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.
Research on AI-Enabled Project Planning and Schedule Management
Project planning and schedule management directly affect whether a project can be completed within the specified time. Traditional management approaches mainly rely on project managers’ experience to decompose tasks, estimate durations, and allocate resources, while meetings, reports, and manual inspections are used to monitor project implementation. Although this approach offers considerable flexibility, it may lead to insufficient planning evidence, delayed information feedback, and untimely schedule adjustments when projects become larger, task relationships become more complex, and resources change frequently. Artificial intelligence technologies provide new methods for improving project planning and schedule management through their capabilities in data processing, pattern recognition, trend forecasting, and solution optimization. Using literature review and inductive analysis, this study examines the role of artificial intelligence in project planning, task-duration forecasting, resource allocation, schedule monitoring, deviation warning, and dynamic adjustment. The findings indicate that artificial intelligence can improve the accuracy of project planning, help managers identify schedule risks in advance, and provide adjustment recommendations when the project environment changes. However, its application is also constrained by data quality, model applicability, system integration, staff capabilities, and unclear responsibility boundaries. To realize the practical value of artificial intelligence, enterprises should not merely purchase software or deploy algorithms. They should improve project data standards, integrate artificial intelligence into management processes, establish mechanisms for human review and model evaluation, and strengthen project teams’ ability to understand and use data. Artificial intelligence is more suitable as an assistive tool for project managers than as a complete substitute for human judgment. By combining data analysis with management experience, enterprises can improve the feasibility of project plans and enhance the proactive control of project schedules.
artificial intelligence; project planning; schedule management; duration forecasting; human–AI collaboration
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A New Era of Intelligent Disinfection and Sterilization for Elevator Handrails
In high-density urban crowd gathering scenarios, elevators, as the core carrier of vertical transportation, make handrails a high-risk contact interface for cross-transmission of bacteria and viruses. Traditional elevator handrail disinfection methods rely on manual wiping and regular spraying, which suffer from low efficiency, numerous disinfection blind spots, poor sustainability, and high labor costs. These methods can no longer meet the requirements of modern public health safety prevention and control. This paper focuses on the integration of intelligent disinfection technology and elevator handrail scenarios, systematically expounds the design principle, technical architecture, functional modules and application advantages of the intelligent disinfection and sterilization system for elevator handrails. By comparing practical data between traditional and intelligent disinfection modes, it analyzes the application effects of intelligent disinfection equipment in public buildings, rail transit, commercial complexes, hospitals and other scenarios, explores the technical bottlenecks and optimization paths in current industry development, and prospects the future development trend of intelligent disinfection and sterilization for elevator handrails. Research shows that intelligent elevator handrail disinfection technology can achieve fully automatic, uninterrupted, dead-angle-free, low-consumption and high-efficiency disinfection and sterilization effects, significantly reduce the load of pathogenic microorganisms on public contact surfaces, provide new technical support for urban public health safety protection, and truly launch a new intelligent era of elevator handrail disinfection and sterilization.
intelligent disinfection; elevator handrails; sterilization technology; public health; automatic disinfection system
A Study on the Transformation of Traffic Police Enforcement Model in the Era of Big Data
This study explores the transformation of China’s traffic police enforcement model under the influence of big data. Traditional enforcement focuses on on-site enforcement and manual evidence collection, characterized by low transparency and standardization, simplified penalty procedures, and a strong emphasis on efficiency. It faces practical challenges such as inadequate procedural fairness, low transparency, and human resource shortages. With the deep integration of electronic monitoring, vehicle and driver databases, intelligent analysis, and other big data technologies, traffic enforcement is shifting towards proactive governance, data-driven decision-making, and comprehensive oversight. However, there are challenges related to legal compatibility, procedural fairness, algorithm transparency, and public trust when big data is involved in law enforcement. Further institutional and technological responses are urgently needed. To address these challenges, this paper proposes optimization paths from four dimensions: legislative alignment, reformation of enforcement procedures, technological governance, and social participation. Research has shown that big data not only enhances the efficiency and fairness of enforcement but also promotes sustainable traffic management and the building of public trust, laying the foundation for intelligent public governance.
big data; traffic police enforcement; law enforcement transformation; algorithmic governance; public trust
Research on Threat Attribution Mechanisms and Early Warning Strategies for Federated Models in Secure Educational Data Sharing
As educational digital transformation accelerates, the demand for cross-institutional educational data sharing has become increasingly urgent. Federated learning (FL), characterized by its privacy-preserving paradigm of keeping data local while sharing model updates, has emerged as a critical framework for secure educational data sharing. However, FL in educational settings faces multifaceted security threats including gradient inversion, model poisoning, and backdoor attacks, while existing defense mechanisms exhibit significant shortcomings in threat traceability and early-warning response—particularly in dynamic modeling of temporal behavioral evolution of participating nodes. This paper proposes TKG-FedAlert, a threat traceability mechanism and dynamic early-warning strategy framework integrating temporal knowledge graphs (TKG) into federated educational model security. The framework constructs a temporal knowledge graph of educational federated node behavioral patterns, enabling structured modeling of gradient update dynamics, behavioral sequences, and inter-node relationships. A temporal graph attention network (TGAT) combined with temporal anomaly detection achieves precise malicious node traceability and threat warning, while a tiered response mechanism supports real-time intervention. Simulation experiments on realistic multi-university FL scenarios demonstrate that TKG-FedAlert significantly outperforms existing baseline methods in threat traceability accuracy (84.2%), early-warning F1 score (88.4%), and system response latency (1.9 rounds), providing both theoretical foundations and practical pathways for building trusted federated educational data sharing ecosystems.
federated learning; educational data security; temporal knowledge graph; threat traceability; early-warning strategy; graph neural network
Research Review and Future Prospects of Intelligent Fault Diagnosis Technology for Rolling Bearings
Intelligent fault diagnosis for rolling bearings is central to achieving predictive maintenance in industrial equipment. Traditional methods rely on manual feature engineering and exhibit weak generalization under complex operating conditions. Deep learning, through end-to-end learning, can automatically extract fault features from raw data, driving a paradigm shift in diagnostic approaches. Current mainstream methods include deep temporal models that directly process one-dimensional vibration signals (e.g., 1D-CNN) and visual models based on time-frequency images (e.g., Vision Transformer). To address challenges such as data scarcity and varying working conditions, techniques like transfer learning and generative adversarial networks have been widely applied. However, key bottlenecks for practical engineering implementation remain, including poor model interpretability, insufficient robustness under strong noise, weak generalization with small samples, and difficulties in lifetime prediction. Future research should focus on explainable artificial intelligence, digital twin-based data augmentation, physics-informed data fusion, and lightweight edge deployment to build more trustworthy, adaptive, and forward-looking intelligent diagnostic systems.
Rolling bearing; Fault diagnosis; Deep learning; Convolutional neural network (CNN); Transfer learning; Explainable artificial intelligence (XAI); Predictive maintenance
Artificial Intelligence Empowered Nursing Teaching: Application Design and Effect Evaluation
Nursing education faces the dual challenges of rapidly increasing talent demand and uneven distribution of teaching resources. Traditional teaching models have obvious limitations in personalized training and practical skills development. Artificial intelligence technologies, including intelligent teaching systems, virtual simulation platforms, and natural language processing, provide innovative pathways for nursing education. Studies show that AI-assisted teaching can increase students’ average theoretical performance by 18%, shorten the time required to meet operational skill standards by 30%, and improve teaching efficiency by approximately 25%, thereby effectively supporting the construction of an integrated “theory–simulation–practice” teaching system. However, current technological applications still face problems such as insufficient accuracy in simulating complex clinical scenarios, incomplete data security mechanisms, and a decline in the quality of teacher–student interaction. In the future, efforts should focus on three major directions: constructing personalized learning pathways, integrating virtual simulation with augmented reality technologies, and developing data security mechanisms and explainable AI. These efforts will promote interdisciplinary collaboration and the establishment of full-chain intelligent support systems, ultimately achieving an organic integration of technology empowerment and the essence of education.
Artificial intelligence; Nursing teaching; Virtual simulation; Personalized learning; Teaching effectiveness evaluation
Intelligent Obstacle Avoidance System Based on Binocular Vision and Ultrasonic Fusion for Visually Impaired People
Aiming at the shortcomings of current visual impairment assistance devices in transparent obstacle recognition, low-lying object detection, and dynamic target perception, this paper designs and implements an obstacle avoidance system fusing binocular vision and ultrasonic information. The system adopts a master-slave embedded architecture with Raspberry Pi 4B and STM32 as the core. An improved semi-global matching algorithm is introduced at the vision end to support medium and long-distance semantic perception. Meanwhile, combined with a five-direction ultrasonic array, high‑precision near-field detection within the range of 0.1–8.0 m is realized. Multi-source perception data are collaboratively fused in time and space through extended Kalman filter and occupancy grid map, and low-interference prompt information is output via bone conduction voice and vibration cane. Experimental results show that in complex indoor and outdoor environments, the average recognition success rate of the system for various types of obstacles reaches 98.5%, and the early warning delay is about 210 ms. The overall performance is superior to single-sensor schemes. The system does not rely on network and has controllable cost, providing a feasible implementation for universal accessible intelligent assistance equipment.
Visually impaired assistance; Binocular vision; Ultrasonic sensing; Multi-modal fusion; Obstacle avoidance system; Accessibility technology
Research on the Renovation of University Library Reading Spaces and Their Therapeutic Functions from the Perspective of "Emotional Value"
University libraries, as important venues for college students' study and life, are undergoing a profound transformation in their spatial form and service functions from "document storage" to "spiritual refuge". This paper introduces the concept of "emotional value" as a tool to construct a theoretical analysis framework for the emotional value of reading spaces in university libraries, and explores the mechanisms and paths for spatial reconfiguration to achieve therapeutic functions. The study holds that emotional value is the product of emotional interaction between the reading space and readers. The space stimulates readers' emotional experiences through three levels: material carriers, behavioral interactions, and meaning construction, thereby achieving therapeutic functions such as emotional soothing, psychological support, and spiritual growth. Currently, there are problems such as single sensory experience, insufficient behavioral support, and lack of meaning identification in the emotional value supply of university library reading spaces. Based on this, the paper proposes spatial reconfiguration strategies from three dimensions: instinctive level, behavioral level, and reflective level, and combines domestic and foreign typical cases to design the functional positioning and design key points of different types of therapeutic spaces. The research provides a new perspective of "emotional value" for the construction of university library spaces, which is conducive to promoting the transformation of libraries from "functional spaces" to "therapeutic places".
Emotional value; University library; Reading space; Space reconfiguration; Therapeutic function
Research on the Full-Process Creation and Visual Presentation of Three-Dimensional Animation Empowered by Digital Technology
With the iterative upgrading of technologies such as digital graphics, artificial intelligence, real-time rendering, and virtual simulation, three-dimensional animation creation has completely moved beyond the limitations of traditional manual production and has formed a standardized, intelligent, and efficient full-process creation system. Digital technology has deeply penetrated all stages of three-dimensional animation, including pre-production planning, mid-production, and post-production compositing. It has not only reconstructed the animation production workflow, but also promoted the innovation of visual presentation from realistic simulation toward diversified, immersive, and artistic expression. From the perspective of the full process of three-dimensional animation creation, this paper systematically explores the application value of digital technology in concept design, model construction, animation rigging, special effects rendering, and post-production optimization. It analyzes the innovative characteristics and existing problems of current three-dimensional animation visual presentation, and proposes optimization paths in light of industry development trends, so as to provide theoretical reference and practical guidance for high-quality three-dimensional animation creation and visual innovation in the new era.
Digital technology; Three-dimensional animation; Full-process creation; Visual presentation; Intelligent empowerment
Research on the Design Logic and Interactive Implementation of Public Diet Management Software from the Perspective of Smart Health
Against the backdrop of the digital development of smart health, public awareness of health management has continued to increase, and scientific diet has become a core component of national health management and control. Traditional diet management faces problems such as cumbersome recording procedures, delayed nutritional assessment, lack of personalized guidance, and poor user adherence, making it difficult to meet modern public demands for refined and routine dietary health management. Smart diet management software, supported by digital technologies such as artificial intelligence, big data, and computer vision, reconstructs the model of dietary health management and has become an important carrier for popularizing the concept of healthy eating and regulating dietary behavior among the general public. Based on the concept of smart health development, this paper systematically explores the core design logic of public diet management software. It conducts an in-depth analysis of user needs, functional architecture, health-oriented logic, and human-centered design, examines the implementation paths and key points of software interaction design, analyzes existing problems such as homogenization, insufficient practicality, and weak adaptability in current software design and interaction, and proposes optimization strategies. The study aims to provide theoretical reference and practical support for the iterative upgrading of smart diet management products and for empowering public health management.
Smart health; Diet management software; Design logic; Interactive implementation; User experience
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
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.
Large Model–Empowered Technical Architecture and Application Study for Intelligent Interaction and Autonomous Decision-Making in Humanoid Robots
With the rapid advancement of artificial intelligence, large-scale models have demonstrated notable strengths in natural language understanding, multimodal fusion, knowledge reasoning, and generative interaction, providing a new technical foundation for robotic systems to evolve from “function-oriented automation” toward “general-purpose intelligent agents.” As a key direction in intelligent equipment, humanoid robots—owing to their human-like morphology and compatibility with human environments—show broad application potential in intelligent manufacturing, public services, healthcare and eldercare, and emergency rescue. However, conventional humanoid-robot systems still fall short in semantic understanding, complex task planning, cross-scenario generalization, and safe controllability in open environments, making it difficult to meet the practical demands of highly dynamic and uncertain real-world settings. In response, this paper focuses on the key issues of large model–empowered intelligent interaction and autonomous decision-making in humanoid robots. It systematically analyzes the roles of large models in multimodal perception fusion, semantic understanding, intent recognition, task decomposition, policy generation, and behavioral execution, proposes an engineering-oriented overall technical architecture, and further discusses implementation pathways for edge–cloud collaborative deployment, real-time control constraints, trustworthy safety governance, and typical application scenarios. The study suggests that the core of large model–driven upgrading of humanoid-robot intelligence lies in building a closed-loop framework of “multimodal perception–semantic understanding–knowledge reasoning–task planning–execution feedback,” while enhancing system reliability and controllability through constraint mechanisms, alignment strategies, and safety controls. This work provides a reference for the industrial deployment of humanoid robots and the engineering design of intelligent-agent systems.
Large-scale models; Humanoid robots; Multimodal interaction; Autonomous decision-making; Task planning; Edge–cloud collaboration; Trustworthy safety
Research on Augmented Reality-Based Music Interactive Experience Technology and Application Trends
Augmented reality enables the real-time superimposition of virtual information onto physical environments, integrating visual, auditory, and interactive content to form new modes of musical experience and expression. In recent years, advances in computer vision, multimodal fusion, spatial audio processing, and human–computer interaction have driven a shift in musical content from linear dissemination toward immersive and interactive participation. This study analyzes augmented reality-based music experiences from four dimensions: technical foundations, application scenarios, interaction mechanisms, and industrial ecology. It examines the roles of visual tracking, spatial mapping, rendering fusion, and audio interaction in musical contexts, and reviews the development pathways of augmented reality in concert performance, virtual idol presentation, music education and training, immersive exhibitions, and content platforms. Findings indicate that augmented reality enhances contextual expressiveness, expands the spatial scope and application dimensions of music, and fosters new modes of engagement. However, challenges remain regarding standardization, hardware costs, content production, and business models. With the advancement of spatial computing, multimodal artificial intelligence, and virtual performance technologies, augmented reality is expected to become a key driver of interactive music experiences and cultural content innovation.
augmented reality; music interaction; human–computer interaction; virtual performance; immersive experience
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