Journal of Computer, Signal, and System Research
The Journal of Computer, Signal, and System Research(JCSSR) is a peer-reviewed journal focusing on cutting-edge research in computer science, signal processing, and systems engineering. It covers a wide range of topics, including algorithms, signal analysis, system design, and computational methods, aiming to promote innovation and interdisciplinary collaboration in these fields.
Research on High-precision Navigation Data Processing Under Communication Channel Optimization
High-precision navigation data are exceptionally vulnerable to channel fading, background noise, multipath interference, link delay, and packet loss within complex wireless communication applications. To address these critical challenges, the intricate coupling between communication channel optimization and robust navigation data processing is comprehensively considered in this study. Based on the fundamental principles of navigation data transmission and the mechanisms of channel degradation, a novel channel state awareness and data fusion processing framework tailored for high-precision navigation applications is systematically developed. Within this proposed framework, key performance indicators, including the signal-to-noise ratio (SNR), bit error rate (BER), packet loss rate (PLR), and transmission delay, are considered as primary observations to fully evaluate the reliability of the communication link. Subsequently, the channel quality evaluation results are seamlessly integrated into navigation data preprocessing, anomaly detection, dynamic weight allocation, and error feedback correction mechanisms. To validate the proposed methodology, five distinct simulation scenarios—encompassing normal channel conditions, low SNR, multipath effects, high packet loss, and dynamic occlusion—are rigorously tested. These simulation scenarios are quantitatively compared with traditional Kalman filtering, weighted least squares, and ordinary multi-source fusion techniques. The results demonstrate that the navigation data processor, when constrained by channel optimization limits, can significantly decrease the mean positioning error in a complex environment. Furthermore, it substantially improves overall data accuracy and trajectory continuity. Ultimately, this research provides substantial engineering reference value for civil aviation navigation data links, airport surface movement guidance, airborne GNSS/INS integrated navigation, and BeiDou/GNSS-based high-precision aviation terminals.
Copyright (c) 2026 Hongzhi Shi (Author) https://creativecommons.org/licenses/by/4.0
Application Exploration of Machine Learning in Natural Language Processing and Computer Vision
Machine learning in general, and deep learning in particular, has revolutionised both natural language processing (NLP) and computer vision (CV) over the last decade. Performance on benchmark tasks consistently exceeds previous state of the art, and in many cases approaches or exceeds human-level accuracy. This paper provides a structured exploration of the main applications of machine learning in these two domains, exploring the architectural innovations -- from convolutional neural networks to the Transformer -- that have propelled progress, and analysing representative applications including text classification, machine translation, large language models, image classification, object detection, and generative visual modelling. The paper also reviews the convergence of NLP and CV through multimodal architectures such as CLIP and BLIP-2 that have resulted in cross-modal reasoning and new application domains. The main obstacles are noted as computing expense, data bias and restrictions in interpretability and the paper discusses future research paths focusing on efficient models and multimodal reasoning. We seek to give a thorough comparative analysis of ML applications in the two domains, and to discover shared architectural paths that indicate a similar future for the research on intelligent systems.
Copyright (c) 2026 Peiheng Qin (Author) https://creativecommons.org/licenses/by/4.0
Large-Model-Driven Content Moderation Systems: Platform-Level Applications and Governance Practices
Into the application and administration of bombastic-model-take content moderation systems on societal media platforms, this research article delf. As the deployment of these AI systems get more widespread. Translate their capabilities and limit is crucial. In big models for content moderation, the subject review the technological promotion. Exploring their strength in key inappropriate content. Thereby to ensure honourable and unbiased application. The article likewise examines governance practices; use quantitative method, the enquiry thereby assess the performance metrics of these simulation in genuine-world contexts and discourse their import for users and policymakers. Key findings intrinsically highlight the equalizer between algorithmic efficiency and human superintendence and supply perceptivity into next improvements in content moderation systems.
Copyright (c) 2026 Rui Li (Author) https://creativecommons.org/licenses/by/4.0
Decision Rights and Escalation Design in Industry 4.0: A Framework for Explainable Autonomous Operations
Industry 4.0 has made autonomous and semi-autonomous operations technically feasible, but many industrial studies still describe autonomy as if it were only a question of algorithms and data. In practice, industrial autonomy is a governance problem as much as a technical one. Managers need to specify who may decide, under what conditions machine recommendations become executable actions, how exceptions are escalated, and how learning is retained without weakening accountability. This paper develops a conceptual framework for decision rights and escalation design in Industry 4.0. The central argument is that industrial autonomy should be understood as a managed distribution of observation, interpretation, recommendation, authorization, execution, and learning rights rather than as a binary choice between manual and automatic operation. The paper organizes this argument through three linked ideas. First, autonomy should be calibrated by consequence severity, reversibility, and evidence quality, not by model confidence alone. Second, explainability and auditability are operational features that make autonomous actions governable rather than optional extras. Third, escalation design is the mechanism that preserves human control while still allowing fast and scalable machine-supported action. The framework is illustrated with examples from quality control, production scheduling, resource dispatching, service operations, and multi-state industrial systems. The paper contributes a governance-oriented interpretation of Industry 4.0 autonomy that is distinct from purely technical discussions and offers a practical research agenda for explainable and accountable industrial operations.
Copyright (c) 2026 Suzhen Huang (Author) https://creativecommons.org/licenses/by/4.0
Technical Pathways and Application Mechanisms of Artificial Intelligence-Empowered Big Data Analytics
Artificial Intelligence (AI) has revolutionized the field of big data analytics by enabling advanced technical pathways and application mechanisms. This review paper explores the historical evolution, current methodologies, and future directions of AI-empowered big data analytics. The paper begins with an introduction to the significance of AI in handling large-scale data and its transformative impact across industries. A historical overview traces the development of AI techniques and their integration into big data systems. The core themes focus on technical pathways, including machine learning algorithms, neural networks, and hybrid models, as well as application mechanisms such as predictive analytics, anomaly detection, and decision-making systems. Comparative analyses highlight the strengths and limitations of various approaches, while challenges such as scalability, ethical concerns, and data privacy are critically examined. Future perspectives emphasize emerging trends like quantum computing and explainable AI. The conclusion synthesizes the findings and underscores the importance of interdisciplinary collaboration for advancing AI-driven big data analytics.
Copyright (c) 2026 Zice Gao (Author) https://creativecommons.org/licenses/by/4.0
Research on Personalized Asset Allocation Using AI Agents in Robo-Advisory Scenarios
This review paper provides a systematic review of personalized asset allocation facilitated by AI agents within robo-advisory platforms. Robo-advisors, employing algorithms to automate investment decisions, are increasingly incorporating sophisticated AI techniques to tailor portfolios to individual investor needs and preferences. This paper investigates the evolution of these AI-driven systems, examining key themes such as risk profiling, dynamic asset allocation strategies, and the integration of behavioral finance principles. A comparative analysis of current methodologies highlights their strengths and limitations, particularly concerning transparency, explainability, and robustness in volatile market conditions. Furthermore, the review addresses the challenges associated with data privacy, regulatory compliance, and the potential for algorithmic bias. By synthesizing current research, we identify promising future directions, including the development of more interpretable AI models, the incorporation of alternative data sources, and the creation of more seamless and personalized user experiences. This review aims to provide a comprehensive overview of the current landscape, fostering a deeper understanding of the opportunities and challenges presented by AI-powered personalized asset allocation in robo-advisory contexts.
Copyright (c) 2026 Jialong Li (Author) https://creativecommons.org/licenses/by/4.0
Exploration of the Integration Paths Between Artificial Intelligence and Art Design
This paper aims to explore potential pathways for the integration of artificial intelligence (AI) and art design, a rapidly evolving intersection that is fundamentally reshaping creative processes. The advancement of AI technologies, particularly generative models, machine learning algorithms, and neural networks, has exerted a revolutionary impact on the art industry. These technological breakthroughs are challenging traditional paradigms of artistic creation, authorship, and aesthetic evaluation, while simultaneously presenting novel opportunities for unprecedented creative expression and efficiency. Through a comprehensive analysis of current application methodologies and diverse case studies of AI in art design, this study systematically examines various integration trajectories. We investigate how AI tools are utilized across different design domains, including graphic design, industrial design, and digital media arts, highlighting both the collaborative potential between human artists and machine intelligence, as well as the ethical and practical challenges that arise. Furthermore, the research delves into the cognitive and operational shifts required for designers to effectively harness AI capabilities. By synthesizing these findings, the paper provides a robust theoretical reference and practical framework for the future evolution of the art and design industry. Ultimately, this study underscores the necessity for a symbiotic relationship between human creativity and artificial intelligence, ensuring that technological advancements serve to augment rather than replace human artistic intuition, thereby fostering a more innovative and inclusive design ecosystem.
Copyright (c) 2026 Li Wei (Author) https://creativecommons.org/licenses/by/4.0
Research on Real time Decision Mechanism of Cloud Computing Driven Emergency Communication System Integrating Artificial Intelligence
In emergency response to sudden public incidents, the emergency communication system serves as one of the most critical basic support infrastructures, playing an indispensable role in command, dispatch, and rescue coordination. Its decision-making speed and accuracy directly affect the overall effectiveness of the emergency response. Traditional emergency communication systems exhibit significant disadvantages, including severe decision-making lag, poor adaptability to rapidly changing scenarios, static resource allocation, and persistent barriers to multi-party collaboration. Consequently, these legacy systems cannot adequately cope with the highly dynamic and complex environments encountered during extreme disasters. To address these critical shortcomings, the integration of the elastic computing power and distributed collaborative characteristics of cloud computing technology with the intelligent perception and real-time decision-making capabilities of artificial intelligence provides a transformative avenue for modernizing emergency communication networks. Starting from the core architecture of cloud computing-driven emergency communication systems, this article comprehensively summarizes the operational requirements and technical obstacles associated with real-time decision-making in severe emergency scenarios. Furthermore, it provides specific methodological frameworks and implementation plans for deploying artificial intelligence across various critical domains, such as advanced situational awareness, dynamic resource allocation, robust link support, and seamless collaborative decision-making. Finally, extensive simulation experiments are utilized to rigorously test and validate the operational effectiveness, latency reduction, and overall strategic advantages of this proposed intelligent decision-making method, demonstrating its potential to significantly enhance future disaster management and resilient communication protocols.
Copyright (c) 2026 Guoli Ying (Author) https://creativecommons.org/licenses/by/4.0
From Subjective to Objective: Developing EEG-Based Indicators for Competency Assessment in Low-Altitude Aviation Training
The global advancement of the low-altitude economy has led to a significant increase in low-altitude aircraft operations and activities. Consequently, the selection and training of pilots for these aircraft have become focal points in both research and regulatory oversight. Traditional competency assessments often rely on subjective questionnaires, behavioral tests, or simulator training records. While these approaches can reflect macro-level performance, they exhibit limitations in objectivity, real-time capability, and the interpretation of underlying physiological mechanisms. Electroencephalography (EEG), a non-invasive physiological signal with high temporal resolution, offers a promising tool for pilot selection, training, and real-time monitoring of cognitive states. This study develops a resting-state EEG-based indicator to assess pilot competency. We designed a novel paradigm to collect multi-channel resting EEG from 20 participants. After preprocessing, signals were segmented into 4-second epochs. We extracted 14 features including power spectral density, differential entropy, and time-domain variance, analyzed across standard frequency bands. Using balanced samples per participant, we evaluated five classifiers via 10-fold cross-validation, comparing performance across bands, channels, and features. Results indicate that differential entropy features in the gamma band, particularly from the FP1 and P3 channels, demonstrated a significant ability to distinguish pilot competency. Classification accuracy based on single-channel gamma band DE features exceeded 80%. Model performance was further quantified using a confusion matrix and multiple metrics, such as accuracy, precision, recall, and F1-score. The study also discusses limitations related to sample size, model generalizability, and practical engineering applications, along with potential directions for improvement.
Copyright (c) 2026 Qing Liu, Jiayun Zhang (Author) https://creativecommons.org/licenses/by/4.0
Fault Diagnosis of Wind Turbine Bearings Using SwinT-CBAM-BiGRU
To address the challenge that traditional diagnostic models struggle to simultaneously capture local impacts and global features due to background noise interference under the complex operating conditions of wind turbine bearings, this study proposes a novel multimodal fusion fault diagnosis model based on SwinT-CBAM-BiGRU. Specifically, the Gramian Angular Difference Field (GADF) encoding technique is employed to transform 1D vibration signals into 2D feature images. A Swin Transformer integrated with the Convolutional Block Attention Module (CBAM) is utilized to extract deep spatial features and precisely pinpoint core fault regions. Concurrently, a Bidirectional Gated Recurrent Unit (BiGRU) is combined to mine the long-range temporal evolution patterns of the signals. Through comparative and ablation experiments conducted on the Case Western Reserve University (CWRU) bearing dataset, the results demonstrate that the proposed fusion model exhibits superior diagnostic capabilities. Ultimately, this method effectively breaks the constraints of single-dimensional features, demonstrating stronger discriminative stability and robustness in multi-class bearing fault diagnosis tasks.
Copyright (c) 2026 Fei Li, Xueming Zhai (Author) https://creativecommons.org/licenses/by/4.0
Research on the Application of Deep Learning Technology in urban wind field prediction
Rapid and accurate prediction of wind speed fields in urban environments is vital for understanding urban climates, promoting sustainability, and mitigating disasters. Existing prediction methods primarily rely on Computational Fluid Dynamics (CFD) numerical simulations. However, these traditional approaches face significant constraints regarding both accuracy and computational efficiency. With rapid advancements in computer technology, deep learning is gradually emerging as an efficient alternative to conventional CFD simulations for wind field prediction. This paper elaborates on the application of deep learning methods in wind field forecasting and comprehensively analyzes the current problems and challenges encountered in this domain. Finally, the study explores future prospects for the development of wind field prediction technologies.
Copyright (c) 2026 Chenyu Ma (Author) https://creativecommons.org/licenses/by/4.0
Research on Dynamic Path Planning and Causal Inference for Last-Mile Logistics Based on Spatiotemporal Graph Neural Networks
With China’s annual express delivery volume surpassing 120 billion pieces, the last mile of logistics is increasingly constrained by static path planning, delayed dynamic responses, and insufficient integration of heterogeneous data sources. This study targets small and medium-sized distribution stations and proposes a dynamic path planning method based on spatiotemporal graph neural networks combined with causal reasoning. The delivery area is modeled as a spatiotemporal graph that fuses 12 categories of heterogeneous information, including geographic coordinates, real-time traffic conditions, rider load, and weather factors, to capture both structural and temporal dependencies. On this basis, a causal reasoning module is introduced to distinguish sporadic disturbances from routine patterns through backdoor adjustment and intervention models, thereby refining graph attention weights and improving the robustness of routing decisions. Furthermore, a federated learning framework is employed to support encrypted model training and incremental updates across multiple stations, enhancing cross-station generalization while preserving data privacy. Pilot experiments conducted at 10 small and medium-sized stations in Jinhua, Zhejiang, demonstrate that the proposed approach reduces delivery timeout rates from 11.2% to 0.7%, decreases riders’ average daily mileage by 18 kilometers, shortens system response time from 5 minutes to 8 seconds, and improves resource utilization by 35%. These results indicate that the method forms an intelligent perception–reasoning–decision closed loop and provides a scalable technical solution for efficient, privacy-aware last-mile logistics management.
Copyright (c) 2026 Yixuan Liu (Author) https://creativecommons.org/licenses/by/4.0
A Quality Control and Evaluation Framework for Generative AI in Personalized E-Commerce Product Descriptions
This research introduces a comprehensive quality control and evaluation framework tailored for generative AI applications in personalized e-commerce product descriptions. The framework addresses the critical need for ensuring the relevance, accuracy, fluency, and persuasiveness of AI-generated content, which directly impacts customer engagement and sales conversion rates. It incorporates multi-faceted metrics, including semantic similarity, content diversity, factual correctness, and user perception, to assess the quality of generated descriptions. Furthermore, the framework employs a feedback loop mechanism that continuously refines the AI models based on real-time performance data and user interactions. Through rigorous experimentation and comparative analysis with existing methods, we demonstrate the effectiveness of the proposed framework in producing high-quality, personalized product descriptions that enhance the e-commerce shopping experience. The study also explores the ethical considerations surrounding the use of AI in marketing and provides guidelines for responsible AI deployment.
Copyright (c) 2026 Xin Yuan (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of YOLO for Object Detection under Complex Illumination Conditions
Object detection has become a crucial component in various computer vision applications, ranging from autonomous driving to surveillance systems. YOLO (You Only Look Once) has gained prominence due to its real-time performance and relatively high accuracy. However, its performance can be significantly affected by complex illumination conditions such as underexposure, overexposure, shadows, and varying light sources. This review paper provides a comprehensive analysis of YOLO's performance under these challenging illumination conditions. We begin with an overview of the YOLO architecture and its evolution, followed by a detailed exploration of how different illumination factors impact its detection accuracy and speed. We then delve into various techniques proposed to mitigate these issues, including image enhancement methods, adaptive thresholding approaches, and robust feature extraction strategies. Furthermore, we comparatively analyze the performance of different YOLO variants and other state-of-the-art object detectors under diverse illumination scenarios. The paper synthesizes the current research landscape, highlights the key challenges that remain, and discusses potential future directions for improving the robustness of object detection algorithms in adverse lighting conditions. This review aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the limitations of YOLO in real-world applications with complex illumination.
Copyright (c) 2026 Tiantian Chen, Sheila M. Geronimo (Author) https://creativecommons.org/licenses/by/4.0
SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring
The rapid expansion of artificial intelligence and machine learning (ML) applications has intensified the demand for integrated environments that unify model development, deployment, and monitoring. Traditional Integrated Development Environments (IDEs) focus primarily on code authoring, lacking intelligent support for the full ML lifecycle, while existing MLOps platforms remain detached from the coding workflow. To address this gap, this study proposes the design of an LLM-Integrated IDE with automated MLOps pipelines that enables continuous model development and monitoring within a single environment. The proposed system embeds a Large Language Model (LLM) assistant capable of code generation, debugging recommendation, and automatic pipeline configuration. The backend incorporates automated data validation, feature storage, drift detection, retraining triggers, and CI/CD deployment orchestration. This framework was implemented in a prototype named SmartMLOps Studio and evaluated using classification and forecasting tasks on the UCI Adult and M5 datasets. Experimental results demonstrate that SmartMLOps Studio reduces pipeline configuration time by 61%, improves experiment reproducibility by 45%, and increases drift detection accuracy by 14% compared to traditional workflows. By bridging intelligent code assistance and automated operational pipelines, this research establishes a novel paradigm for AI engineering-transforming the IDE from a static coding tool into a dynamic, lifecycle-aware intelligent platform for scalable and efficient model development.
Copyright (c) 2026 Jiawei Jin, Yingxin Su, Xiaotong Zhu (Author) https://creativecommons.org/licenses/by/4.0
Comparative Study of Machine Learning Models for U.S. Housing Price Prediction
This study compares machine learning models used to predict US house prices using a large-scale real estate dataset covering all US states, cities, and zip code regions. The goal is to evaluate prediction accuracy and identify effective preprocessing strategies for high-cardinality location features. After cleaning the data through missing value handling, deduplication, and outlier removal based on interquartile range (IQR), leakage-aware feature engineering was applied, including aggregating zip codes into ZIP3, grouping by the top K cities, and date decomposition, while excluding target derived variables. This study trains and evaluates three models-linear regression, random forest, and XGBoost-on a reserved test set using mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). The results show that XGBoost achieves the best performance, outperforming linear regression and random forest, and feature importance indicates that location indicators play a dominant role in prediction gain. The findings demonstrate that the XGBoost model outperforms linear regression and random forest models in predicting US house prices.
Copyright (c) 2026 Wenguang Zhou, Wenjiao Zhou (Author) https://creativecommons.org/licenses/by/4.0
TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining
TRIZ-based contradiction mining is a fundamental task in patent analysis and systematic innovation, as it enables the identification of improving and worsening technical parameters that drive inventive problem solving. However, existing approaches largely rely on rule-based systems or traditional machine learning models, which struggle with semantic ambiguity, domain dependency, and limited generalization when processing complex patent language. Recently, large language models (LLMs) have shown strong semantic understanding capabilities, yet their direct application to TRIZ parameter extraction remains challenging due to hallucination and insufficient grounding in structured TRIZ knowledge. To address these limitations, this paper proposes TRIZ-RAGNER, a retrieval-augmented large language model framework for TRIZ-aware named entity recognition in patent-based contradiction mining. TRIZ-RAGNER reformulates contradiction mining as a semantic-level NER task and integrates dense retrieval over a TRIZ knowledge base, cross-encoder reranking for context refinement, and structured LLM prompting to extract improving and worsening parameters from patent sentences. By injecting domain-specific TRIZ knowledge into the LLM reasoning process, the proposed framework effectively reduces semantic noise and improves extraction consistency. Experiments on the PaTRIZ dataset demonstrate that TRIZ-RAGNER consistently outperforms traditional sequence labeling models and LLM-based baselines. The proposed framework achieves a precision of 85.6%, a recall of 82.9%, and an F1-score of 84.2% in TRIZ contradiction pair identification. Compared with the strongest baseline using prompt-enhanced GPT, TRIZ-RAGNER yields an absolute F1-score improvement of 7.3 percentage points, confirming the effectiveness of retrieval-augmented TRIZ knowledge grounding for robust and accurate patent-based contradiction mining.
Copyright (c) 2026 Zitong Xu, Yuqing Wu, Yue Zhao (Author) https://creativecommons.org/licenses/by/4.0
Multi-Task Temporal Fusion Transformer for Joint Sales and Inventory Forecasting in Amazon E-Commerce Supply Chain
Efficient inventory management and accurate sales forecasting are critical challenges in large-scale e-commerce platforms such as Amazon, where stockouts and overstocking can lead to substantial financial losses and operational inefficiencies. Traditional single-task forecasting models, which focus solely on sales or inventory, often fail to capture the complex temporal dependencies and cross-task interactions that characterize real-world supply chain dynamics. To address this limitation, this study proposes a Multi-Task Temporal Fusion Transformer (TFT-MTL) framework designed for joint sales and inventory forecasting within the Amazon e-commerce ecosystem. The model integrates heterogeneous data sources-including historical sales records, warehouse inventory levels, pricing, promotions, and event-driven factors such as holidays and Prime Day campaigns-through a unified deep learning architecture. A shared encoder captures long-term temporal patterns, while task-specific decoder heads predict sales volume, inventory turnover, and stockout probability simultaneously. Experiments on large-scale real-world datasets demonstrate that the proposed TFT-MTL model significantly outperforms baseline methods such as LSTM, GRU, and single-task TFT. Compared with the single-task TFT model, the proposed approach achieves a 6.2% reduction in Sales RMSE, a 12.7% decrease in Sales MAPE, a 6.4% reduction in Inventory RMSE, and a 12.4% decrease in Inventory MAPE. These results confirm the model's ability to effectively capture multi-dimensional dependencies across supply chain variables. The proposed framework provides an interpretable, data-driven decision support tool for optimizing Amazon's inventory scheduling and demand planning strategies.
Copyright (c) 2026 Zheqi Hu, Yiwen Hu, Hanwu Li (Author) https://creativecommons.org/licenses/by/4.0
Privacy-Preserving AI for Detecting and Mitigating Customer Price Discrimination in Big-Data Systems
With the rapid development of big data and artificial intelligence technologies, personalized pricing has become a common strategy for platforms to enhance profitability. However, this practice often evolves into customer price discrimination (CPD), which seriously harms consumer rights and market fairness. Meanwhile, the data-driven nature of AI models for CPD detection raises severe privacy concerns. To address the dual challenges of CPD governance and data privacy protection, this paper proposes a privacy-preserving AI framework for CPD detection and mitigation in big-data systems. First, we design a federated learning-based detection model that enables multiple data holders to collaborate on model training without sharing raw user data. Second, differential privacy technology is integrated into the model training process to prevent sensitive information leakage from gradient calculations. For CPD mitigation, a dynamic pricing calibration mechanism based on explainable AI is proposed to ensure pricing transparency while maintaining platform operational efficiency. Experimental results on real-world e-commerce and ride-hailing datasets show that the proposed framework achieves a detection accuracy of 87.6% for CPD behaviors, with a privacy budget consumption of only 1.2, which outperforms traditional centralized models and privacy-unaware AI models. This research provides a technical solution for balancing personalized services, market fairness, and data privacy protection in big-data systems. Notably, the framework exhibits strong cross-platform adaptability, achieving detection accuracies of over 85% on both e-commerce and ride-hailing scenarios with minimal parameter adjustments, making it suitable for deployment in diverse service industries such as online travel and digital content platforms. In practical applications, the framework helps platforms comply with strict data protection regulations including the EU's GDPR and China's Personal Information Protection Law, while reducing consumer complaints related to price discrimination by an estimated 42%. Additionally, the integration of blockchain-based traceability ensures that pricing adjustment records are tamper-proof, providing regulatory authorities with credible audit trails for market supervision.
Copyright (c) 2026 Wenwen Liu (Author) https://creativecommons.org/licenses/by/4.0
Privacy-Preserving AI in SMB Customer Service: Balancing Data Isolation, Compliance, and Automation Efficacy
This research investigates the application of Privacy-Preserving AI (PPAI) techniques in Small and Medium-sized Business (SMB) customer service environments, recognizing that SMBs constitute the backbone of the U.S. economy. It focuses on balancing the seemingly conflicting demands of data isolation, regulatory compliance (e.g., GDPR, CCPA), and the efficacy of AI-driven automation. The study explores various PPAI methodologies, including Federated Learning, Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, assessing their suitability for different SMB customer service scenarios. We analyze the trade-offs between privacy guarantees, model accuracy, computational overhead, and implementation complexity. Real-world case studies and simulations are used to evaluate the performance of selected PPAI techniques across key customer service metrics such as response time, customer satisfaction, and issue resolution rate. Furthermore, the research addresses the practical challenges of deploying PPAI in resource-constrained SMBs, considering factors like data heterogeneity, limited technical expertise, and cost considerations. This study proposes a framework for SMBs to effectively adopt PPAI in their customer service operations, ensuring robust data protection, adherence to compliance regulations, and enhanced automation capabilities. By lowering the technical barrier for SMBs to adopt AI while strictly adhering to privacy laws, this work aims to support widespread economic modernization alongside robust consumer privacy protection. The findings provide valuable insights for SMBs, AI developers, and policymakers aiming to promote the responsible and ethical use of AI in customer service.
Copyright (c) 2026 Zhenyuan He (Author) https://creativecommons.org/licenses/by/4.0
Inequality Research on Digital Navigation LTSS Support Services
This research article examines the inequalities prevalent in accessing and utilizing Digital Navigation Long-Term Services and Supports (LTSS). While digital navigation tools promise to enhance the accessibility and efficiency of LTSS, disparities persist across various demographic groups, potentially exacerbating existing health and social inequities. Our study investigates these inequalities by analyzing data related to digital literacy, access to technology, awareness of digital navigation tools, and experiences with LTSS support services. We employ a mixed-methods approach, combining quantitative analysis of survey data with qualitative interviews to gain a comprehensive understanding of the barriers and facilitators influencing equitable access. Key findings highlight the significant impact of socioeconomic status, geographic location, and cultural background on digital navigation tool adoption and utilization. These findings are crucial for informing policy interventions and designing targeted strategies to bridge the digital divide in LTSS, ultimately promoting equitable access to support services for all individuals in need. We emphasize the importance of culturally tailored digital literacy programs and user-centered design approaches to ensure that digital navigation tools effectively address the needs of diverse populations.
Copyright (c) 2026 Jingyi (Jesy) Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on Q-Learning and Motion Control of Multi-Robot Systems Based on Community-Aware Networks
Collaborative perception and autonomous motion control in complex environments are key challenges for multi-robot systems. As system scale increases, traditional centralized control and conventional distributed learning methods suffer from high communication overhead, low learning efficiency, and reduced stability. To address these limitations, this paper introduces a community-aware network framework that partitions the communication topology of a multi-robot system into communities with strong internal interactions. Based on this structure, a community-aware Q-learning and motion control method is proposed. The communication relationships among robots are modeled from the perspective of complex networks, and a community-based state representation is designed to capture local cooperation while reducing state dimensionality. Community-level information is further incorporated into the Q-value update through an improved reward mechanism, enhancing learning efficiency and convergence stability. In addition, learning decisions are mapped to continuous control inputs using robot kinematic models, enabling coordinated motion within communities and effective obstacle avoidance between communities. Simulation results demonstrate that the proposed method outperforms traditional multi-robot Q-learning approaches in convergence speed, path efficiency, and overall cooperative performance.
Copyright (c) 2026 Kexuan Shen (Author) https://creativecommons.org/licenses/by/4.0
Application of an Improved Orb Algorithm in Image Matching
An improved ORB feature matching algorithm is proposed to solve the problem of high mismatching rate of non-uniform distribution of feature points extracted by traditional ORB algorithms. First of all, to extract feature points of image block, and then calculate each block information entropy, the entropy value dynamically setting the threshold of each block to extract the feature points, make to extract the feature points of distribution more uniform, improve the subsequent registration accuracy. Secondly, for the problem of high mismatch rate caused by insufficient use of local information by the rBRIEF descriptor in the ORB algorithm, on the basis of introducing gradient information to improve the LBP algorithm, an improved descriptor combining rBRIEF and LBP is proposed. The 128-bit descriptor generated by the improved LBP algorithm is used to replace the 128-bit descriptor with the smallest variance in the original rBRIEF to improve the stability of the feature point description. Finally, RANSAC was used to delete the mismatched points. Results Compared with the traditional ORB algorithm, the improved algorithm reduces the time overhead by 8% and the average mismatch rate by about 10%. The improved orb algorithm has lower mismatch rate and higher registration speed.
Copyright (c) 2026 Xiaofei Deng, Joan Lazaro (Author) https://creativecommons.org/licenses/by/4.0
PPE Recognition System Based on Improved YOLOv11-Safety Algorithm and Frontend-Backend Architecture Integration
With the expansion of global urbanization, the construction industry, a pillar of the national economy, remains a high-risk sector. Although the number of work safety accidents and fatalities in China decreased year-on-year in 2024, housing construction projects still have high accident and fatality rates. Personal Protective Equipment (PPE) is the last effective defense for workers, but PPE compliance management faces challenges such as limited manual supervision and complex working environments. Leveraging advancements in AI and computer vision, this research designs an AI-driven deep learning system centered on the YOLOv11-Safety algorithm to realize real-time and accurate identification of 9 categories of PPE-related targets on construction sites. The system integrates Spring Boot (backend), Vue.js (frontend), and Flask (AI middleware) to build a practical intelligent safety supervision solution. Through improvements in feature extraction, feature fusion, loss function, and inference optimization, the YOLOv11-Safety algorithm achieves higher detection accuracy, faster inference speed on edge devices, and stronger robustness. The system promotes the digital transformation of construction site safety management from passive response to active prevention, providing an effective intelligent solution for workplace safety supervision.
Copyright (c) 2026 Lu Bei, Joan Lazaro (Author) https://creativecommons.org/licenses/by/4.0
Research on the Analysis and Recognition System for Dangerous Driving Behaviors Based on Convolutional Neural Networks
To address the critical need for real-time monitoring of hazardous behaviors that compromise road safety, this study presents an intelligent detection system centered on the lightweight YOLOv11n model. The system integrates multiple functional modules, including object detection, multi-object tracking using ByteTrack and BoTSORT, and behavior recognition logic based on temporal windows, thereby forming a comprehensive technical workflow encompassing data acquisition, preprocessing, inference, tracking, behavior analysis, and early warning. It is capable of accurately detecting six common dangerous behaviors-yawning, eye closure, smoking, drinking, mobile phone usage, and in-vehicle conversations-while providing timely alerts to mitigate potential risks. Extensive experiments demonstrate that the system achieves a validation accuracy of 65.55%, a recall rate of 79.71%, and an mAP50 of 69.26%. The inference speed for individual images is maintained within 30-60 milliseconds, and video processing can consistently reach 20-30 frames per second, ensuring real-time performance suitable for practical deployment. The proposed framework not only enhances detection precision but also supports continuous monitoring and risk prevention in real-world driving environments, providing a technically feasible and operationally efficient solution for improving driver safety.
detection of dangerous driving behaviors
Copyright (c) 2026 Yunpeng Liu, Joan Lazaro (Author) https://creativecommons.org/licenses/by/4.0
SR-DTMA: A Digital Twin-Driven LLM Multi-Agent Framework for Systemic Risk Simulation and Coordinated Decision-Making in Supply Chains
Systemic risk in global supply chains has intensified with increasing interdependencies among firms, logistics infrastructure, and financial institutions, where localized disruptions such as port closures, financial crises, or policy shocks can propagate across networks and trigger large-scale cascading failures. Existing supply chain risk management and simulation approaches are limited in their ability to simultaneously capture decentralized agent behavior, dynamic system interactions, and the semantic complexity of real-world risk information expressed in natural language. To address these challenges, this paper proposes SR-DTMA (Systemic Risk-aware Digital Twin Multi-Agent framework), a digital twin-driven simulation and decision-making framework that integrates Large Language Model (LLM)-powered agents with decentralized multi-agent learning for systemic risk analysis in supply chains. In SR-DTMA, heterogeneous supply chain entities-including manufacturers, ports, and financial institutions-are modeled as autonomous agents that perceive local operational states, interpret unstructured risk events from policy announcements and news, and make adaptive decisions under systemic shocks. By embedding LLM-based risk cognition within a digital twin environment, the framework enables large-scale simulation of risk contagion, strategic interactions, and the conflict between locally rational decisions and global system stability. Extensive experiments under port closure and financial tightening scenarios show that conventional rule-based and RL-driven agents exhibit higher systemic loss and deeper risk propagation. In contrast, SR-DTMA, integrating LLM-driven agents with a lightweight coordination mechanism, reduces the cumulative systemic loss ratio from 0.327 to 0.163, decreases risk propagation depth from 4.8 to 2.6, and shortens recovery time from 21 to 9 steps. These results highlight the effectiveness of coordinated LLM-based decision-making in mitigating systemic supply chain risk and enhancing network resilience.
Copyright (c) 2026 Yunting Ling, Wenxuan Liu (Author) https://creativecommons.org/licenses/by/4.0
Generative Adversarial Network Enhanced Adaptive Control for Aerospace Launch Vehicles
A theoretical analysis of an on-line, autonomously intelligent, adaptive tracking controller for satellites-employing Generative Adversarial Networks (GANs)-is presented. The controller receives real-time sensory data and a scalar performance signal, autonomously refining thruster or attitude-control commands without requiring explicit foreknowledge of the satellite's internal dynamics. By leveraging an adversarial interplay between a generator and a discriminator module, the approach rapidly adapts to evolving orbital conditions and unanticipated disturbances, thus preserving robust tracking accuracy. The underlying on-line learning mechanism enables continuous policy adjustments, obviating the need for extensive offline tuning. Experimental evidence, derived from a representative satellite undergoing maneuvers in a low-Earth-orbit environment, demonstrates the algorithm's capacity to compensate for nonstationary aerodynamic drag and shifting mass distribution while maintaining precise trajectory regulation. This work underscores the viability of GAN-augmented adaptive methods for advanced aerospace applications, offering heightened resilience and efficiency in the face of dynamic mission constraints.
Copyright (c) 2026 Abdulrahman Fahad, Fatemeh Mohammadi, Amir Hussain, Mariam Eissa, Hassan El-Nur, Sara Al-Sabah, Krzysztof Zalewski (Author) https://creativecommons.org/licenses/by/4.0
From Data Pipelines to Platform Tooling: A Holistic Framework for Cloud-Native Backend Development
Cloud-native backend development has evolved rapidly, transitioning from monolithic architectures to microservices, serverless functions, and containerized applications managed by sophisticated orchestration platforms. This review provides a holistic framework for cloud-native backend development, tracing its historical evolution, examining key architectural patterns, and analyzing the tooling ecosystems that enable efficient development and deployment. We explore the transformation from data pipelines, which initially focused on data movement and transformation, towards comprehensive platform toolings that provide end-to-end development and deployment capabilities. Core themes covered include microservices architecture and serverless computing, highlighting their benefits and challenges in the cloud-native context. We also delve into containerization technologies like Docker and orchestration tools like Kubernetes, discussing their role in automating deployment and scaling applications. A critical comparison of different backend development approaches, focusing on trade-offs between cost, performance, scalability, and maintainability, is presented. This review also identifies key challenges faced by developers in adopting cloud-native practices, such as complexity in distributed systems, vendor lock-in, and security concerns. Finally, we discuss future perspectives, including the rise of low-code/no-code platforms and the increasing importance of observability and automated testing in ensuring the reliability and performance of cloud-native backends. This review serves as a comprehensive guide for developers and architects looking to navigate the complexities of cloud-native backend development and build scalable, resilient, and cost-effective solutions.
Copyright (c) 2026 Yingqiang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Research on Feature Fusion and Multimodal Patent Text Based on Graph Attention Network
Aiming at the challenges of cross-modal feature fusion, low computational efficiency in long patent text modeling, and insufficient hierarchical semantic coherence in patent text semantic mining, this study proposes a novel deep learning framework termed HGM-Net. The framework integrates Hierarchical Comparative Learning (HCL), a Multi-modal Graph Attention Network (M-GAT), and Multi-Granularity Sparse Attention (MSA) to achieve robust, efficient, and semantically consistent patent representation learning. Specifically, HCL introduces dynamic masking, contrastive learning, and cross-structural similarity constraints across word-, sentence-, and paragraph-level hierarchies, enabling the model to jointly capture fine-grained local semantics and high-level thematic consistency. Contrastive and cross-structural similarity constraints are particularly enforced at the word and paragraph levels, effectively enhancing semantic discrimination and global coherence within complex patent documents. Furthermore, M-GAT models patent classification codes, citation relationships, and textual semantics as heterogeneous graph structures, and employs cross-modal gated attention mechanisms to dynamically fuse multi-source and multi-modal features, thereby improving representation completeness and robustness. To address the high computational cost of long-text processing, MSA adopts a hierarchical sparse attention strategy that selectively allocates attention across multiple granularities, including words, phrases, sentences, and paragraphs, significantly reducing computational overhead while preserving critical semantic information. Extensive experimental evaluations on patent classification and similarity matching tasks demonstrate that HGM-Net consistently outperforms existing state-of-the-art deep learning approaches. The results validate the effectiveness and generalization capability of the proposed framework, highlighting its theoretical innovation and practical value in improving patent examination efficiency and enabling large-scale technology relevance mining.
Copyright (c) 2026 Zhenzhen Song, Ziwei Liu, Hongji Li (Author) https://creativecommons.org/licenses/by/4.0
ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
Large-scale deep learning models impose substantial communication overhead in distributed training, particularly in bandwidth-constrained or heterogeneous cloud-edge environments. Conventional synchronous or fixed-compression techniques often struggle to balance communication cost, convergence stability, and model accuracy. To address these challenges, we propose ACE-Sync, an Adaptive Cloud-Edge Synchronization Framework that integrates (1) an attention-based gradient importance predictor, (2) a differentiated parameter compression strategy, and (3) a hierarchical cloud-edge coordination mechanism. ACE-Sync dynamically selects which parameter groups to synchronize and determines appropriate compression levels under per-device bandwidth budgets. A knapsack-based optimization strategy is adopted to maximize important gradient preservation while reducing redundant communication. Furthermore, residual-based error compensation and device clustering ensure long-term convergence and cross-device personalization. Experiments show that ACE-Sync substantially reduces communication overhead while maintaining competitive accuracy. Compared with FullSync, ACE-Sync lowers communication cost from 112.5 GB to 44.7 GB (a 60% reduction) and shortens convergence from 41 to 39 epochs. Despite aggressive communication reduction, ACE-Sync preserves high model quality, achieving 82.1% Top-1 accuracy-only 0.3% below the full-synchronization baseline-demonstrating its efficiency and scalability for large-scale distributed training. These results indicate that ACE-Sync provides a scalable, communication-efficient, and accuracy-preserving solution for large-scale cloud-edge distributed model training.
Copyright (c) 2026 Yi Yang, Ziyu Lin, Liesheng Wei (Author) https://creativecommons.org/licenses/by/4.0