应用人工智能研究
type: channel
description: （AAIR）是一本专注于人工智能领域前沿研究与创新实践的国际期刊，致力于为全球学者、研究人员和行业专家提供一个高端学术交流平台，推动人工智能理论与技术的持续发展，加速其在多领域的深度融合与广泛应用。​ 本期刊聚焦人工智能的核心研究方向，涵盖机器学习、深度学习、自然语言处理、计算机视觉、机器人技术、智能系统、数据挖掘、知识图谱等多个领域，既欢迎基础理论研究的突破性成果，也重视具有重要应用价值的创新性实践探索。通过发表高质量的原创研究论文、综述文章、案例研究及技术报告，全面展示人工智能领域的最新动态与发展趋势。​
publisher: CHINA SCIENCE AND TECHNOLOGY DEVELOPMENT PRESS
language: zh-Hans
publication name: 应用人工智能研究
issn: 3105-0379
items: Post-Human Perspectives on Film: Exploring Innovative Applications of AIGC
items: An AI-Driven Intelligent Health Assessment System for High-Voltage Switchgear Based on Multimodal Sensor Data Fusion
items: Artificial Intelligence for Fracture Risk Prediction: A Bibliometric Analysis of Research Hotspots and Evolutionary Trends
items: Explainable Machine Learning for Telecom Customer Churn Prediction and Actionable Retention Strategies
items: A Comprehensive Survey of Deep Learning–Based Object Tracking for Augmented Reality in Complex Real-World Scenes
items: Personalized Recommendation Algorithms in Digital Media: A Review of Engagement Effects, Information Cocoons, and Marketing Implications
items: Continuous blood pressure estimation and cardiovascular risk assessment based on wearable PPG signals
items: When Algorithms Push Firms to Cheat: AI, Blockchain, and Financing in China
items: Research on Performance Prediction Model of Wind Turbine Gearbox Lubricating Oil Based on Deep Learning
items: Map of Geographical Science Based on Artificial Intelligence
items: https://journal.innoviair.cn/index.php/AAIR/zh_Hans/article/view/42
items: Innovative Research on Cracking the Personalized Service Management of Artificial Intelligence Libraries
items: Application of AI Models in Dynamic Talent Adaptation between Industry and Education: A Case Study of Data from Digital Genealogy Cloud Platform
items: Language, Consciousness, and Manifestation: Towards a Pragmatic-Cognitive Framework for Next-Generation Neural Language Systems
items: Against the Backdrop of the Digital Economy: AI Empowers Innovation and Entrepreneurship Education for the Integration of Industry and Education
items: Research on the Impact Mechanism of Farmers' Digital Literacy on Enhancing Service Satisfaction of Power Grid Companies: An Implementation Path Based on the Interpretability of Machine Learning Models
items: Applying Radiomics and Deep Learning to Investigations into Seafarers' Health Status
items: A Learnable Frequency Gated DCT Denoising Network
items: The coupled application of GIS digital Twin and AI reinforcement learning
items: Ethics-First Lightweight AI: Differentiated English Teaching and Cultural Competence in Junior Middle Schools
items: On How Artificial Intelligence Drives Innovation in International Chinese Language Education
items: AI in Pharmaceutical Formulation: A Comprehensive Review
items: To whom exactly does inspiration belong? The creative rights and aesthetic boundaries behind AI-generated works
items: From Knowing how to farm to Understanding Data - The Reengineering of Farmers' Skills and Rural Development in the Context of Smart Agriculture
items: Intelligent Auxiliary System for Chronic Disease Prevention and Treatment Based on Information Technology and Its application in Improving Patients' Trust Predicament
items: Innovative Application of Artificial Intelligence Algorithms in Credit Scoring: Taking specific products in the field of consumer finance as the research object
items: From "Passive Congestion" to "Active Scheduling" — Rebuilding Travel Experience and Resource Allocation for Intelligent Transportation Networks

Post-Human Perspectives on Film: Exploring Innovative Applications of AIGC
type: item
description: The advent of AIGC(Artificial Intelligence Generated Content) represents a silicon-driven revolution for the carbon-based era of cinema. Human approaches to filmmaking have evolved—from celluloid to digital, from handwritten scripts to computer-generated texts—and are now increasingly oriented toward the development and collaboration with AIGC. During the process of employing AIGC in film production, it has become deeply embedded in cinematic art, whether through the secondary creation of celluloid imagery, collaborative brainstorming with humans, or integration with VR to craft new and enhanced viewing experiences. This study demonstrates that AIGC can significantly enhance the efficiency and creativity of film production, offering innovative solutions for image restoration, facilitating seamless human–machine collaboration, and enabling immersive VR-based cinematic experiences. By systematically analyzing these applications, the research provides a framework for understanding the transformative impact of AIGC on contemporary cinema and highlights its potential to redefine aesthetic and production standards in the film industry.
subject: 研究论文
  topic: section
subject: AIGC
  topic: keywords
subject: Film
  topic: keywords
subject: Creative Practices
  topic: keywords
subject: Virtual Production
  topic: keywords
subject: VR Imagery
  topic: keywords
creator: Zishen Huang
creator: Le Xu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/thwcfx26

An AI-Driven Intelligent Health Assessment System for High-Voltage Switchgear Based on Multimodal Sensor Data Fusion
type: item
description: The function of high-voltage switchgear is to maintain the stability of the power grid. The traditional health evaluation methods generally rely heavily on a single index; The accuracy of this method is relatively low, and it is difficult to detect failures because it cannot take into account multiple factors. In view of the above shortcomings, this paper has designed and developed an AI-intelligent health evaluation System based on multimodal sensor data fusion technology.The system uses the four-tier architecture of sensor layout, data acquisition and transmission, data fusion and processing, and health evaluation and decision-making, and integrates temperature, humidity, partial discharge, vibration, current, and voltage data. It uses a three-tier data fusion approach (weighted average at the data layer, principal component analysis (PCA) at the feature layer, and D-S evidence theory at the decision-making layer) to handle heterogeneous data and combines SVM models for small sample sizes and CNN models for time series data to provide all-around evaluation.Experiments based on a simulated platform show that the system can achieve an average accuracy of 96.8% and perform better than traditional single parameters by about 18.5 per cent. It can identify incipient faults 2-3 hours earlier than traditional methods, maintain 85% assessment accuracy even if one sensor fails, and complete post-fault state assessment and fault location within 500 ms after the relay protection action for short-circuit faults. The integrated three-level warning mechanism can reduce blind maintenance by as much as 60 per cent and lower the cost of maintenance by an average of 35 per cent; At the same time, it has achieved an effective grasp of complex fault conditions, improved the credibility of evaluation, provided scientific basis for preventive maintenance.
subject: 研究论文
  topic: section
subject: High-Voltage Switchgear
  topic: keywords
subject: Multimodal Sensor Data Fusion
  topic: keywords
subject: AI-Driven Health Assessment
  topic: keywords
subject: Fault Detection
  topic: keywords
creator: Huiming Kang
creator: Juanmin Chen
creator: Shaocong Liang
creator: Shidi Liu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/a6eqkh55

Artificial Intelligence for Fracture Risk Prediction: A Bibliometric Analysis of Research Hotspots and Evolutionary Trends
type: item
description: Artificial intelligence (AI) has emerged as a promising approach for improving fracture risk prediction. This study aimed to provide a comprehensive bibliometric overview of research on AI in fracture risk prediction by mapping its temporal evolution, collaborative structure, thematic framework, and disciplinary distribution. Publications were retrieved from the Web of Science Core Collection using a predefined search strategy covering January 2001 to December 2025, including only English-language articles and reviews. After screening and data cleaning, a total of 943 publications were analyzed. Bibliometric analyses were conducted using VOSviewer (v1.6.20) and the Bibliometrix R package (v4.5.1), examining annual publication trends, co-authorship networks at the author, institutional, and country levels, keyword co-occurrence clusters, and subject category distributions. The results showed that research output exhibited three developmental phases, with a marked acceleration after 2020. Collaboration networks demonstrated a clustered and moderately centralized structure, with core institutions and countries—particularly the United States and major European partners—occupying central positions. Keyword co-occurrence analysis identified four principal thematic clusters: AI-based predictive modeling, imaging-derived radiomics and opportunistic screening, FRAX-centered clinical risk assessment, and comorbidity- and pharmacotherapy-related applications. Subject category analysis further revealed progressive interdisciplinary integration, with increasing contributions from radiology and computer science alongside traditional clinical specialties. Overall, AI-based fracture risk prediction research has evolved from clinically grounded risk stratification toward computationally augmented, imaging-integrated, and translationally oriented predictive frameworks. The field demonstrates rapid growth, expanding international collaboration, and increasing interdisciplinary converg…
subject: 研究论文
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Fracture Risk Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
creator: Bo Wang
creator: Yin Lian
creator: Lina Gao
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
doi: 10.65455/7tc70w72

Explainable Machine Learning for Telecom Customer Churn Prediction and Actionable Retention Strategies
type: item
description: Customer churn is a critical issue in customer relationship management (CRM) in the telecommunications industry. Accurately identifying high-risk customers and providing actionable intervention recommendations is crucial for improving customer lifetime value and reducing operating costs. This paper addresses the task of predicting customer churn in the telecommunications industry by constructing an interpretable machine learning analysis framework of "prediction-intervention." On the Telco customer dataset containing 21 customer features, the discriminative performance of three models—XGBoost, Random Forest, and Logistic Regression—is compared. SHAP (SHapley Additive exPlanations) is introduced into the optimal model family to achieve interpretability analysis at both the global and individual levels. Experimental results show that the performance of the three models is similar, with Logistic Regression achieving the highest AUC (0.835) and F1 (0.593), while XGBoost and Random Forest have AUCs of 0.833 and 0.829, respectively. Confusion matrix analysis reveals that the main bottleneck under the current setup is false negative reporting (FN), indicating the need for threshold and cost-sensitive optimization. SHAP results indicate that contract type, tenure, online security, monthly charges, and technical support are the most critical churn drivers. Furthermore, this paper proposes a three-tiered, precise retention strategy targeting different risk levels and driving factors, providing a practical reference for deploying explainable AI-driven churn management systems in real-world telecom operations.
subject: 研究论文
  topic: section
subject: Explainable Machine Learning
  topic: keywords
subject: SHAP
  topic: keywords
subject: Logistic Regression
  topic: keywords
subject: Telecom Operations
  topic: keywords
subject: Customer Retention Strategies
  topic: keywords
creator: Zihan Liu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
doi: 10.65455/1z4v1627

A Comprehensive Survey of Deep Learning–Based Object Tracking for Augmented Reality in Complex Real-World Scenes
type: item
description: Augmented Reality (AR) systems critically depend on accurate, temporally stable, and computationally efficient object tracking to maintain geometric alignment and perceptual coherence between virtual content and the physical world. As AR technologies transition from controlled laboratory prototypes to large-scale deployment in industrial, medical, and consumer scenarios, tracking must operate robustly in complex real-world environments characterized by dynamic objects, occlusions, illumination changes, fast motion, and strict computational constraints. Traditional geometry-driven tracking pipelines often degrade under such conditions, motivating increased adoption of deep learning based approaches. This survey provides a comprehensive review of deep learning based object tracking for AR in complex real-world scenes, with particular emphasis on system-level considerations. Object tracking is treated as a central perception primitive that underpins stable AR experiences and interacts tightly with modules such as visual simultaneous localization and mapping, depth and geometry estimation, and semantic scene understanding. In contrast to prior surveys that emphasize algorithmic accuracy in isolation, we explicitly analyze AR-specific constraints including real-time latency, temporal stability, energy efficiency, long-term robustness, and deployment on mobile and wearable platforms. We review major tracking paradigms, representative datasets and benchmarks, AR-centric evaluation criteria, and common failure modes observed in practice, and we outline future research directions toward scalable, reliable, and trustworthy AR tracking systems.
subject: 综述论文
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Object Tracking
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Complex Scenes
  topic: keywords
subject: Real-Time Systems
  topic: keywords
subject: Mobile and Wearable AR
  topic: keywords
creator: Duoduo Mou
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
doi: 10.65455/ddjkhy91

Personalized Recommendation Algorithms in Digital Media: A Review of Engagement Effects, Information Cocoons, and Marketing Implications
type: item
description: Artificial intelligence (AI) has become a significant force, demonstrating rapid and sustained growth on global digital media platforms. AI technologies on these platforms include machine learning, deep learning, natural language processing, expert systems, and highly advanced personalized recommendation algorithms. These AI-driven algorithms are crucial for enhancing user engagement on digital media platforms, such as smartphone applications, social media, and online media. These algorithms provide customized content based on user preferences, significantly influencing user consumption behavior. Meanwhile, the role of recommendation algorithms in content management and creation promotes the boundaries of personalization, which is effective in social media marketing and e-commerce. However, the rapid development of AI-based recommendation algorithms is likely to pose potential risks, such as the "information cocoon effect," producing homogeneous content and exacerbating social polarization and biases. Additionally, concerns about user privacy and ethical implications have been raised. This review aims to explore the multi-faceted impacts of AI-driven personalized recommendation algorithms on audiences of digital media platforms, focusing on audience engagement, changing user behavior through social media marketing, and the impact of the cocoon effect on users.
subject: 研究论文
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Media Platforms
  topic: keywords
subject: Algorithmic Recommendation Systems
  topic: keywords
subject: Audience Engagement
  topic: keywords
subject: Social Media Marketing
  topic: keywords
subject: The Cocoon Effect
  topic: keywords
creator: Jueling Zhou
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/qnpjvg49

Continuous blood pressure estimation and cardiovascular risk assessment based on wearable PPG signals
type: item
description: This paper systematically studies the non-invasive continuous blood pressure estimation and cardiovascular risk assessment methods based on photoplethysmography (PPG) signals. A complete signal processing flow was constructed using the PhysioNet public dataset, including preprocessing, feature extraction, model construction and validation. The experimental results show that the extraction of heart rate and respiratory rate based on PPG has high accuracy, preliminarily verifying the correlation between the morphological characteristics of PPG and blood pressure parameters. The research further proposed a hierarchical processing architecture to balance the computing efficiency and monitoring depth of wearable devices, providing a reference for the engineering implementation of health monitoring systems.
subject: 研究论文
  topic: section
subject: PPG
  topic: keywords
subject: Blood Pressure Estimation
  topic: keywords
subject: Cardiovascular Risk Assessment
  topic: keywords
subject: Wearable Devices
  topic: keywords
subject: Signal Processing
  topic: keywords
subject: MATLAB
  topic: keywords
creator: Zehui Xu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/6pgttp36

When Algorithms Push Firms to Cheat: AI, Blockchain, and Financing in China
type: item
description: As pillars of China's economy, SMEs(Small and Medium-sized Enterprises) face persistent financing frictions. To address these challenges, the government promotes digital finance tools such as artificial intelligence (AI) and blockchain. AI enables rapid risk assessment and improves efficiency but often imposes rigid standards that overlook SME conditions, creating algorithmic pressures and incentives for data manipulation. Blockchain enhances governance through immutable, transparent records but raises compliance costs and adaptation burdens. Drawing on interviews with SME owners, managers, and financial intermediaries, this study applies grounded theory to analyze how AI and blockchain jointly reconfigure financing governance in China. Findings reveal that algorithmic rigidity and blockchain transparency interact, generating systemic tension while compelling institutional adaptation from firms. This dynamic converges into a state of governance synergy, wherein the technologies' complementary functions are institutionalized, ultimately producing a dual-threshold financing mechanism. Under this mechanism, firms must sequentially pass AI-driven credit screening and blockchain-based data verification. This research advances digital governance theory by framing technology as an active institutional shaper and contributes to SME finance literature by elucidating the paradigm shift from relationship-based to protocol-based financing logic.
subject: 研究论文
  topic: section
subject: SMEs
  topic: keywords
subject: Financing Constraints
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Governance Synergy
  topic: keywords
subject: Dual-Threshold Mechanism
  topic: keywords
creator: Ming Fu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
doi: 10.65455/d2tq5y61

Research on Performance Prediction Model of Wind Turbine Gearbox Lubricating Oil Based on Deep Learning
type: item
description: Gearbox lubricating oil oxidation degradation severely impairs the operational stability of wind turbines and increases maintenance costs. Conventional detection methods (e.g., Rotating Pressure Vessel Oxidation Test (RPVOT), Pressure Differential Scanning Calorimetry (PDSC)) have high instrument dependence and long testing cycles, and may not meet on-site rapid detection demands. This study took in-service industrial gear oil samples as the research object, selected five physicochemical indices as input variables, and systematically optimizes data preprocessing (Standard Normal Variate (SNV)), feature extraction (Principal Component Analysis (PCA)), and machine learning algorithms (Back Propagation Neural Network (BPNN)/Support Vector Machine (SVM)/Random Forest (RF)). The proposed SNV-PCA-BP hybrid model achieved excellent predictive performance with a R2 of 0.9960 and Residual Prediction Deviation (RPD) of 6.1247, which is 360 times more efficient (defined as single-sample detection time) than traditional RPVOT/PDSC methods based on parallel tests of 62 samples. This model provides a low-cost and reliable technical support for the predictive maintenance of wind turbine gearboxes.
subject: 研究论文
  topic: section
subject: Gearbox Lubricating Oil
  topic: keywords
subject: Antioxidant Performance Prediction
  topic: keywords
subject: Data Preprocessing
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: SNV-PCA-BP Model
  topic: keywords
subject: Rapid Detection
  topic: keywords
creator: TongWei Xie
creator: Xikun Zhang
creator: Xiaodong Liu
creator: Xuan Zhang
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
doi: 10.65455/2s6npy06

Map of Geographical Science Based on Artificial Intelligence
type: item
description: As a core tool for integrating the geographical knowledge system and revealing conceptual associations, the traditional construction mode of the basic concept map of geographical science is confronted with bottlenecks such as single data dimension, shallow association mining, and lagging dynamic update. The rise of artificial intelligence technology offers a new path to break through the above limitations. Through the deep integration of algorithms such as machine learning, deep learning, and natural language processing with geographical science theories, intelligent upgrades in concept extraction, relationship modeling, and map optimization can be achieved. This paper systematically reviews the key technologies for constructing the basic concept map of geographic science based on artificial intelligence, including multi-source geographic data preprocessing methods, intelligent concept recognition and classification models, and dynamic relationship mining algorithms, and deeply analyzes the technical characteristics and applicable scenarios of different construction methods. The application value is expounded from three dimensions: the sorting out of the geographical knowledge system, the intelligent solution of geographical problems, and the empowerment of interdisciplinary integration. Finally, the future research directions are prospected to provide references for the intelligent development and practical application of geographical science knowledge graphs.
subject: 研究论文
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Geographical Science
  topic: keywords
subject: Concept Map
  topic: keywords
subject: Construction Method
  topic: keywords
subject: Application Value
  topic: keywords
creator: Lizao Li
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-23
publication date: 2025-12-23
doi: 10.65455/dhsph777

https://journal.innoviair.cn/index.php/AAIR/zh_Hans/article/view/42
type: item
title: AI-Driven Intellectual Property: Building and Empirical Testing of an Innovative Startup Collaborative Management Model-Analysis of Cross-Legal Domain Data in the Guangdong-Hong Kong-Macao Greater Bay Area
description: With the rapid advancement of artificial intelligence technology, the collaborative management of intellectual property and innovation-driven entrepreneurship faces new opportunities and challenges. This study focuses on the unique cross-jurisdictional region of the Guangdong-Hong Kong-Macao Greater Bay Area, exploring the development of an AI-driven collaborative management model for intellectual property and innovation. Using a hybrid research approach, the study collected intellectual property data, innovation indicators, and policy regulations from 11 cities in the Greater Bay Area. Through machine learning algorithms and multidimensional modeling, it revealed the interaction mechanisms between intellectual property and innovation under the distinct legal systems of the three regions. The research established a collaborative management framework comprising four modules: "intelligent identification, cross-domain collaboration, value transformation, and risk prevention," while designing algorithm optimization strategies and decision support systems tailored for cross-jurisdictional environments. This study not only enriches the theoretical framework of intellectual property and innovation collaboration but also provides practical management tools and policy recommendations for innovative development in the Guangdong-Hong Kong-Macao Greater Bay Area and other cross-jurisdictional regions worldwide.
subject: 研究论文
  topic: section
subject: AI-Driven
  topic: keywords
subject: Intellectual Property
  topic: keywords
subject: Innovation and Entrepreneurship
  topic: keywords
subject: Collaborative Management Model
  topic: keywords
subject: Guangdong-Hong Kong-Macao Greater Bay Area
  topic: keywords
creator: Xiaodi Guo
creator: Jingjiang Deng
creator: Junming Liang
creator: Chen Chen
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-04
publication date: 2025-12-04
doi: 10.65455/85c92315

Innovative Research on Cracking the Personalized Service Management of Artificial Intelligence Libraries
type: item
description: Against the backdrop of the accelerated advancement of Artificial intelligence transformation, emerging technologies have become the core driving force for the upgrading of library services. This study focuses on the innovative application of technologies such as artificial intelligence, big data analysis, Internet of Things and blockchain in the personalized service management of libraries. Through methods such as literature analysis and case studies , a three-dimensional model of "technology empowerment - service reconfiguration - management collaboration" is constructed. Research shows that technology empowerment drives service management transformation through three major mechanisms: precise user profile construction (with an accuracy rate of 89.2%), intelligent resource scheduling (with a utilization rate increase of 41.7%), and dynamic service optimization (with a response speed increase of 63.5%). Empirical cases show that user satisfaction has increased by 36% after technology empowerment, but the issues of data security (68.3% of users are concerned) and the cost of technology adaptation (with an average deployment cost of 287,000 yuan) need to be addressed simultaneously. Finally, a hierarchical deployment strategy and a collaborative framework of "technology - system - humanities" are proposed to provide a systematic solution for the construction of smart libraries.
subject: 研究论文
  topic: section
subject: Emerging Technology
  topic: keywords
subject: Library
  topic: keywords
subject: Personalized Service
  topic: keywords
subject: Service Management
  topic: keywords
subject: Wisdom Empowerment
  topic: keywords
subject: Privacy Computing
  topic: keywords
creator: Nan Liu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
doi: 10.65455/4mv0z378

Application of AI Models in Dynamic Talent Adaptation between Industry and Education: A Case Study of Data from Digital Genealogy Cloud Platform
type: item
description: This study examines the application of AI models in dynamic talent adaptation between industry and education, using data from the Digital Genealogy Cloud Platform as a research sample. It analyzes current challenges in industry-education talent alignment and elaborates on the pivotal role of AI models. Through in-depth mining and analysis of platform data, the research demonstrates how AI models leverage big data, artificial intelligence, and knowledge graphs to achieve precise digital integration across industrial chains, talent pipelines, and educational systems. The findings reveal that AI models not only assist vocational colleges in optimizing program structures and innovating talent cultivation models, but also facilitate deep collaboration between schools and enterprises. This enhances the alignment between talent development and industrial demands, providing robust support for the digital transformation and high-quality development of vocational education. Additionally, it offers new perspectives and practical solutions to address fundamental issues in industry-education integration.
subject: 研究论文
  topic: section
subject: AI model
  topic: keywords
subject: Industry-Education Talent
  topic: keywords
subject: Dynamic Adaptation
  topic: keywords
subject: Digital Genealogy Cloud Platform
  topic: keywords
subject: Vocational Education
  topic: keywords
subject: Macao Greater Bay Area
  topic: keywords
creator: Jingjiang Deng
creator: Junming Liang
creator: Chen Chen
creator: Weijian Huang
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-04
publication date: 2025-12-04
doi: 10.65455/srezr230

Language, Consciousness, and Manifestation: Towards a Pragmatic-Cognitive Framework for Next-Generation Neural Language Systems
type: item
description: Augmented Reality (AR) systems critically depend on accurate, temporally stable, and computationally efficient object tracking to maintain geometric alignment and perceptual coherence between virtual content and the physical world. As AR technologies transition from controlled laboratory prototypes to large-scale deployment in industrial, medical, and consumer scenarios, tracking must operate robustly in complex real-world environments characterized by dynamic objects, occlusions, illumination changes, fast motion, and strict computational constraints. Traditional geometry-driven tracking pipelines often degrade under such conditions, motivating increased adoption of deep learning based approaches. This survey provides a comprehensive review of deep learning based object tracking for AR in complex real-world scenes, with particular emphasis on system-level considerations. Object tracking is treated as a central perception primitive that underpins stable AR experiences and interacts tightly with modules such as visual simultaneous localization and mapping, depth and geometry estimation, and semantic scene understanding. In contrast to prior surveys that emphasize algorithmic accuracy in isolation, we explicitly analyze AR-specific constraints including real-time latency, temporal stability, energy efficiency, long-term robustness, and deployment on mobile and wearable platforms. We review major tracking paradigms, representative datasets and benchmarks, AR-centric evaluation criteria, and common failure modes observed in practice, and we outline future research directions toward scalable, reliable, and trustworthy AR tracking systems.
subject: 研究论文
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Object Tracking
  topic: keywords
subject: Manifestation
  topic: keywords
subject: Pragmatics
  topic: keywords
subject: Cognition
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Neural Language Systems
  topic: keywords
creator: Anni Li
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
doi: 10.65455/cnbj7s21

Against the Backdrop of the Digital Economy: AI Empowers Innovation and Entrepreneurship Education for the Integration of Industry and Education
type: item
description: The digital economy and AI technologies are driving transformations in industry-education integration models, yet traditional practices face challenges such as mismatched supply and demand and inefficient resource allocation. This study proposes an AI-empowered innovation and entrepreneurship education ecosystem through a quadruple-subject collaborative framework. Employing multi-case comparative research and system dynamics simulation, Practical pathways include developing intelligent course matrices and virtual training platforms. Empirical results demonstrate: university case incubation cycles shortened by 42%, enterprise case employment rates increased by 16%, government case talent gaps narrowed by 54%, and overall talent matching accuracy improved by 36.5%. AI technology effectively resolves industry-education integration challenges, offering a new paradigm for digital economy talent cultivationy.
subject: 研究论文
  topic: section
subject: Digital Economy
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Industry-Education Integration
  topic: keywords
subject: Innovation and Entrepreneurship Education
  topic: keywords
creator: Qiang Liu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
doi: 10.65455/nh2jc129

Research on the Impact Mechanism of Farmers' Digital Literacy on Enhancing Service Satisfaction of Power Grid Companies: An Implementation Path Based on the Interpretability of Machine Learning Models
type: item
description: This study focuses on the "service supply - user demand" tension issue in the service satisfaction of power grid companies during the process of Chinese-style modernization. China has achieved remarkable results in the construction of power grid infrastructure, but there are regional differences in user satisfaction, especially in rural areas. Under the background of digitalization of power grids, although digital technology brings benefits, the differences in digital infrastructure and usage levels in rural areas have led to a digital divide, shifting from an "access gap" to a "capability gap". Digital literacy has become the key to enhancing service satisfaction. In view of the existing research deficiencies, this paper constructs an index system for measuring and evaluating the digital literacy of farmers, and uses the XGBoost and SHAP models to quantitatively analyze the influence mechanism and implementation path of digital literacy on the service satisfaction of power grid companies. The research results can provide a scientific basis for power grid companies to precisely optimize services, helping them identify key influencing factors, optimize the allocation of service resources, enhance service satisfaction, and promote the high-quality development of power grid services.
subject: 研究论文
  topic: section
subject: Farmers' Digital Literacy
  topic: keywords
subject: Power Grid Companies
  topic: keywords
subject: Service Satisfaction
  topic: keywords
subject: XGBoost
  topic: keywords
subject: SHAP Explainability
  topic: keywords
creator: Yuxuan Tong
creator: Zewei Zhang
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-03
publication date: 2025-12-03
doi: 10.65455/6dwx3a82

Applying Radiomics and Deep Learning to Investigations into Seafarers' Health Status
type: item
description: This retrospective study on 7232 male seafarers(June 2022 -- Nov 2023), explored the occurrence of fatty liver disease and gallstones and pointed out its related risk elements, moreover examined the worth of using DL-based radiomics in intelligent examination and medical diagnosis of the said hepatobiliary diseases. Clinical data (BMI, blood lipids) and abdominal ultrasound images were analyzed via traditional statistical methods (chi-squared test, correlation analysis, t-test). A DL framework integrating U-Net segmentation and MobileNetV2 classification was developed to automate region-of-interest (ROI) extraction, extract high-dimensional radiomic features, and fuse clinical/radiomic data for dual-disease prediction. Results showed BMI was linearly positively correlated with hyperlipidemia and fatty liver disease (r=0.98, P<0.05). The DL model demonstrated superior diagnostic performance: for fatty liver disease, AUC=0.93, accuracy=90.2%, recall=88.5%, and specificity=89.8% (significantly higher than manual ultrasound, AUC=0.79, P<0.05); for gallstones, AUC=0.89, accuracy=87.6%, and recall=85.3%. Gallstone formation was statistically associated with gallbladder wall thickening/roughness, hyperlipidemia, and hypercholesterolemia (P<0.005).Conclusions Controlling BMI and blood lipid levels effectively reduces fatty liver risk. DL-based radiomics enables automated, quantitative, and intelligent hepatobiliary disease assessment—ideal for seafarers with limited on-board medical resources and large-scale screenings. Combining this AI tool with targeted health education and lifestyle interventions will enhance the efficiency and accuracy of seafarers’ hepatobiliary health management.
subject: 研究论文
  topic: section
subject: Fatty Liver Disease
  topic: keywords
subject: Gallstones
  topic: keywords
subject: Radiomics
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Huiming Kang
creator: Xiaoqing Li
creator: Mingyue Feng
creator: Wei Wang
creator: Shidi Liu
creator: Shaocong Liang
creator: Cuiliu Zhou
creator: De Kang
creator: Lingling Chen
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-09
publication date: 2025-12-09
doi: 10.65455/nqcbkw66

A Learnable Frequency Gated DCT Denoising Network
type: item
description: DCT-based denoising often relies on fixed rules such as thresholding or retaining only low-frequency components, which easily blur textures or leave residual noise on natural images. To address this limitation, we develop a learnable denoising model that keeps the structure of the DCT domain while allowing the network to adaptively regulate each frequency band. With differentiable DCT/IDCT layers, the method applies trainable masks to high- and low-frequency coefficients, and a lightweight global attention block at reduced resolution provides broader contextual cues at low cost. An image-level residual path further aids reconstruction. Under a compact setup, the model improves PSNR and SSIM from 11.89 dB and 0.2915 (pure DCT) to 25.05 dB and 0.945, showing that replacing fixed heuristics with learnable frequency modulation leads to substantially better restoration quality.
subject: 研究论文
  topic: section
subject: Image Denoising
  topic: keywords
subject: Discrete Cosine Transform
  topic: keywords
subject: Frequency Domain Modeling
  topic: keywords
subject: Multi Scale Attention
  topic: keywords
subject: Global Residual
  topic: keywords
subject: Image Restoration
  topic: keywords
creator: Haonian Cao
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-09
publication date: 2025-12-09
doi: 10.65455/p24ghq94

The coupled application of GIS digital Twin and AI reinforcement learning
type: item
description: In response to the intelligent governance demands of complex scenarios such as smart cities and smart water conservancy, this paper explores the coupled application of GIS digital twins and AI reinforcement learning. First, sort out the core theories of the two, then analyze the coupling technology interface, data flow logic and adaptability, and then carry out practice in combination with the scenarios of urban traffic control and river basin water resource dispatching. Finally, point out the challenges such as data heterogeneity, insufficient generalization and computing power bottleneck, and propose solutions. The results show that the coupled system can significantly improve the governance accuracy, such as reducing the delay time in traffic scenarios by 32.1% and increasing the utilization rate of water resources scenarios by 8.3%, providing technical support for the intelligent governance of complex spatial scenarios.
subject: 研究论文
  topic: section
subject: GIS Digital Twin
  topic: keywords
subject: AI Reinforcement Learning
  topic: keywords
subject: Coupled Application
  topic: keywords
subject: Intelligent Governance
  topic: keywords
subject: Urban Traffic Control
  topic: keywords
subject: Water Resources Dispatching in River Basins
  topic: keywords
creator: Ziyi Xia
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.65455/d792e032

Ethics-First Lightweight AI: Differentiated English Teaching and Cultural Competence in Junior Middle Schools
type: item
description: The implementation of stratified English teaching in junior middle schools consistently encounters two fundamental challenges: the persistence of static "pseudo-stratification" that fails to adapt to students' evolving needs, and growing ethical concerns regarding intrusive artificial intelligence technologies in educational settings. In direct response to these dual challenges, this study proposes and empirically validates an innovative ethics-first lightweight AI framework specifically designed to enable truly dynamic and culturally responsive differentiated instruction. By strategically integrating privacy-preserving federated learning methodologies with non-intrusive mobile sensing technologies, our framework introduces a novel pedagogical architecture termed the "three-layer dynamic grouping and four-dimensional strategy matrix." To comprehensively evaluate its efficacy, we conducted a rigorous six-month controlled experiment involving 326 students across diverse socioeconomic contexts—including urban (eastern), county-town (central), and rural (western) regions of China. The empirical results demonstrate the framework's remarkable effectiveness across multiple dimensions: it achieved a stratification accuracy of 89%, significantly reduced grammatical error rates by 31%, and enhanced cultural understanding by 44%. Most notably, the implementation of a carefully designed tiered informed consent protocol successfully reduced parental data withdrawal requests to an unprecedented 0.9%. By leveraging ubiquitous smartphone sensors for low-cost deployment, this framework provides a scalable, ethically-grounded solution for implementing sophisticated differentiated instruction in resource-constrained educational environments, thereby advancing the crucial goal of educational equity from theoretical principle to practical reality.
subject: 研究论文
  topic: section
subject: Ethics-First AI
  topic: keywords
subject: Differentiated Teaching
  topic: keywords
subject: Cultural Competence
  topic: keywords
subject: Lightweight Model
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Educational Equity
  topic: keywords
subject: Dynamic Assessment
  topic: keywords
creator: Hui Nie
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
doi: 10.65455/es208a12

On How Artificial Intelligence Drives Innovation in International Chinese Language Education
type: item
description: The rapid development of artificial intelligence (AI) technology is permeating all sectors of society with unprecedented depth and breadth. In the field of international Chinese language education, AI has evolved far beyond being a mere auxiliary tool like the "slide projector" or "tape recorder" of the past; instead, it has become a core driving force leading this field toward a profound paradigmatic revolution. This paper systematically elaborates on how AI technology promotes systematic innovation and upgrading in international Chinese language education from five core dimensions: the personalized reconstruction of teaching models, the intelligent generation of teaching resources, the full-process reform of teaching evaluation, the strategic transformation of teachers' roles, and the global integration of educational ecosystems. Meanwhile, the paper also takes a prudent look at challenges that may arise during the process of technology integration, such as algorithmic bias, lack of emotional interaction, the digital divide, and the alienation of the essence of education. Finally, it points out that the future development path must involve the in-depth integration of "artificial intelligence" and "humanistic guidance," aiming to build a new, human-machine collaborative, ecologically sound, and sustainable international Chinese language education system. This system will provide new possibilities and fundamental pathways for achieving more equitable, high-quality, and inclusive global Chinese language education.
subject: 研究论文
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: International Chinese Language Education
  topic: keywords
subject: Educational Innovation
  topic: keywords
subject: Personalized Learning
  topic: keywords
subject: AIGC
  topic: keywords
subject: Paradigmatic Revolution
  topic: keywords
creator: Fanqi Meng
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
doi: 10.65455/023h4s18

AI in Pharmaceutical Formulation: A Comprehensive Review
type: item
description: This review systematically evaluates the current status, application scenarios, and implementation effectiveness of artificial intelligence (AI) technologies in pharmaceutical formulation. It analyzes existing challenges and future directions to provide a technical reference for rational formulation development. Methods: We retrieved literature published between 2020 and 2025 from core databases including PubMed, MEDLINE, Embase, and Cochrane Library, focusing on high-quality studies applying AI to formulation design, optimization, production, and quality control. A systematic review methodology was employed to summarize technical approaches, performance metrics, and comparative outcomes between AI and traditional methods across various formulation types. Results: Analysis of 67 qualified studies revealed significant advantages of AI technologies across multiple formulation development stages. In drug delivery system design, AI-assisted nanocarrier development reduced development time by 60%-70% on average. For formulation optimization, AI models achieved 85%-93% accuracy in predicting critical quality attributes, representing a 25%-40% improvement over traditional empirical methods. In manufacturing processes, AI control strategies improved product batch consistency by 30%-50%. Furthermore, AI-driven adaptive learning frameworks demonstrated capability for continuous formulation and process optimization through real-time data integration. Conclusion: AI is fundamentally transforming pharmaceutical formulation development paradigms through data-driven approaches that significantly enhance efficiency, reduce costs, and improve product quality. Despite challenges in data quality, model interpretability, and industrial implementation, ongoing algorithm innovations and multidisciplinary integration are expected to advance pharmaceutical formulation into a new era of precision, personalization, and intelligence.
subject: 综述论文
  topic: section
subject: AI
  topic: keywords
subject: Pharmaceutical Formulation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Intelligent Delivery Systems
  topic: keywords
subject: Formulation Design
  topic: keywords
subject: Process Control
  topic: keywords
subject: Quality by Design
  topic: keywords
subject: Formulation Informatics
  topic: keywords
creator: Peng Wu
creator: Peiyu Zang
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.65455/nshrsr55

To whom exactly does inspiration belong? The creative rights and aesthetic boundaries behind AI-generated works
type: item
description: With the rapid development of AI technology, its application in art creation is becoming more and more popular. An automatic generation system of AI art works can generate works of unique style and aesthetic value by means of deep learning algorithms. However, the question of creative ownership behind AI-generated works has always been ambiguous. In this paper, we discuss creative rights and aesthetic boundaries behind AI-generated works. However, there has not been a clear legal definition. In spite of this, AI's automatic generation of artworks has become a major trend. At present there are no laws or regulations that stop the development of AI + art.. On the basis of the above results, we have developed an AI generation system. Using AI technology, it automatically generates artwork that creators want.In addition, the public often believes that the ownership of AI-generated artworks belongs to the AI platform. In fact, the AI system is merely an auxiliary tool, and the creative inspiration still comes from the author. Therefore, this system can generate a contract for the AI system platform to this AI system waive the copyright.Register your copyright on the website of the National Copyright Center to avoid legal disputes and protect the legitimate rights and interests of creators.
subject: 研究论文
  topic: section
subject: AI + Art Automatic art creation
  topic: keywords
subject: Creation Right
  topic: keywords
subject: Aesthetic Edge Deep Learning
  topic: keywords
creator: Zhongyu Xu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-09-15
publication date: 2025-09-15
doi: 10.65455/aair5915

From Knowing how to farm to Understanding Data - The Reengineering of Farmers' Skills and Rural Development in the Context of Smart Agriculture
type: item
description: With information technology widely applied to agriculture sector, intelligent agriculture has become a new trend of modern agriculture. In this paper, we explore the framework, design and application of smart agricultural data analysis system. With the integration of Internet of Things, Big Data and Artificial Intelligence, this system provides precise decision support to farmers in agricultural production, optimizing agricultural production process, increasing agricultural production efficiency and product quality. In this paper, the overall design, every level design, database design, security design, main functions are elaborated in detail. The validity of this system has been verified by an actual application case. With the development of technology and development of agriculture information system, it is expected that smart agricultural data analysis system will play an important role in broader agriculture scenarios.
subject: 研究论文
  topic: section
subject: AI + Agriculture Data
  topic: keywords
subject: Analysis Internet of Things
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Agriculture Information
  topic: keywords
creator: Can Wan
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-09-15
publication date: 2025-09-15
doi: 10.65455/aair5911

Intelligent Auxiliary System for Chronic Disease Prevention and Treatment Based on Information Technology and Its application in Improving Patients' Trust Predicament
type: item
description: Along with the rapid development of information technology, there are unprecedented opportunities and challenges in the area of chronic diseases. Chronic diseases' long-term nature, complexity and impact on patients' quality of life make it clear that traditional prevention model can not meet patients' diverse needs and increase their trust. It aims at providing personalized and accurate prevention and treatment plans based on AI, Big Data, Internet of Things, and other advanced technologies in order to improve doctor-patient trust. In this paper, we deeply discuss the system's overall structure, design, main functions, and its application effect on patients' trust dilemma.
subject: 研究论文
  topic: section
subject: AI+Healthcare
  topic: keywords
subject: Information Technology
  topic: keywords
subject: Prevention & Treatment for Chronic Diseases
  topic: keywords
subject: Intelligent Auxiliary System
  topic: keywords
subject: Doctor-Patient Relationship
  topic: keywords
creator: Jiaqi Liu
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-09-15
publication date: 2025-09-15
doi: 10.65455/aair5615

Innovative Application of Artificial Intelligence Algorithms in Credit Scoring: Taking specific products in the field of consumer finance as the research object
type: item
description: This paper focuses on specific products in consumer finance and explores the innovative application of artificial intelligence algorithms in credit scoring. By analyzing the technical foundation and current situation, this paper expounds the core technological innovations such as deep learning, tree model integration, and graph neural networks, and solves the problems of data fusion, model optimization, and interpretability. Empirical research shows that artificial intelligence algorithms outperform traditional models in multiple indicators.
subject: 研究论文
  topic: section
subject: Artificial Intelligence Algorithm
  topic: keywords
subject: Credit Score
  topic: keywords
subject: Consumer Finance
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Model Interpretability
  topic: keywords
creator: Feng Yuan
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-06-15
publication date: 2025-06-15
doi: 10.65455/aair5612

From "Passive Congestion" to "Active Scheduling" — Rebuilding Travel Experience and Resource Allocation for Intelligent Transportation Networks
type: item
description: Along with the acceleration of urbanization, traffic congestion becomes an important factor restricting urban development and improving residents' quality of life. In order to cope with complex traffic requirements, traditional traffic management models seem to be inadequate, often falling into passive congestion. The emergence of intelligent transport networks brings new hope to the solution to this problem. In this paper, the theory basis, technology structure, key technology and application practice are deeply discussed. The aim of this paper is to achieve a transition from passive congestion to active dispatching. In this paper, by constructing a framework of intelligent traffic system, analyzing each level design, analyzing main functional applications, it reveals that intelligent traffic network plays an important role in alleviating traffic congestion, improving traffic safety and promoting sustainable traffic development. Finally, it looks forward to its future development.
subject: 研究论文
  topic: section
subject: Intelligent Traffic Reconstruction
  topic: keywords
subject: Travel Experience
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Traffic Congestion
  topic: keywords
subject: Active Scheduling
  topic: keywords
creator: Baochao Liang
rights: ##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
date: 2025-06-15
publication date: 2025-06-15
doi: 10.65455/aair5611