The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.Environmental factors induce vibrations in flexible pipelines,thereby affecting the internal flow characteristics.Therefor...Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.Environmental factors induce vibrations in flexible pipelines,thereby affecting the internal flow characteristics.Therefore,real-time monitoring of solid–liquid two-phase flow in pipelines is crucial for system maintenance.This study develops an autoencoder-based deep learning framework to reconstruct three-dimensional solid–liquid two-phase flow within flexible vibrating pipelines utilizing sparse wall information from sensors.Within this framework,separate X-model and F-model with distinct hidden-layer structures are established to reconstruct the coordinates and flow field information on the computational domain grid of the pipeline under traveling wave vibration.Following hyperparameter optimization,the models achieved high reconstruction accuracy,demonstrating R^(2)values of 0.990 and 0.945,respectively.The models’robustness is evaluated across three aspects:vibration parameters,physical fields,and vibration modes,demonstrating good reconstruction performance.Results concerning sensors show that 20 sensors(0.06%of total grids)achieve a balance between accuracy and cost,with superior accuracy obtained when arranged along the full length of the pipe compared to a dense arrangement at the front end.The models exhibited a signal-to-noise ratio tolerance of approximately 27 dB,with reconstruction accuracy being more affected by sensor failures at both ends of the pipeline.展开更多
Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a n...Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds.Our approach eliminates the need to generate dense point clouds,and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data.We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm.This algorithm projects 3D line clouds onto a 2D plane,clusters the projections to identify potential planes,and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction.Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method,demonstrating its superiority over existing techniques in terms of simplicity and efficiency.展开更多
3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safe...3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.展开更多
This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D n...This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.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
Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest....Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks.展开更多
During the oxygen evolution reaction(OER),reconstruction of transition metal sulfides(TMSs)is inevitable.However,the lack of a clear theoretical understanding of this process has impeded the development of effective r...During the oxygen evolution reaction(OER),reconstruction of transition metal sulfides(TMSs)is inevitable.However,the lack of a clear theoretical understanding of this process has impeded the development of effective reconstruction regulation strategies.In this study,we first explored the reconstruction mechanism of CoS_(2)during OER from the perspective of electronic structure and identified two possible pathways:the OH-assisted mechanism and the O-assisted mechanism.Further verification showed that these mechanisms are universally applicable to other TMSs(e.g.,FeS_(2)).Based on the reconstruction mechanism,we investigated the basic reasons for the influence of various regulation strategies,such as vacancy modification and facet engineering,on the reconstruction ability.This verified that the method of analyzing the change in the reconstruction ability of catalysts based on the reconstruction mechanism has a high degree of applicability.Importantly,we proposed a core regulation strategy:the coordination symmetry regulation strategy.Specifically,by breaking the symmetry of the surface coordination environment of TMSs(such as introducing heteroatom doping or strain),the reconstruction process will be facilitated.Our findings provide a comprehensive mechanistic explanation for the reconstruction of TMS catalysts and offer a new idea for the rational design of OER catalysts with controllable reconstruction capacity.展开更多
There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution...There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.展开更多
Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and...Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration.展开更多
Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.Ho...Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features.展开更多
Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to ...Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.展开更多
Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recov...Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.展开更多
Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees rem...Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.展开更多
Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core functi...Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.展开更多
AIM:To evaluate the efficacy and safety of decellularized conjunctival stroma(DCS)as a novel biomaterial by comparing its grafting outcomes with amniotic membrane(AM)when used for conjunctival reconstruction after pri...AIM:To evaluate the efficacy and safety of decellularized conjunctival stroma(DCS)as a novel biomaterial by comparing its grafting outcomes with amniotic membrane(AM)when used for conjunctival reconstruction after primary pterygium excision.METHODS:This randomized,parallel-controlled study with allocation concealment enrolled 40 patients with primary pterygium.Participants were randomly assigned to two groups using the sealed envelope method:the DCS group(n=20)and the AM group(n=18),receiving DCS and AM grafts respectively.Slit-lamp photography of the operative eyes was performed preoperatively and at 1,3,5,7,10,30,90,and 180d postoperatively.Best-corrected visual acuity(BCVA)and symptom scores were recorded simultaneously.In vivo confocal microscopy was conducted at 3 and 6mo postoperatively.RESULTS:All participants exhibited improved postoperative symptoms.The mean age was 60±9y(male/female ratio:6/14)in the DCS group and 56±12y(male/female ratio:7/11)in the AM group.The average epithelial healing time was 9.89±3.54d in the DCS group and 8.17±1.34d in the AM group(P=0.084).One recurrence case was observed in each group.Postoperative graft hemorrhage was significantly more severe in the DCS group than in the AM group only at 30d postoperatively(P=0.011).In vivo confocal microscopy revealed conjunctival epithelial cell growth in both groups at 90d postoperatively,while clear corneo-conjunctival cell boundaries were observed until 180d postoperatively.CONCLUSION:DCS used in primary pterygium surgery has a safety profile comparable to AM.It promotes rapid postoperative conjunctival healing,achieves a relatively low pterygium recurrence rate,and yields outcomes similar to AM.DCS provides a novel biomaterial option for conjunctival reconstruction after pterygium excision and the treatment of other conjunctival injuries.展开更多
Background:Penile augmentation through injectable substances is becoming increasingly common.A growing number of aesthetic clinics are developing penile enlargement procedures using various injectable materials.Althou...Background:Penile augmentation through injectable substances is becoming increasingly common.A growing number of aesthetic clinics are developing penile enlargement procedures using various injectable materials.Although these procedures are now performed in more controlled and medically supervised environments,their long-term outcomes remain poorly understood.The promotion of such medical treatments contributes to an increasing interest among adult males in self-injection as a method to alleviate psychological distress associated with penile size concerns.At the same time,access to injectable substances through unofficial or unregulated sources has become increasingly easy.Tor our knowledge,we report the first documented case of self-injection with Garamycin®(gentamicin)cream,contributing to the literature on the often multidisciplinary management of penile enlargement injections,a field still lacking well-established guidelines.Case Description:This case report describes a young patient who self-injected Garamycin®into the penis for the purpose of enlargement.He presented to our urology department with worsening symptoms,including severe and poorly tolerated pain.His primary request was prompt relief of pain while preserving,as much as possible,the aesthetic appearance and functional integrity of his penis.This case required a multi-stage surgical approach to salvage the penis and preserve both its structural integrity and functional outcome.Conclusions:To our knowledge,this case report documents the first reported instance of Garamycin®injection performed for the purpose of penile enlargement.It provides insight into the clinical course of such penile cream injections,demonstrates that a two-stage scrotal flap can achieve both functional and aesthetic outcomes,and highlights the importance of comprehensive management particularly addressing the traumatic impact of penile deformity secondary to inflammation and/or infection,as well as the body dysmorphic concerns often associated with these cases.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
The application of conventional manganese dioxide(MnO_(2))materials in sodium-ion supercapacitors(Na-SCs)is considerably limited by their low conductivity and structural instability.Biomimetic morphology engineering c...The application of conventional manganese dioxide(MnO_(2))materials in sodium-ion supercapacitors(Na-SCs)is considerably limited by their low conductivity and structural instability.Biomimetic morphology engineering can optimize the electrochemical performance of MnO_(2).Here,based on the metal-organic frameworks(MOFs)-derived method and electrochemical reconstruction,a coral-like MnO_(2)structure integrated with a functional nitrogen-doped carbon(NC)coating is designed for Na-SC application.The bioinspired coral-like structure captures numerous electrolyte ions and increases the Na+concentration on the electrode surface,which is beneficial for optimizing the Na+transport pathway and accelerating the electrode reaction kinetics.Moreover,the coral-like crosslinked structure effectively enhances the mechanical properties,enabling the maintenance of the structure of MnO_(2)-based electrodes during long-term operation.Furthermore,in/ex-situ characterizations are performed to elucidate the mechanism of lattice transformation during electrochemical phase reconstruction.Additionally,the theoretical calculation and simulation results reveal the ion/electron dynamics in the fabricated electrode.The prepared electrode demonstrates excellent capacitance storage ability(340.7 F g^(−1)at 0.5 A g^(−1))and cycling stability(85.1%capacitance retention after 10,000 cycles).The assembled hybrid device exhibits exceptional life-span(82.0%capacitance retention after 10,000 cycles)and exceptional energy density(36.5 Wh kg^(−1)).This study provides a reliable biomimetic morphology design strategy for MnO_(2)cathodes,paving the way for the fabrication of high-performance Na-SCs.展开更多
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金financial support by the National Natural Science Foundation of China (Nos.52471293 and 12372270)the National Youth Science Foundation of China (Nos.52101322 and 52108375)+3 种基金the Program for Intergovernmental International S&T Cooperation Projects of Shanghai Municipality, China (Nos.24510711100 and 22160710200)The Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (No.SL2022PT101)funded by the Open Fund of the State Key Laboratory of Coastal and Offshore Engineering of Dalian University of Technology (No.LP2415)National Key R&D Program of China (No.2023YFC2811600)
文摘Deep-sea mineral resource transportation predominantly utilizes hydraulic pipeline methodology.Environmental factors induce vibrations in flexible pipelines,thereby affecting the internal flow characteristics.Therefore,real-time monitoring of solid–liquid two-phase flow in pipelines is crucial for system maintenance.This study develops an autoencoder-based deep learning framework to reconstruct three-dimensional solid–liquid two-phase flow within flexible vibrating pipelines utilizing sparse wall information from sensors.Within this framework,separate X-model and F-model with distinct hidden-layer structures are established to reconstruct the coordinates and flow field information on the computational domain grid of the pipeline under traveling wave vibration.Following hyperparameter optimization,the models achieved high reconstruction accuracy,demonstrating R^(2)values of 0.990 and 0.945,respectively.The models’robustness is evaluated across three aspects:vibration parameters,physical fields,and vibration modes,demonstrating good reconstruction performance.Results concerning sensors show that 20 sensors(0.06%of total grids)achieve a balance between accuracy and cost,with superior accuracy obtained when arranged along the full length of the pipe compared to a dense arrangement at the front end.The models exhibited a signal-to-noise ratio tolerance of approximately 27 dB,with reconstruction accuracy being more affected by sensor failures at both ends of the pipeline.
基金Supported by the Guangdong Major Project of Basic and Applied Basic Research (2023B0303000016)the National Natural Science Foundation of China (U21A20515)。
文摘Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds.Our approach eliminates the need to generate dense point clouds,and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data.We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm.This algorithm projects 3D line clouds onto a 2D plane,clusters the projections to identify potential planes,and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction.Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method,demonstrating its superiority over existing techniques in terms of simplicity and efficiency.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.
文摘This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.
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supported in part by the National Natural Science Foundation of China:61773330.
文摘Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks.
基金supported by the National Key Research and Development program(2022YFA1504000)the National Natural Science Foundation of China(22302101)+4 种基金the Fundamental Research Funds for the Central Universities(63185015)the Shenzhen Science and Technology Program(JCYJ20210324121002007,JCYJ20230807151503007)the Yunnan Provincial Science and Technology Project at Southwest United Graduate School(202402AO370001)the China Postdoctoral Science Foundation(2022M721699)the Guangdong Basic and Applied Basic Research Foundation(2024A1515010347).
文摘During the oxygen evolution reaction(OER),reconstruction of transition metal sulfides(TMSs)is inevitable.However,the lack of a clear theoretical understanding of this process has impeded the development of effective reconstruction regulation strategies.In this study,we first explored the reconstruction mechanism of CoS_(2)during OER from the perspective of electronic structure and identified two possible pathways:the OH-assisted mechanism and the O-assisted mechanism.Further verification showed that these mechanisms are universally applicable to other TMSs(e.g.,FeS_(2)).Based on the reconstruction mechanism,we investigated the basic reasons for the influence of various regulation strategies,such as vacancy modification and facet engineering,on the reconstruction ability.This verified that the method of analyzing the change in the reconstruction ability of catalysts based on the reconstruction mechanism has a high degree of applicability.Importantly,we proposed a core regulation strategy:the coordination symmetry regulation strategy.Specifically,by breaking the symmetry of the surface coordination environment of TMSs(such as introducing heteroatom doping or strain),the reconstruction process will be facilitated.Our findings provide a comprehensive mechanistic explanation for the reconstruction of TMS catalysts and offer a new idea for the rational design of OER catalysts with controllable reconstruction capacity.
基金supported by the National Nature Science Foundation of China(Nos.12027901 and 12041202)Synchrotron Radiation Joint Fund of University of Science and Technology of China(Nos.KY2090000059 and KY2090000054)。
文摘There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.
基金supported by the National Natural Science Foundation of China(Nos.12205044 and 12265003)2024 Jiangxi Province Civil-Military Integration Research Institute‘BeiDou+’Project Subtopic(No.2024JXRH0Y06).
文摘Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration.
基金supported by the National Science Foundationof China(Nos.52305127 and 52475130)。
文摘Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features.
基金Project supported by the National Natural Science Foun-dation of China(Grant No.62373197)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(Grant No.23KJB120010)+1 种基金the Industry-University-Research Cooperation Project of Jiangsu Province,China(Grant No.BY20251038)the Cultivation and In-cubation Project of the College of Automation,Nanjing Uni-versity of Posts and Telecommunications.
文摘Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.
文摘Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.
基金supported by the Natural Science Foundation of China(Grant No.52574047 and Grant No.52374045)Key Project of Sichuan Provincial Joint Fund for Science Technology and Education,China(Grant No.2025NSFSC2008).
文摘Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.
基金Supported by grants from the National Natural Science Foundation of China(No.82171018,No.82371022)Beijing Hospitals Authority’s Ascent Plan(No.DFL20240202)+2 种基金The Youth Beijing Scholars Program(No.022)High Level Public Health Technical Talents Construction Project from Beijing(Jie Y)Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes(No.2023YFC2410401).
文摘AIM:To evaluate the efficacy and safety of decellularized conjunctival stroma(DCS)as a novel biomaterial by comparing its grafting outcomes with amniotic membrane(AM)when used for conjunctival reconstruction after primary pterygium excision.METHODS:This randomized,parallel-controlled study with allocation concealment enrolled 40 patients with primary pterygium.Participants were randomly assigned to two groups using the sealed envelope method:the DCS group(n=20)and the AM group(n=18),receiving DCS and AM grafts respectively.Slit-lamp photography of the operative eyes was performed preoperatively and at 1,3,5,7,10,30,90,and 180d postoperatively.Best-corrected visual acuity(BCVA)and symptom scores were recorded simultaneously.In vivo confocal microscopy was conducted at 3 and 6mo postoperatively.RESULTS:All participants exhibited improved postoperative symptoms.The mean age was 60±9y(male/female ratio:6/14)in the DCS group and 56±12y(male/female ratio:7/11)in the AM group.The average epithelial healing time was 9.89±3.54d in the DCS group and 8.17±1.34d in the AM group(P=0.084).One recurrence case was observed in each group.Postoperative graft hemorrhage was significantly more severe in the DCS group than in the AM group only at 30d postoperatively(P=0.011).In vivo confocal microscopy revealed conjunctival epithelial cell growth in both groups at 90d postoperatively,while clear corneo-conjunctival cell boundaries were observed until 180d postoperatively.CONCLUSION:DCS used in primary pterygium surgery has a safety profile comparable to AM.It promotes rapid postoperative conjunctival healing,achieves a relatively low pterygium recurrence rate,and yields outcomes similar to AM.DCS provides a novel biomaterial option for conjunctival reconstruction after pterygium excision and the treatment of other conjunctival injuries.
文摘Background:Penile augmentation through injectable substances is becoming increasingly common.A growing number of aesthetic clinics are developing penile enlargement procedures using various injectable materials.Although these procedures are now performed in more controlled and medically supervised environments,their long-term outcomes remain poorly understood.The promotion of such medical treatments contributes to an increasing interest among adult males in self-injection as a method to alleviate psychological distress associated with penile size concerns.At the same time,access to injectable substances through unofficial or unregulated sources has become increasingly easy.Tor our knowledge,we report the first documented case of self-injection with Garamycin®(gentamicin)cream,contributing to the literature on the often multidisciplinary management of penile enlargement injections,a field still lacking well-established guidelines.Case Description:This case report describes a young patient who self-injected Garamycin®into the penis for the purpose of enlargement.He presented to our urology department with worsening symptoms,including severe and poorly tolerated pain.His primary request was prompt relief of pain while preserving,as much as possible,the aesthetic appearance and functional integrity of his penis.This case required a multi-stage surgical approach to salvage the penis and preserve both its structural integrity and functional outcome.Conclusions:To our knowledge,this case report documents the first reported instance of Garamycin®injection performed for the purpose of penile enlargement.It provides insight into the clinical course of such penile cream injections,demonstrates that a two-stage scrotal flap can achieve both functional and aesthetic outcomes,and highlights the importance of comprehensive management particularly addressing the traumatic impact of penile deformity secondary to inflammation and/or infection,as well as the body dysmorphic concerns often associated with these cases.
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 National Natural Science Foundation of China(22409065)the Guangdong Basic and Applied Basic Research Foundation(2022A1515011906)+2 种基金the China Postdoctoral Science Foundation(2023M731153)the Research Fund Program of Guangdong Provincial Key Laboratory of Fuel Cell Technologythe Postdoctoral Fellowship Program of CPSF(GZC20230868).
文摘The application of conventional manganese dioxide(MnO_(2))materials in sodium-ion supercapacitors(Na-SCs)is considerably limited by their low conductivity and structural instability.Biomimetic morphology engineering can optimize the electrochemical performance of MnO_(2).Here,based on the metal-organic frameworks(MOFs)-derived method and electrochemical reconstruction,a coral-like MnO_(2)structure integrated with a functional nitrogen-doped carbon(NC)coating is designed for Na-SC application.The bioinspired coral-like structure captures numerous electrolyte ions and increases the Na+concentration on the electrode surface,which is beneficial for optimizing the Na+transport pathway and accelerating the electrode reaction kinetics.Moreover,the coral-like crosslinked structure effectively enhances the mechanical properties,enabling the maintenance of the structure of MnO_(2)-based electrodes during long-term operation.Furthermore,in/ex-situ characterizations are performed to elucidate the mechanism of lattice transformation during electrochemical phase reconstruction.Additionally,the theoretical calculation and simulation results reveal the ion/electron dynamics in the fabricated electrode.The prepared electrode demonstrates excellent capacitance storage ability(340.7 F g^(−1)at 0.5 A g^(−1))and cycling stability(85.1%capacitance retention after 10,000 cycles).The assembled hybrid device exhibits exceptional life-span(82.0%capacitance retention after 10,000 cycles)and exceptional energy density(36.5 Wh kg^(−1)).This study provides a reliable biomimetic morphology design strategy for MnO_(2)cathodes,paving the way for the fabrication of high-performance Na-SCs.
