GBP Proceedings Series is a multidisciplinary platform dedicated to publishing high-quality conference proceedings across a wide range of academic disciplines. The series provides a forum for researchers, scholars, and professionals to present and disseminate innovative ideas, research findings, and practical solutions. By collaborating with conference organizers worldwide, the series aims to advance knowledge and foster academic exchange in science, technology, engineering, social sciences, and humanities.
Bibliometric Analysis NEV Purchase Intention: Past, Present and Future Trends
Purpose: This study aims to systematically map the intellectual structure and evolution of research on purchase intentions for new energy vehicles (NEVs). It uncovers thematic trends, key contributors, and emerging directions within this evolving field. Design/methodology/approach: A comprehensive bibliometric analysis was conducted using the Web of Science Core Collection database. Tools like VOSviewer enabled citation, co-citation, and co-word analyses of publications from 1982 to 2024 to visualize thematic clusters and identify leading journals, influential scholars, and country-level contributions. Findings: Sustainability, Journal of Cleaner Production, and Energy Policy emerged as the most influential journals in NEV research. Leadership has shifted from Western countries to China, reflecting changes in production, policy, and infrastructure. Three dominant co-citation clusters identified include external policy drivers, consumer behavioral factors, and methodological foundations, while four co-word clusters encompass information influence, technical performance, psychological mechanisms, and policy incentives. Originality: This study offers a multidimensional, data-driven synthesis of NEV purchase intention research, bridging technical and behavioral perspectives. It provides strategic insights for policymakers, manufacturers, and marketers, emphasizing critical areas for R&D investment and the need for integrated marketing and infrastructure strategies to accelerate NEV adoption. An updated agenda for future scholarship and policy intervention is also presented.
Copyright (c) 2025 Zhikai Yang, Walton Wider, Nasyra Ab. Jamil, Muhammad Ashraf Fauzi, Syed Far Abid Hossain (Author)
Key Technologies and Practical Verification of Carbon Emission Reduction in the Whole Chain of Green Operation of Power Transmission and Transformation Projects
This paper presents key technologies for carbon emission reduction across the entire chain of green operation in power transmission and transformation projects. Based on the actual conditions of the Zhejiang power grid, an integrated carbon reduction control system is established, incorporating intelligent perception, data processing, and decision optimization layers. By leveraging IoT, digital twin, blockchain, and advanced optimization algorithms, the system enables dynamic monitoring, modeling, early warning, and collaborative optimization of carbon emissions. Practical applications in typical projects demonstrate significant reductions in operational carbon emissions and substantial economic and social benefits. Looking forward, the deeper integration of artificial intelligence and big data technologies will further enhance the intelligence and automation of carbon reduction strategies, supporting the achievement of China's dual carbon goals.
Copyright (c) 2025 Tianchao Huang, Jian Wu, Hong Ding, Xiang Lin, Xiaoyuan Liu, Jianrong Wang, Guangze Zhu, Ti Liu, Zhengming Ye, Cong Chen (Author)
The Design of Intelligent Gripper and Holding Algorithm Suitable for High Voltage Combined Electrical Appliances Docking
To address the issues of low efficiency, poor accuracy, and safety hazards associated with manual connections of high-voltage gas-insulated switchgear (GIS), this paper introduces a specialized intelligent gripper system and control algorithm. In terms of hardware, an innovative three-finger radial synchronous gripper is developed, featuring a carbon fiber composite finger tip and a parallel disc spring-rail dual-level adaptive mechanism, which compensates for ±3.5mm pose deviation. The perception system integrates six-axis force sensors and laser rangefinders, with dynamic gravity compensation and Kalman filtering, achieving a force resolution of 0.1N and a pose estimation error less than or equal to 0.03 mm. At the algorithm level, a hierarchical control architecture is proposed. Gain-scheduled adaptive gripping control enhances robustness through gain scheduling strategies. Directionally decoupled force-position hybrid compliant control constructs a stiffness matrix to achieve coordinated tangential low stiffness and normal adaptive stiffness. The force-position deviation mapping algorithm achieves angle error convergence at the 0.04° level. After 50 full-process tests on GIS simulation pieces, the system has a 98% connection success rate, 93.2% seal compression uniformity, a 38.7% reduction in contact force peak, and a single operation time of 4.1 minutes, significantly enhancing assembly automation.
high voltage combined electrical appliances
Copyright (c) 2025 Lihui Zhou, Guangze Zhu, Xianhua Meng, Tengfei Chang, Lu Wang, Bixian Zhu, Ti Liu, Boming Li, Qiunan Xu, Xiaojia Ye (Author)
Analysis of Abnormal Nitrogen Leakage in 500KV Circuit Breaker
High-voltage switches are widely used in 500kV substations, where nitrogen leaks are a common occurrence during operation. This paper provides a detailed analysis of the abnormal nitrogen leak incident involving the No.5041 switch of the No.3 main transformer at the 500kV Danxi Substation. By examining the monitoring backend signals, inspecting on-site equipment, and studying the relay circuits, the cause of the anomaly was identified, and effective solutions were proposed. The analysis revealed that the anomaly was caused by a fault in the K15 relay, not a genuine nitrogen leak. The paper summarizes the handling process and the issues exposed, providing a reference for the analysis and handling of similar anomalies.
Copyright (c) 2025 Hui Liang, Haolin Huang, Wang Lu, Pengda Shao, Zhengwei Feng, Jianqiang Zhan, Guangze Zhu, Tengfei Chang, Shengqun Zhu (Author)
Key Technologies for Carbon Reduction throughout the Entire Process of Green Power Construction
In the context of the national "dual carbon" goals — namely, achieving carbon peaking and carbon neutrality — transmission and transformation projects, as an essential component of the power system, play a pivotal role in promoting green construction practices and advancing carbon reduction efforts. These projects not only serve as critical infrastructure for the reliable delivery and transformation of electrical energy but also represent key opportunities for integrating sustainable technologies and achieving substantial reductions in carbon emissions throughout the engineering lifecycle. Against this backdrop, this paper focuses on the development and application of key technologies that support carbon reduction across the entire green construction process of transmission and transformation projects. Specifically, it introduces the FCCSP (Full-process Carbon Control and Smart Planning) management system, which provides an integrated framework for systematically controlling carbon emissions at every stage — from project design and material selection to construction, commissioning, and long-term operation. Furthermore, the paper details a series of core technological innovations that enhance the carbon reduction capability of these projects, including advanced materials, intelligent construction techniques, and data-driven carbon management tools. The effectiveness and practical value of these technologies are rigorously validated through a set of representative engineering case studies, offering both theoretical insights and empirical evidence. In doing so, this research provides a comprehensive reference for advancing the low-carbon transformation of transmission and transformation projects, and contributes valuable guidance for achieving the broader objectives of sustainable power system development.
Copyright (c) 2025 Jian Wu, Hong Ding, Tianchao Huang, Xiang Lin, Xiaoyuan Liu, Jianrong Wang, Guangze Zhu, Ti Liu, Zhengming Ye, Cong Chen (Author)
Research on X-Ray Image Acquisition Technology of High Voltage Transmission Tower Fittings Based on UAV
The safe and stable operation of high-voltage transmission lines is vital to the nation's energy supply, and hidden defects in transmission tower hardware pose a significant risk of major accidents. Traditional detection methods are inefficient, risky, incomplete, and unable to detect internal defects. To address these challenges, this paper introduces and explores an innovative X-ray imaging technology for high-voltage transmission tower hardware using a drone platform. The core of this technology is a high-performance, lightweight micro X-ray imaging system integrated into a drone, which combines high-precision positioning and stable hovering control, stringent radiation safety measures, and efficient image transmission and processing. This system enables non-contact, non-destructive imaging of key tower hardware. The study details the overall architecture design, key component selection, precise positioning and navigation, radiation field modeling, dose control strategies, and image acquisition and transmission methods adapted for the drone platform. Through systematic laboratory testing, simulation environment validation, and on-site application, the technology has been proven to effectively capture clear X-ray images of the internal structure of high-voltage tower hardware, significantly improving the accuracy and efficiency of defect detection, and greatly reducing the safety risks for workers. This technology provides strong technical support for intelligent and precise inspections of transmission lines, with significant engineering application value and broad prospects.
Copyright (c) 2025 Libo Liu, Jiafeng Chen, Lei Yu, Guangze Zhu, Yanchen Cui, Zhicheng Wang, Jiaqiang Sun, Kangxian Tang, Jilei Cai (Author)
Defect Detection Technology of Tension Clamps Based on UAV
The tension clamp, a critical component of transmission lines, can lead to major safety incidents if its defects are not addressed. Traditional manual inspections are inefficient and risky. This paper introduces an intelligent inspection technology for tension clamps using drones, which employs a quad-copter equipped with high-definition visible light and infrared dual-sensor systems. By integrating autonomous flight path planning, this system can collect data from multiple angles at close range. For typical defects such as cracks, rust, and overheating, a two-stage recognition model has been developed, combining YOLOv5 object detection with an improved ResNet34 classification algorithm, and incorporating attention mechanisms to enhance the extraction of features from small targets. Experiments show that on a test set of 2,368 annotated images, the system achieves a positioning accuracy of 96.2%, an average defect recognition accuracy of 92.7%, and reduces the detection time for a single base tower to 8 minutes. This technology significantly enhances inspection efficiency and safety, offering a new solution for the intelligent operation and maintenance of transmission lines.
Copyright (c) 2025 Jiafeng Chen, Hui Xu, Libo Liu, Guangze Zhu, Yanchen Cui, Zhicheng Wang, Lei Yu, Jiaqiang Sun, Kangxian Tang, Jilei Cai (Author)
Suitable for Dynamic Modeling of 3D Vision Algorithm and Motion Control Compensation
The accuracy of 3D modeling in dynamic scenes is constrained by motion blur and system latency. Traditional visual algorithms often produce geometric distortions when reconstructing high-speed moving objects, and the response delay in control loops further exacerbates these modeling errors. This paper introduces a collaborative framework that integrates time-varying perception 3D vision with predictive compensation control: first, a motion state estimation module based on multi-sensor tight coupling is designed. This module fuses RGB-D data and IMU information using adaptive Kalman filtering to achieve real-time decoupling of motion trajectories. Next, a hierarchical control compensation mechanism is developed, which combines feedforward motion prediction from LSTM networks with online tuning of PID parameters based on visual-inertial feedback. This significantly reduces modeling distortions caused by actuator delays. Verification on a robotic arm dynamic grasping platform shows that compared to the ORB-SLAM3 system, the modeling point cloud registration error is reduced by 62.3%, and the root mean square error (RMSE) of trajectory tracking is reduced by 58.1%. This effectively addresses the industry challenge of 'modeling-control' cross-interference in dynamic scenes, providing robust technical support for scenarios such as intelligent manufacturing and unmanned systems.
Copyright (c) 2025 Zhanfeng Wu, Tengfei Chang, Guangze Zhu, Xiaojia Ye, Chengming Luo, Boming Li, Ti Liu, Xin Yang, Qiunan Xu, Jianqiang Zhan (Author)
Large-Scale Mechanical Vibration Compensation Algorithm Based on Six Degrees of Freedom Platform
The harmful vibrations generated by large machinery during operation significantly impact processing accuracy and structural lifespan. This paper addresses this issue by proposing an active vibration compensation algorithm for high-voltage combined electrical equipment in precision docking scenarios, based on a six-degree-of-freedom platform. The study begins with the establishment of a comprehensive dynamic model of the Stewart platform to analyze the typical low-frequency vibration characteristics of large machinery. It then designs a composite control architecture that integrates feedforward and robust feedback. By real-time vibration signal acquisition, the platform generates inverse dynamic compensation commands using model predictive control, while introducing sliding mode control to enhance robustness against external disturbances. After verifying the algorithm's effectiveness in reducing vibration attenuation by over 90% at frequencies between 5 and 50Hz through simulations, a hydraulic-driven 6-DoF experimental platform was constructed to validate the system using a 10-ton simulated load. The experiments show that under diesel engine excitation conditions, the algorithm reduces the RMS value of key position errors by 82% and the peak vibration acceleration by 76%, significantly outperforming traditional PID control. This study provides an engineering solution for precise vibration suppression in large equipment.
Copyright (c) 2025 Lihui Zhou, Zhanfeng Wu, Guangze Zhu, Tengfei Chang, Boming Li, Ti Liu, Bixian Zhu, Xianhua Meng, Lu Wang, Xiangwen He (Author)
Analysis of Abnormal Secondary Voltage of 500 KV HGIS Line
A 500kV HGIS line experienced an abnormal voltage data alarm for the 3U0 voltage. Through on-site sampling verification, loop temperature measurement, and theoretical analysis, it was determined that a grounding fault existed in the secondary internal circuit of the line voltage transformer (PT), causing abnormal secondary voltage sampling outputs from two sets of line protection systems. This article provides a detailed account of the abnormal event, on-site inspection findings, fault cause analysis, and the results of the return-to-factory inspection, along with proposed corrective measures. The aim is to provide a reference for the investigation and prevention of similar issues.
Copyright (c) 2025 Xiangwen He, Shaolin Kuang, Bixian Zhu, Zhengwei Feng, Zhanfeng Wu, An Du, Xiaojia Ye, Xiaohan Gou, Junkang Wang (Author)
Ultra-High Voltage Line UAV Inspection Technology
Ultra-high voltage (UHV) transmission lines, as the backbone of national energy transmission, are crucial for their safe and stable operation. However, traditional manual inspection methods suffer from low efficiency, high labor intensity, significant safety risks, and limitations due to terrain and climate conditions, as well as observation blind spots, making them inadequate for the intelligent operation and maintenance (O&M) needs of modern power grids. The rapid advancement of drone technology has brought revolutionary breakthroughs in UHV line inspections. This article aims to systematically explore the application of drone inspection technology in UHV scenarios, focusing on its technical principles, system composition, core advantages, and challenges. The research covers key technologies such as drone platform selection, the functions and applications of critical mission payloads, high-precision positioning and navigation, autonomous obstacle avoidance, strong electromagnetic environment adaptability, multi-sensor fusion, and intelligent defect recognition based on deep learning. Through case studies, it demonstrates that drone technology significantly enhances inspection efficiency and coverage, reduces operational risks and costs, and achieves precise and intelligent detection. Although there are still challenges in endurance, adaptability to complex environments, data processing efficiency, and standardization, drone inspection technology represents the inevitable direction for the intelligent O&M of UHV lines. Its deep integration with cutting-edge technologies like artificial intelligence, digital twins, and cluster collaboration will usher in a new era of transmission line O&M.
Copyright (c) 2025 Hui Xu, Libo Liu, Jiafeng Chen, Guangze Zhu, Yanchen Cui, Zhicheng Wang, Lei Yu, Jiaqiang Sun, Kangxian Tang, Jilei Cai (Author)
Closed-Loop Health Management System of Relay Protection Device Based on Multi-Modal Perception and Dynamic Target Test
Conventional relay protection testing systems are plagued by a critical limitation: they prioritize functional compliance over long-term health assessment, leading to undetected mechanical wear, component aging, and failures caused by environmental stress. Routine maintenance based on time intervals, rather than actual device conditions, often results in over-maintenance or missed faults, and the lack of data-driven life prediction leads to inefficient asset utilization. To address these challenges, this paper proposes a closed-loop health management system for relay protection devices, introducing three core innovations: a quantitative health evaluation model based on multi-parameter fusion, a multi-modal perception layer that captures degradation signals in real time, and an intelligent decision-making mechanism at the edge. This system enables full-process monitoring, root-cause diagnosis, and adaptive maintenance, shifting the protection paradigm from passive fault response to proactive immunity, and laying the foundation for predictive maintenance and intelligent lifecycle management of protection assets.
Copyright (c) 2025 Yao Xiao, Jian Wu, Xiaoyuan Liu, Jianrong Wang, Guangze Zhu, Junkang Wang, Jiayi Wang (Author)
Research on GIS Pipeline Precision Docking Device Based on Multi-Degree of Freedom Robot Arm Control
Gas-Insulated Switchgear (GIS) is widely used in substations of various voltage levels due to its compact size and high reliability. However, manual installation of GIS equipment faces challenges such as limited space, low alignment accuracy, low efficiency, and safety risks. To address these issues, this paper proposes a precision alignment technology that integrates six-degree-of-freedom robotic arms. By developing a precision alignment device for GIS pipeline busbars using multi-degree-of-freedom robotic arm control, the system can achieve millimeter-level accuracy in automatic pipeline alignment. This platform significantly enhances pipeline installation efficiency and equipment reliability, filling a gap in China's GIS intelligent construction equipment and enhancing the quality and efficiency of power construction.
Copyright (c) 2025 Jinghui Liu, Xiabo Chen, Guangze Zhu, Lihui Zhou, Ti Liu, Boming Li, Zhengming Ye (Author)
Challenges and Countermeasures in Post-Pandemic Teacher Performance Management: Insights from Public Universities in Hong Kong, China
This study focuses on the impact of the blended teaching model on higher education management in the post-pandemic era. Taking eight public universities in Hong Kong, China as cases, it deeply explores the challenges and optimization paths faced by teacher performance management through qualitative research methods. The research adopted a multi-case design and conducted semi-structured interviews with 32 interdisciplinary teachers and 8 administrative managers. Combined with the thematic analysis method, three core issues were identified: The deficiency of the incentive mechanism was manifested as 70% of the respondents pointed out that the rigid senior-salary system was difficult to reflect the value of teaching innovation, and the interdisciplinary achievements had insufficient weight in the promotion evaluation; The imbalance in resource allocation was manifested in 80% of the participants confirming that there was a severe disciplinary bias (for example, business monopolized 70% of the digital teaching budget, and the internship opportunities in science and engineering were eight times that of humanities), resulting in a satisfaction rate of humanities teachers as low as 2.3/5. The insufficiency of professional development support is prominently reflected in the fact that 77.5% of teachers lack training in blended teaching skills (such as asynchronous interaction and LMS data analysis), and 55% believe that mental health support is absent. Based on the emergent theory, the transformational leadership theory and the framework of the TPACK model, a systematic countermeasure is studied and proposed: reconstructing dynamic performance indicators and adopting the Analytic Hierarchy Process (AHP) to integrate dimensions such as the flexibility of online teaching and the application of technology; Establish a performance-oriented resource allocation mechanism and promote fairness through interdisciplinary special funds; Build a teacher learning community to provide customized technical training and psychological support; Innovate diverse incentives such as linking salary with teaching innovation achievements and establishing social impact awards. These measures have been initially verified for effectiveness through practices such as the development of AI courses at the University of Hong Kong, China and the resource pooling at the Hong Kong University of Science and Technology, and can provide a paradigm reference for global higher education institutions to build a resilient performance management system. Future research needs to expand cross-cultural comparisons and long-term tracking to deepen the applicability of the theory.
Copyright (c) 2025 Baoli Huang, Xuanzhi Huang, Mengran Duan, Yalin Li (Author)
Research on the Influence Path of Customer Participation on Brand Loyalty — Double Mediation Model of Psychological Ownership and Relationship Quality
In the current highly competitive brand environment, customer engagement is seen as a key factor in enhancing brand loyalty. To solve this problem, the purpose of this paper is to explore how customer participation affects brand loyalty through the dual intermediary mechanism of psychological ownership and relationship quality. Based on the questionnaire data, the structural equation model is used to construct and verify the theoretical model. It is found that customer participation significantly positively affects brand loyalty, and that psychological ownership and relationship quality play an important mediating role in this process. Specifically, when the psychological ownership of customers increases, their enthusiasm to participate in the behavior increases, thus promoting the quality of the relationship with the brand, which further strengthens the brand loyalty. In addition, the study also reveals an interactive relationship between psychological ownership and relationship quality, that is, the two complement each other, forming a virtuous cycle in the process of promoting brand loyalty. The innovation is the first time to integrate psychological ownership and relationship quality into the influence mechanism between customer participation and brand loyalty, providing a new perspective to understand the complexity of brand building. This paper has practical application value to brand management practice, and suggests that enterprises should actively guide customers to participate, by increasing the psychological sense of ownership and strengthening the quality of relationship, so as to enhance brand loyalty. This research not only enriches the knowledge system of the theories related to brand loyalty, but also provides practical guidance for customer relationship management for enterprises.
Copyright (c) 2025 Luyi Wang, Podobed Natalia Alexandrovna (Author)
An Empirical Study on the Impact of Ultimate Frisbee on College Students' Mental Health: Taking University Physical Education Classes as the Context
In recent years, as attention to students' mental well-being has increased, integrating physical education with psychological development has become a key focus in higher education reform. This study investigates the psychological impact of ultimate frisbee on college students within the context of university physical education classes. Drawing upon empirical data from questionnaires and interviews conducted among 230 undergraduate students in three universities, the research explores how participation in ultimate frisbee influences emotional regulation, stress relief, interpersonal communication, and self-efficacy. The results indicate that regular engagement in ultimate frisbee significantly enhances students' positive emotional experiences, reduces symptoms of anxiety and academic pressure, and fosters a stronger sense of teamwork and social belonging. The findings provide practical implications for incorporating psychologically beneficial sports into the university curriculum, contributing to the holistic development of students.
The Role of English as a Global Language in Shaping Students' Worldviews
As English continues to solidify its status as a global lingua franca, its influence extends far beyond communication, shaping cultural identity, value systems, and individuals' understanding of the world. This study investigates how learning and using English as a second language shapes the worldviews of university students in non-native English-speaking contexts. Drawing on data from surveys and in-depth interviews with 512 Chinese undergraduates, this study explores the cognitive, cultural, and emotional dimensions of English language acquisition. Findings indicate that English proficiency is positively correlated with students' openness to global perspectives, awareness of international issues, and perceived agency in cross-cultural communication. However, the study also reveals tensions between global integration and cultural displacement, as some students report feelings of identity fragmentation and alienation. The research highlights the dual role of English as both a tool for empowerment and a vector of cultural homogenization. These insights call for a more critically reflective approach to English language education that balances global competence with local cultural rootedness.
AI-Powered Software Testing for Heterogeneous Fleet Vehicle Routing Optimization with Time Constraints
The Vehicle Routing Problem (VRP) is crucial in logistics, transportation, and distribution. Traditional VRP focuses on optimizing vehicle routes between a fixed starting point and multiple locations to minimize travel distance or time. However, these models perform inadequately in dynamic environments such as campus student path planning, which involve diverse movement patterns and time window constraints. This paper addresses campus student path planning as a Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HFVRPTW) and introduces the Scooter-Aware Pathfinding with Time Windows (SAPTW) method. Students start from random points and navigate a grid-based campus to fixed destinations like dormitories and cafeterias, choosing either walking or using electric scooters available at specific locations. This study tackles key challenges including diverse movement modes, time windows for reaching destinations, automatic generation of campus maps, and random generation of student starting points and destinations. Additionally, ensuring AI-powered software testing, we developed the Grid-based Campus Map Randomized Generation (GMRG) method, a rule-based approach for creating grid maps with roads, obstacles, and specific buildings. This method provides a realistic and controlled environment for route planning tests and simulations, ensuring the robustness and reliability of the proposed solution in real-world applications. Our approach highlights the potential of integrating artificial intelligence with software testing to optimize complex routing problems with time constraints. Simulation results demonstrate that SAPTW significantly enhances student arrival efficiency, reducing average arrival time by approximately 3% to 44% compared to traditional methods.
Copyright (c) 2025 Zhongbo Liu, Guillermo Palacios-Navarro, Raquel Lacuesta (Author)
Enhancing Intercultural Competence among College Foreign Language Students: The Critical Role of Educators
This study investigates the intercultural competence of college students majoring in foreign languages, focusing on enhancement strategies and the pivotal role of educators. Based on a questionnaire survey of 122 undergraduates and qualitative interviews with two students, the findings reveal that teacher-guided discussions on foreign cultures, in comparison with Chinese culture, significantly foster students' cultural self-confidence and sense of national identity. Furthermore, classroom group discussions are found to be essential for improving interpersonal and oral communication skills. Assignments designed by instructors also promote critical thinking and creativity. Overall, the results highlight the importance of well-structured classroom activities and effective teaching methods in developing intercultural competence within foreign language education.
Copyright (c) 2025 Huiying Zhang, Yiting Li, Shuying Li (Author)
Effect of Hypertension on Carotid Plaque Formation and Outcomes in High-Risk Cerebrovascular Population: A Community-Based Study in Guangzhou
Objective: This study investigates the effects of hypertension on carotid plaque formation and clinical outcomes in a high-risk population for cerebrovascular disease in the Guangzhou communities. Methods: Data from 322 high-risk individuals for cerebrovascular disease in the Guangzhou communities were consecutively collected between January 2018 to December 2023. These participants were hospitalized twice within one year at the Department of Neurology, the First Affiliated Hospital of Guangzhou Medical University, and underwent carotid ultrasound examinations during both admissions. Based on the carotid ultrasound results from the first hospitalization, participants were initially categorized into two groups (with or without plaque) to analyze the association between hypertension and carotid plaque formation using univariate and multivariate methods. Subsequently, the cohort was further divided into three groups according to hypertension control status across both admissions: a non-hypertensive group (n = 48), a hypertension controlled group (average blood pressure (BP) < 130/80 mmHg at both admission, n = 109), and a hypertension uncontrolled group (average BP ≥ 130/80 mmHg at either admission, n = 165). Differences in the variance of Crouse scores (ΔCrouse) between the two ultrasound examinations were analyzed among the three groups. Further stratification analysis was performed by dividing participants into two age subgroups (cutoff: 70 years) and three hypertension control subgroups (average BP < 130/80 mmHg at two-time admissions, one-time admission, or neither). The differences in ΔCrouse were then analyzed across these stratified subgroups. Results: Age (OR = 1.074, P = 0.000), gender (OR = 0.340, P = 0.000), SBP (OR = 1.039, P = 0.006), and DBP (OR = 0.932, P = 0.003) were significantly associated with carotid plaque formation. No statistically difference in ΔCrouse was observed between the hypertension controlled group and the non-hypertensive group; however, ΔCrouses in the above two groups showed significant differences from that in hypertension uncontrolled group (both, P < 0.01). Furthermore, similar trends were found in both age subgroups (≥ 70 years and < 70 years). The two-time controlled hypertension subgroup exhibited significantly smaller ΔCrouse value than the one-time and two-time uncontrolled subgroups (both, P < 0.05), while no significant difference was found between the one-time and two-time uncontrolled subgroups. Conclusions: Age, gender, SBP, and DBP were identified as influential factors for carotid plaque formation. In patients with well-controlled hypertension (average BP < 130/80 mmHg), the progression rate of carotid plaques showed no significant difference from that of non-hypertensive individuals. However, the progression of carotid plaques was significantly accelerated in patients with uncontrolled hypertension.
Copyright (c) 2025 Shunyu Zhang, Xingdong Deng, Huaixiang Liu, Mingshu Mo, Pingyi Xu, Zuojun Tian (Author)
A Review of Industrial Economic Structure and Efficiency from a Statistical Perspective
This paper provides a comprehensive review of industrial economic structure and efficiency through the lens of statistical methodologies. It systematically discusses key concepts such as technical, allocative, and scale efficiency, and critically evaluates widely used measurement techniques including Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The paper further explores a broad spectrum of statistical tools — such as descriptive statistics, multivariate analysis, time series and panel data models, as well as spatial statistics and geographically weighted regression — and their applications in analyzing industrial dynamics. Empirical studies illustrating variations across different industries and regions are summarized, highlighting the importance of data quality and processing methods. The integration of traditional statistical approaches with emerging machine learning techniques is also examined, pointing toward future research directions. Finally, the study reflects on the practical implications for policy and industrial development, emphasizing both the strengths and limitations of statistical perspectives in industrial economics research.
Copyright (c) 2025 Yiming Teng, Xiujie Tian (Author)
Research on Restructuring the Piano Lesson Teaching Model in the Context of Artificial Intelligence
With the vigorous development of artificial intelligence (AI) in education, traditional piano teaching faces challenges such as low personalization, delayed feedback, and underutilization of classroom resources. Based on a systematic review of traditional piano teaching models and AI applications in education, and informed by cognitive psychology and intelligent teaching theories, this study employs a combination of questionnaires, in-depth interviews, and teaching experiments to analyze the current state and problems of piano classrooms. On this basis, we construct a restructured teaching model that integrates an intelligent teaching platform, personalized learning paths, and adaptive feedback, and conduct practical implementation and effectiveness evaluation in representative schools and student groups. The results show that this model significantly enhances student engagement, optimizes classroom management, and improves practice efficiency, while also revealing limitations in platform maturity and teacher training requirements. Finally, we discuss the feasibility and considerations for wider adoption of the model and propose future research directions in multimodal interaction and interdisciplinary integration.
Copyright (c) 2025 Luyao Liu, Weiyu Zhu (Author)
The Natural Picture and Implementation Path of the Construction of Data-Driven Human Resources Agile Service Platform: Take a Case Study of Dalian Example
The advantage of urban talent competition is the embodiment of urban vitality, people go with the industry, the city depends on the industry, how to maximize the integrated efficiency of the "people-industry-city" model has become the key to systematically addressing urban talent challenges. At present, Dalian's "human resources agile service platform" is faced with problems such as insufficient integration of the existing employment information platform system, underdeveloped grassroots public employment service institutions, and unclear funding mechanisms for the construction and upgrading of grassroots information platforms. In order to accelerate the realization of an agile, intelligent and accurate "human resources agile service platform", it is urgent to build an integrated public employment service system, develop a precise human resource management platform, establish a collaborative and targeted employment cooperation mechanism, and improve the guarantee mechanism of the digital employment system.
Copyright (c) 2025 Yangyang Lu, Xiaoshen Cai (Author)
Study on Fiscal Policy Options for Promoting the Development of China's Low-Carbon Economy
In the field of international climate governance, the low-carbon economy has become an important path for China to achieve high-quality development. The current domestic energy structure is still dominated by fossil energy; the low-carbon transformation of industries is facing technological bottlenecks and cost pressure, which urgently requires fiscal policy to play an incentive and regulatory role. The existing fiscal tools also have their own shortcomings regarding support, structural optimization and cross-sector synergies, which limit research and development of green technology, clean energy substitution and carbon market construction. Inspired by the experience of the development of the international carbon tax system and green subsidy mechanism, a multi-level financial support framework should be constructed in line with the national conditions, and policy resources should be promoted for precise allocation towards the low-carbon sector, with the aim of striking a balance between economic growth and ecological protection dual goals.
Research on the Idea of Governing Colleges and Universities According to Law
Higher vocational colleges face issues such as institutional implementation deviations, insufficient legal literacy among faculty and students, and inadequate risk prevention mechanisms in the process of promoting law-based governance. This paper analyzes the current status of law-based governance in higher vocational colleges and explores strategies for improving the legal system, enhancing the legal literacy of faculty and students, and establishing long-term risk prevention mechanisms. Building an internal governance structure based on law, strengthening practical legal education, and establishing diversified supervision mechanisms are crucial for achieving law-based management in higher vocational colleges. The paper also suggests using digital means to enhance the enforcement and transparency of regulations, to promote deeper development of law-based governance in higher vocational colleges.
Artistic Analysis of the Cello Work Fantasia
The research here focuses on exploring the creative background, the structure of music, and the art of the cello composition Fantasia through a literature review and an empirical data analysis. The variety of its thematic material, construction, and expressiveness provided by the score are identified as the primary subjects for observation. We utilized motion-capture and high-resolution audio analysis to perform quantitative assessments of the interpretation behaviors such as tempo fluctuation, intonation, and dynamics. The technical issues involved in the practice like the change of the position of the left hand, different bowing techniques, and the accomplishment of the expressive control are pointed out and explored. The strategies for solving the issues are drawn from the obtained results and through intervention, these are suggested and tested. This demonstrative practice of intervention impact from pre-and post-study intervention on playing fluidly and expressively proves the findings. The research ultimately unearths the core of Fantasia's beauty and performance practice and at the same time provides a new approach to quantitative analysis and pedagogy of cello repertoire.
From the perspective of public administration, this paper reconstructs the "soft resilience" framework within urban resilience theory, breaking away from the traditional focus on physical infrastructure. It highlights the critical role of social, cultural, and institutional factors in enhancing urban adaptability. Through interdisciplinary integration, this paper introduces complexity theory, multi-dimensional value balancing in policy-making, and adaptive management models, systematically exploring flexible policy adjustments and feedback mechanisms in dynamic environments. It reveals the coordinating and driving role of public administration in building urban resilience. "Soft resilience" is conceptualized as a dynamic adaptation process aimed at continuous learning, feedback, and optimization to strengthen cities' capacities to confront globalization, climate change, and social uncertainties. By constructing participatory mechanisms, fostering cross-sector collaboration, and strengthening social capital, public administration emerges not merely as a crisis manager but as a fundamental driver of institutional transformation and urban resilience.
Copyright (c) 2025 Zhijiang Lu, Hongbo Yu (Author)
Analyze the Feasibility of Implementing Traffic Congestion Tax in the First-Tier Cities
With the economic expansion of first-tier cities and the increase of car ownership in recent years, the road congestion in first-tier cities is not optimistic. This paper will review the current domestic congestion situation and countermeasures, and refer to foreign countries that have successfully implemented congestion tax to illustrate the demand and legitimacy of charging congestion tax in China's first-tier cities. At the same time, with reference to the specific programs of other countries, make a preliminary conception and design for the content of the policy, and draw an appropriate charge based on scientific calculation and principle. It does not mean that charging congestion fees would abolish the construction of the public transport system, and urban residents' travel still depends on the subway and the bus to a considerable extent. Therefore, we emphasize that we should pay equal attention to the implementation of congestion tax policy and the construction of public transport system to achieve the purpose of solving road traffic congestion to the greatest extent under the joint action of the two.
Research on New Problems and Reform Prospects of the Budgetary Accounting System of Business Units
As an important pillar of the national public service system, the budgetary accounting system of institutions is directly related to the efficiency of the use of financial funds and the rational allocation of public resources. In recent years, with the increasing complexity of the economic environment, the deepening of the financial system reform and the public's demand for transparency and performance, the traditional budgetary accounting system has gradually revealed a number of non-adaptive problems. For example, such as the rigidity of the budgeting method, lack of supervision in the implementation process, outdated accounting basis, and insufficient scientific guidance in the performance evaluation system, among others. These problems not only restrict the effective performance of the functions of the institutions, but also may lead to the waste of funds and risks to institutional integrity. At the same time, the arrival of the digital economy era has put forward higher requirements for the informatization and refinement of budget management, while the existing system is still insufficient in the application of technology and system innovation. In this context, the study of new issues and reform paths of the budget accounting system of public institutions is not only an inevitable choice to improve the quality of public services, but also an important hand to promote the modernization of financial management.
Strategic Management and Innovation Development of Family Business: Based on the Case Study of Family Business of Ningbo T Group Company
Strategic management plays a vital role in the internal governance of family-owned enterprises. Compared to other types of businesses, family firms often encounter unique challenges during their operation and growth. Without timely adjustments to their strategic direction in line with industry trends and their own organizational realities, these enterprises risk falling into stagnation and losing their competitive edge. This paper takes Ningbo T Group—a representative family-owned business — as the focal point to examine the current status and shortcomings of strategic management in such enterprises. It further investigates the underlying causes of these issues and proposes innovative management approaches to address them. By exploring the company’s brand positioning, innovation initiatives, human resource strategies, and family governance mechanisms, this study uncovers the key drivers behind its sustained success in the highly competitive kitchen appliance industry. The findings aim to offer practical insights and guidance for the strategic development of other family businesses, highlighting the value of quality-driven growth and continuous innovation as cornerstones of long-term viability.
Copyright (c) 2025 Zhiliang Li, Li Yang, Xiaoxi Zhang (Author)