This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper cons...This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.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
β-Ga_(2)O_(3) MOS inverter should play a crucial role in β-Ga_(2)O_(3) electronic circuits. Enhancement-mode(E-mode) MOSFET was fabricated based on β-Ga_(2)O_(3) film grown by atomic layer deposition technology, an...β-Ga_(2)O_(3) MOS inverter should play a crucial role in β-Ga_(2)O_(3) electronic circuits. Enhancement-mode(E-mode) MOSFET was fabricated based on β-Ga_(2)O_(3) film grown by atomic layer deposition technology, and the β-Ga_(2)O_(3) inverter was further monolithically integrated on this basis. The β-Ga_(2)O_(3) n MOSFET exhibits excellent electrical characteristics with an on/off current ratio reaching 10^(5). The logic inverter shows outstanding voltage inversion characteristics under low-frequency from 1 to 400 Hz operation. As the frequency continues to increase to 10 K, the reverse characteristic becomes worse due to parasitic capacitance induced by processes, and the difference between the highest and lowest values of VOUT has an exponential decay relationship with the frequency. This paper provides the practice for the development of β-Ga_(2)O_(3)-based circuits.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 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
Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data st...Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data structure to create virtual samples,which can be used to augment the original dataset.The ALVSG process consists of two steps.First,an average-linkage clustering technique is applied to the dataset to create a dendrogram.The dendrogram represents the hierarchical structure of the dataset,with each merging operation regarded as a linkage.Next,the linkages are combined into an average-based dataset,which serves as a new representation of the dataset.The second step in the ALVSG process involves generating virtual samples using the average-based dataset.The research project generates a set of 100 virtual samples by uniformly distributing them within the provided boundary.These virtual samples are then added to the original dataset,creating a more extensive dataset with improved generalization performance.The efficacy of the ALVSG approach is validated through resampling experiments and t-tests conducted on two small real-world datasets.The experiments are conducted on three forecasting models:the support vector machine for regression(SVR),the deep learning model(DL),and XGBoost.The results show that the ALVSG approach outperforms the baseline methods in terms of mean square error(MSE),root mean square error(RMSE),and mean absolute error(MAE).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
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
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
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
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
Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components,particularly at elevated voltage levels.Addressi...Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components,particularly at elevated voltage levels.Addressing these shortcomings,thiswork presents a robust 15-level PackedUCell(PUC)inverter topology designed for renewable energy and grid-connected applications.The proposed systemintegrates a sensor less proportional-resonant(PR)controller with an advanced carrier-based pulse width modulation scheme.This approach efficiently balances capacitor voltage,minimizes steady-state error,and strongly suppresses both zero and third-order harmonics resulting in reduced total harmonic distortion and enhanced voltage regulation.Additionally,a novel switching algorithm simplifies the design and implementation,further lowering voltage stress across switches.Extensive simulation results validate the performance under various resistive and resistive-inductive load conditions,demonstrating compliance with IEEE-519 THD standards and robust operation under dynamic changes.The proposed sensorless PR-controlled 15-PUC inverter thus offers a compelling,cost-effective solution for efficient power conversion in next-generation renewable energy systems.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
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
Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains chall...Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging,thereby hindering the effective utilization of existing natural fractures.In this study,a phase field model was developed utilizing the finite element method to examine the influence of fluid presence,stress conditions,and natural fractures on the initiation and propagation of hydraulic fractures.The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field,while the phase field theory is applied to simulate fracture behavior.The results show that whenσ_(x0)/σ_(y0)<3 or qf<20 kg/(m^(3)·s),the presence of natural fractures can alter the original propagation direction of hydraulic fractures.Conversely,in the absence of these conditions,the propagation path of natural fractures is predominantly influenced by the initial stress field.Furthermore,based on the analysis of breakdown pressure and damage area,the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45°to 60°.Finally,once a dominant channel forms,initiating and propagating hydraulic fractures in other directions becomes increasingly difficult,even in highly fractured areas.This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions,providing a theoretical basis for optimizing Enhanced Geothermal System(EGS)projects.展开更多
In wind power generation system the grid-connected inverter is an important section for energy conversion and transmission, of which the performance has a direct influence on the entire wind power generation system. T...In wind power generation system the grid-connected inverter is an important section for energy conversion and transmission, of which the performance has a direct influence on the entire wind power generation system. The mathematical model of the grid-connected inverter is deduced firstly. Then, the space vector pulse width modulation （SVPWM） is analyzed. The power factor can be controlled close to unity, leading or lagging, which is realized based on H-type current controller and grid voltage vector-oriented control. The control strategy is verified by the simulation and experimental results with a good sinusoidal current, a small harmonic component and a fast dynamic response.展开更多
Islanding detection is an essential function for safety and reliability in grid-connected distributed generation (DG) systems. Several methods for islanding detection are proposed, but most of them may fail under mult...Islanding detection is an essential function for safety and reliability in grid-connected distributed generation (DG) systems. Several methods for islanding detection are proposed, but most of them may fail under multi-source configurations, or they may produce important power quality degradation which gets worse with increasing DG penetration. This paper presents an active islanding detection algorithm for Voltage Source Inverter (VSI) based multi-source DG systems. The proposed method is based on the Voltage Positive Feedback (VPF) theory to generate a limited active power perturbation. Theoretical analyses were performed and simulations by MATLAB /Simulink /SimPowerSystems were used to evaluate the algorithm’s performance and its advantages concerning the time response and the effects on power quality, which turned out to be negligible. The algorithm performance was tested under critical conditions: load with unity power factor, load with high quality factor, and load matching DER’s powers.展开更多
A quasi resonant pulse width modulation（PWM） inverter is used in a solar power system to convert the solar panel and battery charger＇s direct current（DC） output to alternating current（AC）.Although much has been...A quasi resonant pulse width modulation（PWM） inverter is used in a solar power system to convert the solar panel and battery charger＇s direct current（DC） output to alternating current（AC）.Although much has been published about DC to AC PWM inverters,none of the previous work has shown modeling and simulation results for DC to AC inverters.In this study,we suggest a new topology for a quasi resonant PWM inverter.Experimental results are also presented.展开更多
100-W class power storage systems were developed, which comprised spherical Si solar cells, a maximum power point tracking charge control-ler, a lithium-ion battery, and one of two different types of direct current (D...100-W class power storage systems were developed, which comprised spherical Si solar cells, a maximum power point tracking charge control-ler, a lithium-ion battery, and one of two different types of direct current (DC)-alter- nating current (AC) converters. One inverter used SiC met-al-oxide-semicon-ductor field-effect transistors (MOSFETs) as switching devices while the other used Si MOSFETs. In these 100-W class inverters, the ON resistance was considered to have little influence on the efficiency. Nevertheless, the SiC-based inverter exhibited an approximately 3% higher DC-AC conversion efficiency than the Si-based inverter. Power loss analysis indicated that the higher efficiency resulted predominantly from lower switching and reverse recovery losses in the SiC MOSFETs compared with in the Si MOSFETs.展开更多
基金Supported by the Fundamental Research Funds for the Central Universities(2024ZYGXZR047)the National Natural Science Foundation of China(62373156)the Guangdong Basic and Applied Basic Research Foundation(2024A1515011736)。
文摘This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.
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supported by Natural Science Basic Research Program of Shaanxi Province of China (No. 2023-JC-YB-574)National Natural Science Foundation of China (No. 62304178)。
文摘β-Ga_(2)O_(3) MOS inverter should play a crucial role in β-Ga_(2)O_(3) electronic circuits. Enhancement-mode(E-mode) MOSFET was fabricated based on β-Ga_(2)O_(3) film grown by atomic layer deposition technology, and the β-Ga_(2)O_(3) inverter was further monolithically integrated on this basis. The β-Ga_(2)O_(3) n MOSFET exhibits excellent electrical characteristics with an on/off current ratio reaching 10^(5). The logic inverter shows outstanding voltage inversion characteristics under low-frequency from 1 to 400 Hz operation. As the frequency continues to increase to 10 K, the reverse characteristic becomes worse due to parasitic capacitance induced by processes, and the difference between the highest and lowest values of VOUT has an exponential decay relationship with the frequency. This paper provides the practice for the development of β-Ga_(2)O_(3)-based circuits.
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 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funding support from the National Science and Technology Council(NSTC),under Grant No.114-2410-H-011-026-MY3.
文摘Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data structure to create virtual samples,which can be used to augment the original dataset.The ALVSG process consists of two steps.First,an average-linkage clustering technique is applied to the dataset to create a dendrogram.The dendrogram represents the hierarchical structure of the dataset,with each merging operation regarded as a linkage.Next,the linkages are combined into an average-based dataset,which serves as a new representation of the dataset.The second step in the ALVSG process involves generating virtual samples using the average-based dataset.The research project generates a set of 100 virtual samples by uniformly distributing them within the provided boundary.These virtual samples are then added to the original dataset,creating a more extensive dataset with improved generalization performance.The efficacy of the ALVSG approach is validated through resampling experiments and t-tests conducted on two small real-world datasets.The experiments are conducted on three forecasting models:the support vector machine for regression(SVR),the deep learning model(DL),and XGBoost.The results show that the ALVSG approach outperforms the baseline methods in terms of mean square error(MSE),root mean square error(RMSE),and mean absolute error(MAE).
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 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.
文摘Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components,particularly at elevated voltage levels.Addressing these shortcomings,thiswork presents a robust 15-level PackedUCell(PUC)inverter topology designed for renewable energy and grid-connected applications.The proposed systemintegrates a sensor less proportional-resonant(PR)controller with an advanced carrier-based pulse width modulation scheme.This approach efficiently balances capacitor voltage,minimizes steady-state error,and strongly suppresses both zero and third-order harmonics resulting in reduced total harmonic distortion and enhanced voltage regulation.Additionally,a novel switching algorithm simplifies the design and implementation,further lowering voltage stress across switches.Extensive simulation results validate the performance under various resistive and resistive-inductive load conditions,demonstrating compliance with IEEE-519 THD standards and robust operation under dynamic changes.The proposed sensorless PR-controlled 15-PUC inverter thus offers a compelling,cost-effective solution for efficient power conversion in next-generation renewable energy systems.
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 the National Key Research and Development Program(2021YFB150740401)National Natural Science Foundation of China(42202336)the CAS Pioneer Hundred Talents Program in China(Y826031C01)。
文摘Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging,thereby hindering the effective utilization of existing natural fractures.In this study,a phase field model was developed utilizing the finite element method to examine the influence of fluid presence,stress conditions,and natural fractures on the initiation and propagation of hydraulic fractures.The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field,while the phase field theory is applied to simulate fracture behavior.The results show that whenσ_(x0)/σ_(y0)<3 or qf<20 kg/(m^(3)·s),the presence of natural fractures can alter the original propagation direction of hydraulic fractures.Conversely,in the absence of these conditions,the propagation path of natural fractures is predominantly influenced by the initial stress field.Furthermore,based on the analysis of breakdown pressure and damage area,the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45°to 60°.Finally,once a dominant channel forms,initiating and propagating hydraulic fractures in other directions becomes increasingly difficult,even in highly fractured areas.This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions,providing a theoretical basis for optimizing Enhanced Geothermal System(EGS)projects.
基金supported by Delta Power Electronic Science and Education Development in 2007 (Grant No.DRES2007002)
文摘In wind power generation system the grid-connected inverter is an important section for energy conversion and transmission, of which the performance has a direct influence on the entire wind power generation system. The mathematical model of the grid-connected inverter is deduced firstly. Then, the space vector pulse width modulation （SVPWM） is analyzed. The power factor can be controlled close to unity, leading or lagging, which is realized based on H-type current controller and grid voltage vector-oriented control. The control strategy is verified by the simulation and experimental results with a good sinusoidal current, a small harmonic component and a fast dynamic response.
文摘Islanding detection is an essential function for safety and reliability in grid-connected distributed generation (DG) systems. Several methods for islanding detection are proposed, but most of them may fail under multi-source configurations, or they may produce important power quality degradation which gets worse with increasing DG penetration. This paper presents an active islanding detection algorithm for Voltage Source Inverter (VSI) based multi-source DG systems. The proposed method is based on the Voltage Positive Feedback (VPF) theory to generate a limited active power perturbation. Theoretical analyses were performed and simulations by MATLAB /Simulink /SimPowerSystems were used to evaluate the algorithm’s performance and its advantages concerning the time response and the effects on power quality, which turned out to be negligible. The algorithm performance was tested under critical conditions: load with unity power factor, load with high quality factor, and load matching DER’s powers.
基金supported by the Ming Chuan University Internal Research Fund
文摘A quasi resonant pulse width modulation（PWM） inverter is used in a solar power system to convert the solar panel and battery charger＇s direct current（DC） output to alternating current（AC）.Although much has been published about DC to AC PWM inverters,none of the previous work has shown modeling and simulation results for DC to AC inverters.In this study,we suggest a new topology for a quasi resonant PWM inverter.Experimental results are also presented.
文摘100-W class power storage systems were developed, which comprised spherical Si solar cells, a maximum power point tracking charge control-ler, a lithium-ion battery, and one of two different types of direct current (DC)-alter- nating current (AC) converters. One inverter used SiC met-al-oxide-semicon-ductor field-effect transistors (MOSFETs) as switching devices while the other used Si MOSFETs. In these 100-W class inverters, the ON resistance was considered to have little influence on the efficiency. Nevertheless, the SiC-based inverter exhibited an approximately 3% higher DC-AC conversion efficiency than the Si-based inverter. Power loss analysis indicated that the higher efficiency resulted predominantly from lower switching and reverse recovery losses in the SiC MOSFETs compared with in the Si MOSFETs.
