International Journal of Engineering Advances
type: channel
description: 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.
publisher: George Brown Press
language: en
publication name: International Journal of Engineering Advances
issn: 3078-7688
items: Development Status and Prospects of Orthopedic Internal Fixation Devices in the Context of Digitalization and Personalization
items: Scouring Mechanism of Roadbeds at Curved Sections of River-Adjacent Highways
items: Advances in Die-Casting Mold and Precision Component R&D in the Automotive and Electronics Industries under Advanced Manufacturing Contexts
items: Key Technologies and Development Directions of Die-Casting Mold and Precision Component R&D in Automotive and Electronics Manufacturing
items: Key Technologies and Development Trends of Building Integrated Photovoltaic (BIPV)
items: Design and Implementation of System Extensibility under High Concurrency Environment
items: Application of Large Language Model in Mental Health Clinical Decision Support System
items: Research on 3D Reconstruction Methods of Remote Sensing Images Combined with Deep Learning and GIS
items: High Reliability Architecture and Compliance Design of Enterprise Level Financial Infrastructure
items: LLM Supported Complex System Anomaly Detection and Intelligent Defect Classification Model
items: Policy Challenges and Applications of Hydrogen Energy and Energy Storage Technologies in Energy Transition
items: Adaptive Reuse of Urban Public Space and Optimization of Urban Living Environment
items: Optimization of Neural Motor Control Model Based on EMG Signals
items: Integration and Optimization Strategy of Spatial Video Technology in Virtual Reality Platform
items: Intelligent Sensor and System Integration Optimization of Auto Drive System
items: Deployment of Natural Language Processing Technology as a Service and Front-End Visualization
items: Research on Real-Time User Feedback Acceleration Mechanism Based on Genai Chatbot
items: Research on the Application of Integrating Medical Data Intelligence and Machine Learning Algorithms in Cancer Diagnosis
items: VNA-based Insertion Loss and Return Loss Testing and De-embedding Techniques for High-Speed Serial Links on PCBs
items: Machine Learning-Driven Optimization of Physical Layer Signal Processing in High-Speed Networks
items: Energy Management and Recycling Mechanisms for Degradable Electronic Devices: A Comprehensive Framework for Sustainable Technology Integration in Circular Economy Systems
items: Asset Optimization and Value Enhancement of EV Charging Infrastructure under Aggregated Operation Models: Methods, Frameworks, and Practices
items: Design of a Laser Beam Pointing Stabilization System Based on Dual-Level Feedback for 4-Axis PZT Interlock Control
items: Research on Intelligent Decision-Making Model for Automotive Production Planning Based on Big Data and Artificial Intelligence
items: Risk Control and Financial Analysis in Energy Industry Project Investment
items: Soil Chemistry Enhancement through Coordination Polymer Urease Inhibitors: Environmental Impact Assessment
items: Decomposition Analysis of Carbon Emission Drivers in Thailand's Power Sector
items: AI-Driven Project Management for Construction SMEs: A Framework for Cost and Schedule Optimization
items: Structural Characterization of Novel Copper-Based Metal-Organic Frameworks: X-Ray Crystallography and Analysis
items: Study on the Impact of Water Level Fluctuations on the Stability of Reservoir Bank Landslides in Guangxi

Development Status and Prospects of Orthopedic Internal Fixation Devices in the Context of Digitalization and Personalization
type: item
description: This review delves into the evolution and future trajectory of orthopedic internal fixation devices, with a specific focus on the transformative impact of digitalization and personalization. Orthopedic internal fixation, a cornerstone of fracture management and reconstructive surgery, is undergoing a paradigm shift driven by advancements in digital technologies such as 3D printing, computational modeling, and robotic surgery. Personalization, facilitated by these digital tools, aims to tailor implants and surgical procedures to the unique anatomical and biomechanical characteristics of each patient. This review begins with a historical overview of internal fixation devices, highlighting key milestones and technological innovations. It then explores the core themes of digitalization and personalization, examining how these concepts are shaping the design, manufacturing, and application of orthopedic implants. Specific areas of focus include patient-specific implants, surgical planning software, and intraoperative navigation systems. A comparative analysis of traditional and digitally-enhanced approaches to internal fixation is presented, along with a discussion of the challenges and limitations associated with the adoption of new technologies. These challenges encompass regulatory hurdles, cost considerations, and the need for specialized training. Finally, the review concludes with a forward-looking perspective on the future of orthopedic internal fixation, anticipating further integration of artificial intelligence, machine learning, and biocompatible materials to optimize patient outcomes and reduce the incidence of implant-related complications. This includes exploration of predictive modeling for fracture healing and remote monitoring of implant stability. The ultimate goal is to achieve precision orthopedics, where internal fixation is precisely tailored to the individual patient for optimal healing and functional restoration.
subject: Article
  topic: section
subject: orthopedic internal fixation
  topic: keywords
subject: digitalization
  topic: keywords
subject: personalization
  topic: keywords
subject: 3D printing
  topic: keywords
subject: surgical planning
  topic: keywords
subject: fracture healing
  topic: keywords
subject: implants
  topic: keywords
creator: Wenkui Liang
rights: Copyright (c) 2026 Wenkui Liang (Author)
date: 2026-03-19
publication date: 2026-03-19
starting page: 77
ending page: 85
doi: 10.71222/wvz41031

Scouring Mechanism of Roadbeds at Curved Sections of River-Adjacent Highways
type: item
description: Highways constructed along river bends face heightened vulnerability to scour due to complex hydrodynamic behaviors, intensified outer-bank erosion, and highly erodible riverbank soils. This review synthesizes current knowledge on the flow structures at curved river sections, the mechanisms governing roadbed–river interactions, and the typical failure modes triggered by progressive scour. It further examines the key influencing factors—hydraulic, geometrical, geotechnical, and anthropogenic—that collectively determine scour severity. Existing engineering protection techniques, modern monitoring tools, and advanced numerical and physical modeling methods are evaluated to highlight their applicability and limitations. The review concludes by identifying emerging research priorities, including AI-based scour forecasting, fully coupled hydro–geotechnical–structural modeling, climate-resilient design strategies, and ecological protection approaches. Overall, this study provides an integrated understanding of scour processes affecting river-adjacent highway roadbeds and offers guidance for more resilient and sustainable infrastructure development.
subject: Article
  topic: section
subject: riverbend scour
  topic: keywords
subject: highway roadbed stability
  topic: keywords
subject: hydrodynamic–geotechnical interaction
  topic: keywords
subject: monitoring and modeling
  topic: keywords
subject: climate-resilient infrastructure
  topic: keywords
creator: Binglin Tang
rights: Copyright (c) 2026 Binglin Tang (Author)
date: 2026-03-19
publication date: 2026-03-19
starting page: 66
ending page: 76
doi: 10.71222/p8ben179

Advances in Die-Casting Mold and Precision Component R&D in the Automotive and Electronics Industries under Advanced Manufacturing Contexts
type: item
description: This review paper explores recent advancements in die-casting mold technology and precision component research and development, specifically within the automotive and electronics industries, under the umbrella of advanced manufacturing contexts. We examine historical trends, core technological themes, and challenges facing the industries. The review synthesizes findings from recent scholarly articles and industrial reports, focusing on innovations in mold design, materials, process control, and simulation techniques. A comparative analysis of different approaches is provided, along with an assessment of the limitations and challenges in achieving higher precision and efficiency. Finally, the paper presents future research directions, including the integration of artificial intelligence, advanced materials, and sustainable manufacturing practices. The aim is to provide a comprehensive overview of the state-of-the-art and to identify promising avenues for future innovation in die-casting mold and precision component manufacturing.
subject: Article
  topic: section
subject: die-casting
  topic: keywords
subject: molds
  topic: keywords
subject: precision components
  topic: keywords
subject: automotive industry
  topic: keywords
subject: electronics industry
  topic: keywords
subject: advanced manufacturing
  topic: keywords
subject: R&D
  topic: keywords
creator: Guiming Li
rights: Copyright (c) 2026 Guiming Li (Author)
date: 2026-02-11
publication date: 2026-02-11
starting page: 56
ending page: 65
doi: 10.71222/16fz5w70

Key Technologies and Development Directions of Die-Casting Mold and Precision Component R&D in Automotive and Electronics Manufacturing
type: item
description: This paper provides a comprehensive analysis of the critical technologies and future development trajectories of high-pressure die casting (HPDC) mold R&D within the automotive and electronics sectors. Driven by the dual demands of vehicle lightweighting-specifically the emergence of "Giga-casting"-and the miniature integration of 5G consumer electronics, the research explores advanced mold design strategies, including conformal cooling via additive manufacturing and high-fidelity CAE simulations. The study highlights the shift from traditional empirical methods to data-driven paradigms, emphasizing the role of heat-treatment-free alloys and high-vacuum casting in ensuring structural integrity. Furthermore, it examines the integration of Industry 4.0 technologies, such as digital twins and AI-driven process optimization, as essential tools for balancing mold longevity, cycle efficiency, and dimensional precision. The conclusion asserts that the convergence of material science and digitalization will be the primary catalyst for sustainable "green casting" and the continued evolution of high-precision manufacturing global supply chains.
subject: Article
  topic: section
subject: die-casting mold R&D
  topic: keywords
subject: Giga-casting
  topic: keywords
subject: precision components
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Thin-wall Casting
  topic: keywords
subject: sustainable manufacturing
  topic: keywords
creator: Guiming Li
rights: Copyright (c) 2026 Guiming Li (Author)
date: 2026-01-30
publication date: 2026-01-30
starting page: 49
ending page: 55
doi: 10.71222/8246qm93

Key Technologies and Development Trends of Building Integrated Photovoltaic (BIPV)
type: item
description: Building-integrated photovoltaics (BIPV) have emerged as a sustainable solution to combine renewable energy generation with architectural functionality. This review provides a comprehensive overview of BIPV technologies, including crystalline silicon, thin-film, perovskite, and organic photovoltaic modules, highlighting their efficiency, flexibility, and aesthetic potential. Integration forms such as roofs, facades, and semi-transparent windows are discussed, along with key materials, encapsulation techniques, and energy storage strategies. System design considerations, including electrical and thermal performance optimization, smart monitoring, and real-world case studies, are examined to illustrate practical applications. Challenges such as material degradation, efficiency losses, economic barriers, and environmental concerns are critically analyzed. Future trends emphasize high-efficiency tandem modules, flexible and lightweight materials, smart BIPV systems, standardization, and sustainability within a circular economy framework. By integrating performance, aesthetics, and environmental responsibility, BIPV can play a pivotal role in advancing energy-positive buildings and promoting sustainable urban development.
subject: Article
  topic: section
subject: building-integrated photovoltaics
  topic: keywords
subject: BIPV
  topic: keywords
subject: thin-film solar cells
  topic: keywords
subject: perovskite photovoltaics
  topic: keywords
subject: sustainable architecture
  topic: keywords
creator: Juanjuan Cui
rights: Copyright (c) 2026 Juanjuan Cui (Author)
date: 2026-01-27
publication date: 2026-01-27
starting page: 38
ending page: 48
doi: 10.71222/yx8vev15

Design and Implementation of System Extensibility under High Concurrency Environment
type: item
description: In the high concurrency environment, the design and implementation of system scalability is the key to ensure that the system can carry a large number of concurrent requests, and ensure the stability and response speed. Starting from the definition and classification of system extensibility, this paper discusses the core principles and methods of system extensibility design in high concurrency scenarios. This paper first analyzes the basic principles of scalability design, including the requirements of data throughput, real-time, fault tolerance and so on. This paper discusses in detail how to deal with the challenges of high concurrency environment, such as high load, high concurrency request processing and high availability of the system. The technical details of load balancing, resource scheduling and data analysis modeling framework are analyzed, and a reasonable extensibility design method and technical framework are proposed. Finally, through performance evaluation and experimental results, this paper verifies the effectiveness and feasibility of the proposed extensibility design scheme in high concurrency environment. This study provides theoretical basis and practical guidance for system extensibility design in high concurrency environment.
subject: Article
  topic: section
subject: high concurrency environment
  topic: keywords
subject: system scalability
  topic: keywords
subject: load balancing
  topic: keywords
subject: resource scheduling
  topic: keywords
subject: performance evaluation
  topic: keywords
creator: Yajing Cai
rights: Copyright (c) 2026 Yajing Cai (Author)
date: 2026-01-14
publication date: 2026-01-14
starting page: 31
ending page: 37
doi: 10.71222/kbw0tg49

Application of Large Language Model in Mental Health Clinical Decision Support System
type: item
description: With the rapid advancement of artificial intelligence, large language models (LLMs) have emerged as potentially transformative auxiliary tools for clinical judgment and decision-making within the mental health domain. This paper explores systematic methodologies for integrating these models into psychiatric practice, focusing specifically on technical applications in semantic modeling, sophisticated data processing strategies, and the refinement of prompt engineering alongside output optimization. A robust architectural framework and integration scheme tailored to the specificities of mental health services are constructed, providing a structured approach to clinical deployment. Furthermore, the study establishes comprehensive evaluation model standards and rigorous risk control measures designed to enhance the functional stability and ethical safety of these systems. By addressing critical concerns such as interpretability and clinical reliability, this research aims to provide a clear technical trajectory and implementation support for achieving intelligent, evidence-based, and highly understandable auxiliary decision-making in modern mental health care environments.
subject: Article
  topic: section
subject: large language model
  topic: keywords
subject: mental health
  topic: keywords
subject: clinical decision support
  topic: keywords
subject: prompt engineering
  topic: keywords
subject: interpretability
  topic: keywords
creator: Danyating Shen
rights: Copyright (c) 2026 Danyating Shen (Author)
date: 2026-01-14
publication date: 2026-01-14
starting page: 23
ending page: 30
doi: 10.71222/nbtsxh74

Research on 3D Reconstruction Methods of Remote Sensing Images Combined with Deep Learning and GIS
type: item
description: Three-dimensional reconstruction of remote sensing images represents a key research direction in integrating geographic information systems (GIS) with remote sensing data. This study proposes a comprehensive technical approach that combines deep learning techniques with GIS to enhance the reconstruction of remote sensing imagery, addressing common challenges such as limited accuracy, low efficiency, and difficulties in semantic interpretation. Specifically, an improved U-Net network is employed to perform semantic segmentation on remote sensing images, enabling the extraction of critical land feature information while preserving spatial and structural details. Following feature extraction, a three-dimensional registration method is integrated with dense point clouds to achieve high-precision terrain reconstruction, ensuring accurate spatial alignment and continuity across the reconstructed surface. In addition, GIS-based procedures are applied to perform spatial positioning, attribute integration, and three-dimensional visualization, allowing the reconstructed terrain and land features to be effectively interpreted and analyzed within a geographic context. Compared with traditional reconstruction methods, this integrated approach demonstrates higher positioning accuracy, improved model fidelity, and superior semantic reconstruction capabilities. By combining deep learning-based feature extraction with GIS-enabled spatial analysis, the method offers a more effective and robust solution for three-dimensional remote sensing reconstruction, providing enhanced applicability for geographic analysis, environmental monitoring, and urban planning applications.
subject: Article
  topic: section
subject: deep learning
  topic: keywords
subject: remote sensing imagery
  topic: keywords
subject: three-dimensional reconstruction
  topic: keywords
creator: Chuying Lu
rights: Copyright (c) 2026 Chuying Lu (Author)
date: 2026-01-14
publication date: 2026-01-14
starting page: 15
ending page: 22
doi: 10.71222/favndq45

High Reliability Architecture and Compliance Design of Enterprise Level Financial Infrastructure
type: item
description: Along with the rapid evolution of the modern financial industry, enterprise-level financial infrastructure must increasingly satisfy the dual demands of high-level security and stringent regulatory compliance within the financial sector. This article focuses on the systematic construction of an intelligent and highly efficient infrastructure, integrated with a comprehensive legal and regulatory compliance framework. To significantly enhance system reliability, the study utilizes AI-driven load scheduling and predictive analytics to achieve dynamic resource allocation and intelligent capacity management. Simultaneously, the integration of edge computing technology improves real-time response speeds and supports a robust fault-tolerant, self-healing operational mode, ensuring continuous service availability. In terms of compliance and risk governance, this research designs a standardized permission control model, an automated compliance auditing and response algorithm, and a trusted audit framework capable of full-chain tracking. Through the strategic application of these advanced technologies, the framework achieves enhanced data security and operational transparency, effectively building a future-oriented financial infrastructure. This research provides both a theoretical foundation and a practical implementation roadmap for financial institutions seeking to optimize their underlying technical architecture while maintaining rigorous adherence to industrial standards and safety protocols.
subject: Article
  topic: section
subject: financial infrastructure
  topic: keywords
subject: high reliability architecture
  topic: keywords
subject: compliance design
  topic: keywords
subject: edge computing
  topic: keywords
subject: fault self-healing
  topic: keywords
creator: Yue Qi
rights: Copyright (c) 2026 Yue Qi (Author)
date: 2026-01-12
publication date: 2026-01-12
starting page: 8
ending page: 14
doi: 10.71222/sbh3a492

LLM Supported Complex System Anomaly Detection and Intelligent Defect Classification Model
type: item
description: With the increasingly frequent application of complex systems, anomaly detection and defect classification have become important tasks to ensure the stable operation of systems. This paper is based on a complex system anomaly detection and intelligent defect classification model with Large Language Model (LLM) as the core. Through the data processing and learning capabilities of LLM, multi-source information is fused to establish an efficient and accurate detection and defect classification method. The model extracts abnormal patterns from the system data through LLM and intelligently classifies them against known defects, thereby achieving early identification and classification of potential faults in complex systems. The experimental results show that this model demonstrates excellent detection accuracy and classification ability in fields such as power, manufacturing, and transportation. According to the actual application scenarios, the model can adaptively process, thereby ensuring that complex systems have better robustness and security. This research provides new technical ideas and practical paths for future intelligent operation and maintenance and precise fault diagnosis.
subject: Article
  topic: section
subject: LLM
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: defect classification
  topic: keywords
creator: Mingde Guo
rights: Copyright (c) 2026 Mingde Guo (Author)
date: 2026-01-11
publication date: 2026-01-11
starting page: 1
ending page: 7
doi: 10.71222/x7nj2a82

Policy Challenges and Applications of Hydrogen Energy and Energy Storage Technologies in Energy Transition
type: item
description: With the acceleration of the global energy transition process and the formulation of "carbon neutrality" goals by various countries, new energy means represented by hydrogen energy and energy storage technologies play a key role in building a low-carbon, safe, efficient and resilient energy system. Hydrogen energy can provide clean fuel for sectors that are difficult to reduce emissions, such as transportation, industry, and chemical engineering. Energy storage technology provides effective support for increasing the proportion of renewable energy and enhancing the resilience of the power system. However, although these technologies have achieved a certain degree of scale in the application stage, there are still problems in relevant policies such as the lack of standards, slow commercialization progress, incomplete infrastructure and unbalanced international cooperation. In response to the above situation, it is necessary to further ensure the smooth progress of hydrogen energy and energy storage technologies in the energy transition process through measures such as promoting demonstration projects, optimizing the marketization process, strengthening infrastructure construction and enhancing the level of international cooperation.
subject: Article
  topic: section
subject: hydrogen energy
  topic: keywords
subject: energy storage
  topic: keywords
subject: energy transition
  topic: keywords
subject: policy challenges
  topic: keywords
subject: application path
  topic: keywords
creator: Xiaoshan Chen
rights: Copyright (c) 2025 Xiaoshan Chen (Author)
date: 2025-12-20
publication date: 2025-12-20
starting page: 18
ending page: 25
doi: 10.71222/vaaj9x51

Adaptive Reuse of Urban Public Space and Optimization of Urban Living Environment
type: item
description: 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.
subject: Article
  topic: section
subject: urban public space
  topic: keywords
subject: urban voids
  topic: keywords
subject: adaptive reuse
  topic: keywords
subject: urban quotidian dynamics
  topic: keywords
subject: spatial adaptability
  topic: keywords
subject: environmental optimization
  topic: keywords
creator: Jingyuan Huang
rights: Copyright (c) 2025 Jingyuan Huang (Author)
date: 2025-12-14
publication date: 2025-12-14
starting page: 9
ending page: 17
doi: 10.71222/egsk5x24

Optimization of Neural Motor Control Model Based on EMG Signals
type: item
description: 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.
subject: Article
  topic: section
subject: EMG signal
  topic: keywords
subject: neuromotor control
  topic: keywords
subject: optimization algorithm
  topic: keywords
creator: Jun Ye
rights: Copyright (c) 2025 Jun Ye (Author)
date: 2025-12-09
publication date: 2025-12-09
starting page: 1
ending page: 8
doi: 10.71222/zv591k09

Integration and Optimization Strategy of Spatial Video Technology in Virtual Reality Platform
type: item
description: 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.
subject: Article
  topic: section
subject: spatial video
  topic: keywords
subject: virtual reality
  topic: keywords
subject: system integration
  topic: keywords
creator: Da Xu
rights: Copyright (c) 2025 Da Xu (Author)
date: 2025-12-08
publication date: 2025-12-08
starting page: 131
ending page: 137
doi: 10.71222/tgkjpw51

Intelligent Sensor and System Integration Optimization of Auto Drive System
type: item
description: 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 systemati…
subject: Article
  topic: section
subject: intelligent sensors
  topic: keywords
subject: system integration
  topic: keywords
subject: autonomous driving
  topic: keywords
subject: optimization strategy
  topic: keywords
creator: Junchun Ding
rights: Copyright (c) 2025 Junchun Ding (Author)
date: 2025-12-07
publication date: 2025-12-07
starting page: 124
ending page: 130
doi: 10.71222/c9804347

Deployment of Natural Language Processing Technology as a Service and Front-End Visualization
type: item
description: 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
subject: Article
  topic: section
subject: natural language processing
  topic: keywords
subject: service deployment
  topic: keywords
subject: front-end visualization
  topic: keywords
subject: data security
  topic: keywords
creator: Xindi Wei
rights: Copyright (c) 2025 Xindi Wei (Author)
date: 2025-12-07
publication date: 2025-12-07
starting page: 117
ending page: 123
doi: 10.71222/1bmby689

Research on Real-Time User Feedback Acceleration Mechanism Based on Genai Chatbot
type: item
description: 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 response…
subject: Article
  topic: section
subject: chatbot
  topic: keywords
subject: user feedback
  topic: keywords
subject: real time mechanism
  topic: keywords
subject: model optimization
  topic: keywords
creator: Xiao Liu
rights: Copyright (c) 2025 Xiao Liu (Author)
date: 2025-12-07
publication date: 2025-12-07
starting page: 109
ending page: 116
doi: 10.71222/zjzqka31

Research on the Application of Integrating Medical Data Intelligence and Machine Learning Algorithms in Cancer Diagnosis
type: item
description: 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.
subject: Article
  topic: section
subject: medical data intelligence
  topic: keywords
subject: cancer diagnosis
  topic: keywords
subject: machine learning
  topic: keywords
subject: multimodal fusion
  topic: keywords
subject: model optimization
  topic: keywords
subject: clinical decision support
  topic: keywords
subject: deep learning
  topic: keywords
creator: Xiangtian Hui
rights: Copyright (c) 2025 Xiangtian Hui (Author)
date: 2025-12-06
publication date: 2025-12-06
starting page: 101
ending page: 108
doi: 10.71222/gs918h25

VNA-based Insertion Loss and Return Loss Testing and De-embedding Techniques for High-Speed Serial Links on PCBs
type: item
description: 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.
subject: Article
  topic: section
subject: Vector Network Analyzer
  topic: keywords
subject: PCB
  topic: keywords
subject: high-speed serial link
  topic: keywords
subject: insertion loss
  topic: keywords
subject: return loss
  topic: keywords
subject: de-embedding technology
  topic: keywords
creator: Qitao Pan
rights: Copyright (c) 2025 Qitao Pan (Author)
date: 2025-11-30
publication date: 2025-11-30
starting page: 93
ending page: 100
doi: 10.71222/77tz0j28

Machine Learning-Driven Optimization of Physical Layer Signal Processing in High-Speed Networks
type: item
description: 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.
subject: Article
  topic: section
subject: machine learning
  topic: keywords
subject: physical layer
  topic: keywords
subject: high-speed networks
  topic: keywords
subject: signal processing
  topic: keywords
subject: deep learning
  topic: keywords
subject: adaptive communication
  topic: keywords
creator: Hao Xu
rights: Copyright (c) 2025 Hao Xu (Author)
date: 2025-11-27
publication date: 2025-11-27
starting page: 82
ending page: 92
doi: 10.71222/2wzq7083

Energy Management and Recycling Mechanisms for Degradable Electronic Devices: A Comprehensive Framework for Sustainable Technology Integration in Circular Economy Systems
type: item
description: 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 pr…
subject: Article
  topic: section
subject: sustainable electronics
  topic: keywords
subject: biodegradable materials
  topic: keywords
subject: triboelectric nanogenerators
  topic: keywords
subject: electronic waste recycling
  topic: keywords
subject: energy harvesting
  topic: keywords
subject: lifecycle assessment
  topic: keywords
creator: Youshi Wu
creator: Oliver Fiehn
rights: Copyright (c) 2025 Youshi Wu, Oliver Fiehn (Author)
date: 2025-11-26
publication date: 2025-11-26
starting page: 66
ending page: 81
doi: 10.71222/gy96z072

Asset Optimization and Value Enhancement of EV Charging Infrastructure under Aggregated Operation Models: Methods, Frameworks, and Practices
type: item
description: 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.
subject: Article
  topic: section
subject: EV charging
  topic: keywords
subject: aggregated operation
  topic: keywords
subject: asset optimization
  topic: keywords
subject: V2G
  topic: keywords
subject: grid flexibility
  topic: keywords
subject: multi-stream revenue
  topic: keywords
creator: Yongnian Su
rights: Copyright (c) 2025 Yongnian Su (Author)
date: 2025-11-25
publication date: 2025-11-25
starting page: 51
ending page: 65
doi: 10.71222/a3bs1m81

Design of a Laser Beam Pointing Stabilization System Based on Dual-Level Feedback for 4-Axis PZT Interlock Control
type: item
description: 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.
subject: Article
  topic: section
subject: beam stabilization
  topic: keywords
subject: PZT control
  topic: keywords
subject: decoupling algorithm
  topic: keywords
subject: system modeling
  topic: keywords
subject: experimental validation
  topic: keywords
creator: Jinqiang Chen
rights: Copyright (c) 2025 Jinqiang Chen (Author)
date: 2025-11-12
publication date: 2025-11-12
starting page: 43
ending page: 50
doi: 10.71222/5hqxzx93

Research on Intelligent Decision-Making Model for Automotive Production Planning Based on Big Data and Artificial Intelligence
type: item
description: 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.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: automotive production planning
  topic: keywords
subject: big data
  topic: keywords
subject: scheduling optimization
  topic: keywords
subject: smart manufacturing
  topic: keywords
subject: Industry 4.0
  topic: keywords
creator: Yuxiang Liu
rights: Copyright (c) 2025 Yuxiang Liu (Author)
date: 2025-11-10
publication date: 2025-11-10
starting page: 29
ending page: 42
doi: 10.71222/af8ycw44

Risk Control and Financial Analysis in Energy Industry Project Investment
type: item
description: 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.
subject: Article
  topic: section
subject: risk control
  topic: keywords
subject: financial analysis
  topic: keywords
subject: data security
  topic: keywords
subject: technical uncertainty
  topic: keywords
creator: Lingyun Lai
rights: Copyright (c) 2025 Lingyun Lai (Author)
date: 2025-11-01
publication date: 2025-11-01
starting page: 21
ending page: 28
doi: 10.71222/rt7yy366

Soil Chemistry Enhancement through Coordination Polymer Urease Inhibitors: Environmental Impact Assessment
type: item
description: 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.
subject: Article
  topic: section
subject: coordination polymers
  topic: keywords
subject: urease inhibitors
  topic: keywords
subject: soil enzymes
  topic: keywords
subject: environmental impact
  topic: keywords
subject: soil chemistry
  topic: keywords
subject: sustainable agriculture
  topic: keywords
creator: Anna Müller
rights: Copyright (c) 2025 Anna Müller (Author)
date: 2025-10-11
publication date: 2025-10-11
starting page: 11
ending page: 20
doi: 10.71222/qaw0tg91

Decomposition Analysis of Carbon Emission Drivers in Thailand's Power Sector
type: item
description: 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.
subject: Article
  topic: section
subject: carbon emissions
  topic: keywords
subject: power sector
  topic: keywords
subject: LMDI
  topic: keywords
subject: Thailand
  topic: keywords
creator: Chen Chen
creator: Mohammad Nizamuddin Inamdar
creator: Aiman Al-Odaini
rights: Copyright (c) 2025 Chen Chen, Mohammad Nizamuddin Inamdar, Aiman Al-Odaini (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 1
ending page: 10
doi: 10.71222/r1mnwh13

AI-Driven Project Management for Construction SMEs: A Framework for Cost and Schedule Optimization
type: item
description: 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.
subject: Article
  topic: section
subject: AI-driven project management
  topic: keywords
subject: predictive scheduling
  topic: keywords
subject: resource allocation
  topic: keywords
subject: construction SMEs
  topic: keywords
subject: engineering optimization
  topic: keywords
creator: Yuanfeng Liang
rights: Copyright (c) 2025 Yuanfeng Liang (Author)
date: 2025-09-25
publication date: 2025-09-25
starting page: 93
ending page: 100
doi: 10.71222/qm6ebz81

Structural Characterization of Novel Copper-Based Metal-Organic Frameworks: X-Ray Crystallography and Analysis
type: item
description: 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.
subject: Article
  topic: section
subject: metal-organic frameworks
  topic: keywords
subject: copper coordination
  topic: keywords
subject: x-ray crystallography
  topic: keywords
subject: structural characterization
  topic: keywords
subject: porous materials
  topic: keywords
subject: crystal structure
  topic: keywords
creator: Hao Lin
rights: Copyright (c) 2025 Hao Lin (Author)
date: 2025-09-25
publication date: 2025-09-25
starting page: 85
ending page: 92
doi: 10.71222/dz0mq960

Study on the Impact of Water Level Fluctuations on the Stability of Reservoir Bank Landslides in Guangxi
type: item
description: 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.
subject: Article
  topic: section
subject: water level fluctuations
  topic: keywords
subject: reservoir bank landslides
  topic: keywords
subject: stability
  topic: keywords
subject: permeability
  topic: keywords
creator: Zhangwu Tian
creator: Fengjiao Qin
rights: Copyright (c) 2025 Zhangwu Tian, Fengjiao Qin (Author)
date: 2025-09-05
publication date: 2025-09-05
starting page: 78
ending page: 84
doi: 10.71222/jzp09845