Journal of Electronic Research and Application

An Infrared Small Target Detection Method for Unmanned Aerial Vehicles Integrating Adaptive Feature Focusing Diffusion and Edge Enhancement

Jiale Wang — 2025-10-31

In the context of target detection under infrared conditions for drones, the common issues of high missed detection rates, low signal-to-noise ratio, and blurred edge features for small targets are prevalent. To address these challenges, this paper proposes an improved detection algorithm based on YOLOv11n. First, a Dynamic Multi-Scale Feature Fusion and Adaptive Weighting approach is employed to design an Adaptive Focused Diffusion Pyramid Network (AFDPN), which enhances the feature expression and transmission capability of shallow small targets, thereby reducing the loss of detailed information. Then, combined with an Edge Enhancement (EE) module, the model improves the extraction of infrared small target edge features through low-frequency suppression and high-frequency enhancement strategies. Experimental results on the publicly available HIT-UAV dataset show that the improved model achieves a 3.8% increase in average detection accuracy and a 3.0% improvement in recall rate compared to YOLOv11n, with a computational cost of only 9.1 GFLOPS. In comparison experiments, the detection accuracy and model size balance achieved the optimal solution, meeting the lightweight deployment requirements for drone-based systems. This method provides a high-precision, lightweight solution for small target detection in drone-based infrared imagery.

AW-HRNet: A Lightweight High-Resolution Crack Segmentation Network Integrating Spatial Robustness and Frequency-Domain Enhancement

Dewang Ma — 2025-11-03

The study presents AW-HRNet, a lightweight high-resolution crack segmentation network that couples Adaptive residual enhancement (AREM) in the spatial domain with Wavelet-based decomposition–reconstruction (WDRM) in the frequency domain. AREM introduces a learnable channel-wise scaling after standard 3 × 3 convolution and merges it through a residual path to stabilize crack-sensitive responses while suppressing noise. WDRM performs DWT to decouple LL/LH/HL/HH sub-bands, conducts lightweight cross-band fusion, and applies IDWT to restore detail-enhanced features, unifying global topology and boundary sharpness without deformable offsets. Integrated into a high-resolution backbone with auxiliary deep supervision, AW-HRNet attains 79.07% mIoU on CrackSeg9k with only 1.24M parameters and 0.73 GFLOPs, offering an excellent accuracy–efficiency trade-off and strong robustness for real-world deployment.

Technological Innovation and Clinical Application Prospects of Vascular Interventional Surgical Robots

Bo Xu — 2025-12-09

As a cutting-edge technology in the field of medical robotics, vascular interventional surgical robots have made remarkable progress in recent years. This paper focuses on the technological innovations of vascular interventional surgical robots, with particular attention to the system developed by Academician Xu Bo’s team that rapidly secured national patents through China’s Green Innovation Fast-Track. The article elaborates on its key breakthroughs in haptic force feedback, master–slave control accuracy, and multimodal image fusion, and discusses the potential clinical advantages these innovations confer. By benchmarking the system against comparable technologies at home and abroad, the paper forecasts its future applications in healthcare and provides a reference for subsequent R&D and clinical adoption.

Exploring the Key Supports and Industry Adaptation Strategies of Artificial Intelligence Technology in Medical Data Applications

Chenxi Zhang — 2025-12-16

With the rapid evolution of artificial intelligence (AI) technologies, the medical industry is undergoing a profound transformation driven by data intelligence. As the foundational element for intelligent diagnosis, precision prevention, and public health governance, medical data is characterized by massive volume, complex structure, diverse sources, high dimensionality, strong privacy, and high timeliness. Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security, intelligent processing, and decision support. Through techniques such as machine learning, deep learning, natural language processing, and multimodal fusion, AI provides robust technical support for medical data cleaning, governance, mining, and application. At the data level, intelligent algorithms enable the standardization, structuring, and interoperability of medical data, promoting information sharing across medical systems. At the model level, AI supports auxiliary diagnosis and precision treatment through image recognition, medical record analysis, and knowledge graph construction. At the system level, intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services. However, the widespread adoption of AI in medicine still faces challenges such as privacy protection, data security, model interpretability, and the lack of unified industry standards. Based on a systematic review of AI’s key supporting technologies in medical data processing and application, this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on “technological trustworthiness, data security, and industry collaboration.” The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.

Efficient Control of Mechatronic Systems Enabled by Generative AI for Single-Chip Microcomputers

Hang Xu — 2025-12-16

In recent years, research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing. Within the field of integrating mechatronics and intelligent control, analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications. This paper examines the technical characteristics of generative AI in data generation, multimodal fusion, and dynamic adaptation, proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers, ensuring local real-time inference capabilities. It constructs an edge intelligent control architecture, enabling generative AI to directly participate in decision-making instruction generation, forming a new working system of perception, decision-making, and execution. Additionally, it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance. At the application level, an intelligent fault prediction system is developed for early identification of equipment anomalies, an adaptive parameter optimization module is constructed for dynamically adjusting control strategies, and a multi-device collaborative scheduling engine is established to optimize production processes, providing technical support for embedded intelligent control in Industry 4.0 scenarios.

AI Technology Application Research in the Teaching of Power System Analysis Course

Yutong Liu — 2025-12-16

Traditional approaches to teaching the power system analysis course face challenges such as abstract and difficult-to-understand content, single teaching method, and limited practical links. In response, this paper explores in depth the significance and strategies of applying AI technology in the teaching of the course. The aim is to enhance students’ ability to understand and apply knowledge, and to cultivate well-qualified technical professionals who can adapt to the intelligent development needs of the power industry. The proposed strategies include building an intelligent learning diagnosis platform, providing personalized learning guidance, developing an AI-integrated curriculum system, fostering a “dual-qualified and interdisciplinary” teaching team, and establishing a diversified assessment and evaluation system.

Artificial Intelligence Empowering University Governance: Risks and Countermeasures

Qinglin Feng — 2025-12-16

Against the backdrop of the new era, artificial intelligence (AI), as a key driver for industrial transformation and upgrading, is accelerating innovation and development in the field of education. Obviously, it undoubtedly brings new challenges to university governance. Based on this, this paper mainly conducts relevant analysis and research on the risks and countermeasures of AI empowering university governance. The purpose is to further promote the digital development of universities, thereby providing students with better education and management services, and hoping to offer some references for peers.

Research on Optimization Algorithm for Video Recognition Accuracy in Smart Construction Sites

Xiang Gao — 2025-12-16

Aiming at the problems faced by construction site video management in the recognition of cigarette butts, reflective vests, and other objects, such as small target confusion, high-brightness false alarms, occlusion missed detections, and poor adaptability to complex environments, this study proposes a recognition accuracy optimization algorithm based on multimodal fusion. The research constructs a dataset containing three modalities of data: visible light, infrared, and millimeter-wave. The Dust-GAN algorithm is adopted to realize dust removal and enhancement of dusty images, and the SAA module is introduced into YOLOv8-s to improve the small target recall rate. Meanwhile, three-modal feature fusion is achieved, and channel pruning and quantization-aware training are used to realize algorithm lightweighting. The algorithm was deployed and operated on-site for 3 months, effectively reducing the construction site safety accident rate by 65%, which provides a solution for safety management and control in smart construction sites under complex environments.

Mechanical Design and Control Measures in Smart Home Systems

Zhe Lin — 2025-12-16

This paper focuses on the study of mechanical design and control measures in smart home systems. First of all, it elaborates on the theoretical foundation of mechatronics technology, including its multidisciplinary integration characteristics, system design principles, and constituent elements. It then reviews the research progress in this field, followed by a detailed analysis of mechatronics design in systems such as smart lighting and smart security, as well as the application of control algorithms and communication protocols in smart homes. Finally, it discusses challenges such as system compatibility and data security risks, proposing corresponding solutions to provide theoretical and practical references for the development of smart home systems.

AI-Based Intelligent Information System Operation and Maintenance

Sihao Huang — 2025-12-16

This paper focuses on AI intelligence as the fundamental direction to conduct research on information system operation and maintenance (O&M). Combining current AI-supported technologies in information system O&M, it proposes O&M strategies such as intelligent fault prediction and diagnosis, intelligent system performance optimization, intelligent system security protection, and adaptive system O&M implementation. Practical applications reveal that AI intelligence technology offers significant advantages in information system O&M, effectively addressing pain points of traditional O&M techniques, such as low fault prediction rates, slow repair speeds, poor security interception, and high labor costs. This substantially enhances the effectiveness of information system O&M.

Applied Research on AI Technology Empowerment in Single-Chip Microcomputer Course Teaching

Hang Xu — 2025-12-16

In recent years, the application of various advanced technologies, such as digitization and informatization, has become the primary tool for innovation in education and teaching. For traditional single-chip microcomputer course teaching, it is necessary to emphasize the introduction and application of high-tech innovations in its path of innovative development. This course is a typical representative of multidisciplinary teaching, involving multiple disciplines such as electronic engineering, automation, and computer science. In response to issues faced in traditional teaching, such as rigid organization of teaching content that struggles to keep pace with technological advancements, resulting in a noticeable lag in knowledge transfer, and monotonous teaching methods that fail to precisely meet the diverse learning needs of students, analyzing the innovative applications of this course under the empowerment of AI technology holds significant practical relevance. In this regard, the study relies on AI technology empowerment to analyze the application paths for the deep integration of AI technology and single-chip microcomputer courses, constructing a new teaching model to provide references for enhancing teaching quality and stimulating students’ innovative potential.

Application of Digital Twin Technology in Aerial Techniques of Competitive Cheerleading

Qin Li — 2025-12-16

With the continuous development of science and technology, we have already entered the digital age. While big data, artificial intelligence, and digital twin technology provide convenience for various fields of people’s lives, they also bring new opportunities for the innovation and development of competitive cheerleading. Especially for the training of aerial techniques in competitive cheerleading, which has high requirements for accuracy, coordination, and safety, the traditional training model has problems such as empiricism and insufficient risk prediction, which directly affect the quality of training. This article discusses the application value and application countermeasures of digital twin technology in the aerial techniques of competitive cheerleading, hoping to provide some reference for relevant personnel.

Research on Teaching Strategies of Power System Relay Protection Based on OBE Concept

Yutong Liu — 2025-12-16

With the continuous improvement of current science and technology and residents’ electricity demand, the power system relay protection course has become a key component of the curriculum for students majoring in power engineering. In this context, the teaching model of power system relay protection faces both new challenges and opportunities. By integrating the Outcome-Based Education (OBE) concept, teachers can reconstruct curriculum objectives and teaching frameworks by clearly defining learning outcomes, thereby enhancing students’ practical competencies and innovative thinking. Based on the core connotation of the OBE concept, this paper analyzes the significance of incorporating OBE into Power System Relay Protection teaching and explores effective implementation strategies. The findings aim to offer practical insights for teaching reform and to strengthen the alignment between academic training and industry requirements.

Research on the Teaching Reform of Professional Practical Courses under the Background of Emerging Engineering Education

Yuxiu Li — 2025-12-16

With the in-depth development of emerging engineering education, higher education has put forward higher requirements for the cultivation of engineering professionals. Professional practical courses are an important part of undergraduate teaching plans, aiming to improve students’ practical abilities and their ability to comprehensively apply professional knowledge. To further explore teaching reform and improve the diversified curriculum assessment and evaluation system, this paper proposes a series of teaching reform methods for professional practical courses. It reforms the content and teaching methods of professional practical courses. On the basis of introducing practical application scenarios and cutting-edge technologies, it adds interdisciplinary modules, makes full use of internal and external school resources to break traditional disciplinary boundaries, and integrates interdisciplinary content from cutting-edge fields such as artificial intelligence, big data, and biotechnology. This enhances students’ understanding of industry needs, practical operation capabilities, and awareness of multi-disciplinary integration. Secondly, it promotes a student-centered teaching model, increases students’ opportunities for independent participation, and cultivates their independent thinking and innovation abilities. At the same time, it reforms the evaluation system: by comprehensively considering students’ performance and achievements in the process of professional practical courses, it measures students’ abilities more comprehensively through a multi-dimensional evaluation system. This study provides new ideas for the teaching of professional practical courses.

MADF-YOLOv8: A Lightweight Model for Road Distress Detection Based on Adaptive Multiscale Feature Fusion

Tao OuYang — 2025-12-16

Efficient road distress detection is crucial for transportation safety. To address the challenge of balancing detection accuracy, efficiency, and multi-scale feature fusion in existing methods, this paper proposes a lightweight model named MADF-YOLOv8. The model enhances multi-scale feature extraction capability by introducing the Multi-Scale Ghost Residual Convolution (MSGRConv) and the Multiscale Adaptive Feature Processing Module (MAFP). Furthermore, it constructs a Multi-scale Dynamic sampling Bidirectional Feature Pyramid Network (MD-BiFPN) and incorporates the C2f-Faster module to optimize feature fusion efficiency. Experiments on the RDD2022 dataset demonstrate that the proposed model achieves a mean Average Precision at 0.5 Intersection over Union (mAP@0.5) of 88.6% with only 2.312 million parameters. Its overall performance surpasses various mainstream detectors, achieving an exceptional balance between accuracy and efficiency.

Panoramic Glass Image Segmentation Network

Guanlin Pan — 2025-12-16

In panoramic images, the geometric distortion caused by wide-angle lenses makes traditional semantic segmentation methods difficult to accurately segment the glass areas. To address the challenges of capturing spatial features and integrating context information, we propose the Panoramic Glass Image Segmentation Network (PGISNet). This network integrates the Matrix Decomposition Base Module (MDBM), the Transparent Perception Consistency Module (TACM), the Context and Texture Compensation Module (CTCM), and the Multi-scale Gated Context Attention Module (MGCA), constructing a progressive feature processing flow. Experimental results on the PanoGlassV2 benchmark test show that PGISNet achieved 90.03% IoU, 94.76% F-score, and 94.0% PA, significantly outperforming existing methods, verifying its effectiveness and advancement in the panoramic image glass segmentation task.

Design and Implementation of a Python-Based LIVP Live Photo Conversion Tool

Jiaming Wang — 2025-12-16

This paper presents the design and implementation of a Python-based conversion tool for Apple Live Photo files (.livp). The tool converts .livp files into standard image formats (JPEG/HEIC/PNG) by renaming them to .zip and extracting the static images while optionally removing the embedded short video (.mov). It supports one-click batch processing of all .livp files within a folder. The software adopts a modular design, consisting of file renaming, batch decompression, temporary file cleaning, and a Tkinter-based graphical user interface (GUI). A multithreading mechanism ensures a smooth user experience without interface freezing. With a compact size of approximately 10.7 MB, the software is easy to deploy and suitable for users who need to batch-convert Live Photos into static images. This paper introduces the system design, key implementation details, performance analysis, and potential improvements, providing references for developing lightweight format conversion applications.

System Design of Workpiece Automatic Cleaning Device based on YOLOv5

Ru-Tao Wang — 2025-12-16

The recognition and positioning of material baskets are key links in the automatic workpiece cleaning device. Aiming at the problems of low recognition accuracy and poor precision of traditional visual methods for material basket recognition, a control system of automatic workpiece cleaning device based on YOLOv5 was designed. The YOLOv5 detection algorithm was improved by introducing the attention mechanism and optimizing the loss function, which enhanced the attention to the target area and improved the accuracy of feature extraction, thus realizing the position recognition and coordinate acquisition of workpiece material baskets. In addition, a cleaning system with Siemens S7-1200 PLC as the control core was designed. By controlling servo motors to drive the gantry and adjust the operation of the crane, the automatic grabbing and handling of material baskets were realized, and the automatic control of the cleaning process was achieved. Meanwhile, a human-computer interaction (HMI) and monitoring interface was designed, which could intuitively display the operating status of material baskets and improve the interaction capability of the automatic workpiece cleaning device.

Design and Implementation of a 360° Panorama Imaging System Based on Motor-Driven Fisheye Camera

Yongsheng Sun — 2025-12-16

This paper designs and implements a single-camera 360° panoramic imaging system based on motor-driven fisheye rotation. The system utilizes a stepper motor for precise angular control, enabling the camera to rotate around its optical center to capture multi-view images, thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations. To address the strong distortion characteristics of fisheye images, an equidistant projection model is adopted for distortion correction. On this basis, a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency. By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending, the system achieves high-precision panoramic stitching and seamless visual transitions.

Research on the Application of Three-Phase VIENNA Topology in High-Power Switching Power Supplies

Bai Fusheng — 2025-12-16

The growing demand for efficient high-power switching power supplies has spurred interest in advanced topologies. The three-phase VIENNA converter stands out for its high power factor, simplified structure, and robust performance. Current research focuses on its operational principles, control strategies, and behavior under various load conditions. Key considerations include component selection, thermal management, and EMI/EMC optimization. This topology finds applications across renewable energy systems, industrial equipment, telecommunications, and electric vehicle charging infrastructures. Comparative analyses with alternative topologies and cost-benefit evaluations are also addressed. Future developments are expected to emphasize the integration of wide-bandgap devices and advancements in digital control techniques to further enhance efficiency and system performance.

Design of Elevator Control System Based on S7- 200PLC

Xin Chen — 2025-12-16

With the acceleration of urbanization and the increasing number of high-rise buildings, elevators, as key equipment for vertical transportation, have attracted significant attention regarding the safety, reliability, and efficiency of their operation. This design develops a four-floor elevator control system based on S7-200PLC. It realizes the following functions: the second and third floors support both upward and downward request functions (with corresponding indicator lights turning on), the first floor only supports the upward request function, and the fourth floor only supports the downward request function. Meanwhile, the elevator automatically opens its door after arriving at each floor and closes the door automatically 6 seconds later. The design aims to achieve precise control and efficient operation of the elevator.

Electronic Performance Monitoring: A Literature Review and Research Prospect

Ziyi Zhang — 2025-12-16

Amid the accelerating process of digital transformation, electronic performance monitoring has become an essential tool in organizational management. As a process that utilizes information technology to observe, record, and analyze employees’ work behaviors and performance, electronic performance monitoring not only enhances organizational efficiency and optimizes decision-making support but also exerts a profound influence on employees’ psychological states and behavioral responses. This paper systematically reviews the conceptual evolution, measurement methods, and related research progress of electronic performance monitoring. The findings reveal that the definition of electronic performance monitoring has evolved dynamically from a “technological tool” to an “organizational practice,” and its measurement approaches have developed from a single-dimensional to a multi-dimensional perspective, encompassing aspects such as monitoring purpose, monitoring intensity, and monitoring feedback. Existing empirical studies indicate that different types of electronic performance monitoring, such as developmental and preventive monitoring, have distinct impacts on employees’ job performance, innovative behavior, and psychological responses. Overall, developmental monitoring tends to foster positive behaviors and creativity, whereas preventive monitoring may trigger psychological resistance and counterproductive work behaviors. This review provides a theoretical foundation for understanding the dual-edged effects of electronic performance monitoring and lays the groundwork for developing localized measurement instruments and exploring its underlying mechanisms in future research.

Research on Library Data Governance for Data Factorization

Yan Jiang — 2025-12-16

Data factors are becoming the core driving force in the intelligent transformation of libraries. Based on a systematic review of the progress in data governance practices in libraries both domestically and internationally, this study delves into the mechanism by which data governance promotes data factorization and proposes implementation paths for data governance oriented toward data factorization. The aim is to facilitate the intelligent transformation and high-quality development of libraries.

Health Status Prediction Of Lithium Batteries Based On Deep Learning

Hai-Rui Zhang — 2025-12-16

Aiming at the shortcomings of traditional State of Health (SOH) prediction methods in nonlinear modeling and temporal dependence handling, this paper proposes a hybrid CNN-GRU model integrated with the Dung Beetle Optimization (DBO) algorithm (denoted as DBO-CNN-GRU) for lithium battery SOH prediction. Indirect health factors strongly correlated with SOH are extracted from the NASA public dataset, and their effectiveness is verified using Pearson and Spearman correlation coefficients. A CNN-GRU model is designed: the convolutional neural network (CNN) is used to capture local features, and the gated recurrent unit (GRU) is combined to model the temporal dependence of capacity degradation. Furthermore, the DBO algorithm is introduced to optimize the model’s hyperparameters, enhancing the global search capability. Experiments show that the DBO-CNN-GRU model achieves significantly better test performance on the NASA dataset than the single CNN, GRU, and LSTM models.

Intelligent Agent Analysis and Measurement Application Based on DeepSeek

Fengxi Gao — 2025-12-16

In the context of the integrated development of artificial intelligence and the power industry, the State Grid has carried out in-depth research on the research and development and application of the Guangming Power model. The model is based on the knowledge of the power industry, cognitive computing as the core, and knowledge service as the goal, and has the characteristics of large sample, large calculation, large parameters, large knowledge, and large tasks, providing important support for the intelligent transformation of the power industry. This study focuses on the efficient operation of Guangming Power large model, builds a trinity operation system of “model iterative optimization, sample full-process governance, and computing power resource collaboration”, and drives the continuous improvement of model capabilities and compliance development through a two-level collaboration mechanism. With the goal of “building a strong and excellent Bright Power Large Model”, the study clearly states that it is necessary to accelerate the construction of a two-level collaborative operation system, and promote model iterative optimization, capability evaluation, service monitoring and compliance review on a regular basis. At the same time, focusing on the whole process of R&D and application of large and small models, starting from four aspects: computing power planning and layout, allocation and scheduling, adaptation and optimization, monitoring and analysis, strengthening the application monitoring and analysis of two-level intelligent computing centers, and building an efficient computing power resource application and supply system to continuously improve its operation and service capabilities.

Design and Implementation of Inductive Intelligent Garbage Recycling System Based on Dual Recognition Fusion

Dongxing Wang — 2025-12-16

To solve the problems of traditional garbage classification relying on manual work, low recognition accuracy, and unhygienic contact-based disposal, an inductive intelligent garbage recycling system based on a micro-controller is designed. The system takes STM32F103ZET6 as the core, integrates speech recognition, visual recognition, and infrared induction modules, and adopts a dual recognition fusion strategy of “speech priority + visual verification” to realize con-tactless induction lid opening, accurate garbage classification, and remote status monitoring functions. Speech recognition completes keyword matching through the LD3320 module, visual recognition realizes image feature extraction and classification based on the BP neural network, and the ESP8266 module is responsible for uploading device status to the Cloud platform. It is applicable to multiple scenarios such as households and communities, effectively improving the efficiency and convenience of garbage classification, and has good practical value.

Research on the Collaborative Optimization of the Upgrading, Transformation and Maintenance of Large Equipment for Offshore Oil Extraction

Yanhong Guo — 2025-12-16

This study investigates the collaborative optimization of upgrading, retrofitting, and maintaining large offshore oil extraction equipment. It examines the key challenges and interdependencies inherent in these processes and proposes integrated solutions, including sensor-network-based monitoring and digital twin-driven management platforms. The findings demonstrate notable improvements in operational efficiency and reductions in equipment downtime, underscoring both the economic and safety benefits of the proposed approach and providing a reference framework for future optimization strategies in offshore engineering.

Development of Intelligent Storage and Logistics Management System for Thermal Power Plant

Caizhen Sun — 2025-12-16

As the core industry of energy supply, the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety, economy and sustainability of power generation. At present, the traditional thermal power plant warehousing and logistics system is faced with decentralized management, information lag, high dependence on manual and insufficient cost control, which cannot meet the needs of modern management. The in-depth application of Internet of Things (IoT) technology provides technical support for the digital upgrade of warehousing and logistics system, but there are still challenges in data integration depth, prediction model accuracy and adaptability to complex environment. The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT, AI, and automation technologies to create a smart management platform that covers full lifecycle tracking of material information, automated warehouse scheduling, intelligent logistics path optimization, and multidimensional data analysis. By deploying RFID tags, smart sensor terminals, and AGV logistics equipment, combined with recursive neural networks and reinforcement learning algorithms, the system achieves real-time material status monitoring, precise inventory demand forecasting, and dynamic optimization of transportation routes.

Digital Twin Spatiotemporal Indexing Engine: “Edge Cloud” Layered Storage and Fast Backtracking of Real-Time Streaming Data

Huanjing Huang — 2025-12-16

This article examines the design and implementation of a digital twin spatiotemporal indexing engine. It outlines the core theoretical foundations, including spatiotemporal mapping mechanisms, and discusses key enabling technologies such as hybrid spatiotemporal indexing structures, edge-cloud collaborative storage architectures, and protocol conversion middleware. The study further evaluates system performance through an experimental platform, comparing a layered storage architecture with traditional storage models. The results demonstrate clear advantages in terms of efficiency, scalability, and responsiveness. Finally, the paper explores practical application scenarios and outlines future development directions for next-generation spatiotemporal indexing engines in digital twin systems.

Secure and Privacy-Preserving Cross-Departmental Computation Framework Based on BFV and Blockchain

Peng Zhao — 2025-12-16

As the demand for cross-departmental data collaboration continues to grow, traditional encryption methods struggle to balance data privacy with computational efficiency. This paper proposes a cross-departmental privacy-preserving computation framework based on BFV homomorphic encryption, threshold decryption, and blockchain technology. The proposed scheme leverages homomorphic encryption to enable secure computations between sales, finance, and taxation departments, ensuring that sensitive data remains encrypted throughout the entire process. A threshold decryption mechanism is employed to prevent single-point data leakage, while blockchain and IPFS are integrated to ensure verifiability and tamper-proof storage of computation results. Experimental results demonstrate that with 5,000 sample data entries, the framework performs efficiently and is highly scalable in key stages such as sales encryption, cost calculation, and tax assessment, thereby validating its practical feasibility and security.

Autonomous Obstacle Avoidance Decision Mechanism of Intelligent Robot Based on Multimodal Perception

Jiaming Yan — 2025-12-16

Intelligent robots are increasingly being deployed across industries ranging from manufacturing to household applications and outdoor exploration. Their autonomous obstacle avoidance capabilities in complex environments have become a critical factor determining operational stability. Multimodal perception technology, which integrates visual, auditory, tactile, and LiDAR data, provides robots with comprehensive environmental awareness. By establishing efficient autonomous obstacle avoidance decision-making mechanisms based on this information, the system’s adaptability to challenging scenarios can be significantly enhanced. This study investigates the integration of multimodal perception with autonomous obstacle avoidance decision-making, analyzing the acquisition and processing of perceptual information, core modules and logic of decision-making mechanisms, and proposing optimization strategies for specific scenarios. The research aims to provide theoretical references for advancing autonomous obstacle avoidance technology in intelligent robots, enabling safer and more flexible movement in diverse environments.

Structural Safety Assessment and Improvement Measures in Mechanical Design of Amusement Equipment

Bin Liu — 2025-12-16

This paper focuses on the mechanical structure design of amusement rides, elucidating the application of fundamental theories such as statics and fatigue strength analysis. It introduces special requirements like impact and alternating stress, explores methods for structural safety assessment and their limitations. The paper also covers principles for selecting indicators, the construction of a grading evaluation system, and the application of various materials and technologies in amusement rides, emphasizing the importance of structural safety and future development directions.

Development and Application of Digital Twin Simulation System for Thermal Power Plant

Hui Li — 2025-12-16

As a product of the deep integration between next-generation information technology and industrial systems, digital twin technology has demonstrated significant advantages in real-time monitoring, predictive maintenance, and optimization decision-making for thermal power plants. To address challenges such as low equipment efficiency, high maintenance costs, and difficulties in safety risk management in traditional thermal power plants, this study developed a digital twin simulation system that covers the entire lifecycle of power generation units. The system achieves real-time collection and processing of critical parameters such as temperature, pressure, and flow rate through a collaborative architecture integrating multi-source heterogeneous sensor networks with Programmable Logic Controllers (PLCs). A three-tier processing framework handles data preprocessing, feature extraction, and intelligent analysis, while establishing a hybrid storage system combining time-series databases and relational databases to enable millisecond-level queries and data traceability. The simulation model development module employs modular design methodology, integrating multi-physics coupling algorithms including computational fluid dynamics (CFD) and thermal circulation equations. Automated parameter calibration is achieved through intelligent optimization algorithms, with model accuracy validated via unit-level verification, system-level cascaded debugging tests, and virtual test platform simulations. Based on the modular layout strategy, the user interface and interaction module integrates 3D plant panoramic view, dynamic equipment model and multi-mode interaction channel, supports cross-terminal adaptation of PC, mobile terminal and control screen, and improves fault handling efficiency through AR assisted diagnosis function.

Data-Driven Precision Training Model for Innovation and Entrepreneurship Talents in Universities: Theoretical Framework and Implementation Path

Shuai Yuan — 2025-12-16

Against the backdrop of the national innovation strategy and the digital transformation of education, the traditional “extensive” training model for innovation and entrepreneurship talents struggles to meet the personalized development needs of students, making an urgent shift toward precision and intelligence necessary. This study constructs a four-dimensional integrated framework centered on data, ”Goal-Data-Intervention-Evaluation”, and proposes a data-driven training model for innovation and entrepreneurship talents in universities. By collecting multi-source data such as learning behaviors, competency assessments, and practical projects, the model conducts in-depth analysis of students’ individual characteristics and development potential, enabling precise decision-making in goal setting, teaching intervention, and practical guidance. Based on data analysis, a supportive system for personalized teaching and practical activities is established. Combined with process-oriented and summative evaluations, a closed-loop feedback mechanism is formed to improve training effectiveness. This model provides a theoretical framework and practical path for the scientific, personalized, and intelligent development of innovation and entrepreneurship education in universities.

Research Status and Development Trends of Lattice-Foam Composite Lightweight Porous Metallic Materials

Kerong Zhang — 2025-12-16

This paper reviews the research status and development trends of lattice-foam composite structural materials. It introduces the characteristics and applications of lattice metallic materials and metallic foam materials among ultra-light porous materials, and points out their respective shortcomings. Subsequently, it elaborates on the research progress of lattice-foam composite structural materials at home and abroad, including the research achievements of research teams such as Xi’an Jiaotong University and the University of Virginia in the United States. Then, the key technologies in the preparation process of lattice-foam composite structural materials are discussed in detail, such as the rapid prototyping technology for special-shaped parts of porous materials, the friction stir welding technology for metallic foams, and the large-area joining and composite technology between the core and face sheets of composite structures. Finally, the research on lattice-foam composite structural materials is summarized and prospected.

Application and Research of Electrical Fault Diagnosis and Operation and Maintenance Technology in Mechanical and Electrical Engineering of Property Services

Runfeng Zhou — 2025-12-16

This paper focuses on electrical fault diagnosis and operation and maintenance technology in property service electromechanical engineering. It details core diagnostic methods, application-oriented tools, predictive maintenance frameworks, and enhanced maintenance planning. It also explores wireless sensor networks, big data analytics, and design-phase applications. Case studies in construction and operation phases are presented. Challenges like legacy system retrofitting are noted, and future potential in quantum sensing and edge AI is discussed.

Application of Workstation Robots in Welding Processes of Automotive OEMs

Wencong Deng — 2025-12-16

Workstation robots play an important role in the welding process of automotive OEMs. Its multi axis degrees of freedom, high-precision motion control, and modular design enable it to demonstrate efficient performance in body welding, component optimization, and process support. By integrating intelligent technology with traditional processes, workstation robots have improved welding efficiency and quality, reducing the risk of manual intervention. Despite facing challenges such as complex weld seam recognition and multi machine collaboration, with technological optimization and innovation, its application prospects are broad, which will promote the development of automotive manufacturing towards intelligence and green direction.

Research Status and Trend Analysis of Aging Friendly Bathroom Facilities Based on Citespace

Desen Zhang — 2025-12-16

Based on the CiteSpace visualization knowledge graph, this article conducts a visual analysis of the evolution of publication volume, distribution of national institutions, keyword clustering, and other related literature on aging friendly bathroom facilities in 502 Chinese and English articles, in order to analyze the research status, hotspots, and future research directions in this field. Through analysis, it was found that research in this field is showing an increasing trend, but there is still a need for relevant experts to actively engage in cross institutional, cross disciplinary, and cross national exchanges and cooperation. Emerging concepts such as smart homes and informatization have become new research hotspots. Moreover, research trend has shifted from macro level discussions to specific optimizations, and has shown characteristics of disciplinary integration and technology driven development. Overall, this study has identified the development direction for improving the level of technological application, emphasizing the importance of interdisciplinary and international cooperation, and providing certain guidance for the construction of a research system for aging friendly bathroom facilities.

An Intelligent Operation and Maintenance System for Photovoltaic Station

Qing Yi — 2025-12-16

The rapid expansion of photovoltaic (PV) deployment poses new challenges for large-scale and distributed maintenance, particularly in fishery-PV complementary plants where panels are deployed over water surfaces. This paper presents the design and implementation of an intelligent operation and maintenance (O & M) system that integrates a 3D holographic digital twin cloud platform with UAV-assisted inspection and localized cleaning. The proposed system supports multi-source data acquisition, including UAV imagery, infrared sensing, and DustIQ-based soiling monitoring, and provides real-time visualization of the PV plant through 1:1 3D reconstruction. UAVs are employed for both autonomous inspections, covering defects such as soiling, bird droppings, bypass diode faults, and panel disconnections and targeted cleaning in small water-covered areas. Field trials were conducted at Riyue and Chebu PV plants, with small-scale UAV cleaning validation in Chebu fish ponds. Results demonstrated that the system achieves efficient task scheduling, fault detection, and localized cleaning, thereby improving O & M efficiency, reducing costs, and enabling digitalized and intelligent management for large-scale PV stations.

Artificial Intelligence-Based Crop Disease Identification Technology: Applications, Challenges, and Future Prospects

Yuhan Zhou — 2025-12-16

Crop diseases pose a critical threat to global food security. Traditional diagnostic methods are inefficient and fail to meet the demands of modern precision agriculture. In recent years, artificial intelligence (AI) technologies centered on deep learning have revolutionized the rapid and precise identification of crop diseases. This paper systematically outlines key AI techniques for crop disease recognition, including computer vision-based image recognition, multimodal data fusion, and edge computing for field deployment. By analyzing representative domestic and international application cases, this paper highlights the significant advantages of this technology in terms of accuracy and efficiency. Simultaneously, it delves into current technical bottlenecks and deployment barriers, such as the few-shot learning problem, environmental interference, and low farmer trust. The paper concludes by outlining future directions, including self-supervised learning, digital twins, and industry integration, to advance the deep application and implementation of AI technology in smart agriculture.

Research on the Principle Comparison and Comprehensive Application of High-Availability Clusters and Load-Balancing Clusters

Yue Yang — 2025-12-16

In modern distributed systems and cloud computing architectures, high availability and high scalability are core requirements to ensure the continuous and stable operation of services. As key technologies for achieving these two goals, high-availability clusters and load-balancing clusters have significant differences in their design concepts and application scenarios, while also maintaining close connections. This paper aims to conduct an in-depth analysis of the core objectives, working principles, technical advantages and disadvantages, and typical application cases of high-availability clusters and load-balancing clusters. By introducing an analogical model of a “restaurant kitchen,” the differences between the two are intuitively explained, and their technical characteristics are compared in detail. Additionally, a detailed practical case is included to specifically demonstrate the collaborative work of high-availability and load-balancing technologies through the construction process of Keepalived and HAProxy. Finally, taking the architecture of a typical e-commerce website as an example, this paper demonstrates the best practice of organically combining the two cluster technologies in a production environment to build a robust and high-performance distributed system. Research shows that understanding the differences between the two and implementing collaborative deployment is the cornerstone of designing modern IT infrastructure.

Development and Characterization of a Non- Enzymatic Glucose Biosensor Based on a Gold Film EG-FET and an Inverting Amplifier Readout Circuit

Xintai Dong — 2025-12-16

The development of low-cost, non-enzymatic glucose biosensors is crucial for advancing accessible diabetes management. This paper presents the experimental testing of an extended-gate field-effect transistor (EG-FET) that uses a gold film as the sensing structure. The system innovatively employs a custom-designed inverting operational amplifier circuit for precise signal acquisition and an Arduino Nano platform for real-time data processing and visualization, eliminating the need for expensive laboratory equipment. At the core of the design is a depletion-mode MOSFET, whose current-voltage properties were characterized. The function of the sensor was demonstrated by testing its response to phosphate-buffered saline containing glucose at different concentrations. A clear modulation of the drain current in the linear region of the EG-FET was observed, and a preliminary analysis revealed a linear correlation between the output current and glucose concentration, indicating the system’s potential for quantitative detection. This study successfully validates the feasibility of a compact, cost-effective, and non-enzymatic EG-FET biosensing platform, establishing a solid foundation for future development of point-of-care diagnostic devices.

Research on Reverse Modeling of Parametric CAD Models from Multi-View RGB-D Point Clouds

Yangzhi Zhang — 2025-12-16

Existing reverse-engineering methods struggle to directly generate editable, parametric CAD models from scanned data. To address this limitation, this paper proposes a reverse-modeling approach that reconstructs parametric CAD models from multi-view RGB-D point clouds. Multi-frame point-cloud registration and fusion are first employed to obtain a complete 3-D point cloud of the target object. A region-growing algorithm that jointly exploits color and geometric information segments the cloud, while RANSAC robustly detects and fits basic geometric primitives. These primitives serve as nodes in a graph whose edge features are inferred by a graph neural network to capture spatial constraints. From the detected primitives and their constraints, a high-accuracy, fully editable parametric CAD model is finally exported. Experiments show an average parameter error of 0.3 mm for key dimensions and an overall geometric reconstruction accuracy of 0.35 mm. The work offers an effective technical route toward automated, intelligent 3-D reverse modeling.

Research on the Transformation Mechanism, Challenges, and Development Path of AI Empowering the Logistics Industry

Haiou Xiong — 2025-12-16

Against the background of the integration of the digital economy and industrial intelligence, AI technology has become the core support for the logistics industry to reduce costs, improve efficiency, and break through development bottlenecks. This paper constructs a research framework of “Application Scenarios–Transformation Mechanism–Challenges–Development Path,” systematically analyzing the application value and practical issues of AI in the logistics industry. The core applications of AI are concentrated in three scenarios: intelligent customer service, logistics data analysis and decision optimization, and intelligent inventory management. Through process automation replacement, service model upgrading, and data-driven decision-making, it achieves a systematic transformation of industry operational efficiency improvement, customer experience optimization, and decision-making model transformation. At the same time, AI applications still face practical challenges such as insufficient technical integration compatibility, data security and privacy protection risks, and talent structure adaptation gaps. In the future, the logistics industry needs to fully release the value of AI technology by deepening the integration of AI with cross-technologies, promoting green and flexible development.

Design of Thematic Tour Digital Guidance Platform Integrating Ontology and User Portrait: A Case Study of Situated Learning in Cultural Heritage Sites

Yueying Huang — 2025-12-16

In recent years, the state has vigorously promoted the construction of cultural heritage-themed tourism routes. Policies such as the Notice on the Construction of China Cultural Heritage-Themed Tourism Routes and the Dongpo Journey-China Cultural Heritage-Themed Tourism Route Master Plan have been successively issued, encouraging the creation of cultural-tourism integration projects that primarily feature immovable cultural relics and combine specific themes. However, current digital tours at cultural heritage sites often suffer from fragmented cultural knowledge transmission, a lack of personalized user experience, and insufficient contextual learning effects. To address these issues, this study takes the “Dongpo Travel” digital tour mini-program as a case study to explore the design of a thematic tourism route digital tour platform that integrates ontology and user profiling. The platform achieves structured transmission of Dongpo cultural knowledge by constructing historical relic tour maps, improving cultural tourism route services, setting up post-tour incentive mechanisms, and developing contextual learning modules. This meets the personalized travel needs of different users and helps tourists deepen their cultural learning during visits. Practice shows that the platform can operate stably at the Su Causeway in the West Lake Scenic Area, covering approximately 27 Dongpo cultural sites and serving over 500 people daily. It not only promotes the revitalization and inheritance of Dongpo cultural heritage but also provides a replicable solution for the digital transformation of the cultural tourism industry. Additionally, it effectively enhances the efficiency of contextual learning at cultural heritage sites and offers a reference for the construction of similar thematic tourism route digital tour platforms.

Design of an Abnormal Behavior Monitoring System for Elderly Living Alone Indoors

Dongxing Wang — 2025-12-16

Aiming at the prominent indoor safety hazards and the difficulty in timely detecting abnormal behaviors of elderly living alone against the backdrop of accelerating population aging, an abnormal behavior monitoring system for elderly living alone indoors, integrating multi-sensor technology and intelligent algorithms, is designed. The system adopts a four-layer architecture of “perception layer–processing layer–communication layer–application layer,” integrating hardware modules for visual collection, environmental sensing, physiological monitoring, and posture perception, and realizing collaborative data processing through embedded processors and edge computing devices. At the algorithm level, it optimizes target detection, behavior classification, posture fusion, and multi-modal discrimination models to achieve real-time identification of risks such as falls, posture abnormalities, and physiological abnormalities. The communication link combines short-range and long-range technologies, coupled with a multi-terminal early warning mechanism to ensure efficient transmission of abnormal information. The system balances detection accuracy, privacy protection, and user acceptance, providing technical support for the home safety of elderly living alone. The relevant design ideas can serve as a reference for the development of similar intelligent elderly care equipment.

Multi-Modal UAV Inspection of Photovoltaic Modules Using a YOLOv9-Based Fusion Network

Qing Yi — 2025-12-16

The rapid expansion of photovoltaic (PV) power plants has created a pressing need for efficient and reliable operation and maintenance (O&M). Traditional manual inspection is slow, costly, and prone to error, motivating the use of unmanned aerial vehicles (UAVs) with infrared and visible cameras for automated monitoring. In this paper, we propose a YOLO-based multi-task framework for simultaneous PV defect detection and hazard-level classification. We constructed a dataset of 5,000 annotated UAV images from the Riyue PV power plant, covering ten defect categories and four severity levels (LV1–LV4). To support severity grading, the YOLO architecture was extended with a dual-task head and an ordinal regression scheme. The model was trained with a compound loss combining bounding-box regression, objectness, defect classification, and hazard-level supervision. Experimental evaluation on real UAV inspection data (224 strings, 30 ground-truth defects) shows that the proposed approach achieves mAP50 of 95.6%, recall of 92.7%, and severity classification accuracy of 90.8%. The system detects both minor anomalies (e.g., bird droppings, soiling) and critical faults (e.g., missing panels, disconnections) in real time at over 40 FPS, providing actionable insights for maintenance prioritization. These results demonstrate that YOLO-based UAV inspection offers a robust and scalable solution for intelligent PV O&M.

Design and Implementation of a Family Private Medical Health Intelligent Management System

Yingqing Zeng — 2025-12-16

With technological advancements, traditional medical models have been unable to meet people’s demands for personalized and convenient medical services. Consequently, the family private medical system has emerged. This system aims to achieve health data monitoring, disease prevention, and personalized medical management for family members. The system is developed using framework technologies such as Spring Boot and Vue, with MySQL as the data storage tool. It enables real-time monitoring of health data, dietary and exercise recommendations for diseases, personalized health management, online consultation, and interactive communication with medical experts, thereby enhancing the convenience and personalization level of medical services.

Research on the Safe, Reliable and Low-Cost Transmission of Multiple Signals between Different Control Systems

Yonggang Deng — 2025-12-16

With the gradual development of smart power plants and large-scale centralized control, there is a need to exchange a large number of signals between different DCS systems and between DCS and PLC systems. Different control systems have different brands and cannot communicate directly via networks. Moreover, due to network security concerns, the main control of unit units and the auxiliary control system of the entire plant cannot communicate directly via networks either. The commonly adopted methods for signal exchange between control systems are hardwiring and 485 communications. Both have obvious drawbacks, where hardwiring requires a large number of channels and cable laying; 485 configuration is difficult, not easy to maintain, and faults are hard to locate. This paper studies how to strike a balance between the two, using a minimal amount of hardwiring to transmit a large number of signals, which is safe, reliable, cost-effective, and can be maintained by any control personnel without network security risks.

Visualization Analysis of Research Status and Hotspots of Computerized Respiratory Sound Analysis Based on CiteSpace

Yan Wang — 2025-12-16

Objective: To explore the research landscape and hotspots of Computerized Respiratory Sound Analysis (CORSA) and provide a reference for future in-depth studies. Methods: Literature related to CORSA published up to August 27, 2020, was retrieved from the Web of Science Core Collection. CiteSpace 5.6.R3 was used to perform co-authorship analysis, institutional collaboration analysis, keyword co-occurrence analysis, and co-citation analysis. Results: A total of 1,897 publications were included. Co-authorship analysis identified several influential contributors, including Zahra Moussavi, Kenneth Sundaraj, and H. Pasterkamp. Major research institutions included the University of Manitoba, the University of Queensland, and Aristotle University of Thessaloniki. Keyword co-occurrence analysis indicated that “respiratory sound,” “lung sound,” “asthma,” “children,” and “classification” were major research themes. The most frequently co-cited articles were published by Arati Gurung (2011), Mohammed Bahoura (2009), and H. Pasterkamp (1997). Highly cited journals included Chest, the American Journal of Respiratory and Critical Care Medicine, and IEEE Transactions on Biomedical Engineering. Conclusion: CORSA research is primarily driven by European and North American scholars and institutions, with China still in an early stage of development. Current hotspots include respiratory sound acquisition and processing, feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs), and classification techniques based on machine learning and deep learning. CORSA is suitable for diverse populations and is widely applied in respiratory diseases, especially bronchial asthma. Its non-invasive nature offers particular advantages for infants and pregnant women. Although CORSA demonstrates strong clinical potential, its clinical translation remains limited. Advancing clinical applications and bridging the gap between research and practice will be key directions for future development. The prominence of top-tier respiratory and engineering journals among citations suggests that CORSA is an emerging and influential research frontier.

Engineering Risk Prevention and Control Strategies from the Perspective of Technical Management in the Field of Electromechanical Engineering

Chunhua Liao — 2025-12-16

This study explores engineering risk prevention and control in electromechanical engineering from the technical management perspective. It elaborates on technical management’s role, core concepts, critical risks, and various strategies like fail-safe design, predictive maintenance, ISO-compliant workflows. It also emphasizes future research areas such as AI - driven predictive risk modeling and cross-disciplinary management frameworks.

Detection, Maintenance of Building Lighting Circuits and Central Air Conditioning Units, and Handling of Customer Maintenance Requirements: Practice of Electromechanical Engineering in Building Facility Management

Wang Yongfeng — 2025-12-16

Electromechanical engineering in building facility management is crucial. It involves detecting and maintaining building lighting circuits, central-air-conditioning units, and handling customer maintenance requirements. Approaches like real-time monitoring, scheduled maintenance, non-invasive testing, energy-efficient retrofitting, and client-centric services enhance building operations, reliability, and tenant satisfaction, laying a foundation for sustainable development.

YOLOv11 Optimized Weighted Cross-Correlation High-Temperature Ultrasonic Temperature Measurement Method

Qianxiang Zhang — 2025-12-16

Traditional cross-correlation algorithms are prone to time-of-flight (TOF) calculation errors under conditions of strong noise interference and complex temperature gradients, resulting in a decline in the accuracy of ultrasonic temperature measurement. To this end, this paper proposes an ultrasonic temperature measurement method that combines YOLOv11 target detection with energy-type weighted cross-correlation algorithm. The YOLOv11 model is utilized to conduct target detection and key area positioning on the ultrasonic signal waveform diagram, automatically identifying characteristic waveforms such as node waves and end face waves, and achieving adaptive extraction of the effective signal interval. Further introduce the energy-based weighted cross-correlation algorithm. Based on the signal energy distribution, the cross-correlation results are weighted and processed to enhance the main wave response and suppress noise interference. Experiments show that the YOLOv11 model has high detection accuracy (Precision = 0.987, Recall = 0.958, mAP@50 = 0.988); The proposed method maintains the stability of time delay estimation under strong noise and high temperature (> 1200℃), with the average time delay error reduced by approximately 35% to 50% compared to traditional algorithms. This verifies its high robustness and temperature measurement accuracy in complex environments, and it has a promising engineering application prospect.

An Improved PBFT Algorithm Based on Dual Scoring Mechanism

Peng Zhao — 2025-12-16

The traditional Practical Byzantine Fault Tolerance (PBFT) approach suffers from three critical deficiencies: arbitrary primary node election, excessive network transmission overhead, coupled with the absence of node incentive mechanisms. To address these issues, this study proposes a refined PBFT strategy utilizing dual scoring (Double Scoring Practical Byzantine Fault Tolerance, DS-PBFT). The algorithm innovatively combines hardware performance evaluation with a dual-dimensional node scoring system. The algorithm first employs bucket sorting technology to quantitatively evaluate node hardware resources, followed by constructing a comprehensive scoring model through credit values and recommendation values. According to the scoring outcomes, the framework hierarchically divides nodes into primary node, follower node and backup nodes groups in a 1:4:5 ratio, substantially decreasing the quantity of nodes involved in consensus. Additionally, this approach streamlines the Commit-Reply stages within the consistency protocol, substantially reducing communication overhead. Experimental validation demonstrates that DS-PBFT maintains security while achieving notable improvements in consensus efficiency, significant reductions in communication costs, and enhanced defense capabilities against malicious nodes.

Thematic Tour Interactive Experience Platform Empowered by Dynamic Path Planning and Augmented Reality: An Empirical Study on Enhancing Visitor Engagement and Cultural Identity

Li Shen — 2025-12-16

This study focuses on the application of dynamic route planning and augmented reality (AR) technology within interactive theme trail platforms. Taking the “Dongpo Travelogue” digital guide mini-program as a case study, it employs empirical analysis to explore its impact on enhancing visitor engagement and cultural identification. Employing a combined quantitative and qualitative methodology, the study analyses the platform’s functional implementation in route planning, cultural narration, interactive games, and their impact on visitor experience. Findings indicate that the integration of dynamic route planning and AR technology significantly enhances visitor engagement and cultural identity, offering novel insights for the digital transformation of the cultural tourism industry.

A Study on the Design of Elderly-Following Monitoring Robots Based on AHP

Xiaorong Wei — 2025-12-16

This study constructs a design model for elderly-following monitoring robots based on the Analytic Hierarchy Process (AHP), establishing four evaluation dimensions: functional adaptability, interaction usability, environmental adaptability, and safety reliability. Design priorities were determined through expert scoring and weight analysis, followed by practical optimization of structure and interaction. Results indicated that this approach can effectively enhance the scientific rigor of robot design and improve user satisfaction, providing a feasible solution for the design of intelligent elderly-care products.

A Hybrid Encryption Medical Information Security Collaboration Scheme Based on Blockchain and Federated Learning

Jia Sun — 2025-12-16

In current medical data sharing practices, the tension between data privacy protection and cross-institutional collaboration efficiency has become increasingly prominent. To address existing security challenges in healthcare data sharing, we propose a collaborative data cooperation model based on blockchain and federated learning, Through federated learning technology, data is made “usable but not visible” by enabling medical institutions to share only encrypted model parameters, thereby preventing the leakage of raw data. Meanwhile, blockchain technology is introduced to establish a decentralized trust mechanism, utilizing smart contracts to automate data access management and track training processes. In addition, the dual security protection strategy is designed, where differential privacy and Paillier homomorphic encryption technology are adopted to resist member reasoning attacks and ensure secure storage and sharing of information. Through security analysis and experimental validation, the scheme has been proven to have good security and usability.

Research on Human-Robot Interaction Technology Based on Gesture Recognition

Ming Hu — 2025-12-16

With the growing application of intelligent robots in service, manufacturing, and medical fields, efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user experience. Gesture recognition, as an intuitive and contactless interaction method, can overcome the limitations of traditional interfaces and enable real-time control and feedback of robot movements and behaviors. This study first reviews mainstream gesture recognition algorithms and their application on different sensing platforms (RGB cameras, depth cameras, and inertial measurement units). It then proposes a gesture recognition method based on multimodal feature fusion and a lightweight deep neural network that balances recognition accuracy with computational efficiency. At system level, a modular human-robot interaction architecture is constructed, comprising perception, decision, and execution layers, and gesture commands are transmitted and mapped to robot actions in real time via the ROS communication protocol. Through multiple comparative experiments on public gesture datasets and a self-collected dataset, the proposed method’s superiority is validated in terms of accuracy, response latency, and system robustness, while user-experience tests assess the interface’s usability. The results provide a reliable technical foundation for robot collaboration and service in complex scenarios, offering broad prospects for practical application and deployment.