In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Me...Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice.展开更多
This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with p...This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.展开更多
Large language models(LLMs)excel in various natural language processing tasks and are increasingly applied in specialized fields like medicine.However,their deployment in the medical domain is challenged by limited do...Large language models(LLMs)excel in various natural language processing tasks and are increasingly applied in specialized fields like medicine.However,their deployment in the medical domain is challenged by limited domain-specific data and the tendency to generate inaccurate information,known as“hallucinations.”While domainspecific fine-tuning has improved open-source LLMs,they still underperform compared to proprietary models like ChatGPT and PaLM.To address this gap,retrieval-augmented generation(RAG)techniques have been explored to enhance LLMs by integrating external knowledge bases.Nevertheless,the success of RAG depends on the quality of retrieved documents,and its application within the medical field remains in the early stages.In this paper,we introduce the“Bailicai”framework as an exploratory approach to integrating RAG with LLMs in the medical field.The framework employs fine-tuning to improve the RAG process,where“falsely relevant”and“completely irrelevant”interference documents are intentionally included in the training data.This enables Bailicai to develop the ability to assess the quality of retrieved documents and selectively incorporate them.The framework is organized into four modules:(1)medical knowledge injection,(2)self-knowledge boundary identification,(3)directed acyclic graph task decomposition,and(4)retrieval-augmented generation.Through the synergy of these modules,Bailicai achieves superior performance on multiple medical benchmarks,outperforming existing large models in the medical domain,RAG-based methods,and proprietary models such as GPT-3.5.Furthermore,Bailicai effectively mitigates the hallucination problem common in LLMs applied to medical tasks and enhances the robustness of RAG when dealing with irrelevant or misleading documents,enabling more accurate information retrieval and integration.展开更多
Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fun...Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fundamental challenges in knowledge-intensive tasks.This special issue serves as a dedicated platform to showcase these cutting-edge advancements.It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.展开更多
Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased th...Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased threat detection methods have been enhanced with machine learning and Large Language Models(LLMs),these approaches remain limited in addressing emerging threats.This study evaluates a two-step Retrieval Augmented Generation(RAG)approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance.The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework,AWS Threat Technique Catalogue,and threat reports to overcome limitations of static pre-trained LLMs.We constructed an evaluation dataset of 200 unique CloudTrail events(122 malicious,78 benign)using the Stratus Red Team adversary emulation framework,covering 9 MITRE ATT&CK techniques across 8 tactics.Events were sampled from 1724 total events using stratified sampling.Ground truth labels were created through systematic expert annotation with 90%inter-annotator agreement.The RAG-enabled model achieved estimated 78%accuracy,85%precision,and 79%F1-score,representing 70.5%accuracy improvement and 76.4%F1-score improvement over baseline Gemini 2.5 Pro(46%accuracy,45%F1-score).Performance are based on evaluation results on 200-event dataset.Cost-latency analysis revealed processing time of 4.1 s and cost of$0.00376 per event,comparable to commercial SIEM solutions while providing superior MITRE ATT&CK attribution.The findings demonstrate that RAG substantially enhances context-aware threat detection,providing actionable insights for cloud security operations.展开更多
The global energy crisis and electricity shortage pose unprecedented challenges.Bio-based solar-driven ionic power generation devices with flexibility,photothermal self-healing and scalability hold great promise for s...The global energy crisis and electricity shortage pose unprecedented challenges.Bio-based solar-driven ionic power generation devices with flexibility,photothermal self-healing and scalability hold great promise for sustainable electricity and alleviating energy crisis.Here,inspired by plant transpiration,a multifunctional bio-based ion conductive elastomer with solar power generation capability was designed by engineered synergy among epoxy natural rubber,cellulose nanofibrils,lithium bis(trifluoromethane)sulfonimide and eumelanin.The film exhibits an outstanding stretchability(1072%)and toughness(22.7 MJ m^(-3)).The favorable synergy of low thermal conductivity,high hygroscopicity and photothermal conversion performance endowed the film with a large thermal gradient under light illumination,driving efficient water transpiration.Furthermore,the excellent interfacial compatibility between eumelanin and matrix facilitates the formation of space charge regions,which further enhances Li^(+)transport.The film demonstrates excellent evaporation rate(2.83 kg m^(-2)h^(-1)),output voltage(0.47 V)and conductivity(5.11×10^(-2)S m^(-1)).Notably,the film exhibits remarkable photothermal self-healing performance even in saline environment,achieving 99.6%healing efficiency of output voltage.Therefore,the film demonstrates significant prospects for applications in photo-thermoelectric generation and solar-driven ionic power generation.展开更多
Electroosmotic transport and entropy generation play a decisive role in regulating efficiency,stability,and energy cost of non-Newtonian nanoblood flows in stenosed arteries,particularly with tapered geometries.Thisst...Electroosmotic transport and entropy generation play a decisive role in regulating efficiency,stability,and energy cost of non-Newtonian nanoblood flows in stenosed arteries,particularly with tapered geometries.Thisstudy develops a unified model to analyze ZnO-Williamson nanoblood flow through a stenosed artery with converging,diverging,and non-tapered configurations,incorporating electroosmosis,viscous dissipation,and entropy production.The arterial walls are assumed to be electrically charged with a no-slip condition to induce electroosmotic propulsionalong the endothelial surface.The partial differential equations are nondimensionalized to a coupled system ofnonlinear ordinary differential equations,which are solved numerically using a MATLAB-based shooting technique.Parametric investigation is conducted for Brinkman,Grashof,and Weissenberg numbers,ZnO fractional volume,volumetric flow rate,and Helmholtz-Smoluchowski velocity to quantify their influences on axial velocity,wall shearstress,impedance resistance,temperature distribution,entropy generation,Bejan number,and streamline topology.The axial velocity decreases radially with increasing Brinkman number for all arterial geometries.Increasing ZnOnanoparticles improves thermal transport owing to enhanced effective thermal conductivity but simultaneously elevatesentropy generation due to increased viscous dissipation.Higher Weissenberg numbers suppress entropy production bypromoting elastic stress redistribution and lowering shear-induced irreversibility.Impedance resistance decreases withincreasing stenosis height but increases with stenosis shape parameter and ZnO fractional volume.Streamline analysisshows that buoyancy and viscoelasticity significantly distort flow near the stenosis,while increasing electroosmoticvelocity stabilizes streamlines,suppresses recirculation,and reduces local shear stress and pressure fluctuations.Inconclusion,electroosmotic actuation is most effective in reducing flow resistance in the converging tapered artery,particularly at lower ZnO volume fractions.Overall,the findings highlight the potential of optimized electroosmoticactuation and controlled nanoparticle loading to minimize thermodynamic losses,regulate shear stress,and improveflow uniformity in stenosed vessels,with promising implications for electro-assisted drug delivery,nanotherapeutics,and bio-inspired vascular microfluidic systems.展开更多
The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re...The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.展开更多
With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation ...With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation in crystals driven by optical skyrmions.We find that although the skyrmion number is not conserved,the resulting high-order harmonics can exhibit a distinctive multi-vortex structure,whose features are shaped by both the topology of the optical skyrmions and the rotational symmetry of the crystal.The position of the vortex centers can be effectively tuned by employing different types of optical skyrmions.To elucidate the underlying physics,we develop a multi-absorption channel model based on the conservation laws of spin and orbital angular momentum.Our work explores the role of optical topology in extreme nonlinear light-matter interactions,offering new opportunities for the formation and manipulation of optical vortices and novel structured light fields in the visible and ultraviolet regimes.展开更多
With the rapid development of image-generative AI (artificial intelligence) technology, its application in undergraduate Landscape Architecture education has demonstrated significant potential. Based on this, the pres...With the rapid development of image-generative AI (artificial intelligence) technology, its application in undergraduate Landscape Architecture education has demonstrated significant potential. Based on this, the present study explores the implications of integrating image-generative AI into Landscape Architecture courses from three perspectives: stimulating students creative design potential, expanding approaches to form and concept generation, and enhancing the visualization of spatial scenes. Furthermore, it discusses application strategies from three dimensions: AI-assisted conceptual generation, human-machine collaboration for design refinement, and optimization of scheme presentation and evaluation. This paper aims to provide relevant educators with insights and references.展开更多
With the rise of AI-assisted education,many instructors and engineers seek to deliver high-quality programming courses online.However,crafting effective programming lectures remains a challenge,particularly for instru...With the rise of AI-assisted education,many instructors and engineers seek to deliver high-quality programming courses online.However,crafting effective programming lectures remains a challenge,particularly for instructors lacking pedagogical training or multilingual fluency.We present CourseAgent,a prompt-driven framework that leverages large language models(LLMs)to automatically generate Python tutorials,structured lecture scripts,and accompanying audio narrations.CourseAgent accepts raw code as input and transforms it into segmented,well-commented code blocks,adapting content to different difficulty levels and languages via prompt customization.Our system supports multilingual instruction(e.g.,Chinese,English),fine-grained control of pedagogical depth,and auto-generation of lecture videos.We evaluate the output generated by CourseAgent using real student feedback and feedback from in-service teachers,alongside automated assessments from LLMs.These evaluations demonstrate that the materials produced by CourseAgent are coherent,pedagogically sound,and comparable in quality to those created by experienced instructors.CourseAgent lowers the barrier to quality programming education and shows promise for scalable,personalized,and language-adaptive content generation.展开更多
High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining prec...High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining precise photon frequencies,especially in the ultraviolet or even extreme ultraviolet regimes,is a key goal in both light–matter interaction experiments and engineering applications.High-order harmonic generation(HHG)is an ideal light source for producing such photons.In this work,we propose an optical temporal interference model(OTIM)that establishes an analogy with multi-slit Fraunhofer diffraction(MSFD)to manipulate fine-frequency photon generation by exploiting the temporal coherence of HHG processes.Our model provides a unified physical framework for three distinct non-integer HHG generation schemes:single-pulse,shaped-pulse,and laser pulse train approaches,which correspond to single-MSFD-like,double-MSFD-like,and multi-MSFD-like processes,respectively.Arbitrary non-integer HHG photons can be obtained using our scheme.Our approach provides a new perspective for accurately measuring and controlling photon frequencies in fields such as frequency comb technology,interferometry,and atomic clocks.展开更多
Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step...Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.展开更多
The occurrence of severe thalassemia,an inherited blood disorder that is either blood-transfusiondependent or fatal,can be mitigated through carrier screening.Here,we aim to evaluate the effectiveness and outcomes of ...The occurrence of severe thalassemia,an inherited blood disorder that is either blood-transfusiondependent or fatal,can be mitigated through carrier screening.Here,we aim to evaluate the effectiveness and outcomes of pre-conceptional and early pregnancy screening initiatives for severe thalassemia prevention in a diverse population of 28,043 women.Using next-generation sequencing(NGS),we identify 4,226(15.07%)thalassemia carriers across 29 ethnic groups and categorize them into high-(0.75%),low-(25.86%),and unknown-risk(69.19%)groups based on their spouses'screening results.Post-screening follow-up reveals 59 fetuses with severe thalassemia exclusively in high-risk couples,underscoring the efficacy of risk classification.Among 25,053 live births over 6 months of age,two severe thalassemia infants were born to unknown-risk couples,which was attributed to incomplete screening and late NGS-based testing for a rare variant.Notably,64 rare variants are identified in 287 individuals,highlighting the genetic heterogeneity of thalassemia.We also observe that migrant flow significantly impacts carrier rates,with 93.90%of migrants to Chenzhou originating from high-prevalence regions in southern China.Our study demonstrates that NGS-based screening during pre-conception and early pregnancy is effective for severe thalassemia prevention,emphasizing the need for continuous screening efforts in areas with high and underestimated prevalence.展开更多
Thermomagnetic generation(TMG),a heat-to-electricity conversion technology based on the thermomagnetic effect,offers high reliability and broad adaptability to diverse heat sources.By exploiting the temperature-depend...Thermomagnetic generation(TMG),a heat-to-electricity conversion technology based on the thermomagnetic effect,offers high reliability and broad adaptability to diverse heat sources.By exploiting the temperature-dependent magnetization of thermomagnetic materials,TMG converts thermal energy into electrical energy through cyclic changes in magnetic flux based on Faraday's law.The performance of TMG systems is largely governed by the intrinsic properties of the working materials and the design of device architecture.Ideal TMG materials exhibit sharp and reversible magnetization transitions near the operating temperature,low thermal hysteresis,and high thermal conductivity.Device configurations can be broadly categorized into active and passive systems:active TMG devices rely on controlled thermal cycling and optimized magnetic circuits for enhanced output,whereas passive devices utilize self-actuated mechanical motion to generate electricity.In this topical review,we provide a comprehensive overview of recent advances in TMG materials and device configurations.Furthermore,we discuss future development trends and offer perspectives on experimental strategies to advance this field.展开更多
It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This stu...It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.展开更多
The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances...The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances within a single day can significantly impact on carbon prices.Faced with uncertain future carbon and electricity prices,GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions.In this paper,a two-layered hybrid mathematical-deep reinforcement learning(DRL)optimization framework is proposed.The upper DRL layer tackles the stochastic,year-long carbon trading and allowance usage optimization problem,aiming for long-term optimality and providing guidance for short-term decisions in the lower layer.The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while enforcing strict technical constraints.To accelerate learning of the annual compliance cycle,a decision timeline transfer learning method is proposed,enabling the DRL agent to progressively refine its policy through sequentially training on monthly,weekly and daily decision environments.Case studies demonstrate that,with these methods,a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.展开更多
Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined ...Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.展开更多
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
基金supported by the Young and Middle-aged Research Fund Project of Shenzhen People's Hospital(Grant No.SYHL2024-N0010)the Shenzhen Basic Research Program(General Program,Grant No.JCYJ20240813104409013)。
文摘Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice.
文摘This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.
基金supported by the State Key Program of National Natural Science of China(No.61533018)the National Natural Science Foundation of China(No.61402220)+3 种基金the Philosophy and Social Science Foundation of Hunan Province(No.16YBA323)the Natural Science Foundation of Hunan Province(Nos.2020JJ4525,2022JJ30495,and 2025JJ50384)the Scientific Research Fund of Hunan Provincial Education Department(Nos.18B279,19A439,and 22A0316)the CCF-Zhipu AI Large Model Fund.
文摘Large language models(LLMs)excel in various natural language processing tasks and are increasingly applied in specialized fields like medicine.However,their deployment in the medical domain is challenged by limited domain-specific data and the tendency to generate inaccurate information,known as“hallucinations.”While domainspecific fine-tuning has improved open-source LLMs,they still underperform compared to proprietary models like ChatGPT and PaLM.To address this gap,retrieval-augmented generation(RAG)techniques have been explored to enhance LLMs by integrating external knowledge bases.Nevertheless,the success of RAG depends on the quality of retrieved documents,and its application within the medical field remains in the early stages.In this paper,we introduce the“Bailicai”framework as an exploratory approach to integrating RAG with LLMs in the medical field.The framework employs fine-tuning to improve the RAG process,where“falsely relevant”and“completely irrelevant”interference documents are intentionally included in the training data.This enables Bailicai to develop the ability to assess the quality of retrieved documents and selectively incorporate them.The framework is organized into four modules:(1)medical knowledge injection,(2)self-knowledge boundary identification,(3)directed acyclic graph task decomposition,and(4)retrieval-augmented generation.Through the synergy of these modules,Bailicai achieves superior performance on multiple medical benchmarks,outperforming existing large models in the medical domain,RAG-based methods,and proprietary models such as GPT-3.5.Furthermore,Bailicai effectively mitigates the hallucination problem common in LLMs applied to medical tasks and enhances the robustness of RAG when dealing with irrelevant or misleading documents,enabling more accurate information retrieval and integration.
文摘Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fundamental challenges in knowledge-intensive tasks.This special issue serves as a dedicated platform to showcase these cutting-edge advancements.It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.
文摘Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased threat detection methods have been enhanced with machine learning and Large Language Models(LLMs),these approaches remain limited in addressing emerging threats.This study evaluates a two-step Retrieval Augmented Generation(RAG)approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance.The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework,AWS Threat Technique Catalogue,and threat reports to overcome limitations of static pre-trained LLMs.We constructed an evaluation dataset of 200 unique CloudTrail events(122 malicious,78 benign)using the Stratus Red Team adversary emulation framework,covering 9 MITRE ATT&CK techniques across 8 tactics.Events were sampled from 1724 total events using stratified sampling.Ground truth labels were created through systematic expert annotation with 90%inter-annotator agreement.The RAG-enabled model achieved estimated 78%accuracy,85%precision,and 79%F1-score,representing 70.5%accuracy improvement and 76.4%F1-score improvement over baseline Gemini 2.5 Pro(46%accuracy,45%F1-score).Performance are based on evaluation results on 200-event dataset.Cost-latency analysis revealed processing time of 4.1 s and cost of$0.00376 per event,comparable to commercial SIEM solutions while providing superior MITRE ATT&CK attribution.The findings demonstrate that RAG substantially enhances context-aware threat detection,providing actionable insights for cloud security operations.
基金financially supported by the National Natural Science Foundation of China(22175044)the Guangxi Natural Science Foundation(2023GXNSFDA026049)the Guangxi Major Talents Program(GXR-1BGQ2424023)。
文摘The global energy crisis and electricity shortage pose unprecedented challenges.Bio-based solar-driven ionic power generation devices with flexibility,photothermal self-healing and scalability hold great promise for sustainable electricity and alleviating energy crisis.Here,inspired by plant transpiration,a multifunctional bio-based ion conductive elastomer with solar power generation capability was designed by engineered synergy among epoxy natural rubber,cellulose nanofibrils,lithium bis(trifluoromethane)sulfonimide and eumelanin.The film exhibits an outstanding stretchability(1072%)and toughness(22.7 MJ m^(-3)).The favorable synergy of low thermal conductivity,high hygroscopicity and photothermal conversion performance endowed the film with a large thermal gradient under light illumination,driving efficient water transpiration.Furthermore,the excellent interfacial compatibility between eumelanin and matrix facilitates the formation of space charge regions,which further enhances Li^(+)transport.The film demonstrates excellent evaporation rate(2.83 kg m^(-2)h^(-1)),output voltage(0.47 V)and conductivity(5.11×10^(-2)S m^(-1)).Notably,the film exhibits remarkable photothermal self-healing performance even in saline environment,achieving 99.6%healing efficiency of output voltage.Therefore,the film demonstrates significant prospects for applications in photo-thermoelectric generation and solar-driven ionic power generation.
基金funded by the Ministry of Higher Education,Malaysia,under the Fundamental Research Grant Scheme FRGS/1/2023/STG06/UM/02/4(Project FP069-2023)。
文摘Electroosmotic transport and entropy generation play a decisive role in regulating efficiency,stability,and energy cost of non-Newtonian nanoblood flows in stenosed arteries,particularly with tapered geometries.Thisstudy develops a unified model to analyze ZnO-Williamson nanoblood flow through a stenosed artery with converging,diverging,and non-tapered configurations,incorporating electroosmosis,viscous dissipation,and entropy production.The arterial walls are assumed to be electrically charged with a no-slip condition to induce electroosmotic propulsionalong the endothelial surface.The partial differential equations are nondimensionalized to a coupled system ofnonlinear ordinary differential equations,which are solved numerically using a MATLAB-based shooting technique.Parametric investigation is conducted for Brinkman,Grashof,and Weissenberg numbers,ZnO fractional volume,volumetric flow rate,and Helmholtz-Smoluchowski velocity to quantify their influences on axial velocity,wall shearstress,impedance resistance,temperature distribution,entropy generation,Bejan number,and streamline topology.The axial velocity decreases radially with increasing Brinkman number for all arterial geometries.Increasing ZnOnanoparticles improves thermal transport owing to enhanced effective thermal conductivity but simultaneously elevatesentropy generation due to increased viscous dissipation.Higher Weissenberg numbers suppress entropy production bypromoting elastic stress redistribution and lowering shear-induced irreversibility.Impedance resistance decreases withincreasing stenosis height but increases with stenosis shape parameter and ZnO fractional volume.Streamline analysisshows that buoyancy and viscoelasticity significantly distort flow near the stenosis,while increasing electroosmoticvelocity stabilizes streamlines,suppresses recirculation,and reduces local shear stress and pressure fluctuations.Inconclusion,electroosmotic actuation is most effective in reducing flow resistance in the converging tapered artery,particularly at lower ZnO volume fractions.Overall,the findings highlight the potential of optimized electroosmoticactuation and controlled nanoparticle loading to minimize thermodynamic losses,regulate shear stress,and improveflow uniformity in stenosed vessels,with promising implications for electro-assisted drug delivery,nanotherapeutics,and bio-inspired vascular microfluidic systems.
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金supported by the National Natural Science Foundation of China (Grant Nos. 12234002, 92250303, 12474486, 12504301, and 12504396)the National Key Research and Development Program of China (Grant No. 2024YFA1612101)。
文摘With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation in crystals driven by optical skyrmions.We find that although the skyrmion number is not conserved,the resulting high-order harmonics can exhibit a distinctive multi-vortex structure,whose features are shaped by both the topology of the optical skyrmions and the rotational symmetry of the crystal.The position of the vortex centers can be effectively tuned by employing different types of optical skyrmions.To elucidate the underlying physics,we develop a multi-absorption channel model based on the conservation laws of spin and orbital angular momentum.Our work explores the role of optical topology in extreme nonlinear light-matter interactions,offering new opportunities for the formation and manipulation of optical vortices and novel structured light fields in the visible and ultraviolet regimes.
基金Supported by Applied Brand Course of Mianyang Teacher's College(Investigation and Monitoring of Natural Resources).
文摘With the rapid development of image-generative AI (artificial intelligence) technology, its application in undergraduate Landscape Architecture education has demonstrated significant potential. Based on this, the present study explores the implications of integrating image-generative AI into Landscape Architecture courses from three perspectives: stimulating students creative design potential, expanding approaches to form and concept generation, and enhancing the visualization of spatial scenes. Furthermore, it discusses application strategies from three dimensions: AI-assisted conceptual generation, human-machine collaboration for design refinement, and optimization of scheme presentation and evaluation. This paper aims to provide relevant educators with insights and references.
基金supported by the Zhejiang Province Leading Geese Plan(Grant No.2025C02025)the Guangdong Province Primary and Secondary School Teachers’Digital Literacy Enhancement Project 2025(Grant No.GDSZSYKT2025244).
文摘With the rise of AI-assisted education,many instructors and engineers seek to deliver high-quality programming courses online.However,crafting effective programming lectures remains a challenge,particularly for instructors lacking pedagogical training or multilingual fluency.We present CourseAgent,a prompt-driven framework that leverages large language models(LLMs)to automatically generate Python tutorials,structured lecture scripts,and accompanying audio narrations.CourseAgent accepts raw code as input and transforms it into segmented,well-commented code blocks,adapting content to different difficulty levels and languages via prompt customization.Our system supports multilingual instruction(e.g.,Chinese,English),fine-grained control of pedagogical depth,and auto-generation of lecture videos.We evaluate the output generated by CourseAgent using real student feedback and feedback from in-service teachers,alongside automated assessments from LLMs.These evaluations demonstrate that the materials produced by CourseAgent are coherent,pedagogically sound,and comparable in quality to those created by experienced instructors.CourseAgent lowers the barrier to quality programming education and shows promise for scalable,personalized,and language-adaptive content generation.
基金supported by the National Natural Science Foundation of China(Grant No.12304379)the Natural Science Foundation of Liaoning Province(Grant No.2024BS-269)the Guangdong Basic and Applied Basic Research Foundation(Grant No.025A1515011117)。
文摘High-precision optical frequency measurement serves as a cornerstone of modern science and technology,enabling advancements in fields ranging from fundamental physics to quantum information technologies.Obtaining precise photon frequencies,especially in the ultraviolet or even extreme ultraviolet regimes,is a key goal in both light–matter interaction experiments and engineering applications.High-order harmonic generation(HHG)is an ideal light source for producing such photons.In this work,we propose an optical temporal interference model(OTIM)that establishes an analogy with multi-slit Fraunhofer diffraction(MSFD)to manipulate fine-frequency photon generation by exploiting the temporal coherence of HHG processes.Our model provides a unified physical framework for three distinct non-integer HHG generation schemes:single-pulse,shaped-pulse,and laser pulse train approaches,which correspond to single-MSFD-like,double-MSFD-like,and multi-MSFD-like processes,respectively.Arbitrary non-integer HHG photons can be obtained using our scheme.Our approach provides a new perspective for accurately measuring and controlling photon frequencies in fields such as frequency comb technology,interferometry,and atomic clocks.
文摘Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.
基金supported by the National Natural Science Foundation of China(81760037)Yunling Scholar Project of Yunnan Province(YNWR-YLXZ-2019-0005)+1 种基金Hunan Provincial Innovation Platform and Talent Program(2018SK4004)Hunan Provincial Natural Science Foundation(2019JJ80048).
文摘The occurrence of severe thalassemia,an inherited blood disorder that is either blood-transfusiondependent or fatal,can be mitigated through carrier screening.Here,we aim to evaluate the effectiveness and outcomes of pre-conceptional and early pregnancy screening initiatives for severe thalassemia prevention in a diverse population of 28,043 women.Using next-generation sequencing(NGS),we identify 4,226(15.07%)thalassemia carriers across 29 ethnic groups and categorize them into high-(0.75%),low-(25.86%),and unknown-risk(69.19%)groups based on their spouses'screening results.Post-screening follow-up reveals 59 fetuses with severe thalassemia exclusively in high-risk couples,underscoring the efficacy of risk classification.Among 25,053 live births over 6 months of age,two severe thalassemia infants were born to unknown-risk couples,which was attributed to incomplete screening and late NGS-based testing for a rare variant.Notably,64 rare variants are identified in 287 individuals,highlighting the genetic heterogeneity of thalassemia.We also observe that migrant flow significantly impacts carrier rates,with 93.90%of migrants to Chenzhou originating from high-prevalence regions in southern China.Our study demonstrates that NGS-based screening during pre-conception and early pregnancy is effective for severe thalassemia prevention,emphasizing the need for continuous screening efforts in areas with high and underestimated prevalence.
基金supported by the National Natural Science Foundation of China(Grant Nos.52171169 and 52101210)the National Key Research and Development Program of China(Grant No.2021YFB3501204)+3 种基金the State Key Laboratory for Advanced Metals and Materials(Grant No.2023-ZD01)USTB Concept Verification Funding Project(Grant No.GNYZ-2024-6)Fundamental Research Funds for the Central Universities(Grant No.FRF-TP-24-004A)USTB Research Center for International People-to-people Exchange in Science,Technology and Civilization(Grant Nos.2024KFZD001 and 2024KFYB004)。
文摘Thermomagnetic generation(TMG),a heat-to-electricity conversion technology based on the thermomagnetic effect,offers high reliability and broad adaptability to diverse heat sources.By exploiting the temperature-dependent magnetization of thermomagnetic materials,TMG converts thermal energy into electrical energy through cyclic changes in magnetic flux based on Faraday's law.The performance of TMG systems is largely governed by the intrinsic properties of the working materials and the design of device architecture.Ideal TMG materials exhibit sharp and reversible magnetization transitions near the operating temperature,low thermal hysteresis,and high thermal conductivity.Device configurations can be broadly categorized into active and passive systems:active TMG devices rely on controlled thermal cycling and optimized magnetic circuits for enhanced output,whereas passive devices utilize self-actuated mechanical motion to generate electricity.In this topical review,we provide a comprehensive overview of recent advances in TMG materials and device configurations.Furthermore,we discuss future development trends and offer perspectives on experimental strategies to advance this field.
基金supported by the DH2025-TN07-07 project conducted at the Thai Nguyen University of Information and Communication Technology,Thai Nguyen,Vietnam,with additional support from the AI in Software Engineering Lab.
文摘It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.
基金supported by the Natural Science Foundation of China-Smart Grid Joint Fund of State Grid Corporation of China(No.U2066212)the Na-tional Natural Science Foundation of China(No.52207105)the Key Science and Technology Pro-jects of China Southern Power Grid Corporation(No.066600KK52222023).
文摘The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances within a single day can significantly impact on carbon prices.Faced with uncertain future carbon and electricity prices,GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions.In this paper,a two-layered hybrid mathematical-deep reinforcement learning(DRL)optimization framework is proposed.The upper DRL layer tackles the stochastic,year-long carbon trading and allowance usage optimization problem,aiming for long-term optimality and providing guidance for short-term decisions in the lower layer.The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while enforcing strict technical constraints.To accelerate learning of the annual compliance cycle,a decision timeline transfer learning method is proposed,enabling the DRL agent to progressively refine its policy through sequentially training on monthly,weekly and daily decision environments.Case studies demonstrate that,with these methods,a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.
基金Nguyen Tat Thanh University,Ho Chi Minh City,Vietnam for supporting this study。
文摘Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.
