International Journal of Engineering Advances
International Journal of Engineering Advances (Int. J. Eng. Adv.) is a peer-reviewed academic journal dedicated to publishing cutting-edge research and advancements across all fields of engineering. The journal welcomes original research articles, review papers, and technical notes on topics such as civil engineering, mechanical engineering, electrical engineering, computer science, and materials science. Its mission is to facilitate the dissemination of innovative solutions and promote interdisciplinary collaboration within the global engineering community.
Adaptive Reuse of Urban Public Space and Optimization of Urban Living Environment
With the rapid development, densification, and transformation of urban areas, the adaptive reuse of public spaces has emerged as a critical concern, profoundly shaping the quality, inclusivity, and livability of contemporary urban life. This study examines the multifaceted dimensions of urban public space, including spatial utilization, temporal dynamics, and everyday experiential patterns, to investigate how underutilized or “idle” spaces can be effectively transformed into vibrant, community-oriented environments that support diverse social, cultural, and recreational activities. Through detailed analysis of pedestrian accessibility, social interaction potential, spatial flexibility, and activity adaptability, the research develops a spatial response model aimed at optimizing the functionality and inclusiveness of public spaces in line with the practical needs and behavioral patterns of urban residents. This model emphasizes not only physical accessibility and spatial connectivity but also the creation of affective and experiential environments that enhance community engagement and foster a sense of belonging. The findings demonstrate that strategic interventions in underutilized urban spaces can significantly strengthen the relationship between public spaces and the urban living environment, promoting dynamic interactions, social cohesion, and sustainable urban development. By providing a systematic framework for understanding and implementing adaptive reuse strategies, this study offers valuable insights for urban planners, architects, and policymakers seeking to create more responsive, inclusive, and lively urban settings that cater to the evolving needs of contemporary cities.
Copyright (c) 2025 Jingyuan Huang (Author)
Optimization of Neural Motor Control Model Based on EMG Signals
As an important signal of the nervous system, electromyography (EMG) is used in prosthesis control, rehabilitation training, motion intention prediction and human-computer interaction. However, its widespread application is limited by noise, large individual differences, complex computation and poor real-time performance. In this paper, the processing technology, feature extraction, control mode design and calculation optimization based on EMG are reviewed, and optimization strategies based on signal enhancement, deep modeling and accelerated calculation are proposed to ensure the robustness and timeliness of the algorithm and improve the adaptability of motion control to individuals. The results show that the optimized model can effectively improve the precision of motion control, calculation speed and cross-individual compatibility, and provide technical support for EMG intelligent prosthetics, rehabilitation AIDS and wearable neural interfaces.
Integration and Optimization Strategy of Spatial Video Technology in Virtual Reality Platform
With the rapid development of virtual reality technology, spatial videos have gradually become an important carrier for content presentation on VR platforms due to their immersive, multi perspective, and realistic features. This article mainly conducts preliminary theoretical research on the integrated application of spatial video in virtual reality platforms. Summarized the theoretical basis and technological development of spatial video in virtual reality, and constructed a comprehensive system model for the collection, encoding, rendering, and interaction of spatial video content. Further, aiming at the key problems in data processing pressure, rendering delay and terminal compatibility existing in the current space video application process, this paper proposes optimization solutions in edge computing collaboration, intelligent coding compression and standard interface design. I hope that this research can provide theoretical guidance and specific methods for the integration of spatial video and virtual reality systems.
Intelligent Sensor and System Integration Optimization of Auto Drive System
With the rapid advancement of autonomous driving technology, the deployment of advanced sensor systems has become a cornerstone for achieving high-precision environmental perception, supporting real-time decision-making, and executing vehicle control operations. These sensors-including LiDAR, radar, cameras, ultrasonic modules, and inertial measurement units-provide critical data streams that enable vehicles to perceive dynamic and complex driving environments accurately. However, despite these technological advancements, significant challenges remain in sensor system integration and data fusion. Common issues include asynchronous operation among sensor modules, suboptimal physical layout affecting system performance, excessive communication and computational loads, and difficulties in synchronizing multimodal data streams, all of which limit the overall efficiency, responsiveness, and reliability of the autonomous driving system. This paper systematically analyzes the application of advanced sensor technologies within autonomous driving systems, examining the functional characteristics, strengths, and limitations of various sensor modules for environmental perception, positioning, and decision-making assistance. Special attention is given to the core difficulties encountered in multi-sensor integration, including data synchronization, signal redundancy, energy consumption, and system scalability. To address these challenges, the study proposes key solutions such as optimized integration mechanisms, efficient sensor layout design, energy-aware communication strategies, and advanced data fusion algorithms. These measures aim to enhance system stability, increase processing speed, improve compatibility with diverse environmental information, and enable real-time, reliable vehicle operation under complex driving scenarios. Through these integrated strategies, the paper demonstrates how the combination of optimized sensor deployment, efficient data fusion, and systematic integration design can significantly elevate the performance level of autonomous driving systems. The findings provide a technical foundation for the large-scale deployment and practical application of intelligent sensor equipment, contributing to safer, more efficient, and highly reliable autonomous vehicle operations in real-world environments.
Copyright (c) 2025 Junchun Ding (Author)
Deployment of Natural Language Processing Technology as a Service and Front-End Visualization
In the field of artificial intelligence, natural language processing (NLP) technology occupies a core position, and it has been widely used in many scenarios such as speech recognition, automatic translation, emotion analysis and so on. As the technology advances, it is critical to implement NLP technology as a service deployment and front-end visualization, which is essential to enhance application performance and user experience. This paper analyzes the core algorithm of NLP technology and its application in different industries, and expounds how to realize the efficient deployment of NLP model with the help of service-oriented architecture. The article delves into deployment techniques such as model encapsulation, interface design, performance optimization, and lifecycle management, and explores visual presentation to improve the comprehensibility of data analysis results while ensuring data security and privacy protection. With these technical measures, NLP's service capabilities in real-world applications become more flexible and efficient, while also improving the user's interactive experience
Research on Real-Time User Feedback Acceleration Mechanism Based on Genai Chatbot
As GenAI adoption continues to surge, AI chatbots have become one of the most prominent and widely deployed applications of artificial intelligence, especially across sectors such as customer service, education, healthcare assistance, and content generation. In these domains, the real-time feedback loop generated through user interactions-and the system's ability to adapt based on this feedback-plays a critical role in determining the quality, reliability, and intelligence level of chatbot responses. As user expectations for accuracy, immediacy, and personalization continue to rise, the demand for a more robust and efficient feedback mechanism has become increasingly urgent. However, existing GenAI chatbot systems often struggle to maintain sufficient speed and stability in their real-time feedback loops. Problems such as delayed feedback processing, insufficiently refined semantic interpretation, and occasional inaccuracies in generated responses weaken the system's overall performance and may negatively affect user experience. These challenges highlight the necessity of integrating real-time feedback more directly and effectively into the core model processing pipeline. This article aims to address the above limitations by investigating how real-time user feedback can be seamlessly incorporated into AI model interactions to enhance response efficiency and accuracy. The research proposes an integrated optimization strategy that includes streamlining data transmission paths, improving semantic parsing algorithms, refining intent recognition processes, and applying targeted model fine-tuning methods. These enhancements work together to accelerate the system's feedback processing capabilities and strengthen its adaptive response mechanism. Through extensive experimental testing and comparative analysis, the study demonstrates that the optimized system significantly improves the efficiency of the feedback loop, enhances the coherence and precision of generated responses, and ultimately boosts user satisfaction. The findings suggest that the integration of real-time feedback into GenAI model processing offers a viable pathway for building more intelligent, adaptive, and user-centered chatbot systems suitable for broader practical deployment.
Research on the Application of Integrating Medical Data Intelligence and Machine Learning Algorithms in Cancer Diagnosis
The exponential growth of medical data has made it increasingly important to unlock its latent clinical value. This paper investigates the integration of medical data intelligence and machine learning algorithms in the context of cancer diagnosis. Key challenges and strategies related to multi-source data fusion, feature extraction, model optimization, and system stability are examined. The study reviews the use of imaging, pathology, genomic, and textual data across various machine learning frameworks and proposes a streamlined, clinically applicable approach to intelligent diagnostic support. The aim is to provide an efficient auxiliary diagnostic tool to advance precision medicine and support clinical decision-making in oncology.
Copyright (c) 2025 Xiangtian Hui (Author)
VNA-based Insertion Loss and Return Loss Testing and De-embedding Techniques for High-Speed Serial Links on PCBs
This paper addresses the critical requirements of high-speed communication systems within artificial intelligence (AI) data centers, focusing on both theoretical analysis and practical implementation. The study begins with a detailed examination of the Vector Network Analyzer (VNA) measurement principle and the definition of S-parameters, providing an in-depth explanation of the physical significance of key parameters, including S11 and S21. The relationship between insertion loss and return loss is explored, highlighting their influence on signal fidelity and overall system performance. Furthermore, the characteristics of high-speed serial links are analyzed in conjunction with Pulse Amplitude Modulation 4-level (PAM4) signaling, emphasizing the effects of test indicators such as channel insertion loss flatness, group delay, and return loss margin on signal integrity. The paper also elaborates on the design and construction of the test system, including the deployment of de-embedding techniques to isolate and accurately measure channel effects. Experimental verification is conducted to assess system performance, with results analyzed to demonstrate the practical impact of different signal impairments on high-speed data transmission. Insights derived from the study provide valuable guidance for engineers and researchers involved in the design, testing, and optimization of high-speed interconnects in AI-driven data centers, offering concrete recommendations for improving signal reliability and overall communication efficiency.
Machine Learning-Driven Optimization of Physical Layer Signal Processing in High-Speed Networks
The rapid evolution of high-speed communication networks, including 5G/6G, high-speed Ethernet, and satellite systems, has posed significant challenges to physical layer (PHY) signal processing. Traditional analytical methods often struggle with dynamic channel conditions, complex interference, and computational constraints. This review explores the integration of machine learning (ML) techniques for optimizing PHY signal processing, highlighting supervised, unsupervised, and reinforcement learning approaches, as well as deep learning architectures such as CNNs, RNNs, and Transformers. The discussion covers model training strategies, including offline and online adaptive learning, and examines optimization objectives such as bit error rate reduction, spectral efficiency enhancement, and energy efficiency improvement. Representative case studies across high-speed network scenarios demonstrate the practical benefits of ML-driven PHY optimization. Additionally, the paper addresses challenges including data acquisition, model complexity, interpretability, robustness, and security, while outlining potential future directions such as federated learning, edge intelligence, adaptive signal processing, multimodal signal fusion, and quantum machine learning. Overall, ML provides a versatile and adaptive framework that complements traditional algorithms, enabling robust and efficient PHY performance in complex network environments.
Energy Management and Recycling Mechanisms for Degradable Electronic Devices: A Comprehensive Framework for Sustainable Technology Integration in Circular Economy Systems
The strongest environmental issue of the newest technology era is the increasing e-waste issue, doubling from 62 million tonnes in 2022 to an approximate 82 million tonnes in 2030. This has been increasing at a progressively higher rate. The current book presents a comprehensive overview of energy management process and decomposable electronics recycling facilities with a mixed-method approach integrating quantitative performance measures and qualitative aspects of sustainability. Based on thorough analysis of 247 top-referenced, peer-reviewed articles published in top-tier journals (2022-2025) and market trends from 15 nations, we tested technology maturity and commerciality of top three energy harvesting mechanisms: piezoelectric material with the maximum power density of 5.2 W/m², triboelectric nanogenerators (TENGs) with the maximum power density of 7.11 W/m², and photovoltaic material with indoor maximum power density of 7.95 mW/cm². Our extensive discussion of recycling technology indicates state-of-the-art chemical recycling technology has 65% material recovery of battery resources and controlled biodegradation has reproducible period from 7 days to 24 weeks in optimized thermophilic conditions (55-58°C). Asia-Pacific regional environmental footprint signifies Asia-Pacific generates 48.4% of world e-waste and official e-waste recycling rates of 22.3% with no value added to it in the era of technological innovation. Economic modeling implies deployable biodegradable electronics have the ability to provide 67% carbon savings over conventional devices, whereas market data suggests growth of the biodegradable electronic market from $861 million (2025) to $1.56 billion by 2030, whereas the overall sustainable electronics manufacturing business is escalating from $15.33 billion (2025) to $68.35 billion by 2032. Research status determines major technology issues like temperature stability limits (60°C milestone), humidity sensitivity problems, and standardization protocol loopholes in forming an integrated framework for energy-autonomous degradable electronics in circular economy systems with quantifiable environmental and economic impacts.
Copyright (c) 2025 Youshi Wu, Oliver Fiehn (Author)
Asset Optimization and Value Enhancement of EV Charging Infrastructure under Aggregated Operation Models: Methods, Frameworks, and Practices
The rapid growth of electric vehicles (EVs) has significantly increased the demand for efficient and profitable charging infrastructure. Traditional station-level deployment often suffers from low utilization and delayed return on investment. Aggregated operation models-integrating multi-station, multi-brand, and multi-operator networks-offer new pathways for asset optimization, cost reduction, and value enhancement. This review systematically examines the methods, frameworks, and practices of aggregated EV charging networks. Key aspects include demand and load forecasting, infrastructure capacity planning, intelligent operational control, and digital twin-based simulations. The study further explores mechanisms for CAPEX and OPEX optimization, multi-stream revenue generation, grid integration, and flexibility services such as V1G/V2G participation. Global case studies illustrate successful implementations, while challenges such as interoperability, real-time dispatch scalability, and data security are discussed. Finally, future directions including AI-driven optimization, ultra-large-scale aggregation, and cross-energy-system coordination are proposed, highlighting the strategic potential of aggregation in sustainable EV infrastructure development.
Design of a Laser Beam Pointing Stabilization System Based on Dual-Level Feedback for 4-Axis PZT Interlock Control
To enhance the pointing stability of laser beams in precision optical systems, this study develops a four-axis piezoelectric (PZT)-driven interlinked control architecture equipped with a dual-stage feedback mechanism. The system is designed to suppress multi-axis disturbances that typically degrade beam alignment accuracy in high-frequency operational environments. A coupled mathematical model describing the dynamic interaction between the laser beam and mirror frame is established, forming the foundation for precise motion control analysis. Based on this model, an inverse matrix decoupling algorithm is integrated to compensate for cross-axis coupling effects, thereby enabling real-time fine-tuning of each control channel. The system's dynamic response characteristics are comprehensively analyzed through both simulation and experimental validation, focusing on tracking accuracy, frequency response, and long-term drift behavior. Experimental results indicate that the proposed control system maintains sub-micron beam stability even under high-frequency and multi-directional disturbances. The control loop demonstrates excellent repeatability, minimal overshoot, and strong immunity to environmental vibration and thermal fluctuation. Compared with conventional single-axis or uncoupled PZT control methods, the proposed interlinked configuration exhibits faster convergence, higher positioning precision, and improved disturbance rejection capability. These advantages confirm that the developed system meets the stringent requirements of advanced laser applications, such as beam steering, optical communication alignment, and high-precision material processing in complex operational conditions. The research provides a reliable reference for designing multi-axis active stabilization systems in next-generation high-performance laser platforms.
Copyright (c) 2025 Jinqiang Chen (Author)
Research on Intelligent Decision-Making Model for Automotive Production Planning Based on Big Data and Artificial Intelligence
This study proposes an intelligent decision-making model for automotive production planning, utilizing big data and artificial intelligence (AI) to optimize production scheduling. The AI-based framework dynamically adapts to production fluctuations, such as changes in production cycle time, resource availability, and order demand, by adjusting schedules based on real-time data. The sensitivity analysis demonstrates that the framework significantly improves key performance indicators, including throughput, equipment utilization, and average delay, outperforming traditional ERP systems. The research highlights the potential of this AI-driven approach to enhance smart manufacturing, offering a scalable, flexible solution for production environments characterized by uncertainty and variability. This study contributes to advancing production planning by integrating AI and big data, showcasing their value in improving efficiency and adaptability in automotive manufacturing.
Risk Control and Financial Analysis in Energy Industry Project Investment
With the rapid expansion of the global energy industry, investments in energy projects are increasingly confronted with complex challenges and uncertainties. This paper examines the distinctive risk characteristics inherent to such projects and proposes a range of practical risk management strategies, including enhancing data security, mitigating technical uncertainties, and ensuring robust compliance in cross-border investments. Furthermore, it presents comprehensive financial analysis methods tailored for energy projects, encompassing financial feasibility assessments, evaluation of capital sources and financing structures, financial risk analysis, and sensitivity testing. By integrating thorough risk management with systematic financial evaluation, this study establishes a rational and reliable decision support framework for investors, helping them anticipate potential threats, make informed investment decisions, and promote the sustainable, long-term growth of energy projects.
Soil Chemistry Enhancement through Coordination Polymer Urease Inhibitors: Environmental Impact Assessment
The application of coordination polymer urease inhibitors represents a promising advancement in sustainable agriculture, offering enhanced nitrogen use efficiency while minimizing environmental degradation. This comprehensive study evaluates the environmental impact of copper-based coordination polymers as urease inhibitors in soil systems, examining their effects on soil enzyme activities, microbial communities, and overall soil health. Recent developments in coordination polymer technology have demonstrated significant potential for improving agricultural productivity while reducing ammonia emissions and environmental contamination. The research integrates findings from multiple studies examining the interaction between synthetic urease inhibitors and natural soil processes, with particular emphasis on enzyme activity modulation and microbial community dynamics. Environmental impact assessments reveal that properly designed coordination polymer systems can enhance soil nutrient cycling without compromising biological diversity or soil functionality. The study demonstrates that copper-based coordination polymers exhibit superior urease inhibition efficiency compared to conventional inhibitors, with reduced environmental persistence and improved biodegradability. Furthermore, these novel materials show promise for addressing cadmium contamination while maintaining soil metabolic functions. The findings suggest that coordination polymer urease inhibitors represent a viable solution for sustainable intensification of agricultural systems, providing effective nitrogen management while preserving soil ecosystem integrity and supporting long-term agricultural sustainability.
Copyright (c) 2025 Anna Müller (Author)
Decomposition Analysis of Carbon Emission Drivers in Thailand's Power Sector
This study investigates the driving factors of carbon emissions in Thailand's power sector from 2002 to 2022 using the Logarithmic Mean Divisia Index (LMDI) decomposition method. The objective is to provide a systematic and quantitative assessment of how socioeconomic and technological developments have influenced emission dynamics. The results indicate that total emissions rose from approximately 62 Mt in 2002 to a peak of 90 Mt in 2013, before stabilizing and slightly declining in recent years, reflecting improvements in efficiency and structural transformation. Economic growth was the largest contributor, adding a cumulative 43.83 Mt, followed by population growth (9.52 Mt). In contrast, structural transformation-primarily the gradual substitution of fuel oil with natural gas-reduced emissions by about 21.81 Mt, while enhanced efficiency in coal consumption contributed an additional 20.86 Mt reduction. Electricity intensity exerted a modest positive effect (4.39 Mt), and the influence of the carbon emission factor was minimal (0.61 Mt). The findings suggest that Thailand's power sector has achieved relative decoupling between economic growth and emissions, though absolute decoupling has yet to occur. The analysis underscores the critical importance of accelerating renewable energy deployment and strengthening energy efficiency initiatives to meet Thailand's carbon neutrality target by 2050 and its net-zero emissions goal by 2065. This study fills a gap in the existing literature by providing the first multi-factor decomposition analysis of Thailand's power sector emissions, offering valuable empirical insights and policy implications for sustainable energy transitions in developing economies.
Copyright (c) 2025 Chen Chen, Mohammad Nizamuddin Inamdar, Aiman Al-Odaini (Author)
AI-Driven Project Management for Construction SMEs: A Framework for Cost and Schedule Optimization
Efficient project management remains a persistent challenge for small and medium-sized enterprises (SMEs) in the U.S. construction industry, where delays and budget overruns are prevalent. This study proposes an AI-driven project management framework tailored to SMEs, integrating predictive scheduling, resource allocation, and real-time progress monitoring. By leveraging machine learning models and cloud-based visualization tools, the framework generates adaptive schedules and optimizes task prioritization. A case study using representative project data demonstrates that the AI-enhanced system reduces schedule variance and cost deviation compared to traditional critical path methods. Results indicate that SMEs can achieve significant improvements in project predictability and resource efficiency without incurring the high costs of enterprise-level tools. The proposed framework contributes to national priorities in infrastructure development by enabling SMEs—who comprise the majority of U.S. construction firms—to deliver projects with greater timeliness, cost-efficiency, and resilience.
Copyright (c) 2025 Yuanfeng Liang (Author)
Structural Characterization of Novel Copper-Based Metal-Organic Frameworks: X-Ray Crystallography and Analysis
Metal-organic frameworks (MOFs) have emerged as a versatile class of crystalline materials with extraordinary potential for applications in gas storage, separation, catalysis, and sensing. This study presents a comprehensive structural characterization of novel copper-based MOFs through advanced X-ray crystallographic techniques and detailed structural analysis. The investigation focuses on the synthesis and structural elucidation of three distinct copper-based frameworks incorporating different organic linkers and secondary building units. Single-crystal X-ray diffraction analysis revealed unique topological arrangements and coordination environments that significantly influence the framework properties. The copper centers exhibit diverse coordination geometries ranging from tetrahedral to octahedral configurations, creating distinct pore architectures and surface characteristics. Structural refinement data demonstrates exceptional crystallographic quality with R-factors below 4% for all synthesized frameworks. The frameworks exhibit remarkable thermal stability up to 350°C and display selective gas adsorption properties. Powder X-ray diffraction confirms the phase purity and structural integrity of the bulk materials. The structural diversity observed in these copper-based systems provides valuable insights into structure-property relationships and opens new avenues for targeted framework design. These findings contribute significantly to the fundamental understanding of copper coordination chemistry in porous crystalline materials and establish a foundation for future applications in environmental remediation and industrial processes.
Study on the Impact of Water Level Fluctuations on the Stability of Reservoir Bank Landslides in Guangxi
The unique geological structure and climatic conditions in Guangxi make reservoir bank land-slides particularly problematic, with water level fluctuations being a key factor in inducing them. This paper systematically analyzes the mechanisms by which water level fluctuations affect the stability of reservoir bank landslides from a theoretical perspective, revealing the coupling effects of permeability, softening, buoyancy, and fatigue. By constructing a "hydrological-geological-mechanical" multi-field coupling model, the controlling effects of rock and soil permeability, geological conditions, and water level fluctuation rate on landslide stability are explored. The results indicate that permeability induced by sudden water level drops is the dominant factor in landslide instability, while fatigue damage caused by long-term water level fluctuations significantly reduces the long-term strength of rock and soil. The theoretical framework proposed in this paper provides a scientific basis for preventing and controlling reservoir land-slides in Guangxi and is of great reference value for similar projects.
Copyright (c) 2025 Zhangwu Tian, Fengjiao Qin (Author)
Copper-Based Coordination Polymers: Geometric Ligand Engineering for Targeted Urease Inhibition
Urease is a key enzyme in nitrogen cycling and a critical target in efforts to regulate urea-based fertilization for agricultural sustainability. Copper-based coordination polymers (Cu-CPs) have emerged as potent urease inhibitors, with molecular design playing a central role in dictating their activity. In particular, V-shaped ligands provide unique steric and electronic environments that can fine-tune polymer architecture and bioactivity. This paper presents a comprehensive analysis of V-shaped ligand-assisted engineering strategies for copper-based coordination polymers, focusing on their structure-function relationships, urease inhibition efficiency, synthetic strategies, and agricultural applications. By analyzing ten recent studies, this work synthesizes the state-of-the-art in ligand-based modulation of Cu-CP frameworks. Furthermore, the agricultural relevance of these materials is assessed through the lens of soil–plant nitrogen retention, enzymatic inhibition kinetics, and long-term soil health. Emphasis is placed on the bridging coordination modes, ligand-metal affinity, and cooperative inhibition mechanisms that define bioactivity. This paper provides a structural roadmap for future Cu-CP design, proposing molecular and synthetic benchmarks for sustainable urease inhibition technologies.
Copyright (c) 2025 Fiona L. Maxwell, Gavin T. Fletcher (Author)
Morphological Characterization of Carbon Nanotubes and Their Agglomeration Behavior Revealed by Scanning Electron Microscopy
In this study, the microscopic morphology and agglomeration behavior of carbon nanotubes (CNTs) were systematically investigated using high-resolution scanning electron microscopy (SEM). The SEM analysis revealed that CNTs predominantly exist in the form of irregular, spherical or quasi-spherical macroscopic aggregates, composed of densely entangled, curved, and flexible nanotubes. These aggregates exhibit a porous nanonetwork structure, rather than solid dense blocks, due to the random stacking and bending of individual CNTs. In addition to the major agglomerates, partially dispersed nanotubes were clearly observed at the periphery and in the interstitial spaces between aggregates, forming sparse and loosely connected nanonetworks. These morphological characteristics highlight the intrinsically high surface energy of CNTs and the strong van der Waals forces that drive their aggregation—a phenomenon that presents a critical challenge for achieving uniform dispersion in practical applications. Poor dispersion can severely hinder the mechanical reinforcement, electrical conductivity, and accessible surface area of CNTs in host materials, limiting their effectiveness in various functional systems. The insights gained from this detailed morphological analysis provide direct empirical evidence for the hierarchical structure of CNT agglomerates and the existence of inter-aggregate networks. These findings lay a solid foundation for understanding the complex relationship between the microstructural organization of CNTs and their macroscopic behavior. Furthermore, the results offer valuable guidance for the rational design of advanced dispersion techniques and integration strategies, aiming to fully exploit the exceptional properties of CNTs in fields such as polymer nanocomposites, energy storage and conversion, biomedical engineering, and environmental applications.
Copyright (c) 2025 Sergey Petrov, Alexey Smirnov, Elena Ivanova (Author)
Research on Identification and Analysis of Hazards in High Altitude Tunnel Construction Operations
Tunnel construction is an important safety control scenario in transportation engineering construction. Due to its own characteristics, such as high weight of terrain and landforms, complex geological conditions, numerous environmental factors, and difficult management, tunnel construction safety management has attracted much attention. In different tunnel constructions, the difficulty of managing high-altitude tunnel construction is particularly prominent. This article focuses on the high-altitude areas represented by the Qinghai Tibet Plateau. Taking the Laze Tunnel as the research objective, a combination of on-site investigation, interview, and screening methods were used to identify the hazards of high-altitude tunnels from four aspects: personnel, equipment, environment, and management. Analyzed its existing problems and provided countermeasures and suggestions. This study lays a theoretical foundation and practical significance for the safety management of high-altitude tunnel construction operations.
human machine environment system analysis
Copyright (c) 2025 Defeng Duan, Baofeng Sun, Hai Liu (Author)
Research on Perception and Control System of Small Autonomous Driving Vehicles
With the continuous advancement of autonomous driving technology, the application of small autonomous vehicles in changing environments has gradually attracted attention. This article discusses the core technologies of perception and control systems for small autonomous vehicles. An analysis was conducted on the key issues faced by the perception system of small autonomous vehicles, including improving the accuracy of local and global positioning, as well as the application of multi-source sensor fusion technology. By integrating various sensing devices such as LiDAR and image sensors, the perception accuracy of the system has been enhanced. The improvement of the control system was also discussed, and the overall path planning method based on gridded maps and the improvement strategy of the motion control system were analyzed. With precise path design and efficient motion control algorithms, the driving stability and safety factor of the car are ensured in changing environments. Finally, the integration and testing of perception and control systems were discussed, and solutions for software hardware collaboration enhancement and comprehensive debugging and testing in complex scenarios were proposed.
A New Design and Simulation of a Reciprocating Motion Mechanism of Dynamic Ad Devices
In this paper, a reciprocating motion dynamic advertisement device is designed for a new category of billboards. The core design consists of a conjugate cam and multi-link structure, based on which a kinematic model is established and ADAMS kinematic simulation is performed. The results show that the kinematic model of the designed reciprocating structure is highly accurate with an error of only 4%, and the cam mechanism has less contact force, which is of strong utilization value.
Copyright (c) 2025 Sining Yao, Kai Xu, Yanzhang Ren (Author)
Safety Countermeasures and Accident Prevention Measures in Traffic Design
Traffic design plays a critical role in ensuring traffic safety. This article discusses the close relationship between traffic design and safety, analyzes potential risk factors in traffic design, and proposes targeted safety measures and accident prevention measures. Traffic design must follow the basic principles of science, reasonably lay out roads, set up traffic signs and markings to ensure safety and smoothness. Safety hazards such as unreasonable road layout, improper setting of traffic markings, and road condition changes caused by adverse weather are key issues in traffic safety management. To address these issues, effective solutions have been proposed, including standardized lane and shoulder design, application of intelligent transportation systems (ITS), and improvement of road drainage and anti-skid design. These measures can not only reduce the incidence of traffic accidents, but also improve the safety and efficiency of the overall transportation system.
intelligent transportation management system
Copyright (c) 2025 Naizhong Cui (Author)
Research on the Relevance between the Traditional Buildings of the WA Ethnic Group in Wengding and Their Culture: A Quantitative Analysis Based on Hypothetical Data
This study takes the traditional villages of the Wa ethnic group in Wengding, Yunnan Province as the research object. Based on the constructed hypothetical building survey data set (a total of 50 buildings) and cultural symbol data set (covering 20 types of symbols), methods such as descriptive statistical analysis, spatial clustering, and association rule mining are used to explore the architectural features, the distribution patterns of cultural symbols, and the correlation between the two. The research results show that: (1) Thatched houses for residential purposes account for 70%. Among the decorative elements, animal carvings account for 42.9% and plant patterns account for 28.6%, which have a significant correlation with the construction age and family status; (2) Cultural symbols present a spatial distribution pattern of "sacrificial core-family periphery"; (3) The architectural scale and decorative style can be classified into two types: "traditional type" and "simplified type", which reflect the differences in the social division of labor among families. This study provides a data-driven analysis paradigm for the digital protection of ethnic minority cultural heritage.
Copyright (c) 2025 Yanan Jiang, Qi Liu (Author)
Web Front-End Application Performance Improvement Method Based on Component-Based Architecture
In the context of the continuous evolution of Web front-end technology, performance optimization has become the core work to improve user experience and enhance product value. This paper focuses on analyzing the performance bottlenecks encountered by current Web front-end applications, such as rendering bottlenecks, network bottlenecks, resource bottlenecks and computing bottlenecks, and introduces optimization suggestions. By designing front-end applications with a block architecture, you can reduce redundant rendering, optimize resource loading, split code and lazy loading, and improve the efficiency of state management to significantly improve application speed. At the same time, the influence of the block structure on the front-end application is analyzed, and how to optimize the development process and performance through good structure design is discussed.
Exploration of Process Improvement in Automotive Manufacturing Based on Intelligent Production
With the rapid development of intelligent manufacturing technology, automation has become the main means of improving efficiency, reducing costs, and ensuring product quality in automotive production. This paper discusses the application of automobile intelligent production technology, mainly investigating the current widespread robot applications, the role of robots in automated production assembly lines, the application of the Internet and big data in the production process and other issues. Through robot automation production technology, high precision and efficiency can be achieved, which is reflected in the processes of body welding and production part installation in automobile production. The combination of the Internet and big data makes the monitoring of the production process more intelligent and real-time, increasing the sensitivity of the production line and equipment utilization. This paper explores the obstacles encountered in the implementation of intelligent production technology, such as technology integration, data confidentiality and personal privacy protection, and shortage of technical personnel, and proposes relevant improvement measures, which can be used as a reference for automobile manufacturing enterprises in the process of implementing intelligent production technology transformation.
Research on the Development of Intelligent Ships and Smart Energy Management Technologies
With the maritime industry undergoing digital transformation and facing growing demands for automation, intelligent ships and smart energy management have become central to boosting operational efficiency, cutting fuel consumption, and reducing environmental impact. This paper first reviews the concept and system architecture of intelligent ships and surveys key technological advances in autonomous navigation, condition monitoring, and decision support. It then examines the architectural models of shipboard energy management systems, explores intelligent optimization algorithms, and outlines real-time control strategies. Drawing on representative application cases, the study evaluates both operational performance and economic returns. Finally, it discusses major challenges in engineering rollout, algorithmic reliability, and large-scale deployment, and looks ahead to emerging research directions and industry trends, offering theoretical underpinnings and practical guidance for the evolution of intelligent shipping and energy management.
Sustainable Construction Management: Integrating Carbon Reduction into Planning, Design, and Project Delivery
As global climate change becomes an increasingly pressing issue, the construction industry plays a significant role in carbon emissions, making carbon reduction measures a key focus for its development. This review explores the primary methods and innovations for integrating carbon reduction strategies into construction management, focusing on the planning, design, and construction phases. The paper first discusses the core concepts of sustainable construction management, detailing the integration of carbon reduction goals throughout the construction lifecycle and the role of industry standards and policies. It then examines the importance of carbon footprint assessments and carbon budgeting models during the project planning phase, and the integration of low-carbon design principles, including green building design, material selection, and energy efficiency optimization. In the construction phase, modular building, green construction technologies, and digital tools are identified as effective means of reducing carbon emissions. Finally, the paper highlights the challenges the industry faces in carbon reduction, such as regulatory issues, cost barriers, and data transparency, while pointing out emerging opportunities like green finance and carbon trading. It also outlines future research directions, including full lifecycle carbon tracking and smart construction technologies.