High-resolution reconstruction of solar speckle image is one of the important research contents in astronomical image processing. High-resolution image reconstruction based on deep learning can obtain the end-to-end m...High-resolution reconstruction of solar speckle image is one of the important research contents in astronomical image processing. High-resolution image reconstruction based on deep learning can obtain the end-to-end mapping function from low-resolution image to high-resolution image through neural network model learning, which can recover the high-frequency information of the image. However, when used to reconstruct the sun speckle image with single feature, more noise and fuzzy local details, there are some shortcomings such as too smooth edge and easy loss of high-frequency information. In this paper, the structure features of input image and reconstructed image are added to CycleGAN network to get MCycleGAN. High frequency information is obtained from structural features by generator network, and the feature difference is calculated to enhance the ability of network to reconstruct high-frequency information. The edge of the reconstructed image is clearer. Compared with the speckle mask method level 1+ used by Yunnan Observatory, the results show that the proposed algorithm has the advantages of small error, fast reconstruction speed and high image clarity.展开更多
After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the tim...After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the timing of interventions,combined with the limitations of current methods.To address these challenges,various techniques have been developed to aid in the repair and reconstruction of neural circuits at different stages of injury.Notably,neuromodulation has garnered considerable attention for its potential to enhance nerve regeneration,provide neuroprotection,restore neurons,and regulate the neural reorganization of circuits within the cerebral cortex and corticospinal tract.To improve the effectiveness of these interventions,the implementation of multitarget early interventional neuromodulation strategies,such as electrical and magnetic stimulation,is recommended to enhance functional recovery across different phases of nerve injury.This review concisely outlines the challenges encountered following spinal cord injury,synthesizes existing neurostimulation techniques while emphasizing neuroprotection,repair,and regeneration of impaired connections,and advocates for multi-targeted,task-oriented,and timely interventions.展开更多
Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,w...Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.展开更多
Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-...Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
In Lamb wave-based Structural Health Monitoring(SHM), a high-enough spatial resolution is highly required for Lamb wave signals to ensure the resolution and accuracy of damage detection. However, besides the dispersio...In Lamb wave-based Structural Health Monitoring(SHM), a high-enough spatial resolution is highly required for Lamb wave signals to ensure the resolution and accuracy of damage detection. However, besides the dispersion characteristic, the signal spatial resolution is also largely restricted by the space duration of excitation waveforms, i.e., the Initial Spatial Resolution(ISR)for the signals before travelling. To resolve the problem of inferior signal spatial resolution of Lamb waves, a Lamb Wave Signal Reconstruction(LWSR) method is presented and applied for highresolution damage imaging in this paper. In LWSR, not only a new linearly-dispersive signal is reconstructed from an original Lamb wave signal, but also the group velocity at the central frequency is sufficiently decreased. Then, both dispersion compensation and ISR improvement can be realized to achieve a satisfying signal spatial resolution. After the frequency domain sensing model and spatial resolution of Lamb wave signals are firstly analyzed, the basic idea and numerical realization of LWSR are discussed. Numerical simulations are also implemented to preliminarily validate LWSR. Subsequently, LWSR-based high-resolution damage imaging is developed. An experiment of adjacent multiple damage identification is finally conducted to demonstrate the efficiency of LWSR and LWSR-based imaging methods.展开更多
Direct demodulation method(DDM) was applied to reconstruct γ-ray spectra. Boosted Richardson-Lucy iteration was introduced into DDM. Monte Carlo method(here GEANT 4) was proposed to calibrate response function and es...Direct demodulation method(DDM) was applied to reconstruct γ-ray spectra. Boosted Richardson-Lucy iteration was introduced into DDM. Monte Carlo method(here GEANT 4) was proposed to calibrate response function and establish response matrix. First, gauss function was regarded as total energy peak. Spectra line was simulated with nine gauss functions. And afterwards DDM was applied to reconstruct the simulated spectra line and determine peak positions and areas. Compared with original spectra, for case that peak position interval was about 1/3 full width half maximum(FWHM), the error of rebuilding peak position was 2 channels. The rest of peaks could be searched accurately. The relative errors of all peaks' area were less than 4%. Then, three key factors, including noise, background, response matrix, were discussed. Finally, DDM was applied to calibrate the field NaI gamma spectrometer. The errors of U, Th, K were less than 5%. Comprehensive studies have shown that it is feasible to reconstruct gamma-ray spectra with DDM. DDM can significantly pseudo-improve energy resolution of gamma spectrometer, effectively decompose doublets whose peak potential interval is1/3 FHWM, and accurately search peak and calculate areas. DDM can restrain noise strongly but is greatly influenced by background. And DDM can improve the accuracy of qualitative and quantitative analysis in combination with the conventional spectrum analysis method.展开更多
As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information...As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.展开更多
Extreme drought events have increased,causing serious losses and damage to the social economy under current warming conditions.However,short-term meteorological data limit our understanding and projection of these ext...Extreme drought events have increased,causing serious losses and damage to the social economy under current warming conditions.However,short-term meteorological data limit our understanding and projection of these extremes.With the accumulation of proxy data,especially tree-ring data,large-scale precipitation field reconstruction has provided opportunities to explore underlying mechanisms further.Using point-by-point regression,we reconstructed the April-September precipitation field in China for the past~530 years on the basis of 590 proxy records,including 470 tree-ring width chronologies and 120 drought/flood indices.Our regression models explained average 50%of the variance in precipitation.In the statistical test on calibration and verification,our models passed the significance level that assured reconstruction quality.The reconstruction data performed well,showing consistency and better quality than previously reported reconstructions.The first three leading modes of variability in the reconstruction revealed the main distribution modes of precipitation over China.Wet/drought and extremely wet/drought years accounted for 12.81%/10.92%(68 years/58 years)and 1.69%/3.20%(9 years/17 years)of the past~530 years in China,respectively.Major extreme drought events can be identified explicitly in our reconstruction.The detailed features of the Chongzhen Great Drought(1637-1643),the Wanli Great Drought(1585-1590),and the Ding-Wu Great Famine(1874-1879),indicated the existence of potentially different underlying mechanisms that need further exploration.Although further improvements can be made for remote uninhabited areas and large deserts,our gridded reconstruction of April-September precipitation in China over the past~530 years can provide a solid database for studies on the attribution of climate change and the mechanism of extreme drought events.展开更多
Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver metastases.Computed tomog...Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver metastases.Computed tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm.We hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural networks.This kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases.This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim.Datasets of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency kernels.These data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal dimension.In the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to standard.However,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan.Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is limited.Future work should investigate the informationpreserving kernels in datasets with clinical labels.展开更多
High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-co...High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.展开更多
The impact of heavy reduction on dendritic morphology was explored by combining experimental research and numerical simulation in metallurgy,including a detailed three-dimensional(3D)analysis and reconstruction of den...The impact of heavy reduction on dendritic morphology was explored by combining experimental research and numerical simulation in metallurgy,including a detailed three-dimensional(3D)analysis and reconstruction of dendritic solidification structures.Combining scanning electron microscopy and energy-dispersive scanning analysis and ANSYS simulation,the high-precision image processing software Mimics Research was utilized to conduct the extraction of dendritic morphologies.Reverse engineering software NX Imageware was employed for the 3D reconstruction of two-dimensional dendritic morphologies,restoring the dendritic characteristics in three-dimensional space.The results demonstrate that in a two-dimensional plane,dendrites connect with each other to form irregularly shaped“ring-like”structures.These dendrites have a thickness greater than 0.1 mm along the Z-axis direction,leading to the envelopment of molten steel by dendrites in a 3D space of at least 0.1 mm.This results in obstructed flow,confirming the“bridging”of dendrites in three-dimensional space,resulting in a tendency for central segregation.Dense and dispersed tiny dendrites,under the influence of heat flow direction,interconnect and continuously grow,gradually forming primary and secondary dendrites in three-dimensional space.After the completion of dendritic solidification and growth,these microdendrites appear dense and dispersed on the two-dimensional plane,providing the nuclei for the formation of new dendrites.When reduction occurs at a solid fraction of 0.46,there is a noticeable decrease in dendritic spacing,resulting in improved central segregation.展开更多
This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Th...This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.展开更多
Water splitting hinges crucially on the availability of electrocatalysts for the oxygen evolution reaction.The surface reconstruction has been widely observed in perovskite catalysts,and the reconstruction degree has ...Water splitting hinges crucially on the availability of electrocatalysts for the oxygen evolution reaction.The surface reconstruction has been widely observed in perovskite catalysts,and the reconstruction degree has been often correlated with the activity enhancement.Here,a systematic study on the roles of Fe substitution in activation of perovskite LaNiO_(3)is reported.The substituting Fe content influences both current change tendency and surface reconstruction degree.LaNi_(0.9)Fe_(0.1)O_(3)is found exhibiting a volcano-peak intrinsic activity in both pristine and reconstructed among all substituted perovskites in the LaNi_(1-x)Fe_(x)O_(3)(x=0.00,0.10,0.25,0.50,0.75,1.00)series.The reconstructed LaNi_(0.9)Fe_(0.1)O_(3)shows a higher intrinsic activity than most reported NiFe-based catalysts.Besides,density functional theory calculations reveal that Fe substitution can lower the O 2p level,which thus stabilize lattice oxygen in LaNi0.9Fe0.1O3 and ensure its long-term stability.Furthermore,it is vital interesting that activity of the reconstructed catalysts relied more on the surface chemistry rather than the reconstruction degree.The effect of Fe on the degree of surface reconstruction of the perovskite is decoupled from that on its activity enhancement after surface reconstruction.This finding showcases the importance to customize the surface chemistry of reconstructed catalysts for water oxidation.展开更多
The surgical approach for patellar instability usually refers to reconstruction of the medial patellofemoral ligament associated with an osteotomy of the tibial tuberosity or a trochleoplasty when required.The medial ...The surgical approach for patellar instability usually refers to reconstruction of the medial patellofemoral ligament associated with an osteotomy of the tibial tuberosity or a trochleoplasty when required.The medial patellotibial ligament and the medial patellomeniscal ligament are secondary stabilizers of the patella.Despite this,both the medial patellotibial and patellofemoral ligaments aid in patellar rotation and tilt when the knee is flexed beyond 45°.The medial patellotibial ligament plays a particularly important role in the final stages of stretching in extension and between 40 degrees to 90 degrees of flexion.The clinical relevance and surgical indications for medial patellotibial ligament reconstruction associated with medial patellofemoral ligament reconstruction are still controversial.This editorial explores the surgical indications and clinical results for medial patellotibial ligament reconstruction to improve readers’understanding of this technique,especially because reported clinical outcomes have remained sparse.展开更多
High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuse...High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches:Multi-stereo fusion and multi-view matching.While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity,no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods.This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions.To ensure fairness in accuracy comparison,both methodologies employ non-local dense matching for cost optimization.Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics,exhibiting approximately 1.2%higher average matching accuracy and 10.7%superior elevation precision in the experimental datasets.Therefore,for 3D modeling applications using satellite data,we recommend adopting the multi-stereo fusion approach for digital surface model(DSM)product generation.展开更多
BACKGROUND Bioabsorbable interference screws are a widely used option for graft fixation in anterior cruciate ligament(ACL)reconstruction.Their ability to degrade over time and avoid secondary hardware removal makes t...BACKGROUND Bioabsorbable interference screws are a widely used option for graft fixation in anterior cruciate ligament(ACL)reconstruction.Their ability to degrade over time and avoid secondary hardware removal makes them advantageous.However,complications such as breakage and intra-articular migration of screws can cause significant clinical issues,including joint pain,swelling,and cartilage damage.Early diagnosis and management are critical in such cases.CASE SUMMARY A 26-year-old male presented with knee pain and swelling one year after ACL reconstruction using a hamstring graft and bioabsorbable tibial interference screw.The patient had been engaged in rigorous physical activity as part of military training.Clinical examination revealed mild effusion without instability,and imaging showed screw breakage with intra-articular migration.Therapeutic arthroscopy confirmed intact graft tension,and broken screw fragments were removed successfully.The patient resumed normal activity two weeks after surgery.CONCLUSION This case highlights the potential complications associated with bioabsorbable screws,emphasizing the need for meticulous surgical technique,postoperative monitoring,and timely intervention.A comprehensive review of the literature illustrates the mechanisms,risk factors,and preventive strategies associated with screw-related complications.展开更多
Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO_(2)reduction reaction(CO_(2)RR),whereas the behavior is by far superficially understood.Here,we report that CO_(2)acc...Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO_(2)reduction reaction(CO_(2)RR),whereas the behavior is by far superficially understood.Here,we report that CO_(2)accessibility results in a universal self-adaptive structural reconstruction from Cu_(2)O to Cu@CuxO composites,ending with feeding gas-dependent microstructures and catalytic performances.The CO_(2)-rich atmosphere favors reconstruction for CO_(2)RR,whereas the CO_(2)-deficient one prefers that for hydrogen evolution reaction.With the assistance of spectroscopic analysis and theoretical calculations,we uncover a CO_(2)-induced passivation behavior by identifying a reductionresistant but catalytic active Cu(I)-rich amorphous layer stabilized by*CO intermediates.Additionally,we find extra CO production is indispensable for the robust production of C2H4.An inverse correlation between durability and FECO/FEC2H4 is disclosed,suggesting that the selfstabilization process involving the absorption of*CO intermediates on Cu(I)sites is essential for durable electrolysis.Guided by this insight,we design hollow Cu_(2)O nanospheres for durable and selective CO_(2)RR electrolysis in producing C2H4.Our work recognizes the previously overlooked passivation reconstruction and self-stabilizing behavior and highlights the critical role of the local atmosphere in modulating reconstruction and catalytic processes.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
The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological d...The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological data of these particles has became a key focus in wear debris analysis.Herein,we develop a novel multi-view polarization-sensitive optical coherence tomography(PS-OCT)method to achieve accurate 3D morphology detection and reconstruction of aero-engine lubricant wear particles,effectively resolving occlusion-induced information loss while enabling material-specific characterization.The particle morphology is captured by multi-view imaging,followed by filtering,sharpening,and contour recognition.The method integrates advanced registration algorithms with Poisson reconstruction to generate high-precision 3D models.This approach not only provides accurate 3D morphological reconstruction but also mitigates information loss caused by particle occlusion,ensuring model completeness.Furthermore,by collecting polarization characteristics of typical metals and their oxides in aero-engine lubricants,this work comprehensively characterizes and comparatively analyzes particle polarization properties using Stokes vectors,polarization uniformity,and cumulative phase retardation,and obtains a three-dimensional model containing polarization information.Ultimately,the proposed method enables multidimensional information acquisition for the reliable identification of abrasive particle types.展开更多
文摘High-resolution reconstruction of solar speckle image is one of the important research contents in astronomical image processing. High-resolution image reconstruction based on deep learning can obtain the end-to-end mapping function from low-resolution image to high-resolution image through neural network model learning, which can recover the high-frequency information of the image. However, when used to reconstruct the sun speckle image with single feature, more noise and fuzzy local details, there are some shortcomings such as too smooth edge and easy loss of high-frequency information. In this paper, the structure features of input image and reconstructed image are added to CycleGAN network to get MCycleGAN. High frequency information is obtained from structural features by generator network, and the feature difference is calculated to enhance the ability of network to reconstruct high-frequency information. The edge of the reconstructed image is clearer. Compared with the speckle mask method level 1+ used by Yunnan Observatory, the results show that the proposed algorithm has the advantages of small error, fast reconstruction speed and high image clarity.
基金supported by the National Key Research and Development Program of China,No.2023YFC3603705(to DX)the National Natural Science Foundation of China,No.82302866(to YZ).
文摘After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the timing of interventions,combined with the limitations of current methods.To address these challenges,various techniques have been developed to aid in the repair and reconstruction of neural circuits at different stages of injury.Notably,neuromodulation has garnered considerable attention for its potential to enhance nerve regeneration,provide neuroprotection,restore neurons,and regulate the neural reorganization of circuits within the cerebral cortex and corticospinal tract.To improve the effectiveness of these interventions,the implementation of multitarget early interventional neuromodulation strategies,such as electrical and magnetic stimulation,is recommended to enhance functional recovery across different phases of nerve injury.This review concisely outlines the challenges encountered following spinal cord injury,synthesizes existing neurostimulation techniques while emphasizing neuroprotection,repair,and regeneration of impaired connections,and advocates for multi-targeted,task-oriented,and timely interventions.
基金funded by the National Natural Science Foundation of China(62273213,62472262,62572287)Natural Science Foundation of Shandong Province(ZR2024MF144)+1 种基金Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds(ZR2022LZH001)Taishan Scholarship Construction Engineering.
文摘Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.
基金supported by the Natural Science Foundation of Hubei Provincial Department of Education(D20232101)Shandong Second Medical University 2024 Affiliated Hospital(Teaching Hospital)Scientific Research Development Fund Project(2024FYQ026)+3 种基金the innovative Research Programme of Xiangyang No.1 People’s Hospital(XYY2023ZY01)Faculty Development Grants of Xiangyang No.1 People’s Hospital Affiliated to Hubei University of Medicine(XYY2023D05)Joint supported by Hubei Provincial Natural Science Foundation and Xiangyang of China(2025AFD091)Traditional Chinese Medicine Scientific Research Project of Hubei Provincial Administration of Traditional Chinese Medicine(ZY2025D019).
文摘Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the Fundamental Research Funds for the Central Universities,China(No.NS2016012)
文摘In Lamb wave-based Structural Health Monitoring(SHM), a high-enough spatial resolution is highly required for Lamb wave signals to ensure the resolution and accuracy of damage detection. However, besides the dispersion characteristic, the signal spatial resolution is also largely restricted by the space duration of excitation waveforms, i.e., the Initial Spatial Resolution(ISR)for the signals before travelling. To resolve the problem of inferior signal spatial resolution of Lamb waves, a Lamb Wave Signal Reconstruction(LWSR) method is presented and applied for highresolution damage imaging in this paper. In LWSR, not only a new linearly-dispersive signal is reconstructed from an original Lamb wave signal, but also the group velocity at the central frequency is sufficiently decreased. Then, both dispersion compensation and ISR improvement can be realized to achieve a satisfying signal spatial resolution. After the frequency domain sensing model and spatial resolution of Lamb wave signals are firstly analyzed, the basic idea and numerical realization of LWSR are discussed. Numerical simulations are also implemented to preliminarily validate LWSR. Subsequently, LWSR-based high-resolution damage imaging is developed. An experiment of adjacent multiple damage identification is finally conducted to demonstrate the efficiency of LWSR and LWSR-based imaging methods.
基金Supported by National Science Foundation for Distinguished Young Scholars(No.41025015)Natural Science Foundation of China(No.41274130)+1 种基金Sichuan Youth Science and Technology Innovation Team(No.2011JTD0013)Sichuan Province Science and Technology Support Plan(No.2013FZ0022)
文摘Direct demodulation method(DDM) was applied to reconstruct γ-ray spectra. Boosted Richardson-Lucy iteration was introduced into DDM. Monte Carlo method(here GEANT 4) was proposed to calibrate response function and establish response matrix. First, gauss function was regarded as total energy peak. Spectra line was simulated with nine gauss functions. And afterwards DDM was applied to reconstruct the simulated spectra line and determine peak positions and areas. Compared with original spectra, for case that peak position interval was about 1/3 full width half maximum(FWHM), the error of rebuilding peak position was 2 channels. The rest of peaks could be searched accurately. The relative errors of all peaks' area were less than 4%. Then, three key factors, including noise, background, response matrix, were discussed. Finally, DDM was applied to calibrate the field NaI gamma spectrometer. The errors of U, Th, K were less than 5%. Comprehensive studies have shown that it is feasible to reconstruct gamma-ray spectra with DDM. DDM can significantly pseudo-improve energy resolution of gamma spectrometer, effectively decompose doublets whose peak potential interval is1/3 FHWM, and accurately search peak and calculate areas. DDM can restrain noise strongly but is greatly influenced by background. And DDM can improve the accuracy of qualitative and quantitative analysis in combination with the conventional spectrum analysis method.
文摘As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.
基金National Key Research and Development Program of China(2018YFA0605601)Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20070101)National Natural Science Foundation of China(41572353,41401228,41690113)。
文摘Extreme drought events have increased,causing serious losses and damage to the social economy under current warming conditions.However,short-term meteorological data limit our understanding and projection of these extremes.With the accumulation of proxy data,especially tree-ring data,large-scale precipitation field reconstruction has provided opportunities to explore underlying mechanisms further.Using point-by-point regression,we reconstructed the April-September precipitation field in China for the past~530 years on the basis of 590 proxy records,including 470 tree-ring width chronologies and 120 drought/flood indices.Our regression models explained average 50%of the variance in precipitation.In the statistical test on calibration and verification,our models passed the significance level that assured reconstruction quality.The reconstruction data performed well,showing consistency and better quality than previously reported reconstructions.The first three leading modes of variability in the reconstruction revealed the main distribution modes of precipitation over China.Wet/drought and extremely wet/drought years accounted for 12.81%/10.92%(68 years/58 years)and 1.69%/3.20%(9 years/17 years)of the past~530 years in China,respectively.Major extreme drought events can be identified explicitly in our reconstruction.The detailed features of the Chongzhen Great Drought(1637-1643),the Wanli Great Drought(1585-1590),and the Ding-Wu Great Famine(1874-1879),indicated the existence of potentially different underlying mechanisms that need further exploration.Although further improvements can be made for remote uninhabited areas and large deserts,our gridded reconstruction of April-September precipitation in China over the past~530 years can provide a solid database for studies on the attribution of climate change and the mechanism of extreme drought events.
基金Research reported in this publication was supported by the NIH/NCI,No.R01CA233888the National Science Foundation Graduate Research Fellowship,No.DGE2147721.
文摘Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver metastases.Computed tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm.We hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural networks.This kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases.This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim.Datasets of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency kernels.These data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal dimension.In the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to standard.However,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan.Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is limited.Future work should investigate the informationpreserving kernels in datasets with clinical labels.
基金financial support from the National Natural Science Foundation of China(Grant No.61971201)。
文摘High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.
基金supported by Open Foundation of the State Key Laboratory of Refractories and Metallurgy(No.G201711)the National Natural Science Foundation of China(Nos.52104317 and 51874001).
文摘The impact of heavy reduction on dendritic morphology was explored by combining experimental research and numerical simulation in metallurgy,including a detailed three-dimensional(3D)analysis and reconstruction of dendritic solidification structures.Combining scanning electron microscopy and energy-dispersive scanning analysis and ANSYS simulation,the high-precision image processing software Mimics Research was utilized to conduct the extraction of dendritic morphologies.Reverse engineering software NX Imageware was employed for the 3D reconstruction of two-dimensional dendritic morphologies,restoring the dendritic characteristics in three-dimensional space.The results demonstrate that in a two-dimensional plane,dendrites connect with each other to form irregularly shaped“ring-like”structures.These dendrites have a thickness greater than 0.1 mm along the Z-axis direction,leading to the envelopment of molten steel by dendrites in a 3D space of at least 0.1 mm.This results in obstructed flow,confirming the“bridging”of dendrites in three-dimensional space,resulting in a tendency for central segregation.Dense and dispersed tiny dendrites,under the influence of heat flow direction,interconnect and continuously grow,gradually forming primary and secondary dendrites in three-dimensional space.After the completion of dendritic solidification and growth,these microdendrites appear dense and dispersed on the two-dimensional plane,providing the nuclei for the formation of new dendrites.When reduction occurs at a solid fraction of 0.46,there is a noticeable decrease in dendritic spacing,resulting in improved central segregation.
基金supported by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025).
文摘This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.
基金funded by the National Key R&D Program of China(2021YFA1501101)the National Natural Science Foundation of China(No.22471103,22425105,22201111,21931001,22221001,and 22271124)+5 种基金Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)the Special Fund Project of Guiding Scientific and Technological Innovation Development of Gansu Province(2019ZX-04)the 111 Project(B20027)as well as the National Natural Science Foundation of Gansu Province(22JR5RA470)the Fundamental Research Funds for the Central Universities(lzujbky-2023-eyt03)supported by the Agency for Science,Technology and Research(A*STAR)MTC Individual Research Grants(IRG)M22K2c0078.
文摘Water splitting hinges crucially on the availability of electrocatalysts for the oxygen evolution reaction.The surface reconstruction has been widely observed in perovskite catalysts,and the reconstruction degree has been often correlated with the activity enhancement.Here,a systematic study on the roles of Fe substitution in activation of perovskite LaNiO_(3)is reported.The substituting Fe content influences both current change tendency and surface reconstruction degree.LaNi_(0.9)Fe_(0.1)O_(3)is found exhibiting a volcano-peak intrinsic activity in both pristine and reconstructed among all substituted perovskites in the LaNi_(1-x)Fe_(x)O_(3)(x=0.00,0.10,0.25,0.50,0.75,1.00)series.The reconstructed LaNi_(0.9)Fe_(0.1)O_(3)shows a higher intrinsic activity than most reported NiFe-based catalysts.Besides,density functional theory calculations reveal that Fe substitution can lower the O 2p level,which thus stabilize lattice oxygen in LaNi0.9Fe0.1O3 and ensure its long-term stability.Furthermore,it is vital interesting that activity of the reconstructed catalysts relied more on the surface chemistry rather than the reconstruction degree.The effect of Fe on the degree of surface reconstruction of the perovskite is decoupled from that on its activity enhancement after surface reconstruction.This finding showcases the importance to customize the surface chemistry of reconstructed catalysts for water oxidation.
文摘The surgical approach for patellar instability usually refers to reconstruction of the medial patellofemoral ligament associated with an osteotomy of the tibial tuberosity or a trochleoplasty when required.The medial patellotibial ligament and the medial patellomeniscal ligament are secondary stabilizers of the patella.Despite this,both the medial patellotibial and patellofemoral ligaments aid in patellar rotation and tilt when the knee is flexed beyond 45°.The medial patellotibial ligament plays a particularly important role in the final stages of stretching in extension and between 40 degrees to 90 degrees of flexion.The clinical relevance and surgical indications for medial patellotibial ligament reconstruction associated with medial patellofemoral ligament reconstruction are still controversial.This editorial explores the surgical indications and clinical results for medial patellotibial ligament reconstruction to improve readers’understanding of this technique,especially because reported clinical outcomes have remained sparse.
文摘High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches:Multi-stereo fusion and multi-view matching.While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity,no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods.This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions.To ensure fairness in accuracy comparison,both methodologies employ non-local dense matching for cost optimization.Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics,exhibiting approximately 1.2%higher average matching accuracy and 10.7%superior elevation precision in the experimental datasets.Therefore,for 3D modeling applications using satellite data,we recommend adopting the multi-stereo fusion approach for digital surface model(DSM)product generation.
文摘BACKGROUND Bioabsorbable interference screws are a widely used option for graft fixation in anterior cruciate ligament(ACL)reconstruction.Their ability to degrade over time and avoid secondary hardware removal makes them advantageous.However,complications such as breakage and intra-articular migration of screws can cause significant clinical issues,including joint pain,swelling,and cartilage damage.Early diagnosis and management are critical in such cases.CASE SUMMARY A 26-year-old male presented with knee pain and swelling one year after ACL reconstruction using a hamstring graft and bioabsorbable tibial interference screw.The patient had been engaged in rigorous physical activity as part of military training.Clinical examination revealed mild effusion without instability,and imaging showed screw breakage with intra-articular migration.Therapeutic arthroscopy confirmed intact graft tension,and broken screw fragments were removed successfully.The patient resumed normal activity two weeks after surgery.CONCLUSION This case highlights the potential complications associated with bioabsorbable screws,emphasizing the need for meticulous surgical technique,postoperative monitoring,and timely intervention.A comprehensive review of the literature illustrates the mechanisms,risk factors,and preventive strategies associated with screw-related complications.
基金supported by the National Natural Science Foundation of China(Grant No.22479097)the Shanghai Science and Technology Committee(Grant No.23ZR1433000)the National High-Level Talent Program for Young Scholars,the Start-up Fund(F.S.)from Shanghai Jiao Tong University.
文摘Structural reconstruction of electrocatalysts plays a pivotal role in catalytic performances for CO_(2)reduction reaction(CO_(2)RR),whereas the behavior is by far superficially understood.Here,we report that CO_(2)accessibility results in a universal self-adaptive structural reconstruction from Cu_(2)O to Cu@CuxO composites,ending with feeding gas-dependent microstructures and catalytic performances.The CO_(2)-rich atmosphere favors reconstruction for CO_(2)RR,whereas the CO_(2)-deficient one prefers that for hydrogen evolution reaction.With the assistance of spectroscopic analysis and theoretical calculations,we uncover a CO_(2)-induced passivation behavior by identifying a reductionresistant but catalytic active Cu(I)-rich amorphous layer stabilized by*CO intermediates.Additionally,we find extra CO production is indispensable for the robust production of C2H4.An inverse correlation between durability and FECO/FEC2H4 is disclosed,suggesting that the selfstabilization process involving the absorption of*CO intermediates on Cu(I)sites is essential for durable electrolysis.Guided by this insight,we design hollow Cu_(2)O nanospheres for durable and selective CO_(2)RR electrolysis in producing C2H4.Our work recognizes the previously overlooked passivation reconstruction and self-stabilizing behavior and highlights the critical role of the local atmosphere in modulating reconstruction and catalytic processes.
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The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological data of these particles has became a key focus in wear debris analysis.Herein,we develop a novel multi-view polarization-sensitive optical coherence tomography(PS-OCT)method to achieve accurate 3D morphology detection and reconstruction of aero-engine lubricant wear particles,effectively resolving occlusion-induced information loss while enabling material-specific characterization.The particle morphology is captured by multi-view imaging,followed by filtering,sharpening,and contour recognition.The method integrates advanced registration algorithms with Poisson reconstruction to generate high-precision 3D models.This approach not only provides accurate 3D morphological reconstruction but also mitigates information loss caused by particle occlusion,ensuring model completeness.Furthermore,by collecting polarization characteristics of typical metals and their oxides in aero-engine lubricants,this work comprehensively characterizes and comparatively analyzes particle polarization properties using Stokes vectors,polarization uniformity,and cumulative phase retardation,and obtains a three-dimensional model containing polarization information.Ultimately,the proposed method enables multidimensional information acquisition for the reliable identification of abrasive particle types.
