Applied Artificial Intelligence Research
(AAIR) is an international journal focusing on cutting-edge AI research and innovative practices, offering a high-end academic platform for global scholars, researchers, and industry experts. It covers core AI fields such as machine learning, NLP, computer vision, robotics, etc., and welcomes both theoretical breakthroughs and applied innovations. By publishing original papers, reviews, case studies, and technical reports, it showcases the latest AI developments and trends.
CHINA SCIENCE AND TECHNOLOGY DEVELOPMENT PRESS
Research on Performance Prediction Model of Wind Turbine Gearbox Lubricating Oil Based on Deep Learning
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.
Copyright (c) 2026 Applied Artificial Intelligence Research https://creativecommons.org/licenses/by/4.0
When Algorithms Push Firms to Cheat: AI, Blockchain, and Financing in China
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.
Research on Intelligent Pipeline Robot Navigation and Defect Identification Based on Multi-Sensor Fusion
Faced with the technical bottlenecks of traditional single-sensor methods in dynamic pipeline environments, this study proposes a hierarchical progressive intelligent framework for pipeline robot navigation and defect recognition based on laser-vision-inertial multi-sensor fusion. An innovative Unscented Kalman Filter (UKF)-based nonlinear optimization framework unifies state vectors of navigation and defect recognition to achieve collaborative optimization of pose estimation and feature extraction. The bottom layer realizes multi-source data spatiotemporal alignment via extended Kalman filter; the middle layer completes environmental modeling using improved ICP and visual SLAM（Simultaneous Localization and Mapping）; the top layer adopts lightweight CNN for defect identification. Experiments show that the system achieves centimeter-level positioning accuracy and 92.7% defect recognition accuracy in complex scenes, with cumulative positioning error controlled within ±8 cm and robustness improved by over 40%. It is universal for 200–800 mm pipe diameters, reducing small-diameter navigation drift by 42% and reaching 98.3% detection coverage. Engineering tests verify 4.2× higher inspection efficiency and 65% lower maintenance costs than manual operations. With modular hardware and open software, the system adapts to oil and gas pipelines, utility tunnels and drainage systems, and its cloud-edge knowledge base supports rapid scenario adaptation via transfer learning, providing reusable methodological support for intelligent perception systems.
Map of Geographical Science Based on Artificial Intelligence
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.
Copyright (c) 2025 Applied Artificial Intelligence Research https://creativecommons.org/licenses/by/4.0
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
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.
Guangdong-Hong Kong-Macao Greater Bay Area
Copyright (c) 2025 Applied Artificial Intelligence Research
Innovative Research on Cracking the Personalized Service Management of Artificial Intelligence Libraries
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.
Application of AI Models in Dynamic Talent Adaptation between Industry and Education: A Case Study of Data from Digital Genealogy Cloud Platform
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.
Language, Consciousness, and Manifestation: Towards a Pragmatic-Cognitive Framework for Next-Generation Neural Language Systems
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.
Against the Backdrop of the Digital Economy: AI Empowers Innovation and Entrepreneurship Education for the Integration of Industry and Education
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.
Innovation and Entrepreneurship Education
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
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.
Applying Radiomics and Deep Learning to Investigations into Seafarers' Health Status
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.
A Learnable Frequency Gated DCT Denoising Network
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.
The coupled application of GIS digital Twin and AI reinforcement learning
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.
Water Resources Dispatching in River Basins
Ethics-First Lightweight AI: Differentiated English Teaching and Cultural Competence in Junior Middle Schools
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.
On How Artificial Intelligence Drives Innovation in International Chinese Language Education
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.
International Chinese Language Education
AI in Pharmaceutical Formulation: A Comprehensive Review
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.
To whom exactly does inspiration belong? The creative rights and aesthetic boundaries behind AI-generated works
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.
From Knowing how to farm to Understanding Data - The Reengineering of Farmers' Skills and Rural Development in the Context of Smart Agriculture
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.
Intelligent Auxiliary System for Chronic Disease Prevention and Treatment Based on Information Technology and Its application in Improving Patients' Trust Predicament
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.
Prevention & Treatment for Chronic Diseases
Innovative Application of Artificial Intelligence Algorithms in Credit Scoring: Taking specific products in the field of consumer finance as the research object
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.
From "Passive Congestion" to "Active Scheduling" — Rebuilding Travel Experience and Resource Allocation for Intelligent Transportation Networks
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.