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
Electroosmotic transport and entropy generation play a decisive role in regulating efficiency,stability,and energy cost of non-Newtonian nanoblood flows in stenosed arteries,particularly with tapered geometries.Thisst...Electroosmotic transport and entropy generation play a decisive role in regulating efficiency,stability,and energy cost of non-Newtonian nanoblood flows in stenosed arteries,particularly with tapered geometries.Thisstudy develops a unified model to analyze ZnO-Williamson nanoblood flow through a stenosed artery with converging,diverging,and non-tapered configurations,incorporating electroosmosis,viscous dissipation,and entropy production.The arterial walls are assumed to be electrically charged with a no-slip condition to induce electroosmotic propulsionalong the endothelial surface.The partial differential equations are nondimensionalized to a coupled system ofnonlinear ordinary differential equations,which are solved numerically using a MATLAB-based shooting technique.Parametric investigation is conducted for Brinkman,Grashof,and Weissenberg numbers,ZnO fractional volume,volumetric flow rate,and Helmholtz-Smoluchowski velocity to quantify their influences on axial velocity,wall shearstress,impedance resistance,temperature distribution,entropy generation,Bejan number,and streamline topology.The axial velocity decreases radially with increasing Brinkman number for all arterial geometries.Increasing ZnOnanoparticles improves thermal transport owing to enhanced effective thermal conductivity but simultaneously elevatesentropy generation due to increased viscous dissipation.Higher Weissenberg numbers suppress entropy production bypromoting elastic stress redistribution and lowering shear-induced irreversibility.Impedance resistance decreases withincreasing stenosis height but increases with stenosis shape parameter and ZnO fractional volume.Streamline analysisshows that buoyancy and viscoelasticity significantly distort flow near the stenosis,while increasing electroosmoticvelocity stabilizes streamlines,suppresses recirculation,and reduces local shear stress and pressure fluctuations.Inconclusion,electroosmotic actuation is most effective in reducing flow resistance in the converging tapered artery,particularly at lower ZnO volume fractions.Overall,the findings highlight the potential of optimized electroosmoticactuation and controlled nanoparticle loading to minimize thermodynamic losses,regulate shear stress,and improveflow uniformity in stenosed vessels,with promising implications for electro-assisted drug delivery,nanotherapeutics,and bio-inspired vascular microfluidic systems.展开更多
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
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 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
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
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
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
With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation ...With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation in crystals driven by optical skyrmions.We find that although the skyrmion number is not conserved,the resulting high-order harmonics can exhibit a distinctive multi-vortex structure,whose features are shaped by both the topology of the optical skyrmions and the rotational symmetry of the crystal.The position of the vortex centers can be effectively tuned by employing different types of optical skyrmions.To elucidate the underlying physics,we develop a multi-absorption channel model based on the conservation laws of spin and orbital angular momentum.Our work explores the role of optical topology in extreme nonlinear light-matter interactions,offering new opportunities for the formation and manipulation of optical vortices and novel structured light fields in the visible and ultraviolet regimes.展开更多
Fabricating macroscale smart actuators that can convert light energy into other forms of energy,especially mechanical and electrical energy,is of great significance.Herein,a simple and efficient 4D printed method for ...Fabricating macroscale smart actuators that can convert light energy into other forms of energy,especially mechanical and electrical energy,is of great significance.Herein,a simple and efficient 4D printed method for fabricating photomechanical actuators based on micro/nano-scale crystals is developed.The high versatility and generality of this method are successfully demonstrated using nine different types of photoresponsive crystalline actuators,including acylhydrazone-,anthracene-,olefin-,and azobenzene-based molecular crystals and covalent organic frameworks(COFs).The low-cost neutral silicone sealant elastomer is first chosen as the photomechanical 4D printing matrix.Notably,these actuators can be used to perform bionic motions(the first windmills spin using crystalline material,dragonflies fly,and sunflowers bloom)under the stimulation of visible light and can realize energy conversion from mechanical energy into electricity when coupled with a piezoelectric membrane.This work provides new insights into the design and manufacturing of smart photomechanical actuators and electricity generators and expands the application scope of COFs.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
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
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
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
The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances...The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances within a single day can significantly impact on carbon prices.Faced with uncertain future carbon and electricity prices,GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions.In this paper,a two-layered hybrid mathematical-deep reinforcement learning(DRL)optimization framework is proposed.The upper DRL layer tackles the stochastic,year-long carbon trading and allowance usage optimization problem,aiming for long-term optimality and providing guidance for short-term decisions in the lower layer.The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while enforcing strict technical constraints.To accelerate learning of the annual compliance cycle,a decision timeline transfer learning method is proposed,enabling the DRL agent to progressively refine its policy through sequentially training on monthly,weekly and daily decision environments.Case studies demonstrate that,with these methods,a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.展开更多
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
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funded by the Ministry of Higher Education,Malaysia,under the Fundamental Research Grant Scheme FRGS/1/2023/STG06/UM/02/4(Project FP069-2023)。
文摘Electroosmotic transport and entropy generation play a decisive role in regulating efficiency,stability,and energy cost of non-Newtonian nanoblood flows in stenosed arteries,particularly with tapered geometries.Thisstudy develops a unified model to analyze ZnO-Williamson nanoblood flow through a stenosed artery with converging,diverging,and non-tapered configurations,incorporating electroosmosis,viscous dissipation,and entropy production.The arterial walls are assumed to be electrically charged with a no-slip condition to induce electroosmotic propulsionalong the endothelial surface.The partial differential equations are nondimensionalized to a coupled system ofnonlinear ordinary differential equations,which are solved numerically using a MATLAB-based shooting technique.Parametric investigation is conducted for Brinkman,Grashof,and Weissenberg numbers,ZnO fractional volume,volumetric flow rate,and Helmholtz-Smoluchowski velocity to quantify their influences on axial velocity,wall shearstress,impedance resistance,temperature distribution,entropy generation,Bejan number,and streamline topology.The axial velocity decreases radially with increasing Brinkman number for all arterial geometries.Increasing ZnOnanoparticles improves thermal transport owing to enhanced effective thermal conductivity but simultaneously elevatesentropy generation due to increased viscous dissipation.Higher Weissenberg numbers suppress entropy production bypromoting elastic stress redistribution and lowering shear-induced irreversibility.Impedance resistance decreases withincreasing stenosis height but increases with stenosis shape parameter and ZnO fractional volume.Streamline analysisshows that buoyancy and viscoelasticity significantly distort flow near the stenosis,while increasing electroosmoticvelocity stabilizes streamlines,suppresses recirculation,and reduces local shear stress and pressure fluctuations.Inconclusion,electroosmotic actuation is most effective in reducing flow resistance in the converging tapered artery,particularly at lower ZnO volume fractions.Overall,the findings highlight the potential of optimized electroosmoticactuation and controlled nanoparticle loading to minimize thermodynamic losses,regulate shear stress,and improveflow uniformity in stenosed vessels,with promising implications for electro-assisted drug delivery,nanotherapeutics,and bio-inspired vascular microfluidic systems.
基金supported by the 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 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(81760037)Yunling Scholar Project of Yunnan Province(YNWR-YLXZ-2019-0005)+1 种基金Hunan Provincial Innovation Platform and Talent Program(2018SK4004)Hunan Provincial Natural Science Foundation(2019JJ80048).
文摘The occurrence of severe thalassemia,an inherited blood disorder that is either blood-transfusiondependent or fatal,can be mitigated through carrier screening.Here,we aim to evaluate the effectiveness and outcomes of pre-conceptional and early pregnancy screening initiatives for severe thalassemia prevention in a diverse population of 28,043 women.Using next-generation sequencing(NGS),we identify 4,226(15.07%)thalassemia carriers across 29 ethnic groups and categorize them into high-(0.75%),low-(25.86%),and unknown-risk(69.19%)groups based on their spouses'screening results.Post-screening follow-up reveals 59 fetuses with severe thalassemia exclusively in high-risk couples,underscoring the efficacy of risk classification.Among 25,053 live births over 6 months of age,two severe thalassemia infants were born to unknown-risk couples,which was attributed to incomplete screening and late NGS-based testing for a rare variant.Notably,64 rare variants are identified in 287 individuals,highlighting the genetic heterogeneity of thalassemia.We also observe that migrant flow significantly impacts carrier rates,with 93.90%of migrants to Chenzhou originating from high-prevalence regions in southern China.Our study demonstrates that NGS-based screening during pre-conception and early pregnancy is effective for severe thalassemia prevention,emphasizing the need for continuous screening efforts in areas with high and underestimated prevalence.
基金supported by the National Natural Science Foundation of China(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supported by the National Natural Science Foundation of China (Grant Nos. 12234002, 92250303, 12474486, 12504301, and 12504396)the National Key Research and Development Program of China (Grant No. 2024YFA1612101)。
文摘With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation in crystals driven by optical skyrmions.We find that although the skyrmion number is not conserved,the resulting high-order harmonics can exhibit a distinctive multi-vortex structure,whose features are shaped by both the topology of the optical skyrmions and the rotational symmetry of the crystal.The position of the vortex centers can be effectively tuned by employing different types of optical skyrmions.To elucidate the underlying physics,we develop a multi-absorption channel model based on the conservation laws of spin and orbital angular momentum.Our work explores the role of optical topology in extreme nonlinear light-matter interactions,offering new opportunities for the formation and manipulation of optical vortices and novel structured light fields in the visible and ultraviolet regimes.
基金the National Natural Science Foundation of China(22175099,22205116,22301147)National Key Research and Development Program of China(2021YFC2102100)+1 种基金Frontiers Science Center for New Organic Matter of Nankai University(63181206)111 Project(B12015).
文摘Fabricating macroscale smart actuators that can convert light energy into other forms of energy,especially mechanical and electrical energy,is of great significance.Herein,a simple and efficient 4D printed method for fabricating photomechanical actuators based on micro/nano-scale crystals is developed.The high versatility and generality of this method are successfully demonstrated using nine different types of photoresponsive crystalline actuators,including acylhydrazone-,anthracene-,olefin-,and azobenzene-based molecular crystals and covalent organic frameworks(COFs).The low-cost neutral silicone sealant elastomer is first chosen as the photomechanical 4D printing matrix.Notably,these actuators can be used to perform bionic motions(the first windmills spin using crystalline material,dragonflies fly,and sunflowers bloom)under the stimulation of visible light and can realize energy conversion from mechanical energy into electricity when coupled with a piezoelectric membrane.This work provides new insights into the design and manufacturing of smart photomechanical actuators and electricity generators and expands the application scope of COFs.
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.
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 the DH2025-TN07-07 project conducted at the Thai Nguyen University of Information and Communication Technology,Thai Nguyen,Vietnam,with additional support from the AI in Software Engineering Lab.
文摘It remains difficult to automate the creation and validation of Unified Modeling Language(UML)dia-grams due to unstructured requirements,limited automated pipelines,and the lack of reliable evaluation methods.This study introduces a cohesive architecture that amalgamates requirement development,UML synthesis,and multimodal validation.First,LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements.Then,DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code.Using this dual-LLM pipeline,we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families.Rendering analysis showed that 89.5%of the generated diagrams compile correctly,while invalid cases were detected automatically.To assess quality,we employed a multimodal scoring method that combines Qwen2.5-VL-3B,LLaMA-3.2-11B-Vision-Instruct and Aya-Vision-8B,with weights based on MMMU performance.A study with 94 experts revealed strong alignment between automatic and manual evaluations,yielding a Pearson correlation of r=0.82 and a Fleiss’Kappa of 0.78.This indicates a high degree of concordance between automated metrics and human judgment.Overall,the results demonstrated that our scoring system is effective and that the proposed generation pipeline produces UML diagrams that are both syntactically correct and semantically coherent.More broadly,the system provides a scalable and reproducible foundation for future work in AI-driven software modeling and multimodal verification.
基金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 Natural Science Foundation of China-Smart Grid Joint Fund of State Grid Corporation of China(No.U2066212)the Na-tional Natural Science Foundation of China(No.52207105)the Key Science and Technology Pro-jects of China Southern Power Grid Corporation(No.066600KK52222023).
文摘The annual compliance cycle of the carbon trading system allows generation companies(GenCos)to decouple the timing of carbon allowance purchases from their actual emissions.However,trading a large volume of allowances within a single day can significantly impact on carbon prices.Faced with uncertain future carbon and electricity prices,GenCos must address a challenging multistage stochastic optimization problem to coordinate their carbon trading strategies with daily power generation decisions.In this paper,a two-layered hybrid mathematical-deep reinforcement learning(DRL)optimization framework is proposed.The upper DRL layer tackles the stochastic,year-long carbon trading and allowance usage optimization problem,aiming for long-term optimality and providing guidance for short-term decisions in the lower layer.The lower mathematical optimization layer addresses the deterministic daily power generation schedule problem while enforcing strict technical constraints.To accelerate learning of the annual compliance cycle,a decision timeline transfer learning method is proposed,enabling the DRL agent to progressively refine its policy through sequentially training on monthly,weekly and daily decision environments.Case studies demonstrate that,with these methods,a GenCo can reduce emission costs and increase profits by effectively leveraging carbon price fluctuations within the compliance cycle.
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.
