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 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
1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emer...1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges.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
Standards are the common language that consolidates global consensus and builds the most solid foundation for international partnerships.They are the cornerstone for global sustainable and high-quality development.You...Standards are the common language that consolidates global consensus and builds the most solid foundation for international partnerships.They are the cornerstone for global sustainable and high-quality development.Young students,with their active and vibrant minds,represent the future and hope of standardization.展开更多
Nocardia is an aerobic,gram-positive,and opportunistic bacillus widely distributed in the environment.Nocardia cyriacigeorgica(N.cyriacigeorgica) was first isolated in 2001 from a chronic bronchitis patient,^([1]) and...Nocardia is an aerobic,gram-positive,and opportunistic bacillus widely distributed in the environment.Nocardia cyriacigeorgica(N.cyriacigeorgica) was first isolated in 2001 from a chronic bronchitis patient,^([1]) and has since been reported as an emerging clinically relevant pathogen worldwide.The diagnosis of nocardial infections remains challenging due to nonspecific symptoms and low culture sensitivity,resulting in high mortality.^([2]) Herein,we report a case of N.cyriacigeorgica brain abscess in an immunosuppressed patient who was successfully treated with antibiotics and surgery.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
We are sorry for the mistakes of Affiliation,"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,Donghua University,Shanghai 201620,China"should be replaced by&quo...We are sorry for the mistakes of Affiliation,"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,Donghua University,Shanghai 201620,China"should be replaced by"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,College of Materials Science and Engineering,Donghua University,Shanghai 201620,China".We apologized for the inconvenience caused by this error.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
Next-generation craniomaxillofacial implants(CMFIs) are redefining personalized bone reconstruction by balancing and optimizing biomechanics,biocompatibility,and bioactivity—the "3Bs".This review highlights...Next-generation craniomaxillofacial implants(CMFIs) are redefining personalized bone reconstruction by balancing and optimizing biomechanics,biocompatibility,and bioactivity—the "3Bs".This review highlights recent progress in implant design,material development,additive manufacturing,and preclinical evaluation.Emerging biomaterials,including bioresorbable polymers,magnesium alloys,and composites with bioactive ceramics,enable patient-specific solutions with improved safety and functionality.Triply periodic minimal surface(TPMS) architectures exemplify how structural design can enhance both mechanical performance and biological integration.Additive manufacturing technologies further allow the fabrication of geometrically complex,customized impla nts that meet individual anatomical and pathological needs.In parallel,multiscale evaluation techniques—from mechanical testing to in vitro and in vivo models—provide comprehensive insights into implant performance and safety.Looking ahead,the field is poised to benefit from several transformative trends:the development of smart and multifunctional biomaterials;Al-driven design frameworks that leverage patient-specific data and computational modeling;predictive additive manufacturing with real-time quality control;and advanced biological testing platforms for preclinical evaluation.Together,these advances form the foundation of a data-informed,translational pipeline from bench to bedside.Realizing the full potential of nextgene ration CMFIs will require close interdisciplina ry collaboration across mate rials science,computational engineering,and clinical medicine.展开更多
Due to the complex structural hierarchy,with deeply nested associative relations between entities such as equipment,specifications,and business processes,intelligent power grid engineering is challenging.Meanwhile,lim...Due to the complex structural hierarchy,with deeply nested associative relations between entities such as equipment,specifications,and business processes,intelligent power grid engineering is challenging.Meanwhile,limited by the fragmented data and loss of contextual information,the generated reports are prone to the problems such as content redundancy and omission of critical information,failing to meet the demands of efficient decision-making and accurate management in modern power systems.To address these issues,this paper proposes a knowledge graph(KG)-enhanced framework to automatically generate electric power engineering reports.In the KG construction phase,a feature-fused entity recognition model named BERT-BiLSTM-CRF is adopted to improve the accuracy of entity recognition in scenarios involving power engineering professional terminology,thereby solving the problem of ambiguous entity boundaries in traditional models;then a BERT-attention relation extraction model is proposed to enhance the completeness of extracting complex hierarchical and implicit relations in power grid data.In the report generation phase,an improved Transformer architecture is adopted to accurately transform structured knowledge into natural language reports that comply with engineering specifications,addressing the issue of semantic inconsistency caused by the loss of structural information in existing models.By validating with real-world projects,the results show that the proposed framework significantly outperforms existing baseline models in entity recognition,confirming its superiority and applicability in practical engineering.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.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
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
Hydrogel microcapsules are powerful microreactor vessels that have attracted widespread attention and research.Among the various methods for their generation,the aqueous two-phase system(ATPS)is by far the most straig...Hydrogel microcapsules are powerful microreactor vessels that have attracted widespread attention and research.Among the various methods for their generation,the aqueous two-phase system(ATPS)is by far the most straightforward approach.However,the high viscosity of ATPS solutions significantly limits the generation throughput of hydrogel microcapsule.In this study,we developed a novel high-throughput approach for generating hydrogel microcapsules using a microfluidic bubble-triggering strategy.By integrating constant-pressure air flow with droplet microfluidics devices,we efficiently manipulated the formation of ATPS droplet through bubble-induced Rayleigh-Plateau instability,enabling the production of uniform,monodisperse microcapsules.Additionally,the droplet generation frequency in the bubble-triggering method exceeded 36 kHz.We further demonstrated the encapsulation of genetically engineered Escherichia coli strains,which acted as biosensors for arsenic ions and caprolactam,highlighting the potential of these microcapsules for biosensing applications.This advancement in hydrogel microcapsule generation offers promising implications for scalable applications in biosensing,organoid culture,and high-throughput screening.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.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
Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standa...Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation.Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge,medical records,and chain-ofthought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms→pathogenesis→therapeutic principles→prescriptions)into the model’s reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of established open-source TCM LLMs.Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differentiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the“symptoms→pathogenesis→therapeutic principles→prescriptions”logic chain.It provided complete prescriptions,whereas other models often omitted dosages or generated mismatched prescriptions.Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tuning strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.展开更多
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 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.
文摘1 Introduction The growing connectivity with mobile internet has significantly enhanced our day-to-day life support through various services and applications with on-demand availability at any time or anywhere.As emerging technologies with continuous revolutions in the digital transformations,various add-on technologies such as quantum computing,AI,and next-generation networks such as 6G are becoming an integral support to mobile internet systems.The emerging technologies in the next-generation mobile internet bring a lot of new security and privacy challenges.
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.
文摘Standards are the common language that consolidates global consensus and builds the most solid foundation for international partnerships.They are the cornerstone for global sustainable and high-quality development.Young students,with their active and vibrant minds,represent the future and hope of standardization.
基金funded by grants from Guangdong Basic and Applied Basic Research Foundation (2025A1515011901)Natural Science Foundation of Guangdong Province (2024A1515012228)。
文摘Nocardia is an aerobic,gram-positive,and opportunistic bacillus widely distributed in the environment.Nocardia cyriacigeorgica(N.cyriacigeorgica) was first isolated in 2001 from a chronic bronchitis patient,^([1]) and has since been reported as an emerging clinically relevant pathogen worldwide.The diagnosis of nocardial infections remains challenging due to nonspecific symptoms and low culture sensitivity,resulting in high mortality.^([2]) Herein,we report a case of N.cyriacigeorgica brain abscess in an immunosuppressed patient who was successfully treated with antibiotics and surgery.
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.
文摘We are sorry for the mistakes of Affiliation,"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,Donghua University,Shanghai 201620,China"should be replaced by"a State Key Laboratory of Advanced Fiber Materials,Center for Advanced Low-Dimension Materials,College of Materials Science and Engineering,Donghua University,Shanghai 201620,China".We apologized for the inconvenience caused by this error.
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Financial support from National University of Singapore (NUS)(AcRF A-8000-126-00-00)。
文摘Next-generation craniomaxillofacial implants(CMFIs) are redefining personalized bone reconstruction by balancing and optimizing biomechanics,biocompatibility,and bioactivity—the "3Bs".This review highlights recent progress in implant design,material development,additive manufacturing,and preclinical evaluation.Emerging biomaterials,including bioresorbable polymers,magnesium alloys,and composites with bioactive ceramics,enable patient-specific solutions with improved safety and functionality.Triply periodic minimal surface(TPMS) architectures exemplify how structural design can enhance both mechanical performance and biological integration.Additive manufacturing technologies further allow the fabrication of geometrically complex,customized impla nts that meet individual anatomical and pathological needs.In parallel,multiscale evaluation techniques—from mechanical testing to in vitro and in vivo models—provide comprehensive insights into implant performance and safety.Looking ahead,the field is poised to benefit from several transformative trends:the development of smart and multifunctional biomaterials;Al-driven design frameworks that leverage patient-specific data and computational modeling;predictive additive manufacturing with real-time quality control;and advanced biological testing platforms for preclinical evaluation.Together,these advances form the foundation of a data-informed,translational pipeline from bench to bedside.Realizing the full potential of nextgene ration CMFIs will require close interdisciplina ry collaboration across mate rials science,computational engineering,and clinical medicine.
基金supported by State Grid Shanghai Economic Research Institute under Grant No.SGTYHT/23-JS-004.
文摘Due to the complex structural hierarchy,with deeply nested associative relations between entities such as equipment,specifications,and business processes,intelligent power grid engineering is challenging.Meanwhile,limited by the fragmented data and loss of contextual information,the generated reports are prone to the problems such as content redundancy and omission of critical information,failing to meet the demands of efficient decision-making and accurate management in modern power systems.To address these issues,this paper proposes a knowledge graph(KG)-enhanced framework to automatically generate electric power engineering reports.In the KG construction phase,a feature-fused entity recognition model named BERT-BiLSTM-CRF is adopted to improve the accuracy of entity recognition in scenarios involving power engineering professional terminology,thereby solving the problem of ambiguous entity boundaries in traditional models;then a BERT-attention relation extraction model is proposed to enhance the completeness of extracting complex hierarchical and implicit relations in power grid data.In the report generation phase,an improved Transformer architecture is adopted to accurately transform structured knowledge into natural language reports that comply with engineering specifications,addressing the issue of semantic inconsistency caused by the loss of structural information in existing models.By validating with real-world projects,the results show that the proposed framework significantly outperforms existing baseline models in entity recognition,confirming its superiority and applicability in practical engineering.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
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.
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sponsored by the National Key R&D Program of China(no.2023YFB3208203)the National Natural Science Foundation of China(no.62374170)the Science and Technology Commission of Shanghai Municipality(no.23J21900200).
文摘Hydrogel microcapsules are powerful microreactor vessels that have attracted widespread attention and research.Among the various methods for their generation,the aqueous two-phase system(ATPS)is by far the most straightforward approach.However,the high viscosity of ATPS solutions significantly limits the generation throughput of hydrogel microcapsule.In this study,we developed a novel high-throughput approach for generating hydrogel microcapsules using a microfluidic bubble-triggering strategy.By integrating constant-pressure air flow with droplet microfluidics devices,we efficiently manipulated the formation of ATPS droplet through bubble-induced Rayleigh-Plateau instability,enabling the production of uniform,monodisperse microcapsules.Additionally,the droplet generation frequency in the bubble-triggering method exceeded 36 kHz.We further demonstrated the encapsulation of genetically engineered Escherichia coli strains,which acted as biosensors for arsenic ions and caprolactam,highlighting the potential of these microcapsules for biosensing applications.This advancement in hydrogel microcapsule generation offers promising implications for scalable applications in biosensing,organoid culture,and high-throughput screening.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
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National Key Research and Development Program of China(2024YFC3505400)Capital Clinical Project of Beijing Municipal Science&Technology Commission(Z221100007422092)Capital’s Funds for Health Improvement and Research(2024-1-2231).
文摘Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation.Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge,medical records,and chain-ofthought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms→pathogenesis→therapeutic principles→prescriptions)into the model’s reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of established open-source TCM LLMs.Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differentiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the“symptoms→pathogenesis→therapeutic principles→prescriptions”logic chain.It provided complete prescriptions,whereas other models often omitted dosages or generated mismatched prescriptions.Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tuning strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM.
