Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO...MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO_(x)-CeO_(2)catalyst that achieves enhanced NO conversion rate and stability under harsh conditions.The catalyst was synthesized by decorating MnOx crystals with amorphous CeO_(2),followed by loading hydrophobic silica on the external surfaces.The hydrophobic silica allowed the adsorption of NH_(3)and NO and diffusion of H,suppressed the adsorption of H_(2)O,and prevented SO_(2)interaction with the Mn active sites,achieving selective molecular discrimination at the catalyst surface.At 120℃,under H_(2)O and SO_(2)exposure,the optimal hydrophobic catalyst maintains 82%NO conversion rate compared with 69%for the unmodified catalyst.The average adsorption energies of NH_(3),H_(2)O,and SO_(2)decreased by 0.05,0.43,and 0.52 eV,respectively.The NO reduction pathway follows the Eley-Rideal mechanism,NH_(3)^(*)+*→NH_(2)^(*)+H^(*)followed by NH_(2)^(*)+NO^(*)→N_(2)^(*)+H_(2)O^(*),with NH_(3)dehydrogenation being the rate determining step.Hydrophobic modification increased the activation energy for H atom transfer,leading to a minor decrease in the NO conversion rate at 120℃.This work demonstrates a viable strategy for developing robust NH_(3)-S CR catalysts capable of efficient operation in water-and sulfur-rich environments.展开更多
The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV imag...The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.展开更多
Recently,an article was published on solid effect(SE)dynamic nuclear polarization(DNP)enhancement,where the au-thors reported achieving 1H enhancement factors up to 500 by increasing the microwave power at 9.4 T,marki...Recently,an article was published on solid effect(SE)dynamic nuclear polarization(DNP)enhancement,where the au-thors reported achieving 1H enhancement factors up to 500 by increasing the microwave power at 9.4 T,marking the highest SE enhancement to date[1].展开更多
Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approac...Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.展开更多
In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symm...In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symmetric points,numerical simulations are conducted to one-soliton and two-soliton solutions of a discrete KdV equation,as well as a one-soliton solution of a discrete Toda lattice equation.Compared with the existing discrete deep learning approach,the numerical results reveal that within the specified spatiotemporal domain,the prediction accuracy by SDE-PINN is excellent regardless of the interior or extrapolation prediction,with a significant reduction in training time.The proposed data enhancement technique and symmetric structure development provides a new perspective for the deep learning approach to solve discrete NLEs.The newly proposed SDE-PINN can also be applied to solve continuous nonlinear equations and other discrete NLEs numerically.展开更多
Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face sev...Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.展开更多
In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,th...In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.展开更多
Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevita...Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.展开更多
Cognitive enhancement is essential for maintaining the quality of life in healthy individuals and improving the ability of those with mental impairments.In recent years,noninvasive neuromodulation techniques(such as t...Cognitive enhancement is essential for maintaining the quality of life in healthy individuals and improving the ability of those with mental impairments.In recent years,noninvasive neuromodulation techniques(such as transcranial magnetic stimulation,transcranial direct-current stimulation,and transcranial ultrasound stimulation)have shown significant potential in enhancing cognitive functions[1,2].Existing technologies are limited mainly to superficial cortical regions,with limited efficacy in targeting deep brain areas and inadequate methods for evaluating their modulatory effects.Selecting stimulation parameters(such as locus,depth,and intensity)and assessing the impact of neuromodulation remains incompletely understood.展开更多
Laparoscopic imaging has advanced significantly,with higher resolutions like 4K,and innovative light modes such as narrow band imaging and near-infrared imaging.Recently,yellow enhancement(YE)mode has emerged as a nov...Laparoscopic imaging has advanced significantly,with higher resolutions like 4K,and innovative light modes such as narrow band imaging and near-infrared imaging.Recently,yellow enhancement(YE)mode has emerged as a novel tool that enhances the pale-yellow colour of fat into a fluorescent yellow-green,improving contrast without the need for injected dyes.It can be toggled on and off easily during surgery.YE is still under evaluation,but early experience suggests it helps surgeons differentiate anatomical planes and key intraabdominal structures from surrounding adipose tissue.This is particularly useful in:(1)Dissecting structures surrounded or covered by fat;and(2)operating on patients with obesity,where excess intra-abdominal fat limits visualisation and retraction.By enhancing the visibility of vascular pedicles,ureters,and nerves,YE enables more precise dissections and may reduce the risk of accidental injury.It can also assist less experienced surgeons in identifying important structures,potentially improving efficiency and surgical outcomes.As a training tool,YE may shorten the learning curve,though further study is needed.Overall,YE offers potential benefits in fat-dense surgical fields by improving visualisation,reducing complications,and enhancing patient safety.展开更多
Health monitoring of underwater concrete facility systems is important in civil engineering. Unlike conventional manual inspection techniques, digital image processing offers a more convenient and effective approach, ...Health monitoring of underwater concrete facility systems is important in civil engineering. Unlike conventional manual inspection techniques, digital image processing offers a more convenient and effective approach, becoming an indispensable tool for structural inspection. Cracks, which are pervasive defects, are a central focus of structural deterioration research. However, the complexity of the marine environment poses challenges to underwater visibility.In this study, the underwater environment under controlled laboratory conditions is replicated, where varying turbidity and illumination conditions and images of concrete cracks are captured. An approach combining a defogging algorithm with guided and fast guided filtering techniques is proposed to enhance both natural underwater images and crack images captured through experimental photography. When applied to turbid crack images captured under two different suspension conditions, the method increases the information entropy(IE) by 32.92% and 17.92% and the underwater color image quality evaluation(UCIQE) by 35.76% and 18.36%, respectively. These results demonstrate its efficiency in enhancing image definition. The findings of this study could significantly impact the practical applications of image visualization and evaluation for underwater concrete cracks.展开更多
Background:Long-term exposure to sunlight can lead to inflammatory responses,skin photoaging and cancers.Plant extracts can serve as effective biological UV(ultraviolet)filters for their UV absorption capacity.Red ric...Background:Long-term exposure to sunlight can lead to inflammatory responses,skin photoaging and cancers.Plant extracts can serve as effective biological UV(ultraviolet)filters for their UV absorption capacity.Red rice extract is rich in phenolic acids,flavonoids,anthocyanins and procyanins has the potential as a biological UV filter.Aims:This study aims to evaluate the potential of red rice extract as a biological UV filter.Forthurmore,exploring the ability of red rice extract to enhance SPF performance and serve as a potential substitute for chemical UV filters.Methods:Using UV absorption spectroscopy,the UV absorption performance of red rice extract was compared with that of three known UV filters.Additionally,using the Folin-Ciocalteu reagent to test the total phenolic acids content of red rice extract and the antioxidant capacity of red rice extract was verified through a 2,2-diphenyl-1-picrylhydrazyl(DPPH)free radical clearance assay,confirming its potential as a biological UV filter.Subsequently,the photoprotective enhancement effects of red rice extract in sunscreen formulations were explored by testing the SPF value with UV2000S.Results:Experiments conducted by adding different concentrations of red rice extract to sunscreen formulations demonstrated that its photoprotective enhancement effects were positively correlated with its concentration.The addition of 1%red rice extract to both oil-in-water(O/W)and water-in-oil(W/O)sunscreen formulations demonstrated good photoprotective enhancement effects,with SPF values increasing by over 10%.Further experiments showed that 1%,3%,and 5%red rice extract could replace approximately 12.82%,19.05%,and 26.09%of traditional UV filters in sunscreen formulations,achieving similar SPF values.Conclusions:These findings suggest that red rice extract is a promising biological UV filter,capable of enhancing SPF values and serving as a viable alternative for traditional UV filters.Red rice extract can be used in sunscreen products and provide photoprotection benefit.展开更多
Background:Residual force enhancement(rFE),defined as increased isometric force following active lengthening compared to a fixed-end isometric contraction at the same muscle length and level of activation,is present a...Background:Residual force enhancement(rFE),defined as increased isometric force following active lengthening compared to a fixed-end isometric contraction at the same muscle length and level of activation,is present across all scales of muscle.While rFE is always present at the cellular level,often rFE"non-re sponders"are observed during joint-level voluntary contractions.Methods:We compared rFE between the joint level and single fiber level(vastus lateralis biopsies)in 16 young males.In vivo voluntary kneeextensor rFE was measured by comparing steady-state isometric torque between a stretch-hold(maximal activation at 150°,stretch to 70°,hold)and a fixed-end isometric contraction,with ultrasonographic recording of vastus lateralis fascicle length(FL).Fixed-end contractions were performed at 67.5°,70.0°,72.5°,and 75.0°;the joint angle that most closely matched FL of the stretch-hold contraction's isometric steady-state was used to calculate rFE.The starting and ending FLs of the stretch-hold contraction were expressed as%optimal FL,determined via torqueangle relationship.Resu lts:In single fiber experiments,the starting and ending fiber lengths were matched relative to optimal length determined from in vivo testing,yielding an average sarcomere excursion of~2.2-3.4μm.There was a greater magnitude of rFE at the single fiber(~20%)than joint level(~5%)(p=0.004),with"non-re sponders"only observed at the joint level.Conclusion:By comparing rFE across scales within the same participants,we show the development of the rFE non-responder phenomenon is upstream of rFE's cellular mechanisms,with rFE only lost rather than gained when scaling from single fibers to the joint level.展开更多
Objective: To assess the effectiveness of the created program in enhancing the knowledge of emergency room (ER) nurses in the emergency management of cardiovascular diseases (CVD). Methods: This study used a quasi-exp...Objective: To assess the effectiveness of the created program in enhancing the knowledge of emergency room (ER) nurses in the emergency management of cardiovascular diseases (CVD). Methods: This study used a quasi-experimental design with a one-group pretest-posttest research design to identify the knowledge and skills of emergency department (ED) nurses in managing CVD. There were 16 participants in this study. The mean, standard deviation (SD), and t-test were used to analyze the data. Results: Before the participants undergo the program, they have a mean (SD) score of 17.63 (5.19). After the completion of the didactic part of the program, they garnered a mean (SD) score of 19.94 (5.22). Moreover, after completion of the practicum part of the program, the mean (SD) score was 21.94 (5.04). Comparing the scores before the program and after finishing the didactic part of the program, the t-test scored (t (15) = -3.87, P = 0.001). Further, comparing the scores before the program and after finishing the didactic and practicum parts of the program, the t-test scored (t (15) = -5.57, P = 0.001). Conclusions: Based on the study’s results, the researchers conclude that the respondents had acceptable knowledge regarding the emergency management of CVD before the program. However, the Cardiac Enhancement Program for Emergency Cardiac Care boosted their knowledge. Also, the program is effective in enhancing the participants’ knowledge of the emergency management of CVD.展开更多
Within the domain of low-level vision,enhancing low-light images and removing sand-dust from single images are both critical tasks.These challenges are particularly pronounced in real-world applications such as autono...Within the domain of low-level vision,enhancing low-light images and removing sand-dust from single images are both critical tasks.These challenges are particularly pronounced in real-world applications such as autonomous driving,surveillance systems,and remote sensing,where adverse lighting and environmental conditions often degrade image quality.Various neural network models,including MLPs,CNNs,GANs,and Transformers,have been proposed to tackle these challenges,with the Vision KAN models showing particular promise.However,existing models,including the Vision KAN models use deterministic neural networks that do not address the uncertainties inherent in these processes.To overcome this,we introduce the Uncertainty-Aware Kolmogorov-Arnold Network(UAKAN),a novel structure that integrates KAN with uncertainty estimation.Our approach uniquely employs Tokenized KANs for sampling within a U-Net architecture’s encoder and decoder layers,enhancing the network’s ability to learn complex representations.Furthermore,for aleatoric uncertainty,we propose an uncertainty coupling certainty module that couples uncertainty distribution learning and residual learning in a feature fusion manner.For epistemic uncertainty,we propose a feature selection mechanism for spatial and pixel dimension uncertainty modeling,which captures and models uncertainty by learning the uncertainty contained between feature maps.Notably,our uncertainty-aware framework enables the model to produce both high-quality enhanced images and reliable uncertainty maps,which are crucial for downstream applications requiring confidence estimation.Through comparative and ablation studies on our synthetic SLLIE6K dataset,designed for low-light enhancement and sand-dust removal,we validate the effectiveness and theoretical robustness of our methodology.展开更多
BACKGROUND Three-phase dynamic computed tomography imaging is particularly useful in the liver region.However,dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple im...BACKGROUND Three-phase dynamic computed tomography imaging is particularly useful in the liver region.However,dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple imaging sessions.We hypothesized that the contrast enhancement boost(CE-boost)technique could be used to enhance the contrast in equilibrium phase(EP)images and produce enhancement similar to that of portal vein phase(PVP)images,and if this is possible,EP imaging could play the same role as PVP imaging.We also speculated that this might allow the conversion of three-phase dynamic imaging to biphasic dynamic imaging,reducing patients’radiation exposure.AIM To determine if the CE-boost of EP,CE-boost(EP)is useful compared to a conventional image.METHODS We retrospectively analyzed the cases of 52 patients who were diagnosed with liver cancer between January 2016 and October 2022 at our institution.From these computed tomography images,CE-boost images were generated from the EP and plane images.We compared the PVP,EP,and CE-boost(EP)for blood vessels and hepatic parenchyma based on the contrast-to-noise ratio(CNR),signal-to-noise ratio,and figure-of-merit(FOM).Visual assessments were also performed for vessel visualization,lesion conspicuity,and image noise.RESULTS The CE-boost(EP)images showed significant superiority compared to the PVP images in the CNR,signal-to-noise ratio,and FOM except regarding the hepatic parenchyma.No significant differences were detected in CNR or FOM comparisons within the hepatic parenchyma(P=0.62,0.67).The comparison of the EP and CE-boost(EP)images consistently favored CE-boost(EP).Regarding the visual assessment,the CE-boost(EP)images were significantly superior to the PVP images in lesion conspicuity,and the PVP in image noise.The CE-boost(EP)images were significantly better than the EP images in the vessel visualization of segmental branches of the portal vein and lesion conspicuity,and the EP in image noise.CONCLUSION The image quality of CE-boost(EP)images was comparable or superior to that of conventional PVP and EP.CEboost(EP)images might provide information comparable to the conventional PVP.展开更多
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported by the Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
基金financially sponsored by the National Natural Science Foundation of China(No.52204414)the National Energy-Saving and Low-Carbon Materials Production and Application Demonstration Platform Program,China(No.TC220H06N)+1 种基金the National Key R&D Program of China(No.2021YFC1910504)the Fundamental Research Funds for the Central Universities,China(No.FRFTP-20-097A1Z)。
文摘MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO_(x)-CeO_(2)catalyst that achieves enhanced NO conversion rate and stability under harsh conditions.The catalyst was synthesized by decorating MnOx crystals with amorphous CeO_(2),followed by loading hydrophobic silica on the external surfaces.The hydrophobic silica allowed the adsorption of NH_(3)and NO and diffusion of H,suppressed the adsorption of H_(2)O,and prevented SO_(2)interaction with the Mn active sites,achieving selective molecular discrimination at the catalyst surface.At 120℃,under H_(2)O and SO_(2)exposure,the optimal hydrophobic catalyst maintains 82%NO conversion rate compared with 69%for the unmodified catalyst.The average adsorption energies of NH_(3),H_(2)O,and SO_(2)decreased by 0.05,0.43,and 0.52 eV,respectively.The NO reduction pathway follows the Eley-Rideal mechanism,NH_(3)^(*)+*→NH_(2)^(*)+H^(*)followed by NH_(2)^(*)+NO^(*)→N_(2)^(*)+H_(2)O^(*),with NH_(3)dehydrogenation being the rate determining step.Hydrophobic modification increased the activation energy for H atom transfer,leading to a minor decrease in the NO conversion rate at 120℃.This work demonstrates a viable strategy for developing robust NH_(3)-S CR catalysts capable of efficient operation in water-and sulfur-rich environments.
基金supported by the National Natural Science Foundation of China(Nos.62201454 and 62306235)the Xi’an Science and Technology Program of Xi’an Science and Technology Bureau(No.23SFSF0004)。
文摘The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.
文摘Recently,an article was published on solid effect(SE)dynamic nuclear polarization(DNP)enhancement,where the au-thors reported achieving 1H enhancement factors up to 500 by increasing the microwave power at 9.4 T,marking the highest SE enhancement to date[1].
基金Graduate Student Innovation Projects of Beijing University of Civil Engineering and Architecture(No.PG2024121)。
文摘Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.
基金supported by the National Natural Science Foundation of China(Grant No.12071042)the Beijing Natural Science Foundation(Grant No.1202004)Promoting the Development of University Classification-Student Innovation and Entrepreneurship Training Programme(Grant No.5112410857)。
文摘In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symmetric points,numerical simulations are conducted to one-soliton and two-soliton solutions of a discrete KdV equation,as well as a one-soliton solution of a discrete Toda lattice equation.Compared with the existing discrete deep learning approach,the numerical results reveal that within the specified spatiotemporal domain,the prediction accuracy by SDE-PINN is excellent regardless of the interior or extrapolation prediction,with a significant reduction in training time.The proposed data enhancement technique and symmetric structure development provides a new perspective for the deep learning approach to solve discrete NLEs.The newly proposed SDE-PINN can also be applied to solve continuous nonlinear equations and other discrete NLEs numerically.
文摘Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.
基金supported by the Natural Science Foundation of Shandong Province(nos.ZR2023MF047,ZR2024MA055 and ZR2023QF139)the Enterprise Commissioned Project(nos.2024HX104 and 2024HX140)+1 种基金the China University Industry-University-Research Innovation Foundation(nos.2021ZYA11003 and 2021ITA05032)the Science and Technology Plan for Youth Innovation of Shandong's Universities(no.2019KJN012).
文摘In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.
文摘Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.
基金supported by the National Natural Science Foundation of China(82172018 and 62333002).
文摘Cognitive enhancement is essential for maintaining the quality of life in healthy individuals and improving the ability of those with mental impairments.In recent years,noninvasive neuromodulation techniques(such as transcranial magnetic stimulation,transcranial direct-current stimulation,and transcranial ultrasound stimulation)have shown significant potential in enhancing cognitive functions[1,2].Existing technologies are limited mainly to superficial cortical regions,with limited efficacy in targeting deep brain areas and inadequate methods for evaluating their modulatory effects.Selecting stimulation parameters(such as locus,depth,and intensity)and assessing the impact of neuromodulation remains incompletely understood.
文摘Laparoscopic imaging has advanced significantly,with higher resolutions like 4K,and innovative light modes such as narrow band imaging and near-infrared imaging.Recently,yellow enhancement(YE)mode has emerged as a novel tool that enhances the pale-yellow colour of fat into a fluorescent yellow-green,improving contrast without the need for injected dyes.It can be toggled on and off easily during surgery.YE is still under evaluation,but early experience suggests it helps surgeons differentiate anatomical planes and key intraabdominal structures from surrounding adipose tissue.This is particularly useful in:(1)Dissecting structures surrounded or covered by fat;and(2)operating on patients with obesity,where excess intra-abdominal fat limits visualisation and retraction.By enhancing the visibility of vascular pedicles,ureters,and nerves,YE enables more precise dissections and may reduce the risk of accidental injury.It can also assist less experienced surgeons in identifying important structures,potentially improving efficiency and surgical outcomes.As a training tool,YE may shorten the learning curve,though further study is needed.Overall,YE offers potential benefits in fat-dense surgical fields by improving visualisation,reducing complications,and enhancing patient safety.
基金financially supported by the National Natural Science Foundation of China (Grant No. 52175245)the Natural Science Foundation of Hubei Province (Grant No. 2021CFB462)。
文摘Health monitoring of underwater concrete facility systems is important in civil engineering. Unlike conventional manual inspection techniques, digital image processing offers a more convenient and effective approach, becoming an indispensable tool for structural inspection. Cracks, which are pervasive defects, are a central focus of structural deterioration research. However, the complexity of the marine environment poses challenges to underwater visibility.In this study, the underwater environment under controlled laboratory conditions is replicated, where varying turbidity and illumination conditions and images of concrete cracks are captured. An approach combining a defogging algorithm with guided and fast guided filtering techniques is proposed to enhance both natural underwater images and crack images captured through experimental photography. When applied to turbid crack images captured under two different suspension conditions, the method increases the information entropy(IE) by 32.92% and 17.92% and the underwater color image quality evaluation(UCIQE) by 35.76% and 18.36%, respectively. These results demonstrate its efficiency in enhancing image definition. The findings of this study could significantly impact the practical applications of image visualization and evaluation for underwater concrete cracks.
基金support from Guangdong S&T programme[2024A0505050001].
文摘Background:Long-term exposure to sunlight can lead to inflammatory responses,skin photoaging and cancers.Plant extracts can serve as effective biological UV(ultraviolet)filters for their UV absorption capacity.Red rice extract is rich in phenolic acids,flavonoids,anthocyanins and procyanins has the potential as a biological UV filter.Aims:This study aims to evaluate the potential of red rice extract as a biological UV filter.Forthurmore,exploring the ability of red rice extract to enhance SPF performance and serve as a potential substitute for chemical UV filters.Methods:Using UV absorption spectroscopy,the UV absorption performance of red rice extract was compared with that of three known UV filters.Additionally,using the Folin-Ciocalteu reagent to test the total phenolic acids content of red rice extract and the antioxidant capacity of red rice extract was verified through a 2,2-diphenyl-1-picrylhydrazyl(DPPH)free radical clearance assay,confirming its potential as a biological UV filter.Subsequently,the photoprotective enhancement effects of red rice extract in sunscreen formulations were explored by testing the SPF value with UV2000S.Results:Experiments conducted by adding different concentrations of red rice extract to sunscreen formulations demonstrated that its photoprotective enhancement effects were positively correlated with its concentration.The addition of 1%red rice extract to both oil-in-water(O/W)and water-in-oil(W/O)sunscreen formulations demonstrated good photoprotective enhancement effects,with SPF values increasing by over 10%.Further experiments showed that 1%,3%,and 5%red rice extract could replace approximately 12.82%,19.05%,and 26.09%of traditional UV filters in sunscreen formulations,achieving similar SPF values.Conclusions:These findings suggest that red rice extract is a promising biological UV filter,capable of enhancing SPF values and serving as a viable alternative for traditional UV filters.Red rice extract can be used in sunscreen products and provide photoprotection benefit.
基金supported by the Natural Sciences and Engineering Research Council of Canada(NSERC,Grant No.RGPIN-2024-03782).
文摘Background:Residual force enhancement(rFE),defined as increased isometric force following active lengthening compared to a fixed-end isometric contraction at the same muscle length and level of activation,is present across all scales of muscle.While rFE is always present at the cellular level,often rFE"non-re sponders"are observed during joint-level voluntary contractions.Methods:We compared rFE between the joint level and single fiber level(vastus lateralis biopsies)in 16 young males.In vivo voluntary kneeextensor rFE was measured by comparing steady-state isometric torque between a stretch-hold(maximal activation at 150°,stretch to 70°,hold)and a fixed-end isometric contraction,with ultrasonographic recording of vastus lateralis fascicle length(FL).Fixed-end contractions were performed at 67.5°,70.0°,72.5°,and 75.0°;the joint angle that most closely matched FL of the stretch-hold contraction's isometric steady-state was used to calculate rFE.The starting and ending FLs of the stretch-hold contraction were expressed as%optimal FL,determined via torqueangle relationship.Resu lts:In single fiber experiments,the starting and ending fiber lengths were matched relative to optimal length determined from in vivo testing,yielding an average sarcomere excursion of~2.2-3.4μm.There was a greater magnitude of rFE at the single fiber(~20%)than joint level(~5%)(p=0.004),with"non-re sponders"only observed at the joint level.Conclusion:By comparing rFE across scales within the same participants,we show the development of the rFE non-responder phenomenon is upstream of rFE's cellular mechanisms,with rFE only lost rather than gained when scaling from single fibers to the joint level.
文摘Objective: To assess the effectiveness of the created program in enhancing the knowledge of emergency room (ER) nurses in the emergency management of cardiovascular diseases (CVD). Methods: This study used a quasi-experimental design with a one-group pretest-posttest research design to identify the knowledge and skills of emergency department (ED) nurses in managing CVD. There were 16 participants in this study. The mean, standard deviation (SD), and t-test were used to analyze the data. Results: Before the participants undergo the program, they have a mean (SD) score of 17.63 (5.19). After the completion of the didactic part of the program, they garnered a mean (SD) score of 19.94 (5.22). Moreover, after completion of the practicum part of the program, the mean (SD) score was 21.94 (5.04). Comparing the scores before the program and after finishing the didactic part of the program, the t-test scored (t (15) = -3.87, P = 0.001). Further, comparing the scores before the program and after finishing the didactic and practicum parts of the program, the t-test scored (t (15) = -5.57, P = 0.001). Conclusions: Based on the study’s results, the researchers conclude that the respondents had acceptable knowledge regarding the emergency management of CVD before the program. However, the Cardiac Enhancement Program for Emergency Cardiac Care boosted their knowledge. Also, the program is effective in enhancing the participants’ knowledge of the emergency management of CVD.
基金supported by National Key R&D Program of China(2023YFB2504400).
文摘Within the domain of low-level vision,enhancing low-light images and removing sand-dust from single images are both critical tasks.These challenges are particularly pronounced in real-world applications such as autonomous driving,surveillance systems,and remote sensing,where adverse lighting and environmental conditions often degrade image quality.Various neural network models,including MLPs,CNNs,GANs,and Transformers,have been proposed to tackle these challenges,with the Vision KAN models showing particular promise.However,existing models,including the Vision KAN models use deterministic neural networks that do not address the uncertainties inherent in these processes.To overcome this,we introduce the Uncertainty-Aware Kolmogorov-Arnold Network(UAKAN),a novel structure that integrates KAN with uncertainty estimation.Our approach uniquely employs Tokenized KANs for sampling within a U-Net architecture’s encoder and decoder layers,enhancing the network’s ability to learn complex representations.Furthermore,for aleatoric uncertainty,we propose an uncertainty coupling certainty module that couples uncertainty distribution learning and residual learning in a feature fusion manner.For epistemic uncertainty,we propose a feature selection mechanism for spatial and pixel dimension uncertainty modeling,which captures and models uncertainty by learning the uncertainty contained between feature maps.Notably,our uncertainty-aware framework enables the model to produce both high-quality enhanced images and reliable uncertainty maps,which are crucial for downstream applications requiring confidence estimation.Through comparative and ablation studies on our synthetic SLLIE6K dataset,designed for low-light enhancement and sand-dust removal,we validate the effectiveness and theoretical robustness of our methodology.
文摘BACKGROUND Three-phase dynamic computed tomography imaging is particularly useful in the liver region.However,dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple imaging sessions.We hypothesized that the contrast enhancement boost(CE-boost)technique could be used to enhance the contrast in equilibrium phase(EP)images and produce enhancement similar to that of portal vein phase(PVP)images,and if this is possible,EP imaging could play the same role as PVP imaging.We also speculated that this might allow the conversion of three-phase dynamic imaging to biphasic dynamic imaging,reducing patients’radiation exposure.AIM To determine if the CE-boost of EP,CE-boost(EP)is useful compared to a conventional image.METHODS We retrospectively analyzed the cases of 52 patients who were diagnosed with liver cancer between January 2016 and October 2022 at our institution.From these computed tomography images,CE-boost images were generated from the EP and plane images.We compared the PVP,EP,and CE-boost(EP)for blood vessels and hepatic parenchyma based on the contrast-to-noise ratio(CNR),signal-to-noise ratio,and figure-of-merit(FOM).Visual assessments were also performed for vessel visualization,lesion conspicuity,and image noise.RESULTS The CE-boost(EP)images showed significant superiority compared to the PVP images in the CNR,signal-to-noise ratio,and FOM except regarding the hepatic parenchyma.No significant differences were detected in CNR or FOM comparisons within the hepatic parenchyma(P=0.62,0.67).The comparison of the EP and CE-boost(EP)images consistently favored CE-boost(EP).Regarding the visual assessment,the CE-boost(EP)images were significantly superior to the PVP images in lesion conspicuity,and the PVP in image noise.The CE-boost(EP)images were significantly better than the EP images in the vessel visualization of segmental branches of the portal vein and lesion conspicuity,and the EP in image noise.CONCLUSION The image quality of CE-boost(EP)images was comparable or superior to that of conventional PVP and EP.CEboost(EP)images might provide information comparable to the conventional PVP.
