets–column reduction method,” stressing the refinement of the timber structural system and structural equilibrium. This divergence not only reflects the diversity of technological choices, but also embodies the unique understandings of the relationship between humanity and nature in different civilizations. This paper argues that there is no single superior path in the development of architectural technology; rather, each path represents the crystallization of wisdom through which different civilizations adapted to their own environmental conditions.
A Phenomenological Reflection on the Digitalization of Dance Education
This paper aims to conduct a systematic philosophical reflection and critical analysis of the increasingly prevalent digital wave in the field of dance education. Moving beyond mere praise for the efficiency of technological tools, it delves into the profound impact of digitalization on authentic dance experiences and teaching ethics. Grounded in the dual theoretical frameworks of technological phenomenology and epistemology of the body, the study seeks to reveal how mainstream digital technologies—such as motion capture, virtual reality, and artificial intelligence—systematically simplify and reconstruct the continuous, emotionally rich, and intentional bodily practice of dance into a collection of discrete, quantifiable, and computable movement parameters through their inherent "data-driven" operational paradigms. The core argument of this paper is that this fundamental process of "dimensional reduction" leads to the loss of essential emotional texture, spontaneous creativity, and cultural significance in dance, effectively "obscuring" the ontological essence of dance. To substantiate this claim, the study extensively draws upon and expands Michael Polanyi’s classic theory of "tacit knowledge," eloquently demonstrating that the "kinesthetic intelligence" at the heart of dance, the "vital rhythm" generated within specific cultural contexts, and the "instantaneous ethical choices" embedded in live improvisation all ontologically resist complete formalization and computational modeling by existing algorithmic frameworks. The critique presented here does not aim to negate technology per se but rather advocates for a fundamental shift in the perception of technology: from an instrumental rationality focused on "monitoring" and "replacing" the body to an "embodied-augmentation" paradigm that views technology as an "extension of the body" and an "amplifier of perception." The ethical mission of future dance education technology should be to deepen and expand the experiential dimensions of the body and foster more profound intersubjective interactions, rather than simplifying or disciplining the body. Ultimately, this study calls for the establishment of an ontological value-ethical framework centered on the body in the design, application, and evaluation of dance education technology, ensuring that technological progress consistently serves the generation of artistic aura and the holistic development of individuals, rather than undermining them.
Research on the Collaborative Intervention Model of Digital Empowerment of College Freshmen with Adjustment Disorder
The adaptation problem of college freshmen is essentially a cultural shock and identity reconstruction dilemma encountered by individuals at a critical turning point in their life course. Based on collaborative governance and ecosystem theory, this study attempts to construct a paradigm of adaptation support with digitalization as the medium and relationship reconstruction as the center. This paradigm uses technology as a "social medium" to promote dialogue and collaboration among multiple subjects, and uses digital platforms to reshape support networks and transform interventions from administrative management to community cultivation. Practical cases from Nanjing University and Peking University show that the effectiveness of digital empowerment is not only reflected in the optimization of processes, but also in promoting meaning sharing between homes, schools, and communities, enhancing the sense of belonging and self-efficacy of freshmen, and creating an immersive and supportive cultural environment. This study hopes to fill the gap between technical logic and humanistic care, and reshape the university education ecology with temperature and effectiveness in the digital era.
Research on the Application Effectiveness of Artificial Intelligence in Empowering Automatic Waste Recycling Machines
With the deepening of environmental awareness and the concept of circular economy, the waste recycling industry is undergoing a transformation towards intelligence and automation. This study takes artificial intelligence (AI) technology as the core and analyzes its application efficacy in automated waste recycling machines. Experiments show that the classification accuracy of a deep learning-based image recognition model exceeds 92%. The average daily processing capacity of a single machine has increased by 170%, the purity of resource recycling has improved by 12 - 18%, and each machine can reduce carbon emissions by 1.2 tons annually. Through case studies in Chongqing, Shanghai, and at an enterprise, it has been verified that AI technology possesses universality and industrial synergy effects, addressing the pain points of traditional models. Although the initial equipment investment has increased by 35%, the investment payback period can be controlled within 2.8 years. Based on this, the study proposes recommendations such as strengthening the research and development of multi-modal data fusion technology, promoting the deployment of lightweight and efficient models, and constructing a full-chain intelligent recycling system. In the future, the integration of AI with other technologies can be explored, and cross-disciplinary collaborative innovation can be strengthened. This study provides theoretical support for the industrialization of intelligent recycling equipment and holds significant practical guiding value for promoting the construction of "zero-waste cities" and achieving the "dual carbon" goals.
Synergistic Development of Technological Innovation in the Guangdong-Hong Kong-Macao Greater Bay Area: Realistic Constraints and Optimization Paths
The synergistic development of technological innovation in the Guangdong-Hong Kong-Macao Greater Bay Area serves as a crucial lever for the national strategy to advance regional innovation layouts. It aims to create an international technology and innovation hub with global influence by integrating the innovative resources of Guangdong, Hong Kong, and Macao. In recent years, the Greater Bay Area has made significant progress in collaborative innovation, primarily reflected in increased R&D investment, enhanced capacity for joint research on key technologies, and increasingly abundant innovation resources. However, regional collaborative innovation still faces numerous challenges. These mainly include: poor coordination of rules due to institutional differences; uneven distribution of innovation resources; restrictions on the cross-border flow of research funding and talent; inadequate mechanisms for intellectual property protection and commercialization; and a regional dynamic where internal competition often outweighs cooperation. Based on this analysis, it is necessary to further promote the alignment of institutional mechanisms, deepen reforms in the market-based allocation of production factors, and construct a cross-regional innovation community. These measures are essential to break through the current bottlenecks and unleash the full potential of the Greater Bay Area's innovation system.
Guangdong-Hong Kong-Macao Greater Bay Area
Functional Design and Engineering Application Challenges of Activated Alumina-Based Materials in Groundwater Remediation
Currently, groundwater in China faces significant challenges from heavy metals such as fluorine and arsenic, as well as various emerging organic pollutants. Activated alumina-based materials are evolving from traditional single adsorption functions toward multifunctional composite environmental material systems. From the dual perspectives of practical application and environmental impact, this article briefly reviews recent advances in the functional design and engineering application challenges of activated alumina-based materials in groundwater remediation. Through methods such as surface functionalization, multi-scale structural compositing, and interfacial reaction modulation, activated alumina-based materials can achieve selective treatment and degradation of specific contaminants in groundwater. Concurrently, challenges related to long-term stability, regenerability, and compatibility with multi-technology coupling under dynamic hydrogeological conditions must be addressed. By analyzing experimental approaches such as metal/non-metal modification and organic-inorganic hybridization, which enhance the adsorption and catalytic functions of materials, this article explores novel non-radical catalytic pathways based on Lewis acid sites. Through a multidimensional integration of materials, interfaces, processes, and systems, future research directions and principles for practical engineering application design are proposed, providing theoretical references for the advancement of groundwater remediation technologies.
Research on the Route of Integrating Cultural Confidence into Corporate Culture Construction under the Background of Cultural Power
Corporate culture is the spiritual strength of enterprise development, and cultural confidence is an important guarantee for the cohesion and soul building of corporate culture. Integrating cultural confidence into the construction of corporate culture in China under the background of building a strong cultural nation can help guide enterprises to establish correct business concepts and values, and promote their development towards a healthy and sustainable direction. Firstly, based on a brief description of the connotation of cultural confidence, the importance of cultural confidence in corporate culture construction is proposed. Then, from the perspective of cultural confidence, this article elaborates on the problems in the construction of corporate culture in China from five aspects: traditional culture, advanced culture, regional culture, personal culture, and employee culture. Finally, it is pointed out that the integration of corporate culture construction into cultural confidence needs to be promoted collaboratively from three levels: ideology, system, and practice, and specific integration paths are proposed.
Research on the“Semantic Reversal”Mechanism and Precise Governance of Dialect-Based Public Sentiment in a Multimodal Context:A Cross-Platform Analysis Based on Chongqing's“Fat Cat Incident”
Addressing the challenges of modal fragmentation and dialect-based“semantic divides”in multimodal online public opinion governance, this study employs the Chongqing“Fat Cat Incident”as an empirical case to construct a dialect-context-oriented cross-modal semantic alignment model (D-CMSA).Through cross-platform comparative analysis of 8,542 Weibo posts and 312 high-engagement Douyin videos,we measured the emotional evolution trajectories of dialect vocabulary and the mobilization mechanisms of visual symbols.Empirical validation confirms that the D-CMSA model possesses the capability to uncover deep public sentiment features difficult to capture through single-modality analysis.These features can be distilled into two core effects:First, the sentiment amplification effect,where the co-occurrence of negative audiovisual symbols increases negative sentiment intensity by an average of 42.9%. Second,the semantic reversal effect:28.5% of samples demonstrate that dialect texts with neutral surface semantics are reconfigured into ironic or resentful symbols within specific visual contexts,exhibiting significant phased semantic drift characteristics.This study proposes establishing a dynamic dialect sentiment lexicon to address semantic drift and employs cross-modal consistency verification algorithms to prevent covert dissemination in public opinion governance.
Monitoring and Genesis Analysis of Surface Deformation in Shenxianpo Coal Mine, Liupanshui Using SBAS-InSAR Technology
The karst landscape of Guizhou is notably developed, with complex geological conditions where surface deformation often exhibits characteristics of concealment, abruptness, and nonlinearity. Traditional ground-based monitoring methods are limited by terrain and accessibility, making it difficult to achieve comprehensive coverage of the study area. Differential interferometric radar technology is prone to interference from temporal-spatial decorrelation and atmospheric errors under complex topographic conditions. Based on 54 scenes of Sentinel-1A satellite data from the Shenxianpo Coal Mine in Liupanshui, this study applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to analyze the time-series characteristics of surface deformation. The results indicate that during the period from January 12, 2023, to January 1, 2025, the mining area exhibited a non-uniform subsidence trend from west to east, with a maximum cumulative subsidence of approximately 138 mm and a maximum subsidence rate of about 62 mm/a. As mining activities continued, surface subsidence showed an accelerating trend, and the affected area gradually expanded, revealing a significant correlation between mining intensity and surface deformation. This study verifies the efficiency and reliability of the SBAS-InSAR technique in monitoring surface deformation in mining areas, providing a reference case for early warning of geological hazards and safety management in similar mining regions in southwestern China.
Economic Drive of the Musical Technology Revolution: A Case Study of City Pop
This paper focuses on Japan's bubble economy period from the mid-to-late 1980s to the early 1990s, aiming to delve into how macroeconomic prosperity systematically drove a technological paradigm shift in the field of popular music production and thereby gave birth to and defined the iconic music genre of City Pop. Rather than viewing music styles as mere cultural reflections, the study adopts an interdisciplinary approach from the intersection of technological sociology and industrial economics, demonstrating how economic capital, through the acquisition of cutting-edge equipment, restructuring of production processes, and shaping of consumer markets, completely revolutionized the infrastructure of music production. The core empirical section of the paper centers on the technological practices of two arrangers, Motoyoshi Funayama and Kyohei Tsutsumi, analyzing in detail how they, as "digital pioneers" and "fusion masters" respectively, applied computer music systems such as the Fairlight CMI, multi-track digital recording technology, and complex vocal arrangement concepts to their compositions, laying the precise and ornate auditory foundation for City Pop. Meanwhile, the study uses the production cases of top idols Nakajima Miyuki and Momoko Kikuchi as a lens to reveal how, under high-budget production models, resources were channeled into recording, arrangement, and performance to elevate idol products to the level of near-artistry. Ultimately, this paper posits that City Pop represents the "technological realization" of the bubble economy in the acoustic realm, with its rise and fall not only reflecting economic cycles but also leaving a lasting technological legacy and aesthetic symbols. It offers important insights for understanding the deep interplay between economic conditions and cultural and artistic production.
Research on the Reconstruction Strategy of Rock Music Lyrics from the Perspective of Mediatization
In the era where media platforms deeply participate in content production, the textual presentation logic of Chinese music variety shows has undergone significant changes. After rock works enter mainstream variety shows, they often undergo systematic lyric adjustments. This paper uses the mediaization theory as the analytical framework, based on the "original lyrics - broadcast lyrics" sentence-by-sentence comparison of more than thirty works in programs such as "Summer Band", and adopts descriptive content analysis and text comparison analysis to summarize the structural characteristics and operational paths of lyric rewriting in the program context. The research finds that under the joint influence of platform rules, broadcasting norms, and narrative rhythm, the program production tends to achieve "broadcastable expression" through language neutrality, semantic intensity adjustment, and image generalization; as a result, expressions in the original work that are highly directional, have high emotional intensity, or have a high risk of cross-cultural understanding are weakened or restructured, but social experience and young emotions do not disappear; instead, they are retained in metaphorical, emotional, and positive ways. This paper can provide references for rock lyric creation, variety show text construction, and understanding of youth culture.
Research on the Practice and Optimization of Technology Transfer for Lithium-Ion Battery Cathode Materials
As a core component of power batteries for new energy vehicles and energy storage systems, cathode materials play a pivotal role in determining the pace of industrial chain upgrading through their technology transfer efficiency. This paper takes Fangyuan Group as a case study to examine its approaches and outcomes in technology transfer. It identifies key challenges currently constraining Fangyuan’s scale of technology commercialization, including mismatches between production capacity and market demand, limitations inherent in its demand-driven R&D model, and insufficient talent reserves coupled with inadequate incentive mechanisms. To address these issues, the study proposes targeted optimization strategies: establishing collaborative mechanisms with research institutions and technology transfer agencies, deepening engagement with end-users, strengthening intellectual property protection, and enhancing the human resource system dedicated to technology transfer and commercialization. These recommendations aim to provide practical insights for cathode material enterprises seeking to overcome bottlenecks in technology transfer.
Industry-academia-research collaboration
From Self-help to Transcendence: A Study on the Intervention Mechanism of AI-supported Library Reading Therapy on the Inferiority Complex of Poor Students
Under the background of the popularization of higher education, poor college students, due to multiple factors such as economic pressure, social comparison and resource limitations, generally have varying degrees of inferiority complex, which seriously affects their academic development and personality integrity. The traditional mental health intervention model is confronted with challenges such as limited coverage, obvious privacy concerns and insufficient personalization. This thesis explores the combination of artificial intelligence technology and library reading therapy to construct a new intervention mechanism for the inferiority complex of poor students. The research first analyzes the special manifestations and causes of inferiority complex among impoverished students, and then systematically expounds how AI technology emempower reading therapy to achieve precise identification, personalized matching and dynamic assessment. On this basis, a systematic intervention model including four major links: intelligent recognition and evaluation, personalized resource matching, immersive healing scenarios, and effect evaluation and feedback is designed, and its practical path is illustrated through cases. Finally, countermeasures and suggestions are put forward for potential challenges such as technological dependence, the digital divide, and privacy protection. Research shows that AI-supported library reading therapy can provide low-cost, easily accessible and highly privacy-protected psychological intervention services for impoverished students, helping them achieve psychological growth from "self-help" to "transcendence", and providing a new model for the innovation of mental health education in colleges and universities.
Digital Empowerment: An Initial Exploration of Multidimensional Innovation and Integrated Development in Sports Technology
This article is based on the background of the digital transformation of sports, aiming to systematically sort out the innovative practices and integration paths of sports technology in multiple fields, in order to provide reference for promoting the development of sports research and application. The study first explains the inevitability of digital technology driving the paradigm transformation of sports science, and then constructs the core system of digital sports technology around four pillars: sports physiological and biochemical monitoring, intelligent intervention and health care, sports big data and artificial intelligence applications, and intelligent equipment and scene research and development. On this basis, the article combines special sports digitalization, emerging therapy applications, sports for special populations, and industrial ecology to analyze the current progress and challenges. Finally, countermeasures and suggestions are proposed from the aspects of strengthening top-level policy design, deepening the integration of industry, academia, and research, strengthening ethical and data governance, and promoting inclusive development, in order to provide theoretical support and practical guidance for building a higher-level public service system for national fitness and serving the Healthy China strategy.
National Cultural Digitization Strategy: Connotation, Practice and Prospect
Culture is the soul of a nation. Amid the rapid advance of information technology, a global tide of digitization has swept across every field, making the deep convergence of culture and digital technology an historical inevitability. With keen foresight, the CPC Central Committee and the State Council have taken the major strategic decision to launch the National Cultural Digitization Initiative, charting a clear course for the high-quality development of China's cultural sector and cultural industries. This paper probes the connotation and significance of the National Cultural Digitization Strategy, reviews the policy measures adopted by the central and local governments and their implementation, sums up current achievements and challenges, and looks ahead to future trends. It aims to provide useful reference for fully implementing the National Cultural Digitization Initiative and for accelerating the digital transformation of cultural industries and the building of a culturally strong country.
Technological Innovation and Practical Progress in Mine Ecological Restoration from the Perspective of AI and Big Data Empowerment
With the continuous intensification of mineral resource development in China, ecological issues in mining areas have become increasingly prominent. Traditional ecological restoration technologies exhibit significant limitations in restoration efficiency, monitoring methods, and intelligent decision-making. In recent years, the rapid development of artificial intelligence (AI) and big data technologies has introduced new technical pathways and governance models for mine ecological restoration. From the perspective of AI and big data empowerment, this paper systematically reviews the current research background, mainstream methods, and typical challenges in mine ecological restoration. It focuses on summarizing the latest application advancements of AI and big data in ecological damage identification, dynamic monitoring, restoration effect evaluation, and intelligent decision support. The study reveals that deep learning algorithms based on remote sensing imagery, damage identification systems driven by knowledge graphs, and comprehensive restoration supervision systems built on big data platforms have been successfully implemented in multiple mine restoration cases. The paper also analyzes current technical challenges in data standardization, model interpretability, and cross-regional adaptability, proposing future efforts should focus on multi-source heterogeneous data integration, construction of industry-specific intelligent models, and coordinated policy-technology advancement to achieve digital, intelligent, and refined transformation in mine ecological restoration. This research provides technical support and theoretical references for green mining development and ecological civilization construction.
Paradigm Shift in Banking Compliance Governance Driven by Artificial Intelligence
With the deep integration of technologies such as generative artificial intelligence, compliance governance in the banking sector is facing fundamental challenges arising from "algorithmic black boxes," model hallucinations, and data ethics. Based on representative practices in the global banking industry from 2024 to 2025, this study systematically analyzes the paradigm shift in AI-driven compliance governance—from "passive response" to "active embedding," and from "tool application" to "operating system-level capabilities." The analysis indicates that leading institutions are transforming compliance into a core competitive advantage by constructing automated governance frameworks covering the entire lifecycle, advocating for the ethical principle of "building trust through design," and promoting collaborative innovation within "regulatory sandboxes." The research suggests that the future competitiveness of the banking industry will depend not only on the diversity of technological applications but also on whether governance systems can internalize ethical constraints as technical foundations, thereby establishing a dynamic balance between innovation and stability.
From Linear to Networked: The Paradigm Revolution and Industrial Reconstruction of Documentary Interactive Communication in the Era of Digital Intelligence
In the era of digital intelligence, the deep integration of digital technologies and artificial intelligence has reconstructed the media ecosystem, leading to a profound transformation in documentary communication modes from linear, one-way transmission to networked, bidirectional interactive communication. Based on the theoretical framework of interactive communication and incorporating typical domestic and international cases as well as industry practices, this paper systematically explores the internal mechanisms, technological support, and practical pathways of documentary interactive communication. The study finds that interactive documentaries achieve a dynamic balance among "communicators-content-users" through nonlinear narrative structures, multimodal interactive interfaces, and user-participatory creation. However, they face challenges such as technological adaptability, content ethics, and commercial sustainability. The research proposes a "technology-content-user" three-dimensional collaborative strategy to provide theoretical support and practical references for the digital transformation of the documentary industry.
A Comparative Study on the Language Aesthetics of AI Synthetic Streamers and Real Streamers
With the rapid iteration of "smart media" technology, AI synthetic anchors have widely emerged in scenarios such as news broadcasts, variety shows, and e-commerce live streaming, and they are reshaping the traditional landscape of broadcasting and hosting. Compared with previous studies on technical routes and industrial impacts, the analysis of the differences in "language aesthetics" between AI-generated live-streamers and real live-streamers remains limited. After systematically reviewing relevant literature, this paper introduces theories such as embodied communication, media affordance, and the aesthetics of audible language to conduct a comparative analysis of AI synthetic anchors and real anchors in terms of voice form, language style, emotional expression, and value implications, revealing the advantages of AI synthetic anchors in controllability, efficiency, and standardization. And there are deficiencies in terms of breath rhythm, emotional texture and cultural connotation. In the future practice of news dissemination, a "human-machine collaboration" pattern of anchors may emerge. Ai-synthesized anchors will undertake structured and regular information broadcasting, while real anchors will maintain the high ground of language aesthetics through their presence and individual style.
"AI Poet" in the Classroom: A Study on the Creative Teaching Model of Poetry in Primary and Secondary Schools Empowered by Technology
Currently, poetry teaching in primary and secondary schools faces problems such as rigid teaching models, single teaching methods, and students' lack of emotional experience. To effectively address this dilemma, this study proposes a creative teaching model integrated with generative artificial intelligence (AI) technology. With "AI Poet" as an intelligent auxiliary tool, the core of this model lies in using natural language processing, virtual reality and other technologies to reconstruct the teaching process of "experience-interaction-creation" through approaches such as situational creation, virtual dialogue, intelligent analysis and poetic extension. Combined with the teaching practice case of Sending Yuan Er to Anxi, the study elaborates on the implementation methods and effects of this model. The results show that this model can effectively stimulate students' interest in poetry learning, promote the development of their innovative thinking and aesthetic ability, and drive the transformation of teaching objectives from knowledge transmission to core literacy cultivation. It provides a referential paradigm for the reform and innovation of poetry teaching in primary and secondary schools.
Research on Hybrid Decoding of LDPC Codes Based on QUBO Modeling and Quantum Annealing
Low-Density Parity-Check (LDPC) codes, as a class of high-efficiency error-correcting codes with sparse parity-check matrices, have demonstrated enormous potential in the field of communications since their rediscovery, and have become a core coding scheme especially in the fifth-generation (5G) wireless communication systems. To address the decoding problem of LDPC codes in complex channel environments, this paper proposes an innovative quantum-classical hybrid decoding architecture. By transforming the decoding problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem, the architecture leverages the parallel processing capability of coherent optical quantum computers to accelerate the iterative decoding process. The research focuses on two typical scenarios specified in the 5G standard: the high-throughput scenario (dominated by BG1 with long code length and high code rate) and the low-complexity scenario (dominated by BG2 with short code length and low code rate). Through establishing a rigorous mathematical model, a hybrid decoder based on quantum annealing and the belief propagation algorithm is designed in this paper, and the Bayesian optimization method is adopted to adaptively adjust key parameters such as the maximum number of iterations and damping factor. This research provides an important theoretical foundation and practical scheme for the practical application of quantum computing in the field of communications.
Review of Metal-Organic Frameworks (MOFs) in Electronic Strain Skin
With the continuous development of flexible electronic technology, electronic strain skin, as an important component that mimics human skin's ability to perceive external strains, has attracted increasing attention. Metal-organic framework (MOF) materials are particularly distinctive for electronic strain skin due to their tunable porous structure, high specific surface area, excellent flexibility, and chemical stability. This paper summarizes the latest applications of MOF materials in flexible strain sensors, focusing on their roles in enhancing sensor sensitivity, response speed, and cyclic stability. By rationally designing the structure and composition of MOFs to improve the signal conversion performance and mechanical durability of electronic skin, they can play a significant role in various fields such as health monitoring, human-computer interaction, and soft robotics. Although remarkable achievements have been made in current research, challenges still exist in aspects like large-scale material preparation, environmental adaptability, and long-term stability. Future research should continue to explore methods for improving the interface between MOFs and flexible substrates, and develop novel composite structures, thereby expanding the potential of their applications in multifunctional electronic skin.
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Research Review on AI-Driven Fault Prediction and Diagnosis Methods for Power Systems
In the current era of deep integration between energy transition and digitalization, power systems as critical infrastructure are facing increasingly complex operational challenges. The rapid development of artificial intelligence (AI) technology has injected new theoretical momentum and methodological support into the field of fault prediction and diagnosis. This paper systematically reviews the evolution from traditional physics-based diagnostic mechanisms to data-driven intelligent paradigms, with a focus on analyzing the integration pathways of cutting-edge AI methods such as deep learning, graph neural networks, and transfer learning in power equipment condition monitoring, fault feature extraction, and causal inference. The research demonstrates that AI technology can significantly enhance the representation capabilities of high-dimensional, non-stationary power system data, enabling early warning of potential faults and accurate identification of multi-source faults, while showing distinct advantages in addressing uncertainties brought by renewable energy integration. However, current research still faces challenges including poor model interpretability, inadequate adaptability to small-sample scenarios, and incomplete cross-domain knowledge transfer mechanisms. Future research should focus on constructing hybrid intelligent diagnostic frameworks combining physical constraints with data-driven approaches, exploring lightweight model deployment strategies for edge computing, and strengthening interdisciplinary theoretical integration to promote the autonomous and intelligent development of power system fault management.
AI Technology Reshaping the Securities Industry: Model Transformation, Essence Interpretation, and Development Pathways
With the rapid development of artificial intelligence technology, the securities industry is undergoing unprecedented and profound changes. This paper systematically analyzes the practical application of AI technology in the front, middle, and back offices of securities institutions and across various business processes, delving into the underlying technological driving logic and the essential transformations within the industry. The study reveals that AI technology has evolved from an auxiliary tool to a core infrastructure of the securities industry. Applications such as Huatai Securities' "AI Zhang Le" are propelling intelligent investment services from a "functional" to an "intelligent" leap. This paper interprets the essential characteristics of the AI-driven securities industry from three dimensions: technology acceptance, service models, and risk profiles. It highlights current challenges such as model hallucination, data security, and ethical misconduct, and proposes a tiered development pathway and regulatory recommendations. The research indicates that AI technology is reconstructing the value creation model of the securities industry, driving its transformation from experience-driven to data- and intelligence-driven.