The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which ...The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models.展开更多
With the rising water cut in mature oil fields,polymer flooding has emerged as a critical Enhanced Oil Recovery(EOR)technique.However,high-fidelity numerical simulations for history matching and polymer flooding optim...With the rising water cut in mature oil fields,polymer flooding has emerged as a critical Enhanced Oil Recovery(EOR)technique.However,high-fidelity numerical simulations for history matching and polymer flooding optimization remain computationally intensive,limiting their practicality for ClosedLoop Reservoir Management(CLRM),which is inherently dependent on rapid iterative simulations for real-time model updating and operational decision-making.Although physics-based data-driven flownetwork models,such as General-Purpose Simulator-powered Network model(GPSNet),can accelerate simulations,their lack of geological constraints compromises predictive reliability.To address this limitation,we propose a novel facies-constrained flow-network model(GPSNet-FC)within the GPSNet framework.This model simplifies reservoir geometry into a 1D discretized grid between wells while incorporating sedimentary facies boundaries identified through edge detection and level-set methods.Grid properties are assigned and calibrated based on facies-specific attributes to ensure geological consistency.GPSNet-FC is applied to history matching using the Ensemble Smoother with Multiple Data Assimilation(ESMDA)and to polymer flooding optimization via the Differential Evolution(DE)algorithm.Numerical case studies validate the method,demonstrating that GPSNet-FC outperforms the original GPSNet in both reliability and accuracy.By integrating facies-based geological constraints,this approach reduces non-uniqueness in history matching and enables rapid and accurate decision-making fo r polymer flooding strategies.This work advances the integration of geological data into physics-based data-driven models,offering a robust and efficient tool for the CLRM of polymer flooding reservoirs.展开更多
TFlux dynamics,which describes the behavior of magnetic vortices in type-Ⅱsuperconductors,governs macroscopic electromagnetic properties of superconducting materials.Recently,cutting-edge approaches utilizing artific...TFlux dynamics,which describes the behavior of magnetic vortices in type-Ⅱsuperconductors,governs macroscopic electromagnetic properties of superconducting materials.Recently,cutting-edge approaches utilizing artificial periodic nanostructures for active control of the pinning centers help to deepen the understanding of relevant mechanisms of flux dynamics.This study demonstrates the controlled introduction of large-scale,periodic artificial pinning centers(APCs)in MgB2 superconducting thin films to manipulate flux dynamics.Using focused helium ion beam(He-FIB)irradiation,we fabricated a square array of nanoscale columnar artificial pinning centers with a period of 100 nm on a 30 nm MgB2 superconducting thin film.Magnetoresistance measurements near the critical temperature(Tc)reveal a pronounced vortex matching effect,evidenced by sharp resistance minima(dips)at specific integer and fractional magnetic matching fields.This effect is shown to be highly dependent on external parameters such as temperature,driving current,and the angle of the magnetic field.Furthermore,the large-area irradiation systematically suppresses Tcand broadens the superconducting transition of the film.This work establishes He-FIB as a potent tool for advanced flux pinning engineering and provides a comprehensive understanding of flux dynamics in superconductors with periodic pinning landscapes.展开更多
Dear Editor,Due to the scarcity of high-quality infrared data,translating visible images to infrared has become a practical solution to meet the growing demand for infrared images in low-light and adverse conditions.D...Dear Editor,Due to the scarcity of high-quality infrared data,translating visible images to infrared has become a practical solution to meet the growing demand for infrared images in low-light and adverse conditions.Due to the large modality gap and limited prior information,existing visible-to-infrared(VIS-to-IR)image translation methods often struggle with poor structural preservation,unclear cross-modal correspondence,and loss of thermal details.展开更多
The rapid deployment of Industrial Internet of Things(IIoT)systems,such as large-scale photovoltaic(PV)power stations in modern power grids,has created a strong demand for edge-intelligent fault localization methods t...The rapid deployment of Industrial Internet of Things(IIoT)systems,such as large-scale photovoltaic(PV)power stations in modern power grids,has created a strong demand for edge-intelligent fault localization methods that can operate reliably under strict computational and memory constraints.In this work,we propose an edge-intelligent photovoltaic fault localization framework that integrates intelligent computation with classical sub-pixel optimization.The framework adopts a modular,edge-oriented design in which a radial basis function(RBF)network is first employed as a lightweight screening module to enable conditional execution,thereby reducing unnecessary computation for non-faulty samples.For suspicious samples,a compact convolutional feature extractor is activated to generate discriminative representations.The architecture of this feature extractor is automatically optimized using neural architecture search(NAS)in an offline design stage,explicitly balancing localization accuracy and computational efficiency for industrial edge hardware.Sub-pixel displacement estimation and recursive partitioning are then performed in the learned feature space using a sum of squared differences-based,preserving the mathematical transparency of classical sub-pixel matching while significantly improving robustness to thermal noise and background interference.Unlike large end-to-end detection models,the proposed framework combines intelligent feature representation with interpretable localization mechanisms,resulting in a flexible and resource-efficient solution for edge deployment.Experimental results on a photovoltaic infrared fault image dataset demonstrate that the proposed NAS-optimized feature-space sub-pixel matching framework achieves more stable fault localization than other baselines,with only marginal additional computational overhead.展开更多
Marine magnetotelluric(MMT)sounding is a vital geophysical technique used for imaging subsurface electrical conductivity structures beneath the seafl oor.However,MMT data recorded in oceanic environments are often sev...Marine magnetotelluric(MMT)sounding is a vital geophysical technique used for imaging subsurface electrical conductivity structures beneath the seafl oor.However,MMT data recorded in oceanic environments are often severely contaminated by various noise sources,including oceanic wave and current eff ects,ship movements,and instrumental noise.This noise can signifi cantly degrade data quality,impairing subsequent data processing and interpretation.Traditional global filtering methods often distort or remove valuable signal components along with the noise.Furthermore,simply excising noisy segments creates data gaps that compromise subsequent frequency-domain analysis.Therefore,a targeted,two-stage approach is necessary to first accurately identify localized,transient noise and then reconstruct only the corrupted segments,preserving the integrity of the clean signal.This paper presents a novel approach that eff ectively addresses both challenges.Firstly,a Short-Term Average/Long-Term Average algorithm is applied for the semiautomatic identifi cation and fl agging of noisy segments,successfully detecting both transient bursts and quasiperiodic disturbances.Secondly,to ensure data continuity and fi delity,a data reconstruction algorithm based on Compressive Sensing(CS)theory is employed to reconstruct the corrupted data within the identifi ed noisy sections.Specifi cally,we use Orthogonal Matching Pursuit(OMP)to solve the CS reconstruction problem,taking advantage of the inherent sparsity of the underlying MMT signal in a transformed domain.The proposed methodology aims to enhance the signal-to-noise ratio and recover essential signal features,thus improving the reliability of MMT soundings.Application to real-world datasets demonstrates the effi cacy of the combined approach in suppressing complex noise patterns and reconstructing good-quality MMT time-series data.展开更多
With the increasing use of passive seismic data,developing seismic reflection imaging methods based on passive data is of considerable practical significance.This study presents a waveform-matching reverse time migrat...With the increasing use of passive seismic data,developing seismic reflection imaging methods based on passive data is of considerable practical significance.This study presents a waveform-matching reverse time migration for the primary reflected data from local earthquakes.In order to mitigate inconsistencies in frequency band and energy across earthquakes of different magnitudes,we first establish reference seismic waveform with standardized dominant frequency and magnitude.A matching operator is derived for each event by matching its waveforms with the reference waveform.This operator is then applied via convolution to all waveforms,producing standardized seismic waveforms with consistent wavelet features.The reshaped waveforms are then subjected to reverse time migration using an impedance imaging condition for primary reflections.To suppress strong energy interference near the hypocenters,both illumination compensation and three-dimensional Smoothed Spherical Mask centered on each source are used.Numerical tests using both simple two-layer model and fault-containing model demonstrate that the new method is robust and effective.The reverse time migration of primary reflected data of local earthquakes accurately images underground impedance boundaries such as stratum interfaces and fault planes,showing its promise for future application in seismically active fault zones.展开更多
We propose a novel procedure,Time-Domain De-Dopplerized Orthogonal Matching Pursuit deconvolution approach for the mapping of acoustic sources(TD-OMP-DAMAS),for separating aerodynamic noise sources distributed across ...We propose a novel procedure,Time-Domain De-Dopplerized Orthogonal Matching Pursuit deconvolution approach for the mapping of acoustic sources(TD-OMP-DAMAS),for separating aerodynamic noise sources distributed across wind turbine blades(WTB),a task that is typically hindered by mutual interference and spatial mixing.e proposed procedure is a two-stage,hybrid de-Doppler/sparse-reconstruction algorithm based on timedomain de-Doppler(TD,Stage 1)and an orthogonal matching pursuit(OMP)-based deconvolution scheme(Stage 2),enabling sparse-reconstruction techniques to be eectively applied in rotating-source scenarios.e method is validated using both simulated rotating-source data and wind-tunnel measurements,and its performance is systematically compared with several conventional approaches,including conventional beamforming(CBF),time-domain de-Doppler beamforming(TD-BF),and time-domain de-Doppler deconvolution approach for the mapping of acoustic sources(TD-DAMAS).Numerical results demonstrate that TD-OMP-DAMAS achieves the smallest localization error and the highest spatial resolution among all tested algorithms,while also maintaining strong robustness under low signal-to-noise ratio conditions and requiring signicantly fewer iterations than TD-DAMAS to accurately converge to the true source positions.Wind-tunnel tests further show that,under an inow velocity of 6 m/s and a tip-speed ratio of 4.5,the method improves spatial resolution by approximately 89%compared with CBF,conrming its superior capability in separating aerodynamic sources located on dierent WTB.展开更多
Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the...Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.展开更多
In mobility-as-a-service(MaaS)platforms integrating ridesharing with public transit,each rider-driver pair may have multiple potential matches via different transfer nodes,with users being self-interested with heterog...In mobility-as-a-service(MaaS)platforms integrating ridesharing with public transit,each rider-driver pair may have multiple potential matches via different transfer nodes,with users being self-interested with heterogeneous preferences.After generating all feasible integrated matches,a two-sided one-to-one stable matching model is formulated to maximize platform revenue,where each feasible match corresponds to a stability constraint embedding preference information.To solve this model efficiently,an iterative constraint-generation algorithm is designed.It repeatedly solves a restricted master problem to obtain a temporary solution and a subproblem to identify violated stability constraints,iterating until no violations remain.The proposed algorithm can significantly improve computational efficiency.Compared with a centralized matching benchmark with the blocking rate up to 75%,stable matching increases transit usage and user acceptance at the cost of a 31.43% reduction in average platform revenue.Riders experience longer detours with greater cost savings,whereas drivers exhibit the opposite pattern.展开更多
Qingyuan,a prefecture-level city in Guangdong Province,China,has rich ecological,cultural,rural,rafting,food,and family-oriented tourism resources.Under the“One Belt,One Corridor,and One Zone”cultural tourism strate...Qingyuan,a prefecture-level city in Guangdong Province,China,has rich ecological,cultural,rural,rafting,food,and family-oriented tourism resources.Under the“One Belt,One Corridor,and One Zone”cultural tourism strategy,the city seeks to improve resource integration,communication,and consumption conversion.However,fragmented information,uneven exposure,insufficient tourist-need identification,homogenized promotion,and weak tourist-resource matching still limit tourism development.As a conceptual framework study,this paper constructs a precision recommendation mechanism based on resource tagging,tourist profiling,tourist-resource matching,route-level recommendation,and feedback iteration,aiming to improve matching efficiency,reduce decision-making costs,and promote actual visits and consumption.展开更多
Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this pa...Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this paper,we propose a Semantic-Guided Parallax Attention Stereo Matching Network(SGPASMnet)that can be trained in unsupervised manner,building upon the Parallax Attention Stereo Matching Network(PASMnet).Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets,facilitating robust training across diverse scene-specific datasets and enhancing generalization.SGPASMnet incorporates two novel components:a Cross-Scale Feature Interaction(CSFI)block and semantic feature augmentation using a pre-trained semantic segmentation model,SegFormer,seamlessly embedded into the parallax attention mechanism.The CSFI block enables effective fusion ofmulti-scale features,integrating coarse and fine details to enhance disparity estimation accuracy.Semantic features,extracted by SegFormer,enrich the parallax attention mechanism by providing high-level scene context,significantly improving performance in ambiguous regions.Our model unifies these enhancements within a cohesive architecture,comprising semantic feature extraction,an hourglass network,a semantic-guided cascaded parallax attentionmodule,outputmodule,and a disparity refinement network.Evaluations on the KITTI2015 dataset demonstrate that our unsupervised method achieves a lower error rate compared to the original PASMnet,highlighting the effectiveness of our enhancements in handling complex scenes.By harnessing unsupervised learning without ground truth disparity needed,SGPASMnet offers a scalable and robust solution for accurate stereo matching,with superior generalization across varied real-world applications.展开更多
In this paper,we propose a novel cyclic redundancy check(CRC)-aided method to improve the energy efficiency of distribution matching(DM)algorithms based on fixed empirical distribution codebooks.The core design concep...In this paper,we propose a novel cyclic redundancy check(CRC)-aided method to improve the energy efficiency of distribution matching(DM)algorithms based on fixed empirical distribution codebooks.The core design concept is to map a subset of a DM codebook to the entire codebook.The mapping is identified by a binary vector that is convolved during the CRC encoding;thereby avoiding any additional overhead in a CRC-aided system.At the receiver(RX),the CRC check not only performs error detection but also identifies the mapping of the transmitted symbol sequences to the original input of the DM.With the information delivered by a quarter-sized codebook,fewer occurrence of high-energy symbols effectively reduces the average symbol energy and the rate-loss.The proposed method can be seamlessly integrated into any DM algorithm that uses fixed empirical distribution codebooks.We demonstrate its implementation in a polar-coded probabilistic amplitude shaping(PAS)system with CRC-aided successive cancellation list decoding.Using this architecture,we show an energy efficiency improvement of up to 26%and a signal-to-noise ratio improvement of up to 0.8 dB at a fixed target frame error rate of 10−3.展开更多
Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudass...Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudassisted architecture faces two critical challenges:the untrusted cloud services and the separation of data ownership from control.Although Attribute-based Searchable Encryption(ABSE)provides fine-grained access control and keyword search over encrypted data,existing schemes lack of error tolerance in exact multi-keyword matching.In this paper,we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search(FCS-ABMSE)scheme that avoids computationally expensive bilinear pairing operations on the IoT device side.The scheme supportsmulti-keyword fuzzy search without requiring explicit keyword fields,thereby significantly enhancing error tolerance in search operations.It further incorporates forward-secure ciphertext search to mitigate trapdoor abuse,as well as offline encryption and verifiable outsourced decryption to minimize user-side computational costs.Formal security analysis proved that the FCS-ABMSE scheme meets both indistinguishability of ciphertext under the chosen keyword attacks(IND-CKA)and the indistinguishability of ciphertext under the chosen plaintext attacks(IND-CPA).In addition,we constructed an enhanced variant based on type-3 pairings.Results demonstrated that the proposed scheme outperforms existing ABSE approaches in terms of functionalities,computational cost,and communication cost.展开更多
Background:Dual-phenotype hepatocellular carcinoma(DPHCC)is a recently defined subtype of hepato-cellular carcinoma(HCC)characterized by the simultaneous hepatocellular and biliary epithelial marker expression.This st...Background:Dual-phenotype hepatocellular carcinoma(DPHCC)is a recently defined subtype of hepato-cellular carcinoma(HCC)characterized by the simultaneous hepatocellular and biliary epithelial marker expression.This study aimed to elucidate the clinicopathological features of DPHCC following curative liver resection and its relationship with prognosis.Methods:We analyzed 1493 patients with HCC who underwent curative liver resection at the Peking Union Medical College Hospital from January 2013 to December 2023.All patients were divided into two groups according to immunohistochemical marker expression,with 487 and 1006 cases in the DPHCC and non-DPHCC groups,respectively.Propensity score matching was performed to reduce the deviation caused by baseline characteristics.Results:After 1:2 matching(DPHCCon-DPHCC group=453/771),patients were comparable regard-ing all baseline characteristics.Compared to patients with non-DPHCC,those with DPHCC were signifi-cantly associated with poorer differentiation,microvascular invasion,satellite nodules,and bile duct tu-mor thrombus(P<0.05).Patients with DPHCC also exhibited significantly worse recurrence-free survival(P=0.009)and overall survival(P=0.021).Furthermore,multivariate analysis revealed that DPHCC was an independent risk factor for recurrence-free survival(HR=1.28,95%CI:1.05-1.52,P=0.019)and over-all survival(HR=1.33,95%CI:1.15-1.51,P=0.023).Conclusions:DPHCC,a newly proposed subtype of HCC,is associated with poorer clinicopathological features and adverse prognosis,providing important clinical guidance.展开更多
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec...In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.展开更多
This paper proposes an efficient algorithm for real-time multi-modal image matching based on a lightweight feature fusion network,targeting the challenges of multi-modal image matching in multi-source data analysis.Th...This paper proposes an efficient algorithm for real-time multi-modal image matching based on a lightweight feature fusion network,targeting the challenges of multi-modal image matching in multi-source data analysis.The algorithm addresses significant multi-modal feature differences and real-time processing limitations by incorporating key technologies including reparameterization in convolutional neural networks,multi-scale image pyramids,and feature fusion modules.The matching process employs a coarse-to-fine strategy,ensuring robust performance in complex environments.Experimental results using multi-modal datasets demonstrate that the proposed algorithm achieves superior accuracy and speed,with a success rate of 98.3%and an average matching time of 30.51 ms per 500×500 image pair.These results highlight the practical value and strong generalization capability of the algorithm in real-time applications.展开更多
In the realms of computer vision and remote sensing,the matching of images and point clouds poses significant challenges due to modality discrepancies.This study introduces a crossmodal consistency network,detector-fr...In the realms of computer vision and remote sensing,the matching of images and point clouds poses significant challenges due to modality discrepancies.This study introduces a crossmodal consistency network,detector-free image and point cloud matching via diffusion-guided crossmodal consistency,2D3D-DiffMatch,leveraging diffusion prior information to enhance feature extraction consistency and alignment across modalities.To strengthen cross-modal consistency in complex scenes,diffusion priors generated by a pre-trained diffusion model are used to guide the feature extraction process toward semantically and geometrically consistent representations.These representations are further refined through a hierarchical fusion process,in which the most consistent diffusion features are adaptively selected using centered kernel alignment(CKA)and integrated with multi-scale backbone features,thereby mitigating the impact of modality gaps.Furthermore,to address feature-space misalignment between images and point clouds,we propose a crossmodal feature consistency loss that adaptively constrains correspondences,separates positive and negative pairs,and optimizes the agreement of positive pairs,enabling high-quality,detector-free matching.Experimental results on the 7Scenes and RGB-D Scenes V2 Datasets demonstrate superior registration recall rates of 81.2%and 61.0%,respectively,outperforming state-of-the-art methods and exhibiting robustness in challenging scenarios.This research advances the collaborative processing of multi-modal data,offering a robust solution for image–point cloud matching in challenging scenarios.展开更多
Talent drives efficient human resource allocation.Compared to developed nations,China faces persistent structural and regional imbalances in its share of high-skilled talent.Policy documents,such as the 14th Five-Year...Talent drives efficient human resource allocation.Compared to developed nations,China faces persistent structural and regional imbalances in its share of high-skilled talent.Policy documents,such as the 14th Five-Year Plan for Talent Development,address these gaps.The academic community recognizes the importance of this issue,but systematic literature reviews remain scarce.展开更多
A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However...A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.展开更多
基金supported in part by the National Natural Science Foundation of China(No.42271446)in part by the Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-Temporary Big Data Technology,China(No.TKL2024B13)in part by the Science and Technology Program of Tianjin,China(No.24YFYSHZ00080)。
摘要The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models.
基金supported by the National Natural Science Foundation of China(Nos.52474067,52441411,52325402,52034010,12131014)Natural Science Foundation of Shandong Province,China(No.ZR2024ME005)+1 种基金Fundamental Research Funds for the Central Universities(Nos.25CX02025A and 21CX06031A)Youth Innovation and Technology Support Program for Higher Education Institutions of Shandong Province,China(No.2022KJ070)。
摘要With the rising water cut in mature oil fields,polymer flooding has emerged as a critical Enhanced Oil Recovery(EOR)technique.However,high-fidelity numerical simulations for history matching and polymer flooding optimization remain computationally intensive,limiting their practicality for ClosedLoop Reservoir Management(CLRM),which is inherently dependent on rapid iterative simulations for real-time model updating and operational decision-making.Although physics-based data-driven flownetwork models,such as General-Purpose Simulator-powered Network model(GPSNet),can accelerate simulations,their lack of geological constraints compromises predictive reliability.To address this limitation,we propose a novel facies-constrained flow-network model(GPSNet-FC)within the GPSNet framework.This model simplifies reservoir geometry into a 1D discretized grid between wells while incorporating sedimentary facies boundaries identified through edge detection and level-set methods.Grid properties are assigned and calibrated based on facies-specific attributes to ensure geological consistency.GPSNet-FC is applied to history matching using the Ensemble Smoother with Multiple Data Assimilation(ESMDA)and to polymer flooding optimization via the Differential Evolution(DE)algorithm.Numerical case studies validate the method,demonstrating that GPSNet-FC outperforms the original GPSNet in both reliability and accuracy.By integrating facies-based geological constraints,this approach reduces non-uniqueness in history matching and enables rapid and accurate decision-making fo r polymer flooding strategies.This work advances the integration of geological data into physics-based data-driven models,offering a robust and efficient tool for the CLRM of polymer flooding reservoirs.
基金supported by the National Natural Science Foundation of China(Grant No.12104016)the Natural Science Foundation of Beijing,China(Grant No.2244106)+1 种基金the National Key Research and Development Program of China(Grant Nos.2020YFF01014706 and 2021YFB3800201)the Peking University Funding on“Instrument Innovation and Key Technology R&D”(Grant No.2024)。
摘要TFlux dynamics,which describes the behavior of magnetic vortices in type-Ⅱsuperconductors,governs macroscopic electromagnetic properties of superconducting materials.Recently,cutting-edge approaches utilizing artificial periodic nanostructures for active control of the pinning centers help to deepen the understanding of relevant mechanisms of flux dynamics.This study demonstrates the controlled introduction of large-scale,periodic artificial pinning centers(APCs)in MgB2 superconducting thin films to manipulate flux dynamics.Using focused helium ion beam(He-FIB)irradiation,we fabricated a square array of nanoscale columnar artificial pinning centers with a period of 100 nm on a 30 nm MgB2 superconducting thin film.Magnetoresistance measurements near the critical temperature(Tc)reveal a pronounced vortex matching effect,evidenced by sharp resistance minima(dips)at specific integer and fractional magnetic matching fields.This effect is shown to be highly dependent on external parameters such as temperature,driving current,and the angle of the magnetic field.Furthermore,the large-area irradiation systematically suppresses Tcand broadens the superconducting transition of the film.This work establishes He-FIB as a potent tool for advanced flux pinning engineering and provides a comprehensive understanding of flux dynamics in superconductors with periodic pinning landscapes.
基金supported by the National Natural Science Foundation of China(62506268,62276192)。
摘要Dear Editor,Due to the scarcity of high-quality infrared data,translating visible images to infrared has become a practical solution to meet the growing demand for infrared images in low-light and adverse conditions.Due to the large modality gap and limited prior information,existing visible-to-infrared(VIS-to-IR)image translation methods often struggle with poor structural preservation,unclear cross-modal correspondence,and loss of thermal details.
基金supported by the Key R&D Projects of Liaoning Provincial Department of Science and Technology:Research on Fault Monitoring and Catastrophe Prediction Technologies for New Energy Power Stations Oriented to Wind-Solar-Storage Complementary Systems(2024JH2/102500074).
摘要The rapid deployment of Industrial Internet of Things(IIoT)systems,such as large-scale photovoltaic(PV)power stations in modern power grids,has created a strong demand for edge-intelligent fault localization methods that can operate reliably under strict computational and memory constraints.In this work,we propose an edge-intelligent photovoltaic fault localization framework that integrates intelligent computation with classical sub-pixel optimization.The framework adopts a modular,edge-oriented design in which a radial basis function(RBF)network is first employed as a lightweight screening module to enable conditional execution,thereby reducing unnecessary computation for non-faulty samples.For suspicious samples,a compact convolutional feature extractor is activated to generate discriminative representations.The architecture of this feature extractor is automatically optimized using neural architecture search(NAS)in an offline design stage,explicitly balancing localization accuracy and computational efficiency for industrial edge hardware.Sub-pixel displacement estimation and recursive partitioning are then performed in the learned feature space using a sum of squared differences-based,preserving the mathematical transparency of classical sub-pixel matching while significantly improving robustness to thermal noise and background interference.Unlike large end-to-end detection models,the proposed framework combines intelligent feature representation with interpretable localization mechanisms,resulting in a flexible and resource-efficient solution for edge deployment.Experimental results on a photovoltaic infrared fault image dataset demonstrate that the proposed NAS-optimized feature-space sub-pixel matching framework achieves more stable fault localization than other baselines,with only marginal additional computational overhead.
基金the financial support from the National Natural Science Foundation of China(Grant No.42474108)the Open Fund(Grant Number:36750000-25-FW0399-0004)of SINOPEC Key Laboratory of Geophysicsthe Science and Technology Plan Special Program of Huzhou(No.2024YZ17).
摘要Marine magnetotelluric(MMT)sounding is a vital geophysical technique used for imaging subsurface electrical conductivity structures beneath the seafl oor.However,MMT data recorded in oceanic environments are often severely contaminated by various noise sources,including oceanic wave and current eff ects,ship movements,and instrumental noise.This noise can signifi cantly degrade data quality,impairing subsequent data processing and interpretation.Traditional global filtering methods often distort or remove valuable signal components along with the noise.Furthermore,simply excising noisy segments creates data gaps that compromise subsequent frequency-domain analysis.Therefore,a targeted,two-stage approach is necessary to first accurately identify localized,transient noise and then reconstruct only the corrupted segments,preserving the integrity of the clean signal.This paper presents a novel approach that eff ectively addresses both challenges.Firstly,a Short-Term Average/Long-Term Average algorithm is applied for the semiautomatic identifi cation and fl agging of noisy segments,successfully detecting both transient bursts and quasiperiodic disturbances.Secondly,to ensure data continuity and fi delity,a data reconstruction algorithm based on Compressive Sensing(CS)theory is employed to reconstruct the corrupted data within the identifi ed noisy sections.Specifi cally,we use Orthogonal Matching Pursuit(OMP)to solve the CS reconstruction problem,taking advantage of the inherent sparsity of the underlying MMT signal in a transformed domain.The proposed methodology aims to enhance the signal-to-noise ratio and recover essential signal features,thus improving the reliability of MMT soundings.Application to real-world datasets demonstrates the effi cacy of the combined approach in suppressing complex noise patterns and reconstructing good-quality MMT time-series data.
基金supported by the National Key Research and Development Program of China(No.2020YFA 0710601)the Deep Earth Probe and Mineral Resources Exploration—National Science and Technology Major Project(No.2025ZD1004901).
摘要With the increasing use of passive seismic data,developing seismic reflection imaging methods based on passive data is of considerable practical significance.This study presents a waveform-matching reverse time migration for the primary reflected data from local earthquakes.In order to mitigate inconsistencies in frequency band and energy across earthquakes of different magnitudes,we first establish reference seismic waveform with standardized dominant frequency and magnitude.A matching operator is derived for each event by matching its waveforms with the reference waveform.This operator is then applied via convolution to all waveforms,producing standardized seismic waveforms with consistent wavelet features.The reshaped waveforms are then subjected to reverse time migration using an impedance imaging condition for primary reflections.To suppress strong energy interference near the hypocenters,both illumination compensation and three-dimensional Smoothed Spherical Mask centered on each source are used.Numerical tests using both simple two-layer model and fault-containing model demonstrate that the new method is robust and effective.The reverse time migration of primary reflected data of local earthquakes accurately images underground impedance boundaries such as stratum interfaces and fault planes,showing its promise for future application in seismically active fault zones.
基金funded by the Natural Science Foundation of Inner Mongolia Autonomous Region(Grant No.2024MS05063)the Special Project for First-Class Discipline Research(Grant No.YLXKZXNGD-008)+1 种基金the Basic Research Operating Expenses Program for Colleges and Universities directly under the Inner Mongolia Autonomous Region(JY20220399)the Research Start-up Fund Project of Inner Mongolia University of Technology(Grant No.BS2024022).
摘要We propose a novel procedure,Time-Domain De-Dopplerized Orthogonal Matching Pursuit deconvolution approach for the mapping of acoustic sources(TD-OMP-DAMAS),for separating aerodynamic noise sources distributed across wind turbine blades(WTB),a task that is typically hindered by mutual interference and spatial mixing.e proposed procedure is a two-stage,hybrid de-Doppler/sparse-reconstruction algorithm based on timedomain de-Doppler(TD,Stage 1)and an orthogonal matching pursuit(OMP)-based deconvolution scheme(Stage 2),enabling sparse-reconstruction techniques to be eectively applied in rotating-source scenarios.e method is validated using both simulated rotating-source data and wind-tunnel measurements,and its performance is systematically compared with several conventional approaches,including conventional beamforming(CBF),time-domain de-Doppler beamforming(TD-BF),and time-domain de-Doppler deconvolution approach for the mapping of acoustic sources(TD-DAMAS).Numerical results demonstrate that TD-OMP-DAMAS achieves the smallest localization error and the highest spatial resolution among all tested algorithms,while also maintaining strong robustness under low signal-to-noise ratio conditions and requiring signicantly fewer iterations than TD-DAMAS to accurately converge to the true source positions.Wind-tunnel tests further show that,under an inow velocity of 6 m/s and a tip-speed ratio of 4.5,the method improves spatial resolution by approximately 89%compared with CBF,conrming its superior capability in separating aerodynamic sources located on dierent WTB.
基金The National Natural Science Foundation of China(No.62261032)the Central Government Guiding Funds for Local Scienceand Technology Development Program(No.25ZYJA026).
摘要Current image inpainting models are primarily designed to achieve a large receptive field(RF)using refinement networks to incorporate different scales.However,these models fail to adapt the use of different RFs to the specific patterns of image damage,resulting in artifacts and semantic information confusion in repaired images.To address the problems of artifacts and semantic information confusion,inspired by different sensitivities of different RFs to inpainting the same image damaged patterns,this study proposes an image inpainting method based on multiple receptive fields(MRFs)and dynamic matching of damaged patterns.First,the parallel filter banks are used to extract the MRF feature groups.Second,the features are dynamically weighted and screened,guided by the mask image,to construct a relationship that adaptively matches the most relevant RF to each specific damaged pattern.A fast Fourier convolution based decoder is used to enhance the fusion of global contextual features during the reconstruction of high dimensional features into low dimensional images.Comparative experimental results show that the proposed method achieves better subjective and objective inpainting results on three public datasets:Paris StreetView,CelebA-HQ,and Places2.
基金The National Natural Science Foundation of China(No.72231002,72371070).
摘要In mobility-as-a-service(MaaS)platforms integrating ridesharing with public transit,each rider-driver pair may have multiple potential matches via different transfer nodes,with users being self-interested with heterogeneous preferences.After generating all feasible integrated matches,a two-sided one-to-one stable matching model is formulated to maximize platform revenue,where each feasible match corresponds to a stability constraint embedding preference information.To solve this model efficiently,an iterative constraint-generation algorithm is designed.It repeatedly solves a restricted master problem to obtain a temporary solution and a subproblem to identify violated stability constraints,iterating until no violations remain.The proposed algorithm can significantly improve computational efficiency.Compared with a centralized matching benchmark with the blocking rate up to 75%,stable matching increases transit usage and user acceptance at the cost of a 31.43% reduction in average platform revenue.Riders experience longer detours with greater cost savings,whereas drivers exhibit the opposite pattern.
基金Innovative Practice Research on the“Task-Driven+AI+Platform”Collaborative Teaching Model for Information Technology Application Innovation Operating System Courses From the Perspective of Digital Empowerment.Project No.:GGJG2025C007Research on the Precise Recommendation Mechanism of Qingyuan Tourism Resources from the Perspective of“One Belt,One Corridor and One Zone”.Project No.:QYSK2025141Research on AI-Enabled Pathways and Strategies for Balanced Urban-Rural Education Development in Qingyuan City.Project No.:QYSK2025048.
摘要Qingyuan,a prefecture-level city in Guangdong Province,China,has rich ecological,cultural,rural,rafting,food,and family-oriented tourism resources.Under the“One Belt,One Corridor,and One Zone”cultural tourism strategy,the city seeks to improve resource integration,communication,and consumption conversion.However,fragmented information,uneven exposure,insufficient tourist-need identification,homogenized promotion,and weak tourist-resource matching still limit tourism development.As a conceptual framework study,this paper constructs a precision recommendation mechanism based on resource tagging,tourist profiling,tourist-resource matching,route-level recommendation,and feedback iteration,aiming to improve matching efficiency,reduce decision-making costs,and promote actual visits and consumption.
基金supported by the National Natural Science Foundation of China,No.62301497the Science and Technology Research Program of Henan,No.252102211024the Key Research and Development Program of Henan,No.231111212000.
摘要Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this paper,we propose a Semantic-Guided Parallax Attention Stereo Matching Network(SGPASMnet)that can be trained in unsupervised manner,building upon the Parallax Attention Stereo Matching Network(PASMnet).Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets,facilitating robust training across diverse scene-specific datasets and enhancing generalization.SGPASMnet incorporates two novel components:a Cross-Scale Feature Interaction(CSFI)block and semantic feature augmentation using a pre-trained semantic segmentation model,SegFormer,seamlessly embedded into the parallax attention mechanism.The CSFI block enables effective fusion ofmulti-scale features,integrating coarse and fine details to enhance disparity estimation accuracy.Semantic features,extracted by SegFormer,enrich the parallax attention mechanism by providing high-level scene context,significantly improving performance in ambiguous regions.Our model unifies these enhancements within a cohesive architecture,comprising semantic feature extraction,an hourglass network,a semantic-guided cascaded parallax attentionmodule,outputmodule,and a disparity refinement network.Evaluations on the KITTI2015 dataset demonstrate that our unsupervised method achieves a lower error rate compared to the original PASMnet,highlighting the effectiveness of our enhancements in handling complex scenes.By harnessing unsupervised learning without ground truth disparity needed,SGPASMnet offers a scalable and robust solution for accurate stereo matching,with superior generalization across varied real-world applications.
摘要In this paper,we propose a novel cyclic redundancy check(CRC)-aided method to improve the energy efficiency of distribution matching(DM)algorithms based on fixed empirical distribution codebooks.The core design concept is to map a subset of a DM codebook to the entire codebook.The mapping is identified by a binary vector that is convolved during the CRC encoding;thereby avoiding any additional overhead in a CRC-aided system.At the receiver(RX),the CRC check not only performs error detection but also identifies the mapping of the transmitted symbol sequences to the original input of the DM.With the information delivered by a quarter-sized codebook,fewer occurrence of high-energy symbols effectively reduces the average symbol energy and the rate-loss.The proposed method can be seamlessly integrated into any DM algorithm that uses fixed empirical distribution codebooks.We demonstrate its implementation in a polar-coded probabilistic amplitude shaping(PAS)system with CRC-aided successive cancellation list decoding.Using this architecture,we show an energy efficiency improvement of up to 26%and a signal-to-noise ratio improvement of up to 0.8 dB at a fixed target frame error rate of 10−3.
摘要Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudassisted architecture faces two critical challenges:the untrusted cloud services and the separation of data ownership from control.Although Attribute-based Searchable Encryption(ABSE)provides fine-grained access control and keyword search over encrypted data,existing schemes lack of error tolerance in exact multi-keyword matching.In this paper,we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search(FCS-ABMSE)scheme that avoids computationally expensive bilinear pairing operations on the IoT device side.The scheme supportsmulti-keyword fuzzy search without requiring explicit keyword fields,thereby significantly enhancing error tolerance in search operations.It further incorporates forward-secure ciphertext search to mitigate trapdoor abuse,as well as offline encryption and verifiable outsourced decryption to minimize user-side computational costs.Formal security analysis proved that the FCS-ABMSE scheme meets both indistinguishability of ciphertext under the chosen keyword attacks(IND-CKA)and the indistinguishability of ciphertext under the chosen plaintext attacks(IND-CPA).In addition,we constructed an enhanced variant based on type-3 pairings.Results demonstrated that the proposed scheme outperforms existing ABSE approaches in terms of functionalities,computational cost,and communication cost.
基金supported by grants from the Beijing Natural Science Foundation(L248022)National High Level Hospital Clin-ical Research Funding(2025-PUMCH-C-015,2025-PUMCH-D-001)+1 种基金CAMS Innovation Fund for Medical Sciences(CIFMS)(2021-I2M-1-061,2021-I2M-1-003)Peking Union Medical College Hospital Out-standing Young Talent Development Program(UBJ10528).
摘要Background:Dual-phenotype hepatocellular carcinoma(DPHCC)is a recently defined subtype of hepato-cellular carcinoma(HCC)characterized by the simultaneous hepatocellular and biliary epithelial marker expression.This study aimed to elucidate the clinicopathological features of DPHCC following curative liver resection and its relationship with prognosis.Methods:We analyzed 1493 patients with HCC who underwent curative liver resection at the Peking Union Medical College Hospital from January 2013 to December 2023.All patients were divided into two groups according to immunohistochemical marker expression,with 487 and 1006 cases in the DPHCC and non-DPHCC groups,respectively.Propensity score matching was performed to reduce the deviation caused by baseline characteristics.Results:After 1:2 matching(DPHCCon-DPHCC group=453/771),patients were comparable regard-ing all baseline characteristics.Compared to patients with non-DPHCC,those with DPHCC were signifi-cantly associated with poorer differentiation,microvascular invasion,satellite nodules,and bile duct tu-mor thrombus(P<0.05).Patients with DPHCC also exhibited significantly worse recurrence-free survival(P=0.009)and overall survival(P=0.021).Furthermore,multivariate analysis revealed that DPHCC was an independent risk factor for recurrence-free survival(HR=1.28,95%CI:1.05-1.52,P=0.019)and over-all survival(HR=1.33,95%CI:1.15-1.51,P=0.023).Conclusions:DPHCC,a newly proposed subtype of HCC,is associated with poorer clinicopathological features and adverse prognosis,providing important clinical guidance.
基金Supported by the National Program on Key Basic Research Project(2020YFA0713600)the National Natural Science Foundation of China(62272214)。
摘要In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.
基金supported by National Natural Science Foundation of China Projects of International Cooperation and Exchanges(No.W2411055)。
摘要This paper proposes an efficient algorithm for real-time multi-modal image matching based on a lightweight feature fusion network,targeting the challenges of multi-modal image matching in multi-source data analysis.The algorithm addresses significant multi-modal feature differences and real-time processing limitations by incorporating key technologies including reparameterization in convolutional neural networks,multi-scale image pyramids,and feature fusion modules.The matching process employs a coarse-to-fine strategy,ensuring robust performance in complex environments.Experimental results using multi-modal datasets demonstrate that the proposed algorithm achieves superior accuracy and speed,with a success rate of 98.3%and an average matching time of 30.51 ms per 500×500 image pair.These results highlight the practical value and strong generalization capability of the algorithm in real-time applications.
基金supported by the National Natural Science Foundation of China(No.62561034)Yunnan Provincial International Joint Laboratory of Agricultural Remote Sensing and Digital Technology(No.202503AP140020)+3 种基金“Xingdian”Talent Support Program Project(No.KKRD202221041)Yunnan Fundamental Research Projects(No.202301AT070463)Yunnan International Joint Laboratory for Integrated Sky-Ground Intelligent Monitoring of Mountain Hazards(No.202403AP140002)Yunnan Plateau Remote Sensing Innovation Team(No.202505AS350001).
摘要In the realms of computer vision and remote sensing,the matching of images and point clouds poses significant challenges due to modality discrepancies.This study introduces a crossmodal consistency network,detector-free image and point cloud matching via diffusion-guided crossmodal consistency,2D3D-DiffMatch,leveraging diffusion prior information to enhance feature extraction consistency and alignment across modalities.To strengthen cross-modal consistency in complex scenes,diffusion priors generated by a pre-trained diffusion model are used to guide the feature extraction process toward semantically and geometrically consistent representations.These representations are further refined through a hierarchical fusion process,in which the most consistent diffusion features are adaptively selected using centered kernel alignment(CKA)and integrated with multi-scale backbone features,thereby mitigating the impact of modality gaps.Furthermore,to address feature-space misalignment between images and point clouds,we propose a crossmodal feature consistency loss that adaptively constrains correspondences,separates positive and negative pairs,and optimizes the agreement of positive pairs,enabling high-quality,detector-free matching.Experimental results on the 7Scenes and RGB-D Scenes V2 Datasets demonstrate superior registration recall rates of 81.2%and 61.0%,respectively,outperforming state-of-the-art methods and exhibiting robustness in challenging scenarios.This research advances the collaborative processing of multi-modal data,offering a robust solution for image–point cloud matching in challenging scenarios.
基金supported by the National College Students’Innovative Entrepreneurial Training Plan Program of Beijing University of Technology GJDC-2026-01-74。
摘要Talent drives efficient human resource allocation.Compared to developed nations,China faces persistent structural and regional imbalances in its share of high-skilled talent.Policy documents,such as the 14th Five-Year Plan for Talent Development,address these gaps.The academic community recognizes the importance of this issue,but systematic literature reviews remain scarce.
基金supported by the National Natural Science Foundation of China(42471336,52379021 and 42201278)the Hebei Province Backbone Talent Program,China(Returnee Platform for Overseas Study)(A20240028)+2 种基金the Hebei Province Statistical Science Research Project,China(2024HZ04)the Hebei Province Graduate Education and Teaching Reform Research Project,China(YJG2024046)the Innovation Ability Training Program for Postgraduate Students of Hebei Provincial Department of Education,China(CXZZSS2025048)。
摘要A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.