The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxi...The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.展开更多
Ammonia and nitric acid,versatile industrial feedstocks,and burgeoning clean energy vectors hold immense promise for sustainable development.However,Haber–Bosch and Ostwald processes,which generates carbon dioxide as...Ammonia and nitric acid,versatile industrial feedstocks,and burgeoning clean energy vectors hold immense promise for sustainable development.However,Haber–Bosch and Ostwald processes,which generates carbon dioxide as massive by-product,contribute to greenhouse effects and pose environmental challenges.Thus,the pursuit of nitrogen fixation through carbon–neutral pathways under benign conditions is a frontier of scientific topics,with the harnessing of solar energy emerging as an enticing and viable option.This review delves into the refinement strategies for scale-up mild photocatalytic nitrogen fixation,fields ripe with potential for innovation.The narrative is centered on enhancing the intrinsic capabilities of catalysts to surmount current efficiency barriers.Key focus areas include the in-depth exploration of fundamental mechanisms underpinning photocatalytic procedures,rational element selection,and functional planning,state-of-the-art experimental protocols for understanding photo-fixation processes,valid photocatalytic activity evaluation,and the rational design of catalysts.Furthermore,the review offers a suite of forward-looking recommendations aimed at propelling the advancement of mild nitrogen photo-fixation.It scrutinizes the existing challenges and prospects within this burgeoning domain,aspiring to equip researchers with insightful perspectives that can catalyze the evolution of cutting-edge nitrogen fixation methodologies and steer the development of next-generation photocatalytic systems.展开更多
Due to the low content of alloying elements and the lack of effective nucleation sites,the fusion zone(FZ)of tungsten inert gas(TIG)welded AZ31 alloy typically exhibits undesirable coarse columnar grains,which can res...Due to the low content of alloying elements and the lack of effective nucleation sites,the fusion zone(FZ)of tungsten inert gas(TIG)welded AZ31 alloy typically exhibits undesirable coarse columnar grains,which can result in solidification defects and reduced mechanical properties.In this work,a novel welding wire containing MgO particles has been developed to promote columnar-to-equiaxed transition(CET)in the FZ of TIG-welded AZ31 alloy.The results show the achievement of a fully equiaxed grain structure in the FZ,with a significant 71.9%reduction in grain size to 41 μm from the original coarse columnar dendrites.Furthermore,the combination of using MgO-containing welding wire and pulse current can further refine the grain size to 25.6 μm.Microstructural analyses reveal the homogeneous distribution of MgO particles in the FZ.The application of pulse current results in an increase in the number density of MgO(1-2 μm)from 5.16 × 10^(4) m^(-3) to 6.18 × 10^(4) m^(-3).The good crystallographic matching relationship between MgO and α-Mg matrix,characterized by the orientation relationship of[11(2)0]α-Mg//[0(1)1]MgO and(0002)_(α-Mg)//(111)_(MgO),indicates that the MgO particles can act as effective nucleation sites for α-Mg to reduce nucleation undercooling.According to the Hunt criteria,the critical temperature gradient for CET is greatly enhanced due to the significantly increased number density of MgO nucleation sites.In addition,the correlation with the thermal simulation results reveals a transition in the solidification conditions within the welding pool from the columnar grain zone to the equiaxed grain zone in the CET map,leading to the realization of CET.The exceptional grain refinement has contributed to a simultaneous improvement in the strength and plasticity of welded joints.This study presents a novel strategy for controlling equiaxed microstructure and optimizing mechanical properties in fusion welding or wire and arc additive manufacturing of Mg alloy components.展开更多
Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual...Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.展开更多
To investigate the effect of microstructure evolution on corrosion behavior and strengthening mechanism of Mg-1Zn-1Ca(wt.%)alloys,as-cast Mg-1Zn-1Ca alloys were performed by equal channel angular pressing(ECAP)with 1 ...To investigate the effect of microstructure evolution on corrosion behavior and strengthening mechanism of Mg-1Zn-1Ca(wt.%)alloys,as-cast Mg-1Zn-1Ca alloys were performed by equal channel angular pressing(ECAP)with 1 and 4 passes.The corrosion behavior and mechanical properties of alloys were investigated by optical microscopy(OM),scanning electron microscopy(SEM),electron backscatter diffraction(EBSD),electrochemical tests,immersion tests and tensile tests.The results showed that mechanical properties improved after ECAP 1 pass;however,the corrosion resistance deteriorated due to high-density dislocations and fragmented secondary phases by ECAP.In contrast,synchronous improvement in the mechanical properties and corrosion resistance was achieved though grain refinement after ECAP 4 passes;fine grains led to a significant improvement in the yield strength,ultimate tensile strength,elongation,and corrosion rate of 103 MPa,223 MPa,30.5%,and 1.5843 mm/a,respectively.The enhanced corrosion resistance was attributed to the formation of dense corrosion product films by finer grains and the barrier effect by high-density grain boundaries.These results indicated that Mg-1Zn-1Ca alloy has a promising potential for application in biomedical materials.展开更多
Three types of NdFeB magnets with the same composition and different grain sizes were prepared,and then the grain boundary diffusion was conducted using metal Tb under the same technical parameters.The effect of grain...Three types of NdFeB magnets with the same composition and different grain sizes were prepared,and then the grain boundary diffusion was conducted using metal Tb under the same technical parameters.The effect of grain size on the grain boundary diffusion process and properties of sintered NdFeB magnets was investigated.The diffusion process was assessed using X-ray diffractometer,field emission scanning electron microscope,and electron probe microanalyzer.The magnetic properties of the magnet before and after diffusion were investigated.The results show that the grain refinement of the magnet leads to higher Tb utilization efficiency and results in higher coercivity at different temperatures.It can be attributed to the formation of a deeper and more complete core-shell structure,resulting in better magnetic isolation and higher anisotropy of the Nd_(2)Fe_(14)B grains.This work may shed light on developing high coercivity with low heavy rare earth elements through grain refinement.展开更多
High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiri...High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.展开更多
Al-Cu-Mg-Ag alloys have become a research hotspot because of its good heat resistance.Its excellent mechanical properties are inseparable from the regulation of the structure by researchers.The method of material stru...Al-Cu-Mg-Ag alloys have become a research hotspot because of its good heat resistance.Its excellent mechanical properties are inseparable from the regulation of the structure by researchers.The method of material structure simulation has become more and more perfect.This study employs numerical simulation to investigate the microstructure evolution of Al-Cu-Mg-Ag alloys during solidification with the aim of controlling its structure.The size distribution of Ti-containing particles in an Al-Ti-B master alloy was characterized via microstructure observation,serving as a basis for optimizing the nucleation density parameters for particles of varying radii in the phase field model.The addition of refiner inhibited the growth of dendrites and no longer produced coarse dendrites.With the increase of refiner,the grains gradually tended to form cellular morphology.The refined grains were about 100μm in size.Experimental validation of the simulated as-cast grain morphology was conducted.The samples were observed by metallographic microscope and scanning electron microscope.The addition of refiner had a significant effect on the refinement of the alloy,and the average grain size after refinement was also about 100μm.At the same time,the XRD phase identification of the alloy was carried out.The observation of the microstructure morphology under the scanning electron microscope showed that the precipitated phase was mainly concentrated on the grain boundary.The Al_(2)Cu accounted for about 5%,and the matrix phase FCC accounted for about 95%,which also corresponded well with the simulation results.展开更多
Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic an...Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic and lightweight SR framework designed for arbitrary scaling factors.DDNet integrates a residual learning structure with an Adaptively fusion Feature Block(AFB)and a scale-aware upsampling module,effectively reducing parameter overhead while preserving reconstruction quality.Additionally,we introduce DDNetGAN,an enhanced variant that leverages a relativistic Generative Adversarial Network(GAN)to further improve texture realism.To validate the proposed models,we conduct extensive training using the DIV2K and Flickr2K datasets and evaluate performance across standard benchmarks including Set5,Set14,Urban100,Manga109,and BSD100.Our experiments cover both symmetric and asymmetric upscaling factors and incorporate ablation studies to assess key components.Results show that DDNet and DDNetGAN achieve competitive performance compared with mainstream SR algorithms,demonstrating a strong balance between accuracy,efficiency,and flexibility.These findings highlight the potential of our approach for practical real-world super-resolution applications.展开更多
The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life s...The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.展开更多
This study investigates the inundation depths of urban floods induced by real storm events,focusing on the development and assessment of super-resolution model based on ensemble learning methods.Unlike traditional dee...This study investigates the inundation depths of urban floods induced by real storm events,focusing on the development and assessment of super-resolution model based on ensemble learning methods.Unlike traditional deep neural networks which require extensive training and high parameterization,this study utilizes ensemble learning model to reconstruct high-resolution flood predictions from low-resolution hydrodynamic simulations.Hydrodynamic modeling results of real pluvial flood event at various spatial resolution are used for constructing datasets and for training and testing the point-based super-resolution model.Influencing factors related to urban terrain,subsurface,rainfall inputs and the hydrodynamic modeling results at coarser resolutions are used as features in the super-resolution model on basis of Random Forest,in which hyperparameters are tuned with Bayesian optimization method.The trained super-resolution models effectively reconstruct high-resolution inundation conditions from 30 m to 5 m coarse resolution inputs,highlighting an increase in correlation coefficients and a decrease in root mean squared error(RMSE)as resolution improves.Dominant influencing factors in the super-resolution models are identified together with variances in their contributions to the model performance.Two optimization approaches are applied to enhance accuracy and mitigate overestimation at coarse resolutions for the super-resolution models.The first integrates outputs from various coarse resolution models as features,notably reducing overestimation,especially with finer 5 m resolutions.The second employs ensemble modeling with super-resolution models from different datasets,which improves the performance across all tested resolutions,demonstrating the robustness of combining multiple predictive models for better flood forecasting in urban environments.展开更多
Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provi...Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.展开更多
A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data a...A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.展开更多
The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;howev...The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;however,their low resolution,particularly the loss of high-frequency information at pore edges,presents challenges in analyzing fractures and pores in shale gas reservoirs.This study introduced a novel neural network called the spatial-spectral domain attention network(SSDAN),which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel.The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery,spectral blocks to process Fourier transform information of images and enhance high-frequency recovery,and an adaptive vision transformer to process Fourier transform block information,eliminating the need for a preset image size.The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets,achieving optimal performance on key indicators such as peak signal-to-noise ratio,structural similarity,learned perceptual image patch similarity,and Frechet inception distance while also exhibiting superior visual performance in pore recovery.Ablation experiments further confirmed the effectiveness of the spatial blocks,channel attention,spectral blocks,and frequency loss function in the model.The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges,thereby validating its effectiveness in unconventional natural gas reservoir analyses.展开更多
Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the preva...Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.展开更多
BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI...BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI)in predicting the histopathologic grade in hepatocellular carcinoma.METHODS We retrospectively analyzed a total of 826 patients from two medical centers(training 459;validation 196;test 171).T2-weighted imaging,diffusion-weighted imaging,and portal venous phases were collected.Tumor segmentations were conducted automatically by 3D U-Net.Based on generative adversarial network,we utilized 3D SR reconstruction to produce SR MRI.Radiomics models were developed and validated by XGBoost and Catboost.The predictive efficiency was demonstrated by calibration curves,decision curve analysis,area under the curve(AUC)and net reclassification index(NRI).RESULTS We extracted 3045 radiomic features from both NR and SR MRI,retaining 29 and 28 features,respectively.For XGBoost models,SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts(0.83 vs 0.79;0.80 vs 0.78),respectively.Consistent trends were seen in CatBoost models:SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76.NRI indicated that the SR MRI models could improve the prediction accuracy by-1.6%to 20.9%compared to the NR MRI models.CONCLUSION Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC.It may be a powerful tool for better stratification management for patients with operable HCC.展开更多
Deep convolutional neural networks(CNNs)have demonstrated remarkable performance in video super-resolution(VSR).However,the ability of most existing methods to recover fine details in complex scenes is often hindered ...Deep convolutional neural networks(CNNs)have demonstrated remarkable performance in video super-resolution(VSR).However,the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction.To address this limitation,we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network(3D-ERVSNet).This network employs a forward and backward bidirectional propagation module(FBBPM)that aligns features across frames using explicit optical flow through lightweight SPyNet.By incorporating an enhanced residual structure(ERS)with skip connections,shallow and deep features are effectively integrated,enhancing texture restoration capabilities.Furthermore,3D convolution module(3DCM)is applied after the backward propagation module to implicitly capture spatio-temporal dependencies.The architecture synergizes these components where FBBPM extracts aligned features,ERS fuses hierarchical representations,and 3DCM refines temporal coherence.Finally,a deep feature aggregation module(DFAM)fuses the processed features,and a pixel-upsampling module(PUM)reconstructs the high-resolution(HR)video frames.Comprehensive evaluations on REDS,Vid4,UDM10,and Vim4 benchmarks demonstrate well performance including 30.95 dB PSNR/0.8822 SSIM on REDS and 32.78 dB/0.8987 on Vim4.3D-ERVSNet achieves significant gains over baselines while maintaining high efficiency with only 6.3M parameters and 77ms/frame runtime(i.e.,20×faster than RBPN).The network’s effectiveness stems from its task-specific asymmetric design that balances explicit alignment and implicit fusion.展开更多
Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribu...Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.展开更多
Monitoring the dynamics of cellular pseudopodia at nanoscale has become essential for understanding their diverse and complex functions in living cells.This is made possible by combining single-molecule localization m...Monitoring the dynamics of cellular pseudopodia at nanoscale has become essential for understanding their diverse and complex functions in living cells.This is made possible by combining single-molecule localization microscopy(SMLM)with self-blinking dyes.However,existing self-blinking dyes often face limitations,such as nonspecific blinking and low photostability,which can bring background noise and yield erroneous localization signals,hindering their effectiveness for nanoscale visualization.Here,we present a method for long-term SMLM imaging of cellular pseudopodia dynamics using a blinkogenic probe that exhibits self-blinking activation upon molecular recognition.This approach enabled the precise tracking of various pseudopodia structures,including filopodia,lamellipodia,and(tunneling nanotubes)-nanoscale(TNTs),in living cells.We monitored the growth and fusion of filopodia,as well as the extension and shrinkage of lamellipodia,in real-time.Additionally,we identified two distinct fusion modes between filopodia and lamellipodia and captured the formation of TNTs and their interactions with filopodia,demonstrating the probe's utility in visualizing real-time pseudopodia dynamics at nanoscale.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51977027 and 51967008)the Scientific and Technological Project of Yunnan Precious Metals Lab-oratory(Nos.YPML-2023050250 and YPML-2022050206).
文摘The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.
基金financially supported by the National Natural Science Foundation of China(No.21675131)the Volkswagen Foundation(Freigeist Fellowship No.89592)+1 种基金the Natural Science Foundation of Chongqing(No.2020jcyj-zdxmX0003,CSTB2023NSCQ-MSX0924)the National Research Foundation,Singapore,and A*STAR(Agency for Science Technology and Research)under its LCER Phase 2 Programme Hydrogen&Emerging Technologies FI,Directed Hydrogen Programme(Award No.U2305D4003).
文摘Ammonia and nitric acid,versatile industrial feedstocks,and burgeoning clean energy vectors hold immense promise for sustainable development.However,Haber–Bosch and Ostwald processes,which generates carbon dioxide as massive by-product,contribute to greenhouse effects and pose environmental challenges.Thus,the pursuit of nitrogen fixation through carbon–neutral pathways under benign conditions is a frontier of scientific topics,with the harnessing of solar energy emerging as an enticing and viable option.This review delves into the refinement strategies for scale-up mild photocatalytic nitrogen fixation,fields ripe with potential for innovation.The narrative is centered on enhancing the intrinsic capabilities of catalysts to surmount current efficiency barriers.Key focus areas include the in-depth exploration of fundamental mechanisms underpinning photocatalytic procedures,rational element selection,and functional planning,state-of-the-art experimental protocols for understanding photo-fixation processes,valid photocatalytic activity evaluation,and the rational design of catalysts.Furthermore,the review offers a suite of forward-looking recommendations aimed at propelling the advancement of mild nitrogen photo-fixation.It scrutinizes the existing challenges and prospects within this burgeoning domain,aspiring to equip researchers with insightful perspectives that can catalyze the evolution of cutting-edge nitrogen fixation methodologies and steer the development of next-generation photocatalytic systems.
基金supported by the National Natural Science Foundation of China(No.51871155).
文摘Due to the low content of alloying elements and the lack of effective nucleation sites,the fusion zone(FZ)of tungsten inert gas(TIG)welded AZ31 alloy typically exhibits undesirable coarse columnar grains,which can result in solidification defects and reduced mechanical properties.In this work,a novel welding wire containing MgO particles has been developed to promote columnar-to-equiaxed transition(CET)in the FZ of TIG-welded AZ31 alloy.The results show the achievement of a fully equiaxed grain structure in the FZ,with a significant 71.9%reduction in grain size to 41 μm from the original coarse columnar dendrites.Furthermore,the combination of using MgO-containing welding wire and pulse current can further refine the grain size to 25.6 μm.Microstructural analyses reveal the homogeneous distribution of MgO particles in the FZ.The application of pulse current results in an increase in the number density of MgO(1-2 μm)from 5.16 × 10^(4) m^(-3) to 6.18 × 10^(4) m^(-3).The good crystallographic matching relationship between MgO and α-Mg matrix,characterized by the orientation relationship of[11(2)0]α-Mg//[0(1)1]MgO and(0002)_(α-Mg)//(111)_(MgO),indicates that the MgO particles can act as effective nucleation sites for α-Mg to reduce nucleation undercooling.According to the Hunt criteria,the critical temperature gradient for CET is greatly enhanced due to the significantly increased number density of MgO nucleation sites.In addition,the correlation with the thermal simulation results reveals a transition in the solidification conditions within the welding pool from the columnar grain zone to the equiaxed grain zone in the CET map,leading to the realization of CET.The exceptional grain refinement has contributed to a simultaneous improvement in the strength and plasticity of welded joints.This study presents a novel strategy for controlling equiaxed microstructure and optimizing mechanical properties in fusion welding or wire and arc additive manufacturing of Mg alloy components.
文摘Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
基金financially supported by the National Natural Science Foundation of China(No.52374395)the Natural Science Foundation of Shanxi Province,China(Nos.20210302123135,202303021221143)+5 种基金the Scientific and Technological Achievements Transformation Guidance Special Project of Shanxi Province,China(Nos.202104021301022,202204021301009)the Central Government Guided Local Science and Technology Development Projects,China(No.YDZJSX20231B003)the Ministry of Science and Higher Education of the Russian Federation for financial support under the Megagrant(No.075-15-2022-1133)the National Research Foundation(NRF)grant funded by the Ministry of Science and ICT of Korea through the Research Institute of Advanced Materials(No.2015R1A2A1A01006795)the China Postdoctoral Science Foundation(No.2022M710541)the Research Project supported by Shanxi Scholarship Council of China(No.2022-038)。
文摘To investigate the effect of microstructure evolution on corrosion behavior and strengthening mechanism of Mg-1Zn-1Ca(wt.%)alloys,as-cast Mg-1Zn-1Ca alloys were performed by equal channel angular pressing(ECAP)with 1 and 4 passes.The corrosion behavior and mechanical properties of alloys were investigated by optical microscopy(OM),scanning electron microscopy(SEM),electron backscatter diffraction(EBSD),electrochemical tests,immersion tests and tensile tests.The results showed that mechanical properties improved after ECAP 1 pass;however,the corrosion resistance deteriorated due to high-density dislocations and fragmented secondary phases by ECAP.In contrast,synchronous improvement in the mechanical properties and corrosion resistance was achieved though grain refinement after ECAP 4 passes;fine grains led to a significant improvement in the yield strength,ultimate tensile strength,elongation,and corrosion rate of 103 MPa,223 MPa,30.5%,and 1.5843 mm/a,respectively.The enhanced corrosion resistance was attributed to the formation of dense corrosion product films by finer grains and the barrier effect by high-density grain boundaries.These results indicated that Mg-1Zn-1Ca alloy has a promising potential for application in biomedical materials.
基金Key Research and Development Program of Shandong Province(2021CXGC010310)Shandong Province Science and Technology Small and Medium Sized Enterprise Innovation Ability Enhancement Project(2023TSGC0287,2024TSGC0519)+1 种基金Shandong Provincial Natural Science Foundation(ZR2022ME222)National Natural Science Foundation of China(51702187)。
文摘Three types of NdFeB magnets with the same composition and different grain sizes were prepared,and then the grain boundary diffusion was conducted using metal Tb under the same technical parameters.The effect of grain size on the grain boundary diffusion process and properties of sintered NdFeB magnets was investigated.The diffusion process was assessed using X-ray diffractometer,field emission scanning electron microscope,and electron probe microanalyzer.The magnetic properties of the magnet before and after diffusion were investigated.The results show that the grain refinement of the magnet leads to higher Tb utilization efficiency and results in higher coercivity at different temperatures.It can be attributed to the formation of a deeper and more complete core-shell structure,resulting in better magnetic isolation and higher anisotropy of the Nd_(2)Fe_(14)B grains.This work may shed light on developing high coercivity with low heavy rare earth elements through grain refinement.
文摘High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.
文摘Al-Cu-Mg-Ag alloys have become a research hotspot because of its good heat resistance.Its excellent mechanical properties are inseparable from the regulation of the structure by researchers.The method of material structure simulation has become more and more perfect.This study employs numerical simulation to investigate the microstructure evolution of Al-Cu-Mg-Ag alloys during solidification with the aim of controlling its structure.The size distribution of Ti-containing particles in an Al-Ti-B master alloy was characterized via microstructure observation,serving as a basis for optimizing the nucleation density parameters for particles of varying radii in the phase field model.The addition of refiner inhibited the growth of dendrites and no longer produced coarse dendrites.With the increase of refiner,the grains gradually tended to form cellular morphology.The refined grains were about 100μm in size.Experimental validation of the simulated as-cast grain morphology was conducted.The samples were observed by metallographic microscope and scanning electron microscope.The addition of refiner had a significant effect on the refinement of the alloy,and the average grain size after refinement was also about 100μm.At the same time,the XRD phase identification of the alloy was carried out.The observation of the microstructure morphology under the scanning electron microscope showed that the precipitated phase was mainly concentrated on the grain boundary.The Al_(2)Cu accounted for about 5%,and the matrix phase FCC accounted for about 95%,which also corresponded well with the simulation results.
基金supported by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004].
文摘Recent Super-Resolution(SR)algorithms often suffer from excessive model complexity,high computational costs,and limited flexibility across varying image scales.To address these challenges,we propose DDNet,a dynamic and lightweight SR framework designed for arbitrary scaling factors.DDNet integrates a residual learning structure with an Adaptively fusion Feature Block(AFB)and a scale-aware upsampling module,effectively reducing parameter overhead while preserving reconstruction quality.Additionally,we introduce DDNetGAN,an enhanced variant that leverages a relativistic Generative Adversarial Network(GAN)to further improve texture realism.To validate the proposed models,we conduct extensive training using the DIV2K and Flickr2K datasets and evaluate performance across standard benchmarks including Set5,Set14,Urban100,Manga109,and BSD100.Our experiments cover both symmetric and asymmetric upscaling factors and incorporate ablation studies to assess key components.Results show that DDNet and DDNetGAN achieve competitive performance compared with mainstream SR algorithms,demonstrating a strong balance between accuracy,efficiency,and flexibility.These findings highlight the potential of our approach for practical real-world super-resolution applications.
基金supported by the following grants:National Natural Science Foundation of China(grant nos.92354305,32271428,and 32201132)National Key R&D Program of China(grant no.2022YFC3401100)+1 种基金Fund for Knowledge Innovation of Wuhan Science and Technology Bureau(grant no.2022020801010558)Director Fund of WNLO.
文摘The rapid development of super-resolution microscopy has made it possible to observe subcellular structures and dynamic behaviors in living cells with nanoscale spatial resolution, greatly advancing progress in life sciences. As hardware technology continues to evolve, the availability of new fluorescent probes with superior performance is becoming increasingly important. In recent years, fluorescent nanoprobes (FNPs) have emerged as highly promising fluorescent probes for bioimaging due to their high brightness and excellent photostability. This paper focuses on the development and applications of FNPs as probes for live-cell super-resolution imaging. It provides an overview of different super-resolution methods, discusses the performance requirements for FNPs in these methods, and reviews the latest applications of FNPs in the super-resolution imaging of living cells. Finally, it addresses the challenges and future outlook in this field.
基金supported by the National Natural Science Foundation of China(Grant Nos.42201026,52201325 and 42407619)the Startup Foundation for Introducing Talent of NUIST(Grant No.2023r009).
文摘This study investigates the inundation depths of urban floods induced by real storm events,focusing on the development and assessment of super-resolution model based on ensemble learning methods.Unlike traditional deep neural networks which require extensive training and high parameterization,this study utilizes ensemble learning model to reconstruct high-resolution flood predictions from low-resolution hydrodynamic simulations.Hydrodynamic modeling results of real pluvial flood event at various spatial resolution are used for constructing datasets and for training and testing the point-based super-resolution model.Influencing factors related to urban terrain,subsurface,rainfall inputs and the hydrodynamic modeling results at coarser resolutions are used as features in the super-resolution model on basis of Random Forest,in which hyperparameters are tuned with Bayesian optimization method.The trained super-resolution models effectively reconstruct high-resolution inundation conditions from 30 m to 5 m coarse resolution inputs,highlighting an increase in correlation coefficients and a decrease in root mean squared error(RMSE)as resolution improves.Dominant influencing factors in the super-resolution models are identified together with variances in their contributions to the model performance.Two optimization approaches are applied to enhance accuracy and mitigate overestimation at coarse resolutions for the super-resolution models.The first integrates outputs from various coarse resolution models as features,notably reducing overestimation,especially with finer 5 m resolutions.The second employs ensemble modeling with super-resolution models from different datasets,which improves the performance across all tested resolutions,demonstrating the robustness of combining multiple predictive models for better flood forecasting in urban environments.
基金supported by the following grants:National Key R&D Program of China(Grant no.2022YFC3401100)National Natural Science Foundation of China(Grant nos.32271428,92054110,32201132 and 31600692).
文摘Blood cells are the most integral part of the body,which are made up of erythrocytes,platelets and white blood cells.The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual,accurate diagnosis,and efficient treatment strategies for diseases.Super-resolution microscopy(SRM)has recently emerged as a cutting-edge tool for the study of blood cells,providing numerous advantages over traditional methods for examining subcellular structures and proteins.In this paper,we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells.Furthermore,future prospects of SRM techniques in the analysis of blood cells are also discussed.
基金supported by the National Natural Science Foundation of China(62175034,62175036,32271510)the National Key R&D Program of China(2021YFF0502900)+2 种基金the Science and Technology Research Program of Shanghai(Grant No.19DZ2282100)the Shanghai Key Laboratory of Metasurfaces for Light Manipulation(23dz2260100)the Shanghai Engineering Technology Research Center of Hair Medicine(19DZ2250500).
文摘A new scheme of super-resolution optical fluctuation imaging(SOFI)is proposed to broaden its application in the high-order cumulant reconstruction by optimizing blinking characteristics,eliminating noise in raw data and applying multi-resolution analysis in cumulant reconstruction.A motor-driven rotating mask optical modulation system is designed to adjust the excitation lightfield and allows for fast deployment.Active-modulated fluorescence fluctuation superresolution microscopy with multi-resolution analysis(AMF-MRA-SOFI)demonstrates enhanced resolution ability and reconstruction quality in experiments performed on sample of conventional dyes,achieving a resolution of 100 nm in the fourth order compared to conventional SOFI reconstruction.Furthermore,our approach combining expansion super-resolution achieved a resolution at-57 nm.
基金the National Natural Science Foundation(NNSF)of China under Grant 41927801.
文摘The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;however,their low resolution,particularly the loss of high-frequency information at pore edges,presents challenges in analyzing fractures and pores in shale gas reservoirs.This study introduced a novel neural network called the spatial-spectral domain attention network(SSDAN),which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel.The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery,spectral blocks to process Fourier transform information of images and enhance high-frequency recovery,and an adaptive vision transformer to process Fourier transform block information,eliminating the need for a preset image size.The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets,achieving optimal performance on key indicators such as peak signal-to-noise ratio,structural similarity,learned perceptual image patch similarity,and Frechet inception distance while also exhibiting superior visual performance in pore recovery.Ablation experiments further confirmed the effectiveness of the spatial blocks,channel attention,spectral blocks,and frequency loss function in the model.The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges,thereby validating its effectiveness in unconventional natural gas reservoir analyses.
基金funded in part by theHenan ProvinceKeyR&DProgramProject,“Research and Application Demonstration of Class Ⅱ Superlattice Medium Wave High Temperature Infrared Detector Technology”under Grant No.231111210400.
文摘Infrared imaging technology has been widely adopted in various fields,such as military reconnaissance,medical diagnosis,and security monitoring,due to its excellent ability to penetrate smoke and fog.However,the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents.In addition,deploying super-resolution models on resource-constrained devices faces significant challenges.To address these issues,this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism.The network’s dynamic weighting module automatically adjusts the weights of the attention and nonattention branch outputs based on the network’s characteristics at different levels.Among them,the attention branch is further subdivided into pixel attention and brightness-texture attention,which are specialized for extracting the most informative features in infrared images.Meanwhile,the non-attention branch supplements the extraction of those neglected features to enhance the comprehensiveness of the features.Through ablation experiments,we verify the effectiveness of the proposed module.Finally,through experiments on two datasets,FLIR and Thermal101,qualitative and quantitative results demonstrate that the model can effectively recover high-frequency details of infrared images and significantly improve image resolution.In detail,compared with the suboptimal method,we have reduced the number of parameters by 30%and improved the model performance.When the scale factor is 2,the peak signal-tonoise ratio of the test datasets FLIR and Thermal101 is improved by 0.09 and 0.15 dB,respectively.When the scale factor is 4,it is improved by 0.05 and 0.09 dB,respectively.In addition,due to the lightweight design of the network structure,it has a low computational cost.It is suitable for deployment on edge devices,thus effectively enhancing the sensing performance of infrared imaging devices.
基金Supported by AI+Health Collaborative Innovation Cultivation Project of Beijing City,No.Z221100003522005.
文摘BACKGROUND Deep learning-based super-resolution(SR)reconstruction can obtain high-quality images with more detailed information.AIM To compare multiparametric normal-resolution(NR)and SR magnetic resonance imaging(MRI)in predicting the histopathologic grade in hepatocellular carcinoma.METHODS We retrospectively analyzed a total of 826 patients from two medical centers(training 459;validation 196;test 171).T2-weighted imaging,diffusion-weighted imaging,and portal venous phases were collected.Tumor segmentations were conducted automatically by 3D U-Net.Based on generative adversarial network,we utilized 3D SR reconstruction to produce SR MRI.Radiomics models were developed and validated by XGBoost and Catboost.The predictive efficiency was demonstrated by calibration curves,decision curve analysis,area under the curve(AUC)and net reclassification index(NRI).RESULTS We extracted 3045 radiomic features from both NR and SR MRI,retaining 29 and 28 features,respectively.For XGBoost models,SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts(0.83 vs 0.79;0.80 vs 0.78),respectively.Consistent trends were seen in CatBoost models:SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76.NRI indicated that the SR MRI models could improve the prediction accuracy by-1.6%to 20.9%compared to the NR MRI models.CONCLUSION Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC.It may be a powerful tool for better stratification management for patients with operable HCC.
基金supported in part by the Basic and Applied Basic Research Foundation of Guangdong Province[2025A1515011566]in part by the State Key Laboratory for Novel Software Technology,Nanjing University[KFKT2024B08]+1 种基金in part by Leading Talents in Gusu Innovation and Entrepreneurship[ZXL2023170]in part by the Basic Research Programs of Taicang 2024,[TC2024JC32].
文摘Deep convolutional neural networks(CNNs)have demonstrated remarkable performance in video super-resolution(VSR).However,the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction.To address this limitation,we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network(3D-ERVSNet).This network employs a forward and backward bidirectional propagation module(FBBPM)that aligns features across frames using explicit optical flow through lightweight SPyNet.By incorporating an enhanced residual structure(ERS)with skip connections,shallow and deep features are effectively integrated,enhancing texture restoration capabilities.Furthermore,3D convolution module(3DCM)is applied after the backward propagation module to implicitly capture spatio-temporal dependencies.The architecture synergizes these components where FBBPM extracts aligned features,ERS fuses hierarchical representations,and 3DCM refines temporal coherence.Finally,a deep feature aggregation module(DFAM)fuses the processed features,and a pixel-upsampling module(PUM)reconstructs the high-resolution(HR)video frames.Comprehensive evaluations on REDS,Vid4,UDM10,and Vim4 benchmarks demonstrate well performance including 30.95 dB PSNR/0.8822 SSIM on REDS and 32.78 dB/0.8987 on Vim4.3D-ERVSNet achieves significant gains over baselines while maintaining high efficiency with only 6.3M parameters and 77ms/frame runtime(i.e.,20×faster than RBPN).The network’s effectiveness stems from its task-specific asymmetric design that balances explicit alignment and implicit fusion.
基金supported by the National Natural Science Foundation of China(Nos.12205271,12075217,U20B2011,and 51978218)Sichuan Science and Technology Program(No.2019ZDZX0010)the National Key R&D Program of China(No.2022YFA1604002).
文摘Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.
基金supported by the National Natural Science Foundation of China(Nos.22225806,22078314,22278394,22378385)Dalian Institute of Chemical Physics(Nos.DICPI202227,DICPI202436)。
文摘Monitoring the dynamics of cellular pseudopodia at nanoscale has become essential for understanding their diverse and complex functions in living cells.This is made possible by combining single-molecule localization microscopy(SMLM)with self-blinking dyes.However,existing self-blinking dyes often face limitations,such as nonspecific blinking and low photostability,which can bring background noise and yield erroneous localization signals,hindering their effectiveness for nanoscale visualization.Here,we present a method for long-term SMLM imaging of cellular pseudopodia dynamics using a blinkogenic probe that exhibits self-blinking activation upon molecular recognition.This approach enabled the precise tracking of various pseudopodia structures,including filopodia,lamellipodia,and(tunneling nanotubes)-nanoscale(TNTs),in living cells.We monitored the growth and fusion of filopodia,as well as the extension and shrinkage of lamellipodia,in real-time.Additionally,we identified two distinct fusion modes between filopodia and lamellipodia and captured the formation of TNTs and their interactions with filopodia,demonstrating the probe's utility in visualizing real-time pseudopodia dynamics at nanoscale.
