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.
Social Responsiveness in Architectural Design under Urban Renewal: An Indicator-Based Study
Urban renewal has increasingly shifted the focus of contemporary architectural design from the mere production of isolated, aesthetically driven objects toward the strategic organization of socially durable and inclusive places. As cities undergo rapid transformation, the need to integrate social dimensions into spatial planning becomes paramount. This paper systematically studies how architectural design can effectively respond to core social dimensions—namely publicness, spatial accessibility, collective memory continuity, and civic participation—within complex urban renewal contexts. To achieve this, the research develops a novel, comprehensive Social Responsiveness Index (SRI). This methodological framework combines verified secondary urbanization data with a highly transparent, scenario-based design audit dataset to rigorously evaluate spatial interventions. The empirical results demonstrate that traditional object-led renewal approaches consistently produce the lowest social responsiveness scores. In stark contrast, community co-designed initiatives and street-edge micro-renewal scenarios perform significantly better. These approaches succeed because they actively connect accessible pedestrian routes, foster active ground-floor interfaces, preserve local historical memory, and encourage sustainable post-occupancy governance. The primary contribution of this research is both methodological and conceptual: it successfully translates abstract social-science concerns into concrete, measurable design indicators without reducing the inherent complexity and quality of architectural design to simplistic numerical scoring alone. Ultimately, the study compellingly argues that socially responsive architecture should be fundamentally evaluated by its capacity to facilitate human interaction and by what it actively enables diverse groups of people to do together in their everyday urban life.
Reconstruction of Commercial Building Space Reuse Mode Driven by Composite Business Types
As urban expansion continues to deepen globally, traditional commercial buildings are increasingly no longer able to meet the complex business demands and practical operational requirements of modern enterprises. Consequently, it has become an inevitable and critical trend to re-utilize and adapt commercial buildings based on the drive of composite and complex business models. By systematically integrating fundamental architectural elements such as spatial layout, transportation routes, equipment systems, and spatial carriers, this paper comprehensively sorts out the typical problems and structural bottlenecks faced in the contemporary renovation of commercial buildings. To address these challenges, the study proposes four strategic development directions: the methodology for reconfiguring spatial organization, the methodology for reconfiguring flow systems, the methodology for reconfiguring facility systems, and the innovative concept of applying advanced transportation tools within the built environment. Relevant empirical and theoretical analysis demonstrates that spatial regeneration based on mixed business requirements is not merely a simple functional replacement or aesthetic upgrade. Rather, it encompasses a multifaceted approach that includes various critical factors such as internal space layout optimization, overall structural support strength enhancement, and long-term operational variability. Ultimately, through comprehensive mode reconfiguration, it is possible to effectively enhance the adaptability, functional complexity, and future extensibility of commercial building spaces, thereby ensuring their sustainable economic viability and resilience in a rapidly evolving urban landscape.
Copyright (c) 2026 Qiyuan Liang (Author)
AI-Driven Bio-Inspired Procedural Animation Framework for Dynamic Game Character Locomotion and Facial Expression Generation
This research article introduces an AI-driven bio-inspired procedural animation framework designed for generating dynamic and realistic game character locomotion and facial expressions. The framework integrates artificial intelligence techniques, specifically deep learning and reinforcement learning, with principles of biological motion to create a system capable of producing complex and nuanced animations in real-time. The locomotion component employs a hierarchical control structure inspired by spinal cord organization, enabling adaptive gait patterns across varying terrains and speeds. The facial expression generator utilizes a blendshape model driven by a neural network trained on extensive motion capture data of human facial performances. Furthermore, we introduce a novel reward function that incorporates principles of both realism and expressiveness to optimize animation quality. Rigorous experiments demonstrate the framework's ability to generate compelling character movements and emotional expressions, surpassing traditional methods in terms of realism, adaptability, and computational efficiency. The results highlight the potential of AI-driven bio-inspired animation for enhancing the immersive quality of interactive entertainment experiences whilst reducing dependence on computationally expensive manual animation processes. The proposed system advances the state-of-the-art in procedural animation and provides a valuable tool for game developers and animators seeking to create engaging and lifelike characters.
Copyright (c) 2026 Yilihamujiang Yimier (Author)
Research on Train Dispatch Optimization and Scheduling Strategy for Railway Freight Network
With the continuous and rapid growth of China's railway freight volume, alongside the increasing complexity of modern transportation organization, traditional single-objective-oriented transportation planning methods are becoming increasingly inadequate. Specifically, these conventional approaches can hardly balance the critical trade-offs between minimizing overall transportation costs and maintaining a high service level for customers. To address this pressing industry challenge, this paper constructs a comprehensive bi-objective optimization model tailored for a typical railway freight network. The proposed framework explicitly considers both the minimization of operational transportation costs and the maximization of overall demand satisfaction. Furthermore, the mathematical model fully takes into account a variety of strict operational constraints, such as the total number of available trains, dynamic line capacity limitations, the marshalling capacity of intermediate stations and freight yards, specific operation time windows, and the required minimum service level thresholds. Aiming at the inherent nonlinear and multi-objective characteristics of the formulated model, an advanced genetic algorithm based on the weighted sum strategy is adopted for efficient solving. The fundamental effectiveness of the proposed model and the robust convergence of the applied heuristic algorithm are rigorously verified through extensive numerical simulation experiments. The comprehensive computational results clearly demonstrate that the developed model can effectively reduce daily operation costs while significantly improving the overall demand satisfaction rate. Ultimately, this research provides a highly useful and practical reference for the optimal scheduling and strategic management of complex railway freight systems.
Development Status and Prospects of Orthopedic Internal Fixation Devices in the Context of Digitalization and Personalization
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.
Copyright (c) 2026 Wenkui Liang (Author)
Scouring Mechanism of Roadbeds at Curved Sections of River-Adjacent Highways
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.
Copyright (c) 2026 Binglin Tang (Author)
Advances in Die-Casting Mold and Precision Component R&D in the Automotive and Electronics Industries under Advanced Manufacturing Contexts
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.
Key Technologies and Development Directions of Die-Casting Mold and Precision Component R&D in Automotive and Electronics Manufacturing
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.
Key Technologies and Development Trends of Building Integrated Photovoltaic (BIPV)
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.
Copyright (c) 2026 Juanjuan Cui (Author)
Design and Implementation of System Extensibility under High Concurrency Environment
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.
Application of Large Language Model in Mental Health Clinical Decision Support System
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.
Copyright (c) 2026 Danyating Shen (Author)
Research on 3D Reconstruction Methods of Remote Sensing Images Combined with Deep Learning and GIS
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.
High Reliability Architecture and Compliance Design of Enterprise Level Financial Infrastructure
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.
LLM Supported Complex System Anomaly Detection and Intelligent Defect Classification Model
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.
Policy Challenges and Applications of Hydrogen Energy and Energy Storage Technologies in Energy Transition
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.
Copyright (c) 2025 Xiaoshan Chen (Author)
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)