Journal of science and technology exploration
《科学与技术探索》（Journal of Science and Technology Exploration，ISSN 3105-5915，eISSN 3106-6313）是一份致力于推动科学研究与技术创新发展的中文学术期刊。本刊以服务科研人员、工程技术人员与高校师生为宗旨，聚焦科学前沿突破与技术应用转化，关注基础科学进展、工程技术创新、智能化与信息化发展，以及科技成果产业化进程，致力于为科学研究与技术创新搭建高水平学术交流与成果展示的平台。
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.
Copyright (c) 2026 Journal of science and technology exploration https://creativecommons.org/licenses/by-nc/4.0
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.
Copyright (c) 2025 Journal of science and technology exploration https://creativecommons.org/licenses/by-nc/4.0
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.
Research on the planning and application of artificial intelligence data mining in intelligent automobile enterprises
As the global automotive industry accelerates its"data-driven"transformation, intelligent automakers face challenges in effectively leveraging massive data assets through traditional methods. AI data mining has emerged as a critical breakthrough. This paper explores the application of AI data mining technologies in smart automotive enterprises, proposing key technologies (data preprocessing, machine learning, deep learning), comprehensive value chain planning (R&D, design, production, operations, services, user engagement), and practical implementations (BYD's end-to-end data applications, NVIDIA security solutions). It summarizes core AI data mining technologies (data security, algorithm stability, talent development) and corresponding strategies. Through strategic planning and practical applications of AI data mining, this study drives the"data assetization"of intelligent automakers, providing valuable insights for the industry's intelligent transformation.
Digital Transformation Ushers in a New Era for the Insurance Industry
Amid the global wave of digital transformation in the insurance industry, artificial intelligence technology is evolving from an auxiliary tool to core infrastructure, driving a profound paradigm revolution within the sector. Based on the agent theory framework, this paper systematically explains the application mechanisms of AI technology in key areas such as insurance business process re-engineering, product innovation, and risk management. By constructing an agent model of "Environmental Perception - Autonomous Decision-Making - Collaborative Execution - Continuous Evolution," it provides an in-depth analysis of the internal operational logic and development path of insurance agents. Research indicates that insurance agents, through four core processes—multi-source data perception, deep learning decision-making, automated execution, and continuous evolution—propel the industry's transformation from traditional experience-driven models to data-and-intelligence-driven ones. This paper conducts in-depth analyses of representative cases, including ZhongAn Insurance, Taikang Online, and Pacific Health Insurance, to validate the applicability and effectiveness of agent theory in insurance practice, offering both theoretical support and practical references for the industry's intelligent transformation. The study finds that AI technology not only significantly enhances the operational efficiency of insurance businesses but also exerts a profound impact on risk pricing, product innovation, and service experience. However, it simultaneously faces multiple challenges such as model reliability, data security, and ethical governance, necessitating the synergistic advancement of technological iteration, regulatory innovation, and organizational adaptation.
AI-empowered Library Reading Therapy: A Study on the Path to Alleviate the Inferiority Complex of Poor Students in Western Universities
This article focuses on the issue of inferiority complex among impoverished students in western universities, exploring how AI technology can empower library reading therapy and establish an effective psychological intervention path. Studies show that poor students in western colleges and universities are prone to have a strong sense of inferiority due to factors such as economic pressure, social environment, school education and personal cognition. Traditional reading therapy has problems such as limited resources and insufficient personalization. By constructing a four-dimensional path of "data-driven - resource construction - technology application - effect evaluation", AI technology can achieve precise identification, personalized book recommendation for reading, intelligent interactive companionship and dynamic effect evaluation, forming a systematic intervention plan. Finally, this paper puts forward relevant implementation suggestions and future prospects, providing theoretical and practical references for alleviating the inferiority complex of poor students in western colleges and universities.
Generative Artificial Intelligence Empowering Education on Forging a Strong Sense of Community for the Chinese Nation: Connections, Dilemmas and Strategies
With the explosive development of generative artificial intelligence (GAI), it has served as a new engine for advancing China's economic and cultural progress, while presenting new opportunities and challenges for education on forging a strong sense of community among the Chinese nation. It is bound to become a novel tool facilitating the development of the Chinese national community. Currently, to better achieve the goal of forging a strong sense of community among the Chinese nation, we should proactively embrace technological advancements and explore the applicability of GAI in this educational domain. GAI can be harnessed to provide new research perspectives and methodological support for such education, mitigate the multiple problems and impacts arising from GAI, and further promote the construction of the Chinese national community.
Analysis of Development Paths and Feasibility Demonstration of Integrated PV-Storage-Charging Solutions
With the continuous growth of electric vehicle ownership and the in-depth advancement of the "dual carbon" goals, traditional charging infrastructure faces challenges such as tight power supply, high capacity expansion costs, and insufficient renewable energy absorption capacity. The integrated PV-storage-charging system, by integrating photovoltaic power generation, energy storage regulation, and intelligent charging technologies, has become an effective path to promote the green transformation of the energy structure. Based on the technical architecture and application scenarios of the integrated PV-storage-charging system, this paper systematically explores its development paths and demonstrates its implementation feasibility from multiple dimensions including technology, economy, and policies, aiming to provide theoretical reference for practice in related fields.
Research status and prospect of intelligent networked line control chassis system development based on artificial intelligence
With the deep integration of the automotive industry and information technology, intelligent connected vehicles have become a major trend in industry development. As a crucial component of smart connected vehicles, the development of wire-controlled chassis systems has garnered significant attention. The research focuses on analyzing the coupling mechanisms between key technologies such as wire-controlled drive, wire-controlled braking, wire-controlled steering, and chassis domain control. By applying artificial intelligence (AI) for environmental perception, decision-making planning, and multi-source information fusion, this approach aims to achieve high dynamic response and collaborative control capabilities in chassis platforms, enabling excellent adaptability in complex scenarios. This paper explores the application of AI technology in the development of intelligent connected wire-controlled chassis systems, outlines key technical pathways, and proposes innovative development solutions. The study aims to provide reference solutions for the engineering implementation of intelligent connected chassis technologies.
With the deep application of artificial intelligence (AI) technology in fields such as computer vision, natural language processing, deep learning training, and inference, AI workloads exhibit characteristics such as high computational intensity, large data throughput, and frequent memory access. Traditional GPU hardware configurations are no longer able to meet their efficient operation requirements. This article aims to improve the efficiency of AI workload operation and reduce resource consumption, focusing on optimizing GPU core hardware components. Firstly, analyze the typical characteristics of AI workloads, including computational parallelism, data locality, and memory access patterns; Subsequently, control experiments were designed to quantify the impact of GPU core frequency, VRAM bandwidth, CUDA core quantity, and multi GPU interconnect architecture on the performance of AI tasks (image classification, Transformer model inference); Finally, an adaptive optimization strategy for GPU configuration based on workload types is proposed, which achieves a balance between performance and energy consumption by dynamically adjusting hardware parameters. The experimental results show that in the ResNet-50 image classification task, the optimized GPU configuration can increase training speed by 23.5% and reduce energy consumption by 18.2%; In the BERT model inference task, latency decreased by 19.8% and throughput increased by 21.1%. This study provides theoretical basis and practical reference for GPU hardware selection and configuration optimization of AI servers.
Research on Teaching Reform of Multi-Agent Applications Based on the Digital-Intelligent Pharmacopoeia Large Language Model
With the deepening advancement of the national strategies "Healthy China" and "Revitalization and Development of Traditional Chinese Medicine (TCM)", the TCM industry is ushering in a historic opportunity for digitalization and intellectualization. However, the current TCM education system suffers from a severe shortage of teaching resources integrating data science and artificial intelligence with medicine, making it difficult to meet the industry's urgent demand for interdisciplinary talents. This project, centered on the "Digital-Intelligent Pharmacopoeia" platform, constructs a teaching experimental platform for TCM data governance based on a multi-agent large language model. It aims to promote systematic reform in the teaching content, methods, and practical mechanisms of higher education in TCM through a deeply integrated "AI + Medicine" teaching model. The platform's core technical feature is its multi-agent collaborative architecture. Through the synergistic workflow of agents such as Classifier, Extractor, Validator, and Matcher, it simulates the real-world data governance processes of the industry, providing students with comprehensive practical scenarios covering the entire workflow from data classification, information extraction, compliance validation, to data fusion. The project represents not only an innovation in technical tools but also a reform in educational philosophy. It is dedicated to cultivating interdisciplinary talents who possess both professional knowledge of TCM and mastery of AI technology, thereby providing intellectual support and talent assurance for the modernization of TCM.
Research on Preventing Religious Networks' Infiltration in Universities: Mechanisms and Implementation Strategies in the AIGC
With the rapid advancement of internet and artificial intelligence technologies, universities have become primary battlegrounds for religious infiltration and ideological competition. Under the AIGC framework, religious forces infiltrating higher education institutions exhibit new characteristics: diversified infiltration vectors, covert methods, and politically charged content. Universities face challenges in preventing such cyber infiltration, including inadequate governance systems, difficulties in identifying religious activities, and insufficient awareness and education among faculty and students. To address these issues, universities must uphold the guiding role of Marxism while establishing effective pathways through technological innovation, institutional reforms, and ideological education. This approach will promote standardized, scientific, and sustained efforts to prevent religious infiltration in academic environments.
Research on Cloud Computing Information System Planning
With the in-depth penetration of cloud computing technology, enterprise information system construction has undergone a comprehensive transformation from "local deployment" to "cloud-native architecture". Cloud computing, with its characteristics such as on-demand elasticity, resource pooling, and cost optimization, has not only reconstructed the technical foundation of information systems but also subverted traditional planning logic. This paper focuses on the planning methodology of information systems in the cloud computing environment. By analyzing core issues faced by enterprises in digital transformation, such as technical adaptation, resource scheduling, and security compliance, it constructs a "strategy-business-technology" three-dimensional collaborative planning framework. It refines the implementation paths of key links including demand modeling, architecture design, resource allocation, and security governance. Combined with a practical case of an automobile manufacturing enterprise in Shenzhen, the feasibility and effectiveness of the framework are verified. This research supplements the theoretical system of information system planning in cloud computing scenarios, provides reusable planning tools and implementation guidelines for enterprises in different industries, and helps them maximize business value and technical efficiency in the cloud environment.
Copyright (c) 2025 Journal of science and technology exploration
Research on One-Stop Construction and Real-Time Monitoring System of Virtual Power Plant Based on Blockchain Technology: Also on the Implementation Path of RWA Tokenization
Driven by the global energy transition and the goal of carbon neutrality, new energy technologies such as energy storage, photovoltaics, and wind power have developed rapidly. The large-scale integration of Distributed Energy Resources (DER) poses severe challenges to the traditional power grid. As a key technology for integrating distributed energy resources, the Virtual Power Plant (VPP) aggregates scattered energy resources through advanced Information and Communication Technology (ICT), enabling optimal resource scheduling and market transactions, and is becoming a core component of the Energy Internet.This paper systematically demonstrates the one-stop construction process and path design of virtual power plants, elaborates on the architectural design and implementation methods of real-time remote monitoring systems, and innovatively introduces blockchain technology to ensure data transparency, security, and traceability. Meanwhile, it deeply analyzes the multi-dimensional application potential of blockchain technology in virtual power plants, providing feasible technical interfaces and channels for the tokenization of Real World Assets (RWA) in the future.By constructing a complete theoretical framework and combining practical case studies, this paper proposes a set of standardized and modular virtual power plant construction systems, covering key links such as resource planning, technical architecture, monitoring systems, and blockchain integration. Research shows that blockchain-based virtual power plants can not only improve the intelligence level and operational efficiency of energy systems but also provide a technical foundation for financial innovation of energy assets, promoting the energy system towards decentralization, transparency, and marketization.
Research on a Three-Level Integrated AI Teaching System Driven by Dynamic Knowledge Graphs
Addressing common challenges in higher education such as complex course knowledge points, fragmented student cognition, and weak practical abilities, this study constructs a three-level integrated AI teaching system characterized by "global navigation via teaching knowledge graphs, in-depth empowerment through AI toolchains, and dynamic iteration of personal knowledge graphs." Teaching experiments were conducted in pilot institutions, where three core graphs—target graphs, dynamic knowledge graphs, and networked problem graphs—were established at the teaching graph level. At the AI tool level, intelligent data analysis was realized through automatic code generation, modeling, and simulation. At the personal graph level, the DeepSeek model was employed to generate personalized knowledge mind maps, which in turn fed back into the teaching knowledge graphs. Experimental results demonstrate that this system effectively enhances students' knowledge integration and practical capabilities, providing a scalable paradigm for the digital transformation of higher education.
Application of Natural Fiber Composites in Lightweight and Environmental Design of Walking Aids for the Elderly
With the acceleration of China's social aging process, changes have been triggered in the elderly product market and industrial structure. At present, China's elderly industry is in the emerging stage, facing problems such as gaps in industrial structure and imbalance between market supply and demand. This paper takes the elderly aged 70-80, who are in the assisted living stage with mild or moderate disabilities, as the target group, and conducts design research on multi-functional intelligent walking aids for the elderly. The purpose is to design a pressure-free walking aid product that meets ergonomic standards, is safe and stable, has relatively complete functions, and conforms to the intelligent elderly care concept in the current mobile Internet era, so as to better meet the elderly's needs for home and travel activities. This paper adopts methods such as literature survey, case analysis, and comparative research [1].This paper focuses on the material design and optimization of walking aids for the elderly, and deeply explores the application of natural fiber composites in the lightweight and environmental design of walking aids for the elderly. By analyzing the performance characteristics of natural fiber composites and combining the functional requirements and usage scenarios of walking aids for the elderly, it studies the application methods and technical paths of natural fiber composites in the structural design, processing technology, and surface treatment of walking aids, so as to realize the lightweight, pressure-free, and environmental protection characteristics of walking aids for the elderly. It also understands the domestic and foreign research status of walking aids for the elderly, as well as the development status of China's elderly industry and the domestic natural fiber composite product market; by summarizing and analyzing the existing walking aid products for the elderly on the market, it identifies the existing pain points and main problems.Experimental and test results show that the use of natural fiber composites can effectively reduce the weight of walking aids while meeting the mechanical performance requirements, and has good environmental advantages. From the five dimensions of safety, stability, functionality, environmental protection, comfort, and aesthetics [4], it provides new ideas and directions for the innovative design of walking aids for the elderly.
The Social Mechanism and Significance of Change in AI-Driven Evolution of Digital Literacy Forms
The popularization of artificial intelligence (AI) reconstructs the interaction logic of the digital world, forcing digital literacy to evolve from the traditional "tool adaptation ability" to an "AI collaborative comprehensive ability" that covers AI collaboration, algorithm cognition, ethical judgment, and content traceability. Through speculative analysis, this paper sorts out the vein of digital literacy's morphological leap from "tool use" to "AI collaboration", analyzes the internal functional logic of four major social mechanisms—technological penetration, demand-driven, institutional guidance, and cultural reconstruction—and explains the in-depth significance of the evolution from three dimensions: the reconstruction of individual competitiveness, the improvement of social inclusion and efficiency, and the consolidation of national digital competitiveness. It provides theoretical support for the cultivation of digital literacy in the AI era.
Constructing and Implementing a Compliance Self-Inspection System for E-Commerce Enterprises under Data-Based Supervision
As tax big-data supervision deepens—linking e-commerce platform data directly with tax authorities’ systems and enabling end-to-end traceability of fund flows—traditional business models face mounting compliance challenges. Taking as an entry point a case in Jiaxing where an e-commerce firm was assessed back taxes and penalties for concealing income by routing receipts through personal accounts, this paper proposes a four-step compliance self-inspection system: “business review – reconstruction of accounting rules – data reconciliation – institutional implementation.” We delineate operational essentials for each phase, including business process mapping, standardized recognition of revenues and expenses, data verification mechanisms, and the establishment of long-term institutional arrangements, thereby providing a practical framework for e-commerce enterprises to mitigate tax risks and achieve compliant operations.