For decades,Xu has been committed to fulfilling the duty and mission of a scientist and educator—diving into the laws of nature,caring deeply for the nation,and earnestly cultivating younger generations.
The integration of interfacial photothermal conversion and hydrovoltaic effect into bifunctional evaporators has emerged as a hopeful approach to address water and energy scarcities.However,developing low-cost bifunct...The integration of interfacial photothermal conversion and hydrovoltaic effect into bifunctional evaporators has emerged as a hopeful approach to address water and energy scarcities.However,developing low-cost bifunctional evaporators and elucidating the freshwater-electricity co-generation mechanism remain challenging.In this work,we prepare porous carbon from waste polyester through a metalorganic framework(MOF)-assisted carbonization strategy and subsequently fabricate a bifunctional evaporator for freshwater-hydroelectricity co-generation.The porous carbon contains rich oxygen-containing groups and shows hierarchical micro-and mesopores with a high specific surface area of 904 m^(2)g^(-1).The porous carbon-based evaporator shows broadband and high light absorption,localized thermal management,good hydrophilicity,and high flexibility.Benefiting from these merits,it achieves high-performance freshwater and hydroelectricity co-generation,with the opencircuit voltage of 250 mV,the short-circuit current of 14μA,and the evaporation rate of 2.34 kg m^(-2)h^(-1).Hence,it is ranked among the most efficient freshwater-hydroelectricity co-generator.Additionally,the weakened hydrogen-bonding network reduces water evaporation enthalpy to 1.7 kJ g^(-1).Mechanistic investigations reveal that selective Na+interaction induces differential ion migration rate to generate streaming potential,as evidenced by molecular dynamics simulations.Meanwhile,the photothermal effect enhances voltage output by promoting interfacial ion concentration gradients.During the outdoor freshwater-electricity co-generation,it shows the voltage output of 250 mV and freshwater production of 2.34 kg m-2.This work not only puts forward a new platform to fabricate advanced evaporators from low-cost waste plastics but also unravels the freshwater-electricity co-generation mechanism,offering scalable strategies to tackle freshwater and energy crises.展开更多
The geothermal resources in China are primarily found in its sedimentary basins,particularly in the large basins located in eastern China,which hold significant potential for geothermal energy development.The Songliao...The geothermal resources in China are primarily found in its sedimentary basins,particularly in the large basins located in eastern China,which hold significant potential for geothermal energy development.The Songliao,North China,and Zhangzhou basins are of special interest due to their considerable exploration depths,extensive development history,and high levels of research activity.This study focuses on the three basins to analyze their thermal reservoir characteristics in eastern China.Between 2017 and 2023,the research team carried out a comprehensive analysis involving deep boreholes that exceeded 4000 m in depth within these three basins.They meticulously created detailed physical profiles that captured essential characteristics such as porosity,permeability,and thermal properties,reaching down to the basement of each basin.The findings indicated that variations in thermal conductivity within shallow geotechnical layers significantly influence the redistribution of deep thermal energy in the upper layers of the earth.Furthermore,differences in physical properties notably affect heat transport processes.The research proposes distinct heat models tailored for each basin:For the Songliao Basin,a low-permeability model with homogeneous thermal properties is constructed;for the North China Basin,high permeability and thermal conductivity layers are highlighted;and a fracture network controlling water and heat is presented in the Zhangzhou Basin.To elucidate the thermal structure of these basins,the Curie surface and Moho surface were analyzed.The shallow Curie surface indicates ongoing intense thermal activity stemming from crustal heat sources,while a shallow Moho surface signifies historical vigorous mantle thermal activity associated with mantle source heat production.Furthermore,the research evaluates the geothermal resources and the potential for carbon emission reduction in these basins.Total volume of exploitable geothermal fluid is estimated to be 76.9×10^(9) m^(3)/a,corresponding to an annual renewable geothermal energy 1.47×10^(16)k J.The implementation of geothermal energy could lead to a reduction in annual CO_(2)emissions by nearly 2×10^(9) t,which constitutes about 17.4%of China’s national carbon emissions in 2022.This estimation provides invaluable theoretical insights and data support for geothermal exploration and sustainable development in eastern China.展开更多
To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.T...To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.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
Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for ...Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for renewable energy and constructing self-powered electronics.In this review,we begin by outlining the fundamental mechanisms—ion diffusion,electric double layer formation,and streaming potential—that govern charge transport for MEG in moist environments.A comprehensive survey of material innovations follows,highlighting breakthroughs in carbon-based materials,conductive polymers,hydrogels,and bio-inspired systems that enhance MEG performance,scalability,and biocompatibility.We then explore a range of device architectures,from planar and layered systems to flexible,miniaturized,and textile-integrated designs,engineered for both energy conversion and sensor integration.Key challenges are analyzed,along with strategies for overcoming them.We conclude with a forward-looking perspective on future directions,including hybrid energy systems,AI-assisted material design,and real-world deployment.This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.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
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been i...Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.展开更多
Al/NH_(4)CoF_(3)-Φ(Φ=0.5,1.0,1.5,2.0,and 3.0)binary composites and Al-NH_(4)CoF_(3)@P(VDF-HFP)ternary composites are fabricated via ultrasonication-assisted blending and electrostatic spraying.The effect of equivale...Al/NH_(4)CoF_(3)-Φ(Φ=0.5,1.0,1.5,2.0,and 3.0)binary composites and Al-NH_(4)CoF_(3)@P(VDF-HFP)ternary composites are fabricated via ultrasonication-assisted blending and electrostatic spraying.The effect of equivalence ratio(Φ)on the reaction properties is systematically investigated in the binary Al/NH_(4)CoF_(3)system.For ternary systems,electrostatic spraying allows both components to be efficiently encapsulated by P(VDF-HFP)and to achieve structural stabilization and enhanced reactivity through synergistic interfacial interactions.Morphological analysis using SEM/TEM revealed that P(VDF-HFP)formed a protective layer on Al and NH_(4)CoF_(3)particles,improving dispersion,hydrophobicity(water contact angle increased by 80.5%compared to physically mixed composites),and corrosion resistance.Thermal decomposition of NH_(4)CoF_(3)occurred at 265℃,releasing NH_(3)and HF,which triggered exothermic reactions with Al.The ternary composites exhibited a narrowed main reaction temperature range and concentrated heat release,attributed to improved interfacial contact and polymer decomposition.Combustion tests demonstrated that Al-NH_(4)CoF_(3)@P(VDF-HFP)achieved self-sustaining combustion.In addition,a simple validation was done by replacing the Al component in the aluminium-containing propellant,demonstrating its potential application in the propellant field.This work establishes a novel strategy for designing stable,high-energy composites with potential applications in advanced propulsion systems.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
Attention,All Villagers Chief Director:Jin Ying Length:Five Episodes Producer:bilibili Broadcasting Platform:bilibili This film series tells the stories of the new generation of farmers in China in a vibrant narrative...Attention,All Villagers Chief Director:Jin Ying Length:Five Episodes Producer:bilibili Broadcasting Platform:bilibili This film series tells the stories of the new generation of farmers in China in a vibrant narrative style.By documenting five villagers’daily lives,each episode highlights the challenges young people are facing in building their life and career in their rural hometowns.展开更多
Previous solar probes have relied on solar energy for power,but in the near-solar environment,traditional solar panels are prone to overheating and radiation damage,increasing system complexity and reducing power reli...Previous solar probes have relied on solar energy for power,but in the near-solar environment,traditional solar panels are prone to overheating and radiation damage,increasing system complexity and reducing power reliability.This study introduces a dual-power system integrating solar-thermal thermoelectric generation with photovoltaic technology.First,suitable thermoelectric materials are screened,the geometric structure of the thermoelectric devices is simulated,and then the fabricated thermoelectric devices are subjected to cyclic heatingcooling power generation tests and long-duration high-temperature power generation tests.The results demonstrate that a single thermoelectric device can stably provide 3.5 W of power with excellent cycling stability.Additionally,this study discusses design concepts for energy storage and intelligent energy management systems required by the dualpower system.Designed for the Solar Close Observations and Proximity Experiments(SCOPE)mission,this dualpower supply system integrates the benefits of both to address demands under varying environmental conditions.展开更多
During the image generation phase,the parserfree Flow-Style-VTON model(PF-Flow-Style-VTON),which utilizes distilled appearance flows,faces two main challenges:blurring,deformation,occlusion,or loss of the arm or palm ...During the image generation phase,the parserfree Flow-Style-VTON model(PF-Flow-Style-VTON),which utilizes distilled appearance flows,faces two main challenges:blurring,deformation,occlusion,or loss of the arm or palm regions in the generated image when these regions of the person occlude the garment;blurring and deformation in the generated image when the person performs large pose movements and the target garment is complex with detailed patterns.To solve these two problems,an improved virtual try-on network model,denoted as IPF-Flow-Style-VTON,is proposed.Firstly,a target warped garment mask refinement module(M-RM)is introduced to refine the warped garment mask and remove erroneous information in the arm and palm regions,thereby improving the quality of subsequent image generation.Secondly,an improved global attention module(GAM)is integrated into the original image generation network,enhancing the ResUNet’s understanding of global context and optimizing the fusion of local features and global information,thereby further improving image generation quality.Finally,the UniPose model is used to provide the pose keypoint information of the target person image,guiding the task execution during the image generation phase.Experiments conducted on the VITON dataset show that the proposed method outperforms the original method,Flow-Style-VTON,by 5.4%,0.3%,6.7%,and 2.2%in Frchet inception distance(FID),structural similarity index measure(SSIM),learned perceptual image patch similarity(LPIPS),and peak signal-to-noise ratio(PSNR),respectively.Overall,the proposed method effectively improves upon the shortcomings of the original network and achieves better visual results.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
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
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
Laser-induced aerosols,predominantly submicron in size,pose significant environmental and health risks during the decommissioning of nuclear reactors.This study experimentally investigated the removal of laser-generat...Laser-induced aerosols,predominantly submicron in size,pose significant environmental and health risks during the decommissioning of nuclear reactors.This study experimentally investigated the removal of laser-generated aerosol particles using a water spray system integrated with an innovative system for pre-injecting electrically charged mist in our facility.To simulate aerosol generation in reactor decommissioning,a high-power laser was used to irradiate various materials(including stainless steel,carbon steel,and concrete),generating aerosol particles that were agglomerated with injected water mist and subsequently scavenged by water spray.Experimental results demonstrate enhanced aerosol removal via aerosol-mist agglomeration,with charged mist significantly improving particle capture by increasing wettability and size.The average improvements for the stainless steel,carbon steel,and concrete were 40%,44%,and 21%,respectively.The results of experiments using charged mist with different polarities(both positive and negative)and different surface coatings reveal that the dominant polarity of aerosols varies with the irradiated materials,influenced by their crystal structure and electron emission properties.Notably,surface coatings such as ZrO_(2)and CeO_(2)were found to possibly alter aerosol charging characteristics,thereby affecting aerosol removal efficiency with charged mist configurations.The innovative aerosol-mist agglomeration approach shows promise in mitigating radiation exposure,ensuring environmental safety,and reducing contaminated water during reactor dismantling.This study contributes critical knowledge for the development of advanced aerosol management strategies for nuclear reactor decommissioning.The understanding obtained in this work is also expected to be useful for various environmental and chemical engineering applications such as gas decontamination,air purification,and pollution control.展开更多
Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement ...Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials.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
文摘For decades,Xu has been committed to fulfilling the duty and mission of a scientist and educator—diving into the laws of nature,caring deeply for the nation,and earnestly cultivating younger generations.
基金supported by the National Natural Science Foundation of China(52373099)Interdisciplinary Research Program of Huazhong University of Science and Technology(5003013161)+1 种基金Innovation and Talent Recruitment Base of New Energy Chemistry and Device(B21003)Hubei Integrative Technology and Innovation Center for Advanced Fiberous Materials(XC202502)。
文摘The integration of interfacial photothermal conversion and hydrovoltaic effect into bifunctional evaporators has emerged as a hopeful approach to address water and energy scarcities.However,developing low-cost bifunctional evaporators and elucidating the freshwater-electricity co-generation mechanism remain challenging.In this work,we prepare porous carbon from waste polyester through a metalorganic framework(MOF)-assisted carbonization strategy and subsequently fabricate a bifunctional evaporator for freshwater-hydroelectricity co-generation.The porous carbon contains rich oxygen-containing groups and shows hierarchical micro-and mesopores with a high specific surface area of 904 m^(2)g^(-1).The porous carbon-based evaporator shows broadband and high light absorption,localized thermal management,good hydrophilicity,and high flexibility.Benefiting from these merits,it achieves high-performance freshwater and hydroelectricity co-generation,with the opencircuit voltage of 250 mV,the short-circuit current of 14μA,and the evaporation rate of 2.34 kg m^(-2)h^(-1).Hence,it is ranked among the most efficient freshwater-hydroelectricity co-generator.Additionally,the weakened hydrogen-bonding network reduces water evaporation enthalpy to 1.7 kJ g^(-1).Mechanistic investigations reveal that selective Na+interaction induces differential ion migration rate to generate streaming potential,as evidenced by molecular dynamics simulations.Meanwhile,the photothermal effect enhances voltage output by promoting interfacial ion concentration gradients.During the outdoor freshwater-electricity co-generation,it shows the voltage output of 250 mV and freshwater production of 2.34 kg m-2.This work not only puts forward a new platform to fabricate advanced evaporators from low-cost waste plastics but also unravels the freshwater-electricity co-generation mechanism,offering scalable strategies to tackle freshwater and energy crises.
基金funded by the Basic Scientific Research of China Geological Academy(YK202305)National Key R&D Program of China(2019YFB1504101)+1 种基金National Natural Science Foundation of China(41602271)China Geological Survey(DD20160207 and DD20189112)。
文摘The geothermal resources in China are primarily found in its sedimentary basins,particularly in the large basins located in eastern China,which hold significant potential for geothermal energy development.The Songliao,North China,and Zhangzhou basins are of special interest due to their considerable exploration depths,extensive development history,and high levels of research activity.This study focuses on the three basins to analyze their thermal reservoir characteristics in eastern China.Between 2017 and 2023,the research team carried out a comprehensive analysis involving deep boreholes that exceeded 4000 m in depth within these three basins.They meticulously created detailed physical profiles that captured essential characteristics such as porosity,permeability,and thermal properties,reaching down to the basement of each basin.The findings indicated that variations in thermal conductivity within shallow geotechnical layers significantly influence the redistribution of deep thermal energy in the upper layers of the earth.Furthermore,differences in physical properties notably affect heat transport processes.The research proposes distinct heat models tailored for each basin:For the Songliao Basin,a low-permeability model with homogeneous thermal properties is constructed;for the North China Basin,high permeability and thermal conductivity layers are highlighted;and a fracture network controlling water and heat is presented in the Zhangzhou Basin.To elucidate the thermal structure of these basins,the Curie surface and Moho surface were analyzed.The shallow Curie surface indicates ongoing intense thermal activity stemming from crustal heat sources,while a shallow Moho surface signifies historical vigorous mantle thermal activity associated with mantle source heat production.Furthermore,the research evaluates the geothermal resources and the potential for carbon emission reduction in these basins.Total volume of exploitable geothermal fluid is estimated to be 76.9×10^(9) m^(3)/a,corresponding to an annual renewable geothermal energy 1.47×10^(16)k J.The implementation of geothermal energy could lead to a reduction in annual CO_(2)emissions by nearly 2×10^(9) t,which constitutes about 17.4%of China’s national carbon emissions in 2022.This estimation provides invaluable theoretical insights and data support for geothermal exploration and sustainable development in eastern China.
基金supported by the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2024ZD0302502 for WZ)the National Natural Science Foundation of China(Grant No.92365210 for WZ)+1 种基金Tsinghua Initiative Scientific Research Program (for WZ)the project of Tsinghua University-Zhuhai Huafa Industrial Share Company Joint Institute for Architecture Optoelectronic Technologies (JIAOT,for YH)。
文摘To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.
基金supported by the Natural Science Foundation of Fujian Province of China(2025J01380)National Natural Science Foundation of China(No.62471139)+3 种基金the Major Health Research Project of Fujian Province(2021ZD01001)Fujian Provincial Units Special Funds for Education and Research(2022639)Fujian University of Technology Research Start-up Fund(GY-S24002)Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare(GY-H-24179).
文摘The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence.
基金supported by the National Natural Science Foundation of China(52305388,BE0200030)Shanghai Pujiang Program(22PJ1407600)+1 种基金SJTU Explore X programShanghai Jiao Tong University Initiative Scientific Research Program(WH220402021)。
文摘Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for renewable energy and constructing self-powered electronics.In this review,we begin by outlining the fundamental mechanisms—ion diffusion,electric double layer formation,and streaming potential—that govern charge transport for MEG in moist environments.A comprehensive survey of material innovations follows,highlighting breakthroughs in carbon-based materials,conductive polymers,hydrogels,and bio-inspired systems that enhance MEG performance,scalability,and biocompatibility.We then explore a range of device architectures,from planar and layered systems to flexible,miniaturized,and textile-integrated designs,engineered for both energy conversion and sensor integration.Key challenges are analyzed,along with strategies for overcoming them.We conclude with a forward-looking perspective on future directions,including hybrid energy systems,AI-assisted material design,and real-world deployment.This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.
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financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.
基金supported by the National Natural Science Foundation of China(No.51706105)。
文摘Al/NH_(4)CoF_(3)-Φ(Φ=0.5,1.0,1.5,2.0,and 3.0)binary composites and Al-NH_(4)CoF_(3)@P(VDF-HFP)ternary composites are fabricated via ultrasonication-assisted blending and electrostatic spraying.The effect of equivalence ratio(Φ)on the reaction properties is systematically investigated in the binary Al/NH_(4)CoF_(3)system.For ternary systems,electrostatic spraying allows both components to be efficiently encapsulated by P(VDF-HFP)and to achieve structural stabilization and enhanced reactivity through synergistic interfacial interactions.Morphological analysis using SEM/TEM revealed that P(VDF-HFP)formed a protective layer on Al and NH_(4)CoF_(3)particles,improving dispersion,hydrophobicity(water contact angle increased by 80.5%compared to physically mixed composites),and corrosion resistance.Thermal decomposition of NH_(4)CoF_(3)occurred at 265℃,releasing NH_(3)and HF,which triggered exothermic reactions with Al.The ternary composites exhibited a narrowed main reaction temperature range and concentrated heat release,attributed to improved interfacial contact and polymer decomposition.Combustion tests demonstrated that Al-NH_(4)CoF_(3)@P(VDF-HFP)achieved self-sustaining combustion.In addition,a simple validation was done by replacing the Al component in the aluminium-containing propellant,demonstrating its potential application in the propellant field.This work establishes a novel strategy for designing stable,high-energy composites with potential applications in advanced propulsion systems.
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Attention,All Villagers Chief Director:Jin Ying Length:Five Episodes Producer:bilibili Broadcasting Platform:bilibili This film series tells the stories of the new generation of farmers in China in a vibrant narrative style.By documenting five villagers’daily lives,each episode highlights the challenges young people are facing in building their life and career in their rural hometowns.
基金supported by National Key R&D Program of China(2022YFF0503804).
文摘Previous solar probes have relied on solar energy for power,but in the near-solar environment,traditional solar panels are prone to overheating and radiation damage,increasing system complexity and reducing power reliability.This study introduces a dual-power system integrating solar-thermal thermoelectric generation with photovoltaic technology.First,suitable thermoelectric materials are screened,the geometric structure of the thermoelectric devices is simulated,and then the fabricated thermoelectric devices are subjected to cyclic heatingcooling power generation tests and long-duration high-temperature power generation tests.The results demonstrate that a single thermoelectric device can stably provide 3.5 W of power with excellent cycling stability.Additionally,this study discusses design concepts for energy storage and intelligent energy management systems required by the dualpower system.Designed for the Solar Close Observations and Proximity Experiments(SCOPE)mission,this dualpower supply system integrates the benefits of both to address demands under varying environmental conditions.
基金National Key R&D Program of China(No.2019YFC1521300)。
文摘During the image generation phase,the parserfree Flow-Style-VTON model(PF-Flow-Style-VTON),which utilizes distilled appearance flows,faces two main challenges:blurring,deformation,occlusion,or loss of the arm or palm regions in the generated image when these regions of the person occlude the garment;blurring and deformation in the generated image when the person performs large pose movements and the target garment is complex with detailed patterns.To solve these two problems,an improved virtual try-on network model,denoted as IPF-Flow-Style-VTON,is proposed.Firstly,a target warped garment mask refinement module(M-RM)is introduced to refine the warped garment mask and remove erroneous information in the arm and palm regions,thereby improving the quality of subsequent image generation.Secondly,an improved global attention module(GAM)is integrated into the original image generation network,enhancing the ResUNet’s understanding of global context and optimizing the fusion of local features and global information,thereby further improving image generation quality.Finally,the UniPose model is used to provide the pose keypoint information of the target person image,guiding the task execution during the image generation phase.Experiments conducted on the VITON dataset show that the proposed method outperforms the original method,Flow-Style-VTON,by 5.4%,0.3%,6.7%,and 2.2%in Frchet inception distance(FID),structural similarity index measure(SSIM),learned perceptual image patch similarity(LPIPS),and peak signal-to-noise ratio(PSNR),respectively.Overall,the proposed method effectively improves upon the shortcomings of the original network and achieves better visual results.
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.
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financial support from the Nuclear Energy Science&Technology and Human Resource Development Project of the Japan Atomic Energy Agency/Collaborative Laboratories for Advanced Decommissioning Science(No.R04I034)The author Ruicong Xu appreciates the scholarship(financial support)from the China Scholarship Council(CSC,No.202106380073).
文摘Laser-induced aerosols,predominantly submicron in size,pose significant environmental and health risks during the decommissioning of nuclear reactors.This study experimentally investigated the removal of laser-generated aerosol particles using a water spray system integrated with an innovative system for pre-injecting electrically charged mist in our facility.To simulate aerosol generation in reactor decommissioning,a high-power laser was used to irradiate various materials(including stainless steel,carbon steel,and concrete),generating aerosol particles that were agglomerated with injected water mist and subsequently scavenged by water spray.Experimental results demonstrate enhanced aerosol removal via aerosol-mist agglomeration,with charged mist significantly improving particle capture by increasing wettability and size.The average improvements for the stainless steel,carbon steel,and concrete were 40%,44%,and 21%,respectively.The results of experiments using charged mist with different polarities(both positive and negative)and different surface coatings reveal that the dominant polarity of aerosols varies with the irradiated materials,influenced by their crystal structure and electron emission properties.Notably,surface coatings such as ZrO_(2)and CeO_(2)were found to possibly alter aerosol charging characteristics,thereby affecting aerosol removal efficiency with charged mist configurations.The innovative aerosol-mist agglomeration approach shows promise in mitigating radiation exposure,ensuring environmental safety,and reducing contaminated water during reactor dismantling.This study contributes critical knowledge for the development of advanced aerosol management strategies for nuclear reactor decommissioning.The understanding obtained in this work is also expected to be useful for various environmental and chemical engineering applications such as gas decontamination,air purification,and pollution control.
文摘Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials.
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.
