（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。​
CHINA SCIENCE AND TECHNOLOGY DEVELOPMENT PRESS
A Case Study of the Influence of Multifarious Factors on Traffic Flow Forecasting
Accurate traffic flow forecasting serves as the core foundation for intelligent transport systems to achieve efficient traffic management and optimized resource allocation, holding significant importance for alleviating congestion in modern cities. Influenced by the complex interplay of diverse heterogeneous factors such as meteorological conditions, temporal cycles, and unforeseen events, urban traffic flow data exhibits pronounced nonlinearity and random fluctuations, rendering high-precision forecasting exceptionally challenging. While mainstream deep learning models have advanced prediction accuracy, they struggle to quantify the specific contributions of different factors and often overlook significant multicollinearity issues among multi-source data. Addressing these challenges, this paper introduces several improvements: Firstly, it constructs a fused dataset incorporating multidimensional external factors such as meteorological conditions and events; secondly, it proposes an explainable prediction framework based on Random Forest with raw feature analysis by Principal Component Analysis (PCA); Thirdly, the SHAP game-theoretic method is introduced to achieve transparent attribution of prediction results. This paper first employs PCA to extract principal components from high-dimensional multi-source factors, effectively eliminating multicollinearity in the data. Subsequently, a robust random forest regression model is constructed for prediction. Based on four independent datasets from Beijing's TaxiBJ service, the proposed framework undergoes comprehensive performance validation and analysis. Results demonstrate that the model maintains a coefficient of determination consistently above 0.85 across all annual datasets, exhibiting outstanding predictive accuracy and robustness across temporal cycles. SHAP analysis further reveals a stable decision mechanism characterized by the first principal component driving periodicity, with subsequent components providing dynamic fine-tuning, successfully achieving traffic flow prediction that combines high precision with strong interpretability.
##submission.copyrightStatement## https://creativecommons.org/licenses/by/4.0
Constructing Multimodal Wireless Knowledge Graphs for Large Language Model–Based Network Reasoning
The increasing complexity of wireless networks and the proliferation of heterogeneous data sources pose significant challenges to intelligent network management and decision-making. Although Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, their direct application in wireless network tasks is limited due to the lack of structured domain knowledge and reliable contextual foundations. This paper proposes an intelligent knowledge graph enhanced retrieval-enhanced generation (KG-RAG) framework for multimodal wireless networks. First, through structured entity and relationship modeling, multimodal wireless data including network measurements, configuration parameters and domain knowledge are transformed into a unified knowledge graph. The constructed knowledge graph, as a clear semantic backbone, supports efficient knowledge retrieval and reasoning. By integrating graph-based retrieval results with LLMs, the proposed framework achieves context-aware and interpretable network reasoning. The effectiveness of this method is evaluated through a case study based on lightweight network slicing. The experimental results show that in different LLMs, compared with the traditional text-based RAG method, the KG-enhanced RAG method improves the reasoning accuracy, reasoning consistency and interpretive integrity. These findings indicate that structured knowledge representation plays a crucial role in enhancing the reliability and interpretability of LLM-driven wireless network intelligence.
AI Transparency and Employee Innovation: The Mediating Role of Psychological Safety and the Moderating Effect of AI Self-Efficacy
As AI technologies become more integrated into everyday organizational workflows, it is essential to understand how employees interpret and interact with these systems to support innovative outcomes.This study investigates how transparency in AI systems influences employee innovation, considering the role of psychological safety and individual confidence in using AI. Data were gathered from 447 knowledge workers in small and medium-sized enterprises in China who regularly interact with AI tools. Analysis using structural equation modeling shows that clearer AI processes enhance employees’ sense of psychological safety, which in turn supports innovative actions. Moreover, employees with greater confidence in using AI benefit more from transparency, amplifying its positive impact on innovation. The results underscore that promoting innovation requires not only transparent system design but also initiatives that strengthen employees’ AI skills and create psychologically supportive environments. This research contributes to understanding the human side of AI adoption by linking system transparency to practical organizational outcomes and offering guidance for effective AI–human integration in workplaces.
A Comparative Study of the Data Ethical Foundations of the EU's GDPR and China's Personal Information Protection Law from the Perspective of Global Integration
Data drives the development of the global economy, but at the same time, countries face the same challenge: how to protect personal information without hindering its utilization.In view of the lack of systematic ethical comparison between the two major privacy frameworks in Europe and China in the existing literature, this paper aims to examine the ethical basis of the EU's General Data Protection Regulation (GDPR) and China's Personal Information Protection Law (PIPL).This paper uses a comparative analysis method to systematically compare the similarities and differences between GDPR and PIPL in terms of ethical paradigm, system design, and governance logic. The study found that although GDPR and PIPL represent different regulatory paradigms, they are converging in global data governance.The GDPR follows a rights-based paradigm, which originates from the European tradition of emphasizing individual rights. PIPL adopts the development-security balance paradigm based on the need to balance personal protection, industry needs and national security. Although the origins are different, both laws emphasize the combination of personal data control empowerment and supervision mechanisms, and pay attention to the established system of enterprises, not just ex post punishment. By clarifying the ethical basis of the global data governance model, this study provides an analytical framework for understanding regulatory convergence in different cultural and institutional contexts.
Research on the Evolution of AI Copyright Attribution Mechanisms
With the continuous iteration of artificial intelligence technology, the AI-generated content has penetrated into various fields of social production and daily life. The copyright ownership mechanism of artificial intelligence generated content evolves with technological progress, legal change and industrial development. This evolution can be divided into three stages : the embryonic stage, the development stage and the mature stage. The core of the conflict is to coordinate the contradiction between technological innovation and the lag of legal regulation. The primary goal is to balance the rights and public interests of multiple stakeholders. This paper systematically combs the evolution of this attribution mechanism by means of literature research, historical analysis, comparative research and case study. This paper makes an in-depth analysis of the core issues such as the absence of legal personality of artificial intelligence, the inconsistency of originality identification standards, and the imbalance of rights distribution among multiple stakeholders. On this basis, it clearly puts forward targeted solutions such as the legal status of artificial intelligence, the standardization of identification standards, and the establishment of a multi-stakeholder rights allocation system. This study improves the theoretical framework of artificial intelligence copyright protection, and provides practical guidance for legislative revision, judicial adjudication and the development of artificial intelligence industry, thus promoting the benign synergy between technological innovation and rights protection.
Design and implementation of public transportation emergency command and assistance platform from the perspective of smart policing
In view of the pain points faced by the current urban public transportation public security management, such as high security pressure of large-scale activities, lagging police response, and insufficient integration of multi-source data, this paper designs and implements a public transportation emergency command and assistance platform. The system adopts Spring Boot + Vue.js front-end and back-end separation architecture, integrating five core modules: vehicle scheduling, route optimization, passenger feedback, real-time monitoring and data analysis. On this basis, from the perspective of smart policing, the adaptation design of the system in scenarios such as security emergency vehicle scheduling for large-scale activities, public security risk investigation of passenger feedback data, real-time monitoring and linkage with public security command center is studied. At the same time, ideas for expanding police functions such as early warning of key personnel and one-click alarm linkage are proposed. Practice shows that the platform can effectively improve the informatization and intelligence level of public transportation security work, and provide technical support for the construction of an integrated police mechanism of "emotion, guidance, and action".
Post-Human Perspectives on Film: Exploring Innovative Applications of AIGC
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.
An AI-Driven Intelligent Health Assessment System for High-Voltage Switchgear Based on Multimodal Sensor Data Fusion
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.
Artificial Intelligence for Fracture Risk Prediction: A Bibliometric Analysis of Research Hotspots and Evolutionary Trends
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 convergence, suggesting continued advancement toward real-world clinical implementation.
Explainable Machine Learning for Telecom Customer Churn Prediction and Actionable Retention Strategies
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.
A Comprehensive Survey of Deep Learning–Based Object Tracking for Augmented Reality in Complex Real-World Scenes
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.
Personalized Recommendation Algorithms in Digital Media: A Review of Engagement Effects, Information Cocoons, and Marketing Implications
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.
Continuous blood pressure estimation and cardiovascular risk assessment based on wearable PPG signals
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.
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 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.
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.
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
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
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.