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
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
The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent ...The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.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
Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challe...Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware.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
Perihilar cholangiocarcinoma(pCCA)and intrahepatic cholangiocarcinoma(iCCA)are highly malignant neoplasms with a 5-year overall survival rate of approximately 30%[1,2].Surgical resection remains the only potentially c...Perihilar cholangiocarcinoma(pCCA)and intrahepatic cholangiocarcinoma(iCCA)are highly malignant neoplasms with a 5-year overall survival rate of approximately 30%[1,2].Surgical resection remains the only potentially curative treatment,yet only one-fifth of patients are eligible for resection at initial diagnosis[3].Threedimensional(3D)reconstruction technology provides precise preoperative visualization of complex hilar anatomy,significantly enhancing surgical planning and outcomes[4].Recent advances in 3D reconstruction technology have enhanced preoperative planning by providing precise anatomical mapping of tumor-vessel relationships and biliary variations[4,5].Therefore,this report describes a case of left iCCA successfully resected with biliary reconstruction guided by 3D visualization.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
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.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
The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,...The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.展开更多
Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstructio...Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstruction methods either compromise on accuracy with iterative algorithms or suffer from limited generalizability with task-specific deep learning approaches.Methods:We present LDM-PIR,a lightweight physics-conditioned diffusion multi-model for medical image reconstruction that addresses key challenges in magnetic resonance imaging(MRI),CT,and low-photon imaging.Unlike traditional iterative methods,which are computationally expensive,or task-specific deep learning approaches lacking generalizability,integrates three innovations.A physics-conditioned diffusion framework that embeds acquisition operators(Fourier/Radon transforms)and noise models directly into the reconstruction process.A multi-model architecture that unifies denoising,inpainting,and super-resolution via shared weight conditioning.A lightweight design(2.1M parameters)enabling rapid inference(0.8s/image on GPU).Through self-supervised fine-tuning with measurement consistency losses adapts to new imaging modalities using fewer annotated samples.Results:Achieves state-of-the-art performance on fastMRI(peak signal-to-noise ratio(PSNR):34.04 for single-coil/31.50 for multi-coil)and Lung Image Database Consortium and Image Database Resource Initiative(28.83 PSNR under Poisson noise).Clinical evaluations demonstrate superior preservation of anatomical structures,with SSIM improvements of 8.8%for single-coil and 4.36%for multi-coil MRI over uDPIR.Conclusion:It offers a flexible,efficient,and scalable solution for medical image reconstruction,addressing the challenges of noise,undersampling,and modality generalization.The model’s lightweight design allows for rapid inference,while its self-supervised fine-tuning capability minimizes reliance on large annotated datasets,making it suitable for real-world clinical applications.展开更多
This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrate...This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free,high-resolution PCB images,which are stitched into a single orthographic composite.A YOLO-based detection model,trained on a dataset of 270 PCB images across 23 component classes with data augmentation,identifies and localizes electronic components with a mean average precision of 0.932.Detected components are automatically matched to corresponding 3D CAD models from a part library and assembled within a Fusion 360 environment,producing a 3D digital replica.Experimental results show a similarity score of 0.894 and dimensional deviations below 2%,outperforming both SensoPart image measurement and manual vernier methods.The proposed approach bridges optical metrology and CAD automation,providing a scalable solution for AI-assisted reverse engineering,digital archiving,and intelligent manufacturing.展开更多
[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of ...[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.展开更多
The advancement of effective and stable non-precious metal-based catalysts for oxygen evolution reactions(OER)with a lowcost and simple technique is essential for the practical application of rechargeable zinc–air ba...The advancement of effective and stable non-precious metal-based catalysts for oxygen evolution reactions(OER)with a lowcost and simple technique is essential for the practical application of rechargeable zinc–air battery(ZAB).However,facilitating the deep reconstruction of electrocatalysts to form active species remains a significant challenge.Here,a simple two-step method composed of impregnation and carbonization process is proposed to synthesize N,S co-doped microcrystalline cellulose-derived carbon-supported nickel sulfide(Ni_(3)S_(2))nanoparticles.The in situ Raman reveals that Fe substitution promotes the reconstruction of Ni_(3)S_(2),accompanied by the cleavage of the Ni–S bond,leading to the deep reconstruction into(Ni,Fe)OOH(DR-(Ni,Fe)OOH)during the OER.Moreover,density functional theory calculations reveal that Fe substitution induces a downshift in the energy band structure,which lowers the energy barriers and thereby improves the kinetics of the OER.The generated DR-(Ni,Fe)OOH delivers a relatively low overpotential of 260 m V and superior durability for 50 h under OER condition.The ZAB incorporating DR-(Ni,Fe)OOH+Pt/C as the air cathode demonstrates superior efficiency and durability,achieving a peak power density of 188.3 m W cm^(-2),a specific capacity of 811.1 m Ah g^(-1),and long-term stability exceeding 200 h.展开更多
Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods...Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.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
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
Transition metal phosphides exhibit excellent efficiency in the oxygen evolution reaction under alkaline conditions,and they have garnered widespread recognition.Currently,most studies have focused on the evolution an...Transition metal phosphides exhibit excellent efficiency in the oxygen evolution reaction under alkaline conditions,and they have garnered widespread recognition.Currently,most studies have focused on the evolution and role of metal cations in the oxygen evolution reaction process,while attention to phosphorus elements is relatively scarce.Actually,phosphides possess unique properties that distinguish them from other metal compounds,and the role of phosphorus in them cannot be ignored.This study used nickel phosphide(Ni_(2)P)as a model catalyst to reveal the reconstruction and dynamic behavior of anions under alkaline conditions through cyclic voltammetry.The results indicate that as the cycle progresses,surface phosphides are converted into active oxyhydroxides.It is worth noting that the presence of the P element accelerates the rapid completion of the reconstruction process but also exhibits triple synergistic functions.First,the internal phosphorus nuclei of the active layer act as conductive scaffolds,effectively enhancing the efficiency of electron conduction.Second,the oxygen-containing anions formed in situ on metal hydroxides optimize the adsorption of reaction intermediates.Finally,the phosphorus atoms dissolved in the electrolyte suppress nickel loss,improve stability,and increase the electrochemical activity specific surface area,exposing more active sites.This study elucidates the oxygen evolution reaction mechanism of phosphides from a novel perspective,enhancing comprehension of surface reconstruction phenomena and the characteristics of active sites,guiding the rational design of phosphide pre-catalysts.展开更多
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 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 by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.
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.
基金supported by the National Natural Science Foundation of China(Nos.62301092 and 62301093).
文摘Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware.
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 grants from the National Natural Science Foundation of China(82170412)Shanghai Municipal Science and Technology Commission Clinical Innovation Research Special Project(23Y11905400).
文摘Perihilar cholangiocarcinoma(pCCA)and intrahepatic cholangiocarcinoma(iCCA)are highly malignant neoplasms with a 5-year overall survival rate of approximately 30%[1,2].Surgical resection remains the only potentially curative treatment,yet only one-fifth of patients are eligible for resection at initial diagnosis[3].Threedimensional(3D)reconstruction technology provides precise preoperative visualization of complex hilar anatomy,significantly enhancing surgical planning and outcomes[4].Recent advances in 3D reconstruction technology have enhanced preoperative planning by providing precise anatomical mapping of tumor-vessel relationships and biliary variations[4,5].Therefore,this report describes a case of left iCCA successfully resected with biliary reconstruction guided by 3D visualization.
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 Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
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funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia—under supervision Energy,Industry,and Advanced Technologies Research Center,Taibah University,Madinah,Saudi Arabia with grant number(12979-iau-2023-TAU-R-3-1-EI-).
文摘The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.
文摘Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstruction methods either compromise on accuracy with iterative algorithms or suffer from limited generalizability with task-specific deep learning approaches.Methods:We present LDM-PIR,a lightweight physics-conditioned diffusion multi-model for medical image reconstruction that addresses key challenges in magnetic resonance imaging(MRI),CT,and low-photon imaging.Unlike traditional iterative methods,which are computationally expensive,or task-specific deep learning approaches lacking generalizability,integrates three innovations.A physics-conditioned diffusion framework that embeds acquisition operators(Fourier/Radon transforms)and noise models directly into the reconstruction process.A multi-model architecture that unifies denoising,inpainting,and super-resolution via shared weight conditioning.A lightweight design(2.1M parameters)enabling rapid inference(0.8s/image on GPU).Through self-supervised fine-tuning with measurement consistency losses adapts to new imaging modalities using fewer annotated samples.Results:Achieves state-of-the-art performance on fastMRI(peak signal-to-noise ratio(PSNR):34.04 for single-coil/31.50 for multi-coil)and Lung Image Database Consortium and Image Database Resource Initiative(28.83 PSNR under Poisson noise).Clinical evaluations demonstrate superior preservation of anatomical structures,with SSIM improvements of 8.8%for single-coil and 4.36%for multi-coil MRI over uDPIR.Conclusion:It offers a flexible,efficient,and scalable solution for medical image reconstruction,addressing the challenges of noise,undersampling,and modality generalization.The model’s lightweight design allows for rapid inference,while its self-supervised fine-tuning capability minimizes reliance on large annotated datasets,making it suitable for real-world clinical applications.
基金funded by the Ratchadaphiseksomphot Endowment Fund,Chulalongkorn University grant number:RSF-AnH-69-06-21-01.
文摘This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free,high-resolution PCB images,which are stitched into a single orthographic composite.A YOLO-based detection model,trained on a dataset of 270 PCB images across 23 component classes with data augmentation,identifies and localizes electronic components with a mean average precision of 0.932.Detected components are automatically matched to corresponding 3D CAD models from a part library and assembled within a Fusion 360 environment,producing a 3D digital replica.Experimental results show a similarity score of 0.894 and dimensional deviations below 2%,outperforming both SensoPart image measurement and manual vernier methods.The proposed approach bridges optical metrology and CAD automation,providing a scalable solution for AI-assisted reverse engineering,digital archiving,and intelligent manufacturing.
文摘[Significance]In alignment with the national germplasm security strategy,current research efforts are accelerating the adoption of precision breeding in sheep.Within the whole-genome selection,accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value.Traditional manual measurements are inefficient,prone to human error,and may cause stress to sheep,limiting their suitability for precision sheep management.By summarizing the applications of sheep body size measurement technologies and analyzing their development directions,this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement.[Progress]This review synthesizes progress across three principal methodological paradigms:two-dimensional(2D)image-based techniques,three-dimensional(3D)point cloud-based approaches,and integrated 2D-3D fusion systems.2D methods,employing either handcrafted geometric features or deep learning-based keypoint detector algorithms,are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics.3D point-cloud approaches enable precise reconstruction of full animal morphology,supporting comprehensive body-size acquisition with higher accuracy,yet face challenges including high hardware costs,complex data workflows,and sensitivity to posture variability.Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds.Having been effectively validated in other livestock specise,e.g.,cattle and pigs,these fusion systems have demonstrated excellent performance,providing important technical references and practical insights for sheep body size measurement.[Conclusions and Prospects]Firstly,future research should focus on constructing large-scale,high-quality datasets for sheep body size measurement that encompass diverse breeds,growth stages,and environmental conditions,thereby enhancing model robustness and generalization.Secondly,the development of lightweight artificial intelligence models is essential.Techniques such as model compression,quantization,and algorithmic optimization can substantially reduce computational complexity and storage requirements,facilitating deployment in resource-constrained environments.Thirdly,the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition,filtering,registration,and segmentation,while promoting the integration of low-cost,high-resilience vision systems into practical farming scenarios.Fourthly,specific emphasis should be placed on improving the accuracy of curved-dimensional measurements,such as chest circumference,abdominal circumference,and shank circumference,through advances in pose standardization,refined 3D segmentation strategies,and multimodal data fusion.Finally,the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation,accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent,data-driven pasture management practices.
基金financially supported by the National Natural Science Foundation of China(32301514)the Young Elite Scientist Sponsorship Program by CAST(YESS20220240)。
文摘The advancement of effective and stable non-precious metal-based catalysts for oxygen evolution reactions(OER)with a lowcost and simple technique is essential for the practical application of rechargeable zinc–air battery(ZAB).However,facilitating the deep reconstruction of electrocatalysts to form active species remains a significant challenge.Here,a simple two-step method composed of impregnation and carbonization process is proposed to synthesize N,S co-doped microcrystalline cellulose-derived carbon-supported nickel sulfide(Ni_(3)S_(2))nanoparticles.The in situ Raman reveals that Fe substitution promotes the reconstruction of Ni_(3)S_(2),accompanied by the cleavage of the Ni–S bond,leading to the deep reconstruction into(Ni,Fe)OOH(DR-(Ni,Fe)OOH)during the OER.Moreover,density functional theory calculations reveal that Fe substitution induces a downshift in the energy band structure,which lowers the energy barriers and thereby improves the kinetics of the OER.The generated DR-(Ni,Fe)OOH delivers a relatively low overpotential of 260 m V and superior durability for 50 h under OER condition.The ZAB incorporating DR-(Ni,Fe)OOH+Pt/C as the air cathode demonstrates superior efficiency and durability,achieving a peak power density of 188.3 m W cm^(-2),a specific capacity of 811.1 m Ah g^(-1),and long-term stability exceeding 200 h.
基金National Key Scientific Instrument and Equipment Development Project under Grant No.61827801the open research fund of State Key Laboratory of Integrated Services Networks,No.ISN22-11+1 种基金Natural Science Foundation of Jiangsu Province,No.BK20211182open research fund of National Mobile Communications Research Laboratory,Southeast University,No.2022D04。
文摘Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.
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(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support of the National Natural Science Foundation of China(22275035).
文摘Transition metal phosphides exhibit excellent efficiency in the oxygen evolution reaction under alkaline conditions,and they have garnered widespread recognition.Currently,most studies have focused on the evolution and role of metal cations in the oxygen evolution reaction process,while attention to phosphorus elements is relatively scarce.Actually,phosphides possess unique properties that distinguish them from other metal compounds,and the role of phosphorus in them cannot be ignored.This study used nickel phosphide(Ni_(2)P)as a model catalyst to reveal the reconstruction and dynamic behavior of anions under alkaline conditions through cyclic voltammetry.The results indicate that as the cycle progresses,surface phosphides are converted into active oxyhydroxides.It is worth noting that the presence of the P element accelerates the rapid completion of the reconstruction process but also exhibits triple synergistic functions.First,the internal phosphorus nuclei of the active layer act as conductive scaffolds,effectively enhancing the efficiency of electron conduction.Second,the oxygen-containing anions formed in situ on metal hydroxides optimize the adsorption of reaction intermediates.Finally,the phosphorus atoms dissolved in the electrolyte suppress nickel loss,improve stability,and increase the electrochemical activity specific surface area,exposing more active sites.This study elucidates the oxygen evolution reaction mechanism of phosphides from a novel perspective,enhancing comprehension of surface reconstruction phenomena and the characteristics of active sites,guiding the rational design of phosphide pre-catalysts.
