Journal of science and technology exploration
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.
AI-Driven Adaptive Scheduling and Dynamic Allocation of Manufacturing Resources
As global market competition intensifies and customer demand becomes increasingly personalized, the limitations of traditional rigid production models have become salient, making flexible manufacturing a core mandate of intelligent manufacturing. Focusing on the digital thread spanning design, process planning, and production, this paper investigates how artificial intelligence (AI) enables adaptive scheduling and dynamic allocation of manufacturing resources. We first analyze the constraints of conventional production scheduling systems in responding to real-time disruptions, and then construct a digital-thread-based AI adaptive scheduling framework. Integrating multi-source heterogeneous data—such as real-time order streams, machine status, and material inventories—and leveraging AI algorithms including deep learning and reinforcement learning, the framework achieves autonomous decision-making and dynamic optimization of production planning. We then examine disturbance-aware dynamic scheduling strategies, load-balancing-based elastic resource allocation, and the application of digital twin technology for strategy simulation and validation. Finally, a simulation case demonstrates the effectiveness of the proposed approach in shortening order lead times, improving equipment utilization, and enhancing system robustness against disruptions, followed by a discussion of future research directions.
Construction and Application of an Intelligent Q&A System for Traditional Chinese Medicine Based on Knowledge Graphs and Artificial Intelligence
With the deep integration of artificial intelligence (AI) and traditional medicine, the digitization and intelligentization of Traditional Chinese Medicine (TCM) have become important research directions. This paper proposes an intelligent TCM question-answering (QA) system based on knowledge graphs and natural language processing (NLP). By structurally representing TCM knowledge and constructing a multi-dimensional relational graph, and by combining deep learning models, the system enables intelligent QA and personalized health analysis. It integrates classical TCM texts, formulas, medicinal materials, and clinical data, and employs techniques such as BERT fine-tuning and graph neural network (GNN)–based dynamic inference to achieve precise understanding and answering of users’ natural-language questions. Experiments show strong performance in answer accuracy, response speed, and user satisfaction, demonstrating solid practicality and scalability. The study provides a feasible pathway for the modernization and intelligent service of TCM knowledge.
A Study on the Interrelationship between Information System Construction and Sustainable Development
Against the backdrop of a steadily deepening global commitment to sustainable development, information system construction—as a core driver of socioeconomic progress—has become increasingly intertwined with sustainability goals. This paper focuses on the interactive relationship between information system construction and sustainable development, analyzing the pathways through which information systems contribute to sustainability processes, as well as the new requirements that the sustainability paradigm imposes on information system construction. By examining practical applications of information systems in areas such as efficient resource allocation, environmental regulation and governance, and the coordination of social equity, the study identifies current problems and challenges that arise when information system construction serves sustainable development, and proposes targeted optimization pathways. The findings indicate that deep integration between information system construction and sustainable development is a key initiative for achieving coordinated advancement across the economy, society, and the environment. Looking ahead, it will be essential to strengthen technological innovation, improve institutional systems, and enhance public participation to foster a virtuous cycle of mutual reinforcement between the two.
Deep Integration of Artificial Intelligence and FinTech: Application Scenarios, Practice Cases, and Governance Pathways
In the digital economy era, the key for the financial industry to keep pace with the times lies in rapidly adapting to and appropriately leveraging iterative technologies to empower finance, making it more “precise,” “rapid,” and “secure.” Confronted with a fast-changing market environment, the deep application of artificial intelligence (AI) facilitates innovation in product forms, service models, and business processes within financial institutions. Through the use of intelligent algorithms, deep learning, and data mining, innovative solutions are proposed for subfields such as green finance and technology finance, further advancing the digital-intelligent transformation of financial services, reshaping the connotation and extension of the financial industry, and effectively improving the quality and efficiency with which finance serves the real economy. AI has become the core engine driving FinTech innovation; via the co-evolution of “data integration–algorithmic evolution–computing power support,” it reshapes the boundaries of financial services. This paper reviews the differentiated practices of the Industrial and Commercial Bank of China (ICBC), Agricultural Bank of China (ABC), Bank of China (BOC), China Construction Bank (CCB), Bank of Communications (BoCom), and China Merchants Bank (CMB); constructs a regulatory system featuring “tiered and categorized approaches + regulatory technology (RegTech) + international coordination”; and, in response to challenges such as talent gaps (120,000 in 2024) and insufficient technological maturity, proposes countermeasures of “industry–academia–research cultivation, finance-oriented optimization, and multi-dimensional risk control.” The study shows that in 2024 China’s AI penetration in finance reached 48.7%, with average industry operational efficiency improving by 35%. Looking ahead, the sector will move toward greater “precision, security, and inclusiveness.”