Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time se...Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.展开更多
Theoretical and experimental studies indicate that complete Green＇s Function can be retrieved from cross-correlation in a diffuse field. High SNR（signal-to-noise ratio） surface waves have been extracted from cross-...Theoretical and experimental studies indicate that complete Green＇s Function can be retrieved from cross-correlation in a diffuse field. High SNR（signal-to-noise ratio） surface waves have been extracted from cross-correlations of long-duration ambient noise across the globe. Body waves, not extracted in most of ambient noise studies, are thought to be more difficult to retrieve from regular ambient noise data processing. By stacking cross-correlations of ambient noise in 50 km inter-station distance bins in China, western United States and Europe, we observed coherent 20–100 s core phases（Sc S, PKIKPPKIKP, PcP PKPPKP） and crustal-mantle phases（Pn, P, PL, Sn, S, SPL, SnS n, SS, SSPL） at distances ranging from 0 to 4000 km. Our results show that these crustal-mantle phases show diverse characteristics due to different substructure and sources of body waves beneath different regions while the core phases are relatively robust and can be retrieved as long as stations are available. Further analysis indicates that the SNR of these body-wave phases depends on a compromise between stacking fold in spatial domain and the coherence of pre-stacked cross-correlations. Spatially stacked cross-correlations of seismic noise can provide new virtual seismograms for paths that complement earthquake data and that contain valuable information on the structure of the Earth. The extracted crustal-mantle phases can be used to study lithospheric heterogeneities and the robust core phases are significantly useful to study the deep structure of the Earth, such as detecting fine heterogeneities of the core-mantle boundary and constraining differential rotation of the inner core.展开更多
To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ens...To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.展开更多
The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured o...The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS.展开更多
Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with ot...Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.展开更多
Traditional cross-correlation algorithms are prone to time-of-flight(TOF)calculation errors under conditions of strong noise interference and complex temperature gradients,resulting in a decline in the accuracy of ult...Traditional cross-correlation algorithms are prone to time-of-flight(TOF)calculation errors under conditions of strong noise interference and complex temperature gradients,resulting in a decline in the accuracy of ultrasonic temperature measurement.To this end,this paper proposes an ultrasonic temperature measurement method that combines YOLOv11 target detection with energy-type weighted cross-correlation algorithm.The YOLOv11 model is utilized to conduct target detection and key area positioning on the ultrasonic signal waveform diagram,automatically identifying characteristic waveforms such as node waves and end face waves,and achieving adaptive extraction of the effective signal interval.Further introduce the energy-based weighted cross-correlation algorithm.Based on the signal energy distribution,the cross-correlation results are weighted and processed to enhance the main wave response and suppress noise interference.Experiments show that the YOLOv11 model has high detection accuracy(Precision=0.987,Recall=0.958,mAP@50=0.988);The proposed method maintains the stability of time delay estimation under strong noise and high temperature(>1200℃),with the average time delay error reduced by approximately 35%to 50%compared to traditional algorithms.This verifies its high robustness and temperature measurement accuracy in complex environments,and it has a promising engineering application prospect.展开更多
Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefor...Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.展开更多
Investigating urban spatial structures(USSs)and their influencing factors at different spatial scales is crucial for promoting sustainable urban transformation.Based on nighttime light datasets and the Herfindahl-Hirs...Investigating urban spatial structures(USSs)and their influencing factors at different spatial scales is crucial for promoting sustainable urban transformation.Based on nighttime light datasets and the Herfindahl-Hirschman index(HHI),this study analyzes USS characteristics in China from 2007 to 2023 on two spatial scales-prefecture-level cities and urban agglomerations.It also explores structural influencing factors,including the economy,infrastructure,society,and government intervention.We find that:(1)HHI values for both cities and urban agglomerations exhibit a decreasing trend,indicating a USS for both that is evolving toward polycentricity;(2)economic development promotes a polycentric structure at both spatial scales,whereas government intervention drives a monocentric structure;and(3)postal and communication infrastructure have conflicting effects on USSs,encouraging a monocentric structure at the city scale but fostering polycentricity at the urban agglomeration scale.展开更多
The suppression of ablative Rayleigh–Taylor instability(ARTI)by a spatially modulated laser in inertial confinement fusion(ICF)is studied through numerical simulations.The results show that in the acceleration phase ...The suppression of ablative Rayleigh–Taylor instability(ARTI)by a spatially modulated laser in inertial confinement fusion(ICF)is studied through numerical simulations.The results show that in the acceleration phase of ICF implosion,the growth of ARTI can be suppressed by using a short-wavelength spatially modulated laser.The ARTI growth rate decreases as the wavelength of the spatially modulated laser decreases,and ARTI is completely suppressed after a certain wavelength has been reached.A spatially uniform laser is introduced to keep the state of motion of the implosion fluid consistent,and it is found that the proportion of the spatially modulated laser required for complete suppression of ARTI decreases as the wavelength continues to decrease.We also optimize the spatial intensity distribution of the spatially modulated laser.In addition,as the duration of the spatially modulated laser decreases,the proportion required for completely suppressing ARTI increases,but the required energy decreases.When the perturbation wavenumber decreases,the wavelength of the spatially modulated laser required for complete suppression of ARTI becomes longer.In the case of multimode perturbation,ARTI can also be significantly suppressed by a spatially modulated laser,and the perturbation amplitude can be reduced to less than 10% of that without a spatially modulated laser.We believe that the conclusions drawn from our simulations can provide the basis for new approaches to control ARTI in ICF.展开更多
Background Mammalian spermatogenesis is critical for the transmission of male genetic information,and singlecell sequencing technology can reveal its complex process.However,at present,there is no research on the dyna...Background Mammalian spermatogenesis is critical for the transmission of male genetic information,and singlecell sequencing technology can reveal its complex process.However,at present,there is no research on the dynamic transcription of bovine germ cell population.Results In this study,we used Stereo-seq to construct a spatial transcription map of bovine testicular tissue at two ages.Four germ cell groups and five somatic cell groups were determined,and functional enrichment characterized their different biological functions and the differences between calves and adult bulls.At the same time,we also defined the subpopulations of cells and marker genes,then,clarified the communications between germ cells.Conclusion Our study constructed a spatial transcription map of bovine testicular tissue for the first time,and systematically described the dynamic transcription changes during spermatogenesis.These data laid the foundation for the study of spermatogenesis in large mammals and elucidated the transcriptional dynamics underlying male germ cell development.展开更多
Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other...Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.展开更多
Grouting with water–cement mixtures is the most widely used and cost-effective method for managing excess water inflow during tunnel construction.Due to uncertain geological and hydrological conditions,current grouti...Grouting with water–cement mixtures is the most widely used and cost-effective method for managing excess water inflow during tunnel construction.Due to uncertain geological and hydrological conditions,current grouting design relies heavily on the experience of onsite engineers.Recent advances in machine learning offer a promising alternative to traditional design to predict grout volume and improve grouting efficiency.Here,an artificial neural network(ANN)model was developed using the data set from an operation tunnel of Jurong Rock Caverns in Singapore to showcase an efficient and physics-guided training strategy.The ANN model was refined by incorporating the spatial scenarios,including the number of grouting holes in four quadrants of tunneling faces,the sequence of grouting screens along the tunnel axis,and the order of grouting rounds on the tunneling faces.The results indicate that an improved training strategy should encompass the grouting process,from Round 1 with grouting holes uniformly distributed around the tunnel periphery,to Round 2 with grouting holes drilled midway between neighboring first-round holes,and to Round 3 with grouting holes determined by onsite engineers.This model,trained based on the order of grouting rounds,performs better than the other models,highlighting the importance of establishing machine learning models grounded in physical principles.The finding was verified by the data set from another operation tunnel and concluded with a perspective on future grouting research.展开更多
While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput...While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments.These technologies are emerging as transformative tools in molecular diagnostics and medical research.By integrating histopathological morphology with spatial multi-omics profiling(genome,transcriptome,epigenome,and proteome),spatial omics technologies open an avenue for understanding disease progression,therapeutic resistance mechanisms,and precise diagnosis.It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states,which may greatly facilitate tumor molecular subtyping,prognostic assessment,and prediction of the radiotherapy and chemotherapy efficacy.Despite the substantial advancements in spatial omics,the translation of spatial omics into clinical applications remains challenging due to robustness,efficacy,clinical validation,and cost constraints.In this review,we summarize the current progress and prospects of spatial omics technologies,particularly in medical research and diagnostic applications.展开更多
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
The brain's functions are governed by molecular metabolic networks.However,due to the sophisticated spatial organization and diverse activities of the brain,characterizing both the minute and large-scale metabolic...The brain's functions are governed by molecular metabolic networks.However,due to the sophisticated spatial organization and diverse activities of the brain,characterizing both the minute and large-scale metabolic activity across the entire brain and its numerous micro-regions remains incredibly challenging.Here,we offer a high-definition spatially resolved metabolomics technique to better understand the metabolic specialization and interconnection throughout the mouse brain using improved ambient mass spectrometry imaging.This method allows for the simultaneous mapping of thousands of metabolites at a 30 μm spatial resolution across the mouse brain,ranging from structural lipids to functional neurotransmitters.This approach effectively reveals the distribution patterns of delicate microregions and their distinctive metabolic characteristics.Using an integrated database,we annotated 259 metabolites,demonstrating that the metabolome and metabolic pathways are unique to each brain microregion.The distribution of metabolites,closely linked to functionally connected brain regions and their interactions,offers profound insights into the complexity of chemical processes and their roles in brain function.An initial dataset for future metabolomics research might be obtained from the high-definition mouse brain's spatial metabolome atlas.展开更多
Agglomeration supports the high-quality development of the manufacturing industry,and its associated resource and environmental effects play a crucial role in driving green economic development.Based on data from pref...Agglomeration supports the high-quality development of the manufacturing industry,and its associated resource and environmental effects play a crucial role in driving green economic development.Based on data from prefecture-level cities in China from 2005 to 2019,this study employs the inverse distance weighting method,the bivariate local indicator of spatial association model,the spatial Durbin model,and other techniques to explore the relationship between manufacturing agglomeration and PM_(2.5)concentrations,and to assess the impact of its manufacturing agglomeration.Four correlation patterns are observed:high-high,low-low,high-low,and low-high.Among these,high-high and low-low patterns dominate in terms of number of cities.These correlation patterns demonstrate strong temporal stability,with a clear“Matthew effect”.The effect of manufacturing agglomeration on PM_(2.5)levels is significantly negative and helps reduce concentrations regionally,indicating the need to further enhance agglomeration levels regionally.However,it can increase PM_(2.5)levels in neighboring areas due to a siphon effect,and the impact of varies across regions.Compared with levels in 2005-2013,the significance of the relationship between manufacturing agglomeration and PM_(2.5)weakened in the 2013-2019 period.Accordingly,this study proposes countermeasures and policy recommendations aimed at strengthening regional collaborative governance and inspiring differentiated agglomeration strategies to support sustainable economic development in China.展开更多
Differentiation in housing costs reinforces the concentration of low-income groups in lowrent residential areas through residential location sorting,making the surrounding employment opportunity environment a crucial ...Differentiation in housing costs reinforces the concentration of low-income groups in lowrent residential areas through residential location sorting,making the surrounding employment opportunity environment a crucial perspective for assessing urban inclusiveness.Using residential areas as the unit of analysis,this study proposed a multidimensional framework for evaluating the spatial equity of urban employment by jointly capturing disparities between opportunity supply and access across three dimensions: employment opportunity quantity,wage levels,and commuting accessibility.In addition,we compared spatial differentiation among residential area types under rentbased stratification.This study focused on Urumqi,a major city in Northwest China,and integrated multisource geospatial data for 3465 residential areas,including points of interest(POIs),online job postings,and rental data for residential areas.Empirical analyses were conducted using the Gini coefficient,location quotient,and Geographically Weighted Regression(GWR) model.The findings reveal marked disparities in employment access across ring road areas and rent-based groups.In the urban core,low-rent residential areas benefit from relatively favorable commuting conditions;however,the accessible employment opportunities are concentrated in low-wage service sectors.In the peripheral zone,low-rent residential areas face a dual disadvantage of limited nearby employment supply and longer commuting distances.Even when spatial conditions are comparable,low-rent residential areas are systematically disadvantaged relative to non-low-rent residential areas in realized access to both employment opportunity quantity and wage levels.This pattern indicated that capability constraints impede the conversion of spatial resources into effective access.Further analyses highlight housing costs,infrastructure quality,and residential location as key associated factors.The findings underscored the importance of coordinated,targeted policies in affordable housing delivery,the spatial distribution of employment opportunities,and improvements in transport accessibility to promote urban spatial justice.展开更多
Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecol...Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.展开更多
Sluggish kinetics coupled with parasitic shuttling reactions are pivotal challenges hindering the performance of lithium-sulfur(Li-S)batteries.Improving areal capacity and cyclability of Li-S batteries can be achieved...Sluggish kinetics coupled with parasitic shuttling reactions are pivotal challenges hindering the performance of lithium-sulfur(Li-S)batteries.Improving areal capacity and cyclability of Li-S batteries can be achieved by addressing these challenges.A composite sulfur host material is synthesized herein by in situ anchoring ultrafine cobalt-iron phosphide nanoparticles(5-7 nm)onto a hollow mesoporous carbon sphere(HMCS)framework.This strategy achieved exceptional spatial restriction and a high density of catalytically active sites through the encapsulation of sulfur within a hollow-structured framework.Specifically,HMCS expedites rapid Li_(2)S nucleation kinetics,while CoFeP facilitates robust Li_(2)S dissolution kinetics by mitigating decomposition barriers.This synergistic integration equips CoFeP@HMCS with robust bi-directional catalytic activity,significantly promoting interracial charge-transfer,facilitate sulfu r multistep catalytic conversion,and inhibiting shuttling.Consequently,the battery exhibits excellent rate performance(991 mA h g^(-1) at 5.0 C)and retains a high areal capacity of 6.06 mA h cm^(-2) after 200 cycles under a high areal sulfur loading of 8.2 mg cm^(-2) but a low electrolyte/sulfur ratio of 4.8μL mg^(-1).This work contributes to enhancing the practical specific capacity of lithium-sulfur batteries and deepens the understanding of catalysts enabling bidirectional electrocatalytic sulfur conversion.展开更多
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ...The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.展开更多
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.
基金supported by the National Science Foundation of China (No. 41374059)the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences (Wuhan) (Nos. CUG090106 and #CUGL100402).
文摘Theoretical and experimental studies indicate that complete Green＇s Function can be retrieved from cross-correlation in a diffuse field. High SNR（signal-to-noise ratio） surface waves have been extracted from cross-correlations of long-duration ambient noise across the globe. Body waves, not extracted in most of ambient noise studies, are thought to be more difficult to retrieve from regular ambient noise data processing. By stacking cross-correlations of ambient noise in 50 km inter-station distance bins in China, western United States and Europe, we observed coherent 20–100 s core phases（Sc S, PKIKPPKIKP, PcP PKPPKP） and crustal-mantle phases（Pn, P, PL, Sn, S, SPL, SnS n, SS, SSPL） at distances ranging from 0 to 4000 km. Our results show that these crustal-mantle phases show diverse characteristics due to different substructure and sources of body waves beneath different regions while the core phases are relatively robust and can be retrieved as long as stations are available. Further analysis indicates that the SNR of these body-wave phases depends on a compromise between stacking fold in spatial domain and the coherence of pre-stacked cross-correlations. Spatially stacked cross-correlations of seismic noise can provide new virtual seismograms for paths that complement earthquake data and that contain valuable information on the structure of the Earth. The extracted crustal-mantle phases can be used to study lithospheric heterogeneities and the robust core phases are significantly useful to study the deep structure of the Earth, such as detecting fine heterogeneities of the core-mantle boundary and constraining differential rotation of the inner core.
基金supported by National Natural Science Foundation of China(No.61973234)Tianjin Science and Technology Plan Project(No.22YDTPJC00090)。
文摘To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.
文摘The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11875042 and 11505114)the Shanghai Project for Construction of Top Disciplines (Grant No. USST-SYS-01)。
文摘Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.
文摘Traditional cross-correlation algorithms are prone to time-of-flight(TOF)calculation errors under conditions of strong noise interference and complex temperature gradients,resulting in a decline in the accuracy of ultrasonic temperature measurement.To this end,this paper proposes an ultrasonic temperature measurement method that combines YOLOv11 target detection with energy-type weighted cross-correlation algorithm.The YOLOv11 model is utilized to conduct target detection and key area positioning on the ultrasonic signal waveform diagram,automatically identifying characteristic waveforms such as node waves and end face waves,and achieving adaptive extraction of the effective signal interval.Further introduce the energy-based weighted cross-correlation algorithm.Based on the signal energy distribution,the cross-correlation results are weighted and processed to enhance the main wave response and suppress noise interference.Experiments show that the YOLOv11 model has high detection accuracy(Precision=0.987,Recall=0.958,mAP@50=0.988);The proposed method maintains the stability of time delay estimation under strong noise and high temperature(>1200℃),with the average time delay error reduced by approximately 35%to 50%compared to traditional algorithms.This verifies its high robustness and temperature measurement accuracy in complex environments,and it has a promising engineering application prospect.
基金Under the auspices of the National Natural Science Foundation of China(No.42371222,41971167)Fundamental Scientific Research Funds of Central China Normal University(No.CCNU24ZZ120)。
文摘Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.
基金supported by the National Natural Science Foundation of China[Grant No.72373084]Taishan Scholar Foundation of Shandong Province[Grant No.tsqn202408139].
文摘Investigating urban spatial structures(USSs)and their influencing factors at different spatial scales is crucial for promoting sustainable urban transformation.Based on nighttime light datasets and the Herfindahl-Hirschman index(HHI),this study analyzes USS characteristics in China from 2007 to 2023 on two spatial scales-prefecture-level cities and urban agglomerations.It also explores structural influencing factors,including the economy,infrastructure,society,and government intervention.We find that:(1)HHI values for both cities and urban agglomerations exhibit a decreasing trend,indicating a USS for both that is evolving toward polycentricity;(2)economic development promotes a polycentric structure at both spatial scales,whereas government intervention drives a monocentric structure;and(3)postal and communication infrastructure have conflicting effects on USSs,encouraging a monocentric structure at the city scale but fostering polycentricity at the urban agglomeration scale.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.12074399,12204500,and 12004403)the Key Projects of Intergovernmental International Scientific and Technological Innovation Cooperation(No.2021YFE0116700)+1 种基金the Shanghai Natural Science Foundation(No.20ZR1464400)the Shanghai Sailing Program(No.22YF1455300).
文摘The suppression of ablative Rayleigh–Taylor instability(ARTI)by a spatially modulated laser in inertial confinement fusion(ICF)is studied through numerical simulations.The results show that in the acceleration phase of ICF implosion,the growth of ARTI can be suppressed by using a short-wavelength spatially modulated laser.The ARTI growth rate decreases as the wavelength of the spatially modulated laser decreases,and ARTI is completely suppressed after a certain wavelength has been reached.A spatially uniform laser is introduced to keep the state of motion of the implosion fluid consistent,and it is found that the proportion of the spatially modulated laser required for complete suppression of ARTI decreases as the wavelength continues to decrease.We also optimize the spatial intensity distribution of the spatially modulated laser.In addition,as the duration of the spatially modulated laser decreases,the proportion required for completely suppressing ARTI increases,but the required energy decreases.When the perturbation wavenumber decreases,the wavelength of the spatially modulated laser required for complete suppression of ARTI becomes longer.In the case of multimode perturbation,ARTI can also be significantly suppressed by a spatially modulated laser,and the perturbation amplitude can be reduced to less than 10% of that without a spatially modulated laser.We believe that the conclusions drawn from our simulations can provide the basis for new approaches to control ARTI in ICF.
基金supported by Biological Breeding-Major Projects to Yun Ma(Grant No.2023ZD0404803)Key R&D Program of Ningxia Hui Autonomous Region to Lingkai Zhang(2023BBF01007)and(2023BCF01006)。
文摘Background Mammalian spermatogenesis is critical for the transmission of male genetic information,and singlecell sequencing technology can reveal its complex process.However,at present,there is no research on the dynamic transcription of bovine germ cell population.Results In this study,we used Stereo-seq to construct a spatial transcription map of bovine testicular tissue at two ages.Four germ cell groups and five somatic cell groups were determined,and functional enrichment characterized their different biological functions and the differences between calves and adult bulls.At the same time,we also defined the subpopulations of cells and marker genes,then,clarified the communications between germ cells.Conclusion Our study constructed a spatial transcription map of bovine testicular tissue for the first time,and systematically described the dynamic transcription changes during spermatogenesis.These data laid the foundation for the study of spermatogenesis in large mammals and elucidated the transcriptional dynamics underlying male germ cell development.
文摘Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.
基金Ministry of Education-Singapore,Grant/Award Number:RG143/23。
文摘Grouting with water–cement mixtures is the most widely used and cost-effective method for managing excess water inflow during tunnel construction.Due to uncertain geological and hydrological conditions,current grouting design relies heavily on the experience of onsite engineers.Recent advances in machine learning offer a promising alternative to traditional design to predict grout volume and improve grouting efficiency.Here,an artificial neural network(ANN)model was developed using the data set from an operation tunnel of Jurong Rock Caverns in Singapore to showcase an efficient and physics-guided training strategy.The ANN model was refined by incorporating the spatial scenarios,including the number of grouting holes in four quadrants of tunneling faces,the sequence of grouting screens along the tunnel axis,and the order of grouting rounds on the tunneling faces.The results indicate that an improved training strategy should encompass the grouting process,from Round 1 with grouting holes uniformly distributed around the tunnel periphery,to Round 2 with grouting holes drilled midway between neighboring first-round holes,and to Round 3 with grouting holes determined by onsite engineers.This model,trained based on the order of grouting rounds,performs better than the other models,highlighting the importance of establishing machine learning models grounded in physical principles.The finding was verified by the data set from another operation tunnel and concluded with a perspective on future grouting research.
基金supported by the National Natural Science Foundation of China(32171022,32221005,and 32401246).
文摘While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments.These technologies are emerging as transformative tools in molecular diagnostics and medical research.By integrating histopathological morphology with spatial multi-omics profiling(genome,transcriptome,epigenome,and proteome),spatial omics technologies open an avenue for understanding disease progression,therapeutic resistance mechanisms,and precise diagnosis.It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states,which may greatly facilitate tumor molecular subtyping,prognostic assessment,and prediction of the radiotherapy and chemotherapy efficacy.Despite the substantial advancements in spatial omics,the translation of spatial omics into clinical applications remains challenging due to robustness,efficacy,clinical validation,and cost constraints.In this review,we summarize the current progress and prospects of spatial omics technologies,particularly in medical research and diagnostic applications.
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金financial support from the National Natural Science Foundation of China (Nos.82473887 and 21927808)the Scientific and Technological Innovation Program of Shanghai (No.23DZ2202500)the CAMS Innovation Fund for Medical Sciences (No.2021-1-I2M-026)。
文摘The brain's functions are governed by molecular metabolic networks.However,due to the sophisticated spatial organization and diverse activities of the brain,characterizing both the minute and large-scale metabolic activity across the entire brain and its numerous micro-regions remains incredibly challenging.Here,we offer a high-definition spatially resolved metabolomics technique to better understand the metabolic specialization and interconnection throughout the mouse brain using improved ambient mass spectrometry imaging.This method allows for the simultaneous mapping of thousands of metabolites at a 30 μm spatial resolution across the mouse brain,ranging from structural lipids to functional neurotransmitters.This approach effectively reveals the distribution patterns of delicate microregions and their distinctive metabolic characteristics.Using an integrated database,we annotated 259 metabolites,demonstrating that the metabolome and metabolic pathways are unique to each brain microregion.The distribution of metabolites,closely linked to functionally connected brain regions and their interactions,offers profound insights into the complexity of chemical processes and their roles in brain function.An initial dataset for future metabolomics research might be obtained from the high-definition mouse brain's spatial metabolome atlas.
基金supported by the National Natural Science Foundation of China“Research on the Multi-scale Regional Industrial Spatial Evolution Mechanism,Resource and Environmental Effects,and Green Transformation in the Yellow River Basin”[Grant No.42371194]Taishan Scholar Foundation of Shandong Province[Grant Nos.tsqn202408148 and tstp20240821].
文摘Agglomeration supports the high-quality development of the manufacturing industry,and its associated resource and environmental effects play a crucial role in driving green economic development.Based on data from prefecture-level cities in China from 2005 to 2019,this study employs the inverse distance weighting method,the bivariate local indicator of spatial association model,the spatial Durbin model,and other techniques to explore the relationship between manufacturing agglomeration and PM_(2.5)concentrations,and to assess the impact of its manufacturing agglomeration.Four correlation patterns are observed:high-high,low-low,high-low,and low-high.Among these,high-high and low-low patterns dominate in terms of number of cities.These correlation patterns demonstrate strong temporal stability,with a clear“Matthew effect”.The effect of manufacturing agglomeration on PM_(2.5)levels is significantly negative and helps reduce concentrations regionally,indicating the need to further enhance agglomeration levels regionally.However,it can increase PM_(2.5)levels in neighboring areas due to a siphon effect,and the impact of varies across regions.Compared with levels in 2005-2013,the significance of the relationship between manufacturing agglomeration and PM_(2.5)weakened in the 2013-2019 period.Accordingly,this study proposes countermeasures and policy recommendations aimed at strengthening regional collaborative governance and inspiring differentiated agglomeration strategies to support sustainable economic development in China.
基金supported by the National Key Research and Development Program of China (2024YFF0809304)the Third Xinjiang Scientific Expedition Program,China (2021xjkk0905)。
文摘Differentiation in housing costs reinforces the concentration of low-income groups in lowrent residential areas through residential location sorting,making the surrounding employment opportunity environment a crucial perspective for assessing urban inclusiveness.Using residential areas as the unit of analysis,this study proposed a multidimensional framework for evaluating the spatial equity of urban employment by jointly capturing disparities between opportunity supply and access across three dimensions: employment opportunity quantity,wage levels,and commuting accessibility.In addition,we compared spatial differentiation among residential area types under rentbased stratification.This study focused on Urumqi,a major city in Northwest China,and integrated multisource geospatial data for 3465 residential areas,including points of interest(POIs),online job postings,and rental data for residential areas.Empirical analyses were conducted using the Gini coefficient,location quotient,and Geographically Weighted Regression(GWR) model.The findings reveal marked disparities in employment access across ring road areas and rent-based groups.In the urban core,low-rent residential areas benefit from relatively favorable commuting conditions;however,the accessible employment opportunities are concentrated in low-wage service sectors.In the peripheral zone,low-rent residential areas face a dual disadvantage of limited nearby employment supply and longer commuting distances.Even when spatial conditions are comparable,low-rent residential areas are systematically disadvantaged relative to non-low-rent residential areas in realized access to both employment opportunity quantity and wage levels.This pattern indicated that capability constraints impede the conversion of spatial resources into effective access.Further analyses highlight housing costs,infrastructure quality,and residential location as key associated factors.The findings underscored the importance of coordinated,targeted policies in affordable housing delivery,the spatial distribution of employment opportunities,and improvements in transport accessibility to promote urban spatial justice.
基金National Key Research and Development Program of China,No.2019YFD1101304National Natural Science Foundation of China,No.52278059+1 种基金Natural Science Foundation of Hunan Province of China,No.2024JJ8316Hunan Provincial Innovation Foundation For Postgraduate,No.CX20250634。
文摘Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.
基金financially supported by the Nation Key R&D Program China(2018YFA0703200)the Key Research and Development Program of Hubei Province(2022BAA026)+1 种基金the National Natural Science Foundation of China(51772110)the Open Research Fund(2024JYBKF01)of Key Laboratory of Material Chemistry for Energy Conversion and Storage(HUST),Ministry of Education。
文摘Sluggish kinetics coupled with parasitic shuttling reactions are pivotal challenges hindering the performance of lithium-sulfur(Li-S)batteries.Improving areal capacity and cyclability of Li-S batteries can be achieved by addressing these challenges.A composite sulfur host material is synthesized herein by in situ anchoring ultrafine cobalt-iron phosphide nanoparticles(5-7 nm)onto a hollow mesoporous carbon sphere(HMCS)framework.This strategy achieved exceptional spatial restriction and a high density of catalytically active sites through the encapsulation of sulfur within a hollow-structured framework.Specifically,HMCS expedites rapid Li_(2)S nucleation kinetics,while CoFeP facilitates robust Li_(2)S dissolution kinetics by mitigating decomposition barriers.This synergistic integration equips CoFeP@HMCS with robust bi-directional catalytic activity,significantly promoting interracial charge-transfer,facilitate sulfu r multistep catalytic conversion,and inhibiting shuttling.Consequently,the battery exhibits excellent rate performance(991 mA h g^(-1) at 5.0 C)and retains a high areal capacity of 6.06 mA h cm^(-2) after 200 cycles under a high areal sulfur loading of 8.2 mg cm^(-2) but a low electrolyte/sulfur ratio of 4.8μL mg^(-1).This work contributes to enhancing the practical specific capacity of lithium-sulfur batteries and deepens the understanding of catalysts enabling bidirectional electrocatalytic sulfur conversion.
基金sponsored by the National Natural Science Foundation of China(Grant No.52178100).
文摘The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.
