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
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
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
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
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
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
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
Low-frequency signals play a crucial role in seismic inversion of thin-layer structure and reservoir prediction.However,during seismic exploration,the low-frequency signals are often contaminated,distorted,or even mis...Low-frequency signals play a crucial role in seismic inversion of thin-layer structure and reservoir prediction.However,during seismic exploration,the low-frequency signals are often contaminated,distorted,or even missing due to acquisition limitations,processing artifacts,and ambient noise.Although compressive sensing theory-based sparse inversion can partially recover low-frequency signals,the reconstruction results suffer from significant non-uniqueness.To address this challenge,we propose a sparse inversion approach incorporating spatial structural regularization to enhance low-frequency signal recovery.Due to the interference among seismic waveforms,spatial reflection structure exhibits frequency dependency.Consequently,the spatial structure estimated directly from seismic data differs significantly from the actual low-frequency spatial structure.Therefore,the proposed method estimates spatial reflection structure from seismic data in the neighboring frequency band of the low-frequency signals to be recovered,aiming to reduce the impact of frequency dependency on estimation accuracy.Subsequently,both the sparse structure of reflection coefcients and spatial structure of low-frequency signals are incorporated as regularization terms into the inversion framework,enabling geologically guided recovery of low-frequency components.The proposed method was successfully applied in the Tarim Oileld,eectively restoring low-frequency signals and providing reliable foundational seismic data for reservoir prediction.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
Rapid urbanization and digital transformation are reshaping how cities address challenges related to security,governance,and sustainable development.Geospatial information technology(GIT)has become a base infrastructu...Rapid urbanization and digital transformation are reshaping how cities address challenges related to security,governance,and sustainable development.Geospatial information technology(GIT)has become a base infrastructure for smart cities,where the gathering,synthesis,and examination of spatially explicit information are used to deliver data to make decisions in cities.Even after its increasing significance,the current body of research on geospatial innovation is still divided into the spheres of urban security,spatial governance,and smart city development.Such fragmentation restricts the integration of theoretical work,as well as limits the possibility of developing coherent policies and governance institutions.This article includes a systematic and integrative review of innovation in geospatial information technology to analyze the relationship between technological,data-driven,and institutional innovation and urban security practices,spatial governance processes,and smart city initiatives.Based on peer-reviewed sources on urban studies,geography,planning,and information science,the review generalizes the main trends in real-time spatial analytics,artificial intelligence,participatory geospatial platforms,and urban digital twins.The review shows that geospatial systems facilitate anticipatory governance,cross-sector coordination,and evidence-based urban management,and that it provides an integrative conceptual lens on the existing literature on smart cities and urban governance,as it positions geospatial information technology as a socio-technical infrastructure,as opposed to a technical tool.The paper recognizes the voids in critical research and the directions into the future on how to build ethical,inclusive,and context-sensitive geospatial systems that can allow the creation of secure,governable,and sustainable urban futures.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
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
Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters...Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters for species with range-wide genetic structure.To investigate the extent of spatially biased sampling in living collections and the coverage of wild genetic clusters in plant populations under ex situ conservation worldwide,we combined a global synthesis of ex situ conservation efforts with a case study of an endangered riparian plant species,Myricaria laxiflora.Our analysis of ex situ conservation worldwide revealed that the majority(82.6%)of ex situ populations fail to cover all wild genetic clusters,largely due to spatially biased sampling with low geographic coverage.Our case study of M.laxiflora showed that genetic diversity differed between the ex situ and upstream populations,while it was comparable between ex situ populations and other wild populations.However,current ex situ populations did not cover all wild genetic clusters,as the upstream genetic cluster was previously uncollected.Our study suggests that the failure to cover all wild genetic clusters in ex situ populations is a widespread issue,and ex situ populations with high genetic diversity can also fail to cover all wild genetic clusters.In future ex situ conservation programs,both the importance of high genetic diversity and the high coverage of wild genetic clusters should be prioritized.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
Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge.However,the extent to which agricultural modernization in China enhances grain y...Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge.However,the extent to which agricultural modernization in China enhances grain yield and contributes to food security remains unclear.Therefore,using panel data from 327 Chinese cities(2013–2021),this study employs spatial econometric models to analyze the spatial spillover effects of agricultural modernization level(AML)on grain yield and to reveal regional heterogeneity across nine major agricultural zones.The results showed a cumulative grain yield increase of 23.7 million tons,with peak productivity concentrated along the Hu Line and declining eastward and westward.AML also exhibited a steady increase but a clear spatial gradient,decreasing from coastal to inland regions,with the highest level observed in Southern China(SC).A key finding was that a 1%increase in AML directly raised local grain yield by an average of 4.185%,accompanied by significant positive spillover effects on neighboring regions.Regional variations revealed distinct patterns:the direct effects of AML were more pronounced in southern and eastern zones,while spillover effects dominated in northern and western zones.The largest positive direct impact of AML on grain yield was observed in the SC(8.499%),while Middle-Lower Yangtze Plain ranked second but exhibited the strongest positive spatial spillover effect(4.534%).These findings highlight the critical role of agricultural modernization in promoting grain production and provide a solid basis for optimizing regional agricultural systems,ensuring food security,and advancing sustainable agriculture.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
In this paper,we propose a novel probabilistic method for predicting the undrained bearing capacity of spatially variable soils.Our approach combines a Gaussian process regression(GPR)-based surrogate model with rando...In this paper,we propose a novel probabilistic method for predicting the undrained bearing capacity of spatially variable soils.Our approach combines a Gaussian process regression(GPR)-based surrogate model with random cell-based smoothed finite analysis.The Gaussian process emulator(GPE)serves as a statistical tool for making predictions from a data set.First,we validate the accuracy and efficiency of kinematic limit analysis using the cell-based smoothed finite element method(CS-FEM)against the standard finite element method(FEM)and edge-based smoothed FEM(ES-FEM).The numerical results demonstrate that the CS-FEM framework surpasses traditional numerical approaches,establishing its reliability in computing collapse loads.Subsequently,we conduct several hundred simulations to develop a surrogate model for predicting the undrained bearing capacity of shallow foundations.By utilizing various kernel functions,we enhance the accuracy of the GPE in these predictions.This method offers a practical and efficient solution,effectively addressing multiple uncertainties.Numerical results indicate that the GPE significantly boosts computational efficiency,achieving satisfactory outcomes within minutes compared to the days required for conventional simulations.Notably,the mean absolute percentage error(MAPE)decreases from 2.38%to 1.82%for rough foundations when employing Matérn and rational quadratic kernel functions,respectively.Additionally,combining different kernel functions further enhances the accuracy of collapse load predictions.展开更多
Quantifying spatial heterogeneity in soil water retention properties(SWRP)is crucial for enhancing the accuracy of hydrogeological simulations.However,studies on the spatial heterogeneity of SWRP in the Chinese Loess ...Quantifying spatial heterogeneity in soil water retention properties(SWRP)is crucial for enhancing the accuracy of hydrogeological simulations.However,studies on the spatial heterogeneity of SWRP in the Chinese Loess Plateau(CLP)remain scarce,especially at the vertical scale.We conducted laboratory tests on undisturbed loess cores collected from boreholes in CLP to analyze soil physical parameters(SPPs)and SWRP.Measured soil water characteristic curves(SWCCs)were fitted to the Brooks-Corey(BC),Fredlund-Xing(FX),and van Genuchten(vG)models.It was revealed that the FX and vG models outperformed the BC model.The geostatistical analysis identified the Gaussian model as optimal for describing the semivariograms of both SPPs and SWCC fitting parameters(FPs).Strikingly,over 90%of these parameters exhibited strong vertical spatial dependence,with an average autocorrelation length of 213.878 cm for SPPs and 320.678 cm for FPs.Moreover,SWRP was found to be significantly influenced by both SPPs and the vertical position relative to the loess ridge slope surface.Parameters near the ridge slope surface showed significantly degraded spatial dependence.These findings provide valuable insights for parameterizing the spatial heterogeneity of soil water retention properties,which are beneficial for hydrogeological modelling in shallow CLP loess strata.展开更多
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(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.
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.
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supported by the National Natural Science Foundation of China(Grant Number:42574160)the Open Fund(Grant Number:36750000-24-FW0399-0011)of SINOPEC Key Laboratory of Geophysics.
文摘Low-frequency signals play a crucial role in seismic inversion of thin-layer structure and reservoir prediction.However,during seismic exploration,the low-frequency signals are often contaminated,distorted,or even missing due to acquisition limitations,processing artifacts,and ambient noise.Although compressive sensing theory-based sparse inversion can partially recover low-frequency signals,the reconstruction results suffer from significant non-uniqueness.To address this challenge,we propose a sparse inversion approach incorporating spatial structural regularization to enhance low-frequency signal recovery.Due to the interference among seismic waveforms,spatial reflection structure exhibits frequency dependency.Consequently,the spatial structure estimated directly from seismic data differs significantly from the actual low-frequency spatial structure.Therefore,the proposed method estimates spatial reflection structure from seismic data in the neighboring frequency band of the low-frequency signals to be recovered,aiming to reduce the impact of frequency dependency on estimation accuracy.Subsequently,both the sparse structure of reflection coefcients and spatial structure of low-frequency signals are incorporated as regularization terms into the inversion framework,enabling geologically guided recovery of low-frequency components.The proposed method was successfully applied in the Tarim Oileld,eectively restoring low-frequency signals and providing reliable foundational seismic data for reservoir prediction.
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project supported by the Scientific Research Fund of the Zhejiang Provincial Education Department(grant number Y202456064).
文摘Rapid urbanization and digital transformation are reshaping how cities address challenges related to security,governance,and sustainable development.Geospatial information technology(GIT)has become a base infrastructure for smart cities,where the gathering,synthesis,and examination of spatially explicit information are used to deliver data to make decisions in cities.Even after its increasing significance,the current body of research on geospatial innovation is still divided into the spheres of urban security,spatial governance,and smart city development.Such fragmentation restricts the integration of theoretical work,as well as limits the possibility of developing coherent policies and governance institutions.This article includes a systematic and integrative review of innovation in geospatial information technology to analyze the relationship between technological,data-driven,and institutional innovation and urban security practices,spatial governance processes,and smart city initiatives.Based on peer-reviewed sources on urban studies,geography,planning,and information science,the review generalizes the main trends in real-time spatial analytics,artificial intelligence,participatory geospatial platforms,and urban digital twins.The review shows that geospatial systems facilitate anticipatory governance,cross-sector coordination,and evidence-based urban management,and that it provides an integrative conceptual lens on the existing literature on smart cities and urban governance,as it positions geospatial information technology as a socio-technical infrastructure,as opposed to a technical tool.The paper recognizes the voids in critical research and the directions into the future on how to build ethical,inclusive,and context-sensitive geospatial systems that can allow the creation of secure,governable,and sustainable urban futures.
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.
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 National Key Research and Development Program of China(2024YFF1307400)Hubei Provincial Natural Science Foundation and Three Gorges Innovation Development Joint Fund(Grant No.2023AFD195)China Three Gorges Corporation(NBZZ202300130).
文摘Successful ex situ conservation of plant populations requires a high degree of genetic representativeness.However,spatially biased sampling in ex situ conservation efforts may fail to capture all wild genetic clusters for species with range-wide genetic structure.To investigate the extent of spatially biased sampling in living collections and the coverage of wild genetic clusters in plant populations under ex situ conservation worldwide,we combined a global synthesis of ex situ conservation efforts with a case study of an endangered riparian plant species,Myricaria laxiflora.Our analysis of ex situ conservation worldwide revealed that the majority(82.6%)of ex situ populations fail to cover all wild genetic clusters,largely due to spatially biased sampling with low geographic coverage.Our case study of M.laxiflora showed that genetic diversity differed between the ex situ and upstream populations,while it was comparable between ex situ populations and other wild populations.However,current ex situ populations did not cover all wild genetic clusters,as the upstream genetic cluster was previously uncollected.Our study suggests that the failure to cover all wild genetic clusters in ex situ populations is a widespread issue,and ex situ populations with high genetic diversity can also fail to cover all wild genetic clusters.In future ex situ conservation programs,both the importance of high genetic diversity and the high coverage of wild genetic clusters should be prioritized.
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National Natural Science Foundation of China,No.42471455,No.42230113National Key Research and Development Program of China,No.2022YFC3800804-01。
文摘Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge.However,the extent to which agricultural modernization in China enhances grain yield and contributes to food security remains unclear.Therefore,using panel data from 327 Chinese cities(2013–2021),this study employs spatial econometric models to analyze the spatial spillover effects of agricultural modernization level(AML)on grain yield and to reveal regional heterogeneity across nine major agricultural zones.The results showed a cumulative grain yield increase of 23.7 million tons,with peak productivity concentrated along the Hu Line and declining eastward and westward.AML also exhibited a steady increase but a clear spatial gradient,decreasing from coastal to inland regions,with the highest level observed in Southern China(SC).A key finding was that a 1%increase in AML directly raised local grain yield by an average of 4.185%,accompanied by significant positive spillover effects on neighboring regions.Regional variations revealed distinct patterns:the direct effects of AML were more pronounced in southern and eastern zones,while spillover effects dominated in northern and western zones.The largest positive direct impact of AML on grain yield was observed in the SC(8.499%),while Middle-Lower Yangtze Plain ranked second but exhibited the strongest positive spatial spillover effect(4.534%).These findings highlight the critical role of agricultural modernization in promoting grain production and provide a solid basis for optimizing regional agricultural systems,ensuring food security,and advancing sustainable agriculture.
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.
文摘In this paper,we propose a novel probabilistic method for predicting the undrained bearing capacity of spatially variable soils.Our approach combines a Gaussian process regression(GPR)-based surrogate model with random cell-based smoothed finite analysis.The Gaussian process emulator(GPE)serves as a statistical tool for making predictions from a data set.First,we validate the accuracy and efficiency of kinematic limit analysis using the cell-based smoothed finite element method(CS-FEM)against the standard finite element method(FEM)and edge-based smoothed FEM(ES-FEM).The numerical results demonstrate that the CS-FEM framework surpasses traditional numerical approaches,establishing its reliability in computing collapse loads.Subsequently,we conduct several hundred simulations to develop a surrogate model for predicting the undrained bearing capacity of shallow foundations.By utilizing various kernel functions,we enhance the accuracy of the GPE in these predictions.This method offers a practical and efficient solution,effectively addressing multiple uncertainties.Numerical results indicate that the GPE significantly boosts computational efficiency,achieving satisfactory outcomes within minutes compared to the days required for conventional simulations.Notably,the mean absolute percentage error(MAPE)decreases from 2.38%to 1.82%for rough foundations when employing Matérn and rational quadratic kernel functions,respectively.Additionally,combining different kernel functions further enhances the accuracy of collapse load predictions.
基金supported by the National Natural Science Foundation of China(Grant No.52379097)the National Natural Science Foundation of China(No.52509138)+2 种基金the Water Conservancy Science and Technology Project of Jiangxi Province(Grant No.202426ZDKT27)Chongqing Natural Science Foundation Doctoral Program(CSTB2025NSCQ-BSX0020)the Research and Innovation Program for Graduate Students of Chongqing Municipality(Grant No.CYB23251).
文摘Quantifying spatial heterogeneity in soil water retention properties(SWRP)is crucial for enhancing the accuracy of hydrogeological simulations.However,studies on the spatial heterogeneity of SWRP in the Chinese Loess Plateau(CLP)remain scarce,especially at the vertical scale.We conducted laboratory tests on undisturbed loess cores collected from boreholes in CLP to analyze soil physical parameters(SPPs)and SWRP.Measured soil water characteristic curves(SWCCs)were fitted to the Brooks-Corey(BC),Fredlund-Xing(FX),and van Genuchten(vG)models.It was revealed that the FX and vG models outperformed the BC model.The geostatistical analysis identified the Gaussian model as optimal for describing the semivariograms of both SPPs and SWCC fitting parameters(FPs).Strikingly,over 90%of these parameters exhibited strong vertical spatial dependence,with an average autocorrelation length of 213.878 cm for SPPs and 320.678 cm for FPs.Moreover,SWRP was found to be significantly influenced by both SPPs and the vertical position relative to the loess ridge slope surface.Parameters near the ridge slope surface showed significantly degraded spatial dependence.These findings provide valuable insights for parameterizing the spatial heterogeneity of soil water retention properties,which are beneficial for hydrogeological modelling in shallow CLP loess strata.
