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 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 northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic ...The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.展开更多
Urban environmental quality research is crucial,as cities become competitive centers concentrating human talent,industrial activity,and financial resources,contributing significantly to national economies.Municipal an...Urban environmental quality research is crucial,as cities become competitive centers concentrating human talent,industrial activity,and financial resources,contributing significantly to national economies.Municipal and government priorities include retaining residents,preventing skilled worker outflow,and meeting the evolving needs of urban populations.The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk.Using advanced geoinformatics,GIS techniques,and an expert knowledge base,the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize the Scenario-based Urban Environment Quality Index across 87,905 standardized unit areas.The methodology incorporates comprehensive criteria aligned with existing urban planning frameworks and includes demographic targeting to address the city’s heterogeneous population.Validation against expert evaluations demonstrates high accuracy and consistency,while dynamicmodeling capabilities facilitate monitoring the effects of planned urban development initiatives.This approach bridges a critical gap in urban planning by providing granular,data-driven insights that reflect residents’real needs and spatial inequalities.The tool greatly benefits municipal authorities by enabling evidence-based prioritization of interventions,fostering inclusive and sustainable urban growth,and enhancing transparency and participatory governance.Its implementation as a no-code/low-code QGIS plugin ensures wide accessibility and practical application in strategic urban development,marking a significant advancement in urban environment quality assessment science and practice.展开更多
We present a spatial analysis of Bitcoin-accepting merchants using BTC Map,a global crowdsourced dataset built on OpenStreetMap,to provide ground-level evidence on Bitcoin’s payment ecosystem.While prior research emp...We present a spatial analysis of Bitcoin-accepting merchants using BTC Map,a global crowdsourced dataset built on OpenStreetMap,to provide ground-level evidence on Bitcoin’s payment ecosystem.While prior research emphasizes macroeconomic drivers,our analysis of approximately 11,000 merchants shows that local adoption is more strongly shaped by community dynamics and sectoral niches.Acknowledging quality variance in crowdsourced data,we focus on verified regional clusters.We find a global concentration of adoption in the hospitality sector,localised clusters driven by grassroots initiatives rather than national policy and significant presence in alternative healthcare and IT services.These findings highlight the limits of top-down interventions such as El Salvador’s legal tender law and underscore the role of social networks in sustaining adoption.By contrasting spatial micro-level evidence with national studies,this work positions merchant data as a key lens for understanding Bitcoin’s evolving role as a medium of exchange.展开更多
Reconfigurable intelligent surface(RIS)is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic waves.In the prior literature,the...Reconfigurable intelligent surface(RIS)is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic waves.In the prior literature,the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are deployed.In this paper we investigate a general double-RIS assisted multiple-input multiple-output(MIMO)wireless communication system under spatially correlated non line-of-sight propagation channels,where the cooperation of the double RISs is also considered.The design objective is to maximize the achievable ergodic rate based on full statistical channel state information(CSI).Specifically,we firstly present a closedform asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical physics.Then a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled design.To further reduce the signal processing overhead and lower the complexity for practical realization,a common-phase scheme is proposed to design the double RISs.Simulation results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna arrays.And the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and complexity.Furthermore,the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.展开更多
Sandy cobble soil exhibits pronounced heterogeneity.The assessment of the uncertainty surrounding its properties is crucial for the analysis of settlement characteristics resulting from volume loss during shield tunne...Sandy cobble soil exhibits pronounced heterogeneity.The assessment of the uncertainty surrounding its properties is crucial for the analysis of settlement characteristics resulting from volume loss during shield tunnelling.In this study,a series of probabilistic analyses of surface and subsurface settlements was conducted considering the spatial variability of the friction angle and reference stiffness modulus,under different volumetric block proportions(Pv)and tunnel volume loss rates(ηt).The non-intrusive random finite difference method was used to investigate the probabilistic characteristics of maximum surface settlement,width of subsurface settlement trough,maximum subsurface settlement,and subsurface soil volume loss rate through Monte Carlo simulations.Additionally,a comparison between stochastic and deterministic analysis results is presented to underscore the significance of probabilistic analysis.Parametric analyses were subsequently conducted to investigate the impacts of the key input parameters in random fields on the settlement characteristics.The results indicate that scenarios with higher Pv or greaterηt result in a higher dispersion of stochastic analysis results.Neglecting the spatial variability of soil properties and relying solely on the mean values of material parameters for deterministic analysis may result in an underestimation of surface and subsurface settlements.From a probabilistic perspective,deterministic analysis alone may prove inadequate in accurately capturing the volumetric deformation mode of the soil above the tunnel crown,potentially affecting the prediction of subsurface settlement.展开更多
Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the vary...Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.展开更多
With China being gradually transformed into an open society where population can flow freely,it deserves more attention that interregional population flow will bring about the interactive growth of insurance.Based on ...With China being gradually transformed into an open society where population can flow freely,it deserves more attention that interregional population flow will bring about the interactive growth of insurance.Based on the traditional insurance growth theory,this paper focuses on the internal mechanism how interregional population flow can affect insurance growth,uses the provincial panel data from 2012 to 2015 to construct a flow spatial weighting matrix based on the interregional population flow scale,and sets up a spatial econometric model for empirical analysis.Results show that,if the population flow increases by 1 percentage point,the region's insurance industry will grow 0.0794 percentage points,and other regions'insurance will grow 0.184 percentage points,making the national insurance industry increase by 0.264 percentage points.,which is to say,the indirect effects of spatial knowledge spillover on insurance growth account for more than two thirds of the overall effects.This conclusion provides the policy enlightenment for promoting the interregional population flow,adjusting the product structure and marketing strategy in time by insurance companies,and promoting the balanced and coordinative development of the insurance industry in China.展开更多
Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of dat...Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.展开更多
[Objectives]To elucidate the spatial variation characteristics and fertility status of soil nutrients in small-scale mountain tea gardens and to inform precise fertilization and nutrient management practices in these ...[Objectives]To elucidate the spatial variation characteristics and fertility status of soil nutrients in small-scale mountain tea gardens and to inform precise fertilization and nutrient management practices in these tea gardens.[Methods]Based on soil nutrient data collected from 72 sampling points in the tea garden in 2021,which covers an area of approximately 2.4 km^(2),the spatial variation characteristics were analyzed using geostatistical methods.Spatial distribution maps of soil pH,total nitrogen,available phosphorus,and available potassium were generated employing the ordinary Kriging interpolation method in Surfer 23 software.Furthermore,a quantitative assessment of soil fertility was performed utilizing the fuzzy comprehensive evaluation method.[Results]The majority of the soil in the tea garden was acidic.The average values for pH,organic matter,total nitrogen,available phosphorus,and available potassium were 4.66,14.4 g/kg,0.9 g/kg,6.2 mg/kg,and 78.1 mg/kg,respectively.The pH exhibited the lowest coefficient of variation at 12.85%,indicating low variability.The coefficients of variation for organic matter,total nitrogen,and available potassium ranged from 31.94%to 49.88%,reflecting moderate variability.In contrast,the coefficient of variation for available phosphorus was 243.41%,indicating high variability.The distribution of soil pH and available phosphorus in the study area was relatively uniform.In contrast,total nitrogen content exhibited a spatial pattern characterized by higher concentrations in the western region and lower concentrations in the eastern region.Organic matter content displayed a spatial distribution pattern with lower values centrally and higher values along the periphery.The distribution of available potassium content was marked by several pronounced"elevations"and"depressions",with notably lower levels observed in the northeastern region of the garden.Total nitrogen and organic matter were the most significant contributors to the integrated fertility index(I_(IFI)),each with a weight value of 0.29,whereas pH had the lowest weight value of 0.14.The proportions of tea garden soils categorized under I_(IFI)grades I to V were 0.26%,69.55%,25.89%,4.30%,and 0.0022%,respectively.[Conclusions]It is recommended that the application of phosphorus fertilizer should be reduced in the study area,whereas the use of potassium fertilizer should be increased in the northeastern region.Additionally,the incorporation of organic and nitrogen fertilizers is advised to improve the soil s capacity for water and nutrient retention.展开更多
This study analyzes the spatial accessibility of key services in Caen,France,focusing on how different transport modes(car,bicycle,and public transit)influence access to essential services across the urban and suburba...This study analyzes the spatial accessibility of key services in Caen,France,focusing on how different transport modes(car,bicycle,and public transit)influence access to essential services across the urban and suburban landscape.Indeed,the introduction of traffic restrictions in towns with low emission zones encourages a detailed study,on a fine spatial scale,of the differences in accessibility between different modes of transport,for different services and for different journey times.Using spatial analysis techniques,we examine accessibility patterns in relation to services such as shops,healthcare,education,and tourism,highlighting significant disparities between transport modes.The findings reveal that car travel provides the highest accessibility across all service categories,particularly for healthcare and recreational services,while bicycle and public transit accessibility is more limited,especially in peripheral areas.A Principal Component Analysis(PCA)synthesizes the multimodal accessibility data,and hierarchical clustering identifies distinct patterns of accessibility using different transport modes across the city.The study further explores temporal trends in accessibility,showing how different modes perform over varying travel times.Based on these findings,we propose targeted policy interventions aimed at improving public transit,enhancing cycling infrastructure,decentralizing essential services,and promoting mixed-use urban development.Future research directions include examining socio-economic disparities,the impact of emerging mobility technologies,and the environmental implications of accessibility patterns.This research provides valuable insights for urban planners seeking to improve mobility equity and sustainability in urban areas.展开更多
Based on the data of NDVI and meteorological factors in Siziwang Banner from 2000 to 2021,the temporal and spatial variation characteristics of NDVI in the grassland of Siziwang Banner and its responses to climate cha...Based on the data of NDVI and meteorological factors in Siziwang Banner from 2000 to 2021,the temporal and spatial variation characteristics of NDVI in the grassland of Siziwang Banner and its responses to climate change were analyzed.The results show that the NDVI of grassland in Siziwang Banner tended to rise overall,with the average tendency rate of 0.05/10 a.The annual variation of NDVI was mainly driven by precipitation,and there was an extremely significant positive correlation between the two.During the growing season,temperature was positively correlated with NDVI in May,but then the correlation gradually turned negative.NDVI was generally positively correlated with precipitation,and there was a significant lag.展开更多
The High Mountain Asia(HMA)is a prominent global mountain system characterized by an average altitude exceeding 4,000 m,intricate topography,and significant spatial variability in climatic conditions.Despite its impor...The High Mountain Asia(HMA)is a prominent global mountain system characterized by an average altitude exceeding 4,000 m,intricate topography,and significant spatial variability in climatic conditions.Despite its importance,there has been a relative paucity of research focusing on the spatiotemporal variations of snow cover,key controlling factors,and variability within HMA sub-basins.This study aims to address this gap by extracting snow cover percentage(SCP)and snow cover days(SCD)data from MOD10A2 snow products,integrating these with precipitation(P)and temperature(T)data from ERA5.Our objective is to analyze the spatiotemporal distribution characteristics of snow cover and to use path analysis to elucidate the key climatic factors and spatial differences influencing snow cover changes.The findings indicate that,on a temporal scale,the overall SCP in HMA exhibited a declining trend from 2001 to 2021.Interannual variations in SCP across HMA sub-basins revealed a decreasing trend in the Pamir(PAM),Western Tibetan Plateau(WTS),Eastern Tibetan Plateau(ETS),Western Kunlun(WKL),Qilian Shan(QLS),and Himalaya(HDS)regions,while an increasing trend was observed in other areas.Spatially,22.97%of the HMA regions experienced an increase in SCD,primarily in the Western Himalaya(WHL),Central Himalaya(CHL),and Southeastern Xizang(SET)regions.Conversely,28.08%of the HMA regions showed a decrease in SCD,predominantly in the Eastern Himalaya(EHL),HDS,and WTS regions.Temperature(T)emerged as the primary influencing factor of SCD change in most HMA sub-basins.However,in the Eastern Kunlun(EKL)and WHL sub-basins,precipitation(P)was identified as the main driver of SCD change,affecting all elevation zones in these regions.Additionally,other climatic conditions can also impact snow cover beyond the primary controlling factor.展开更多
Antarctic krill(Euphausia superba),widely distributes around Antarctica,is a key species supporting the biodiversity of the Southern Ocean ecosystem.The Commission for the Conservation of Antarctic Marine Living Resou...Antarctic krill(Euphausia superba),widely distributes around Antarctica,is a key species supporting the biodiversity of the Southern Ocean ecosystem.The Commission for the Conservation of Antarctic Marine Living Resources(CCAMLR)has thus managed the krill fishery according to a precautionary way.Currently,CCAMLR is making effort to develop a refined krill fishery management approach based on more solid science,which requires accurate predictions of krill distribution.To address this need,this study investigated the effects of algorithm and spatial resolution on the performance of Antarctic krill distribution modelling.We integrated acoustic data from 4 surveys conducted in the waters adjacent to the Antarctic Peninsula with 11 environmental variables characterizing krill prey conditions,water mass properties,and seafloor topography.These data were processed at 4 spatial resolutions(5,10,15,and 20 km)to fit distribution models using 4 algorithms:Random Forests(RF),Generalized Additive Models(GAM),Extreme Gradient Boosting(XGBoost),and Artificial Neural Networks(ANN).Model performance was assessed and compared in terms of goodness-of-fit and predictive accuracy.The results showed that RF achieved the highest predictive performance at most resolutions,whereas GAM performed best at the coarsest resolution(20 km).XGBoost closely following RF in accuracy and demonstrated robustness as evidenced by the highly consistent partial dependence curves across resolutions.In contrast,ANN exhibited limitations with smaller sample sizes,resulting in comparatively poorer predictive performance.The analysis revealed a trade-off whereby reducing spatial resolution improved model fit and mitigated zero-inflation at the expense of fine-scale information and overall predictive accuracy.Ensemble models,integrating RF,GAM,and XGBoost,are proposed as potential balanced solutions to improve predictive stability,offering a more robust scientific basis for the refinement of krill management.展开更多
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal ...Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.展开更多
Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macro...Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.展开更多
Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.Thi...Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System(RHEAS).This system,with a high-resolution of 0.05°,effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index(SMDI)-based agricultural drought monitoring.The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022.The SMDI,also provides a comprehensive understanding of regional variations in drought severity(S),duration(D),and frequency(F),through S-D-F analysis to identify key drought hotspots across Senegal.Findings reveal a distinct north-south gradient in drought conditions,with the northern and central Senegal experiencing more frequent and severe droughts.The study highlights that Senegal experiences frequent short-duration droughts with high severity,resulting in extensive spatial impact.Addition ally,increasing trends in drought severity and duration suggest evolving climate change effects.These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productiv ity.Specifically,the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice,as well as cash crops like peanuts.The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies,ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.展开更多
Recent data suggest that vascular endothelial growth factor receptor inhibitor(VEGFRi)can enhance the anti-tumor activity of the anti-programmed cell death-1(anti-PD-1)antibody in colorectal cancer(CRC)with microsatel...Recent data suggest that vascular endothelial growth factor receptor inhibitor(VEGFRi)can enhance the anti-tumor activity of the anti-programmed cell death-1(anti-PD-1)antibody in colorectal cancer(CRC)with microsatellite stability(MSS).However,the comparison between this combination and standard third-line VEGFRi treatment is not performed,and reliable biomarkers are still lacking.We retrospectively enrolled MSS CRC patients receiving anti-PD-1 antibody plus VEGFRi(combination group,n=54)or VEGFRi alone(VEGFRi group,n=32),and their efficacy and safety were evaluated.We additionally examined the immune characteristics of the MSS CRC tumor microenvironment(TME)through single-cell and spatial transcriptomic data,and an MSS CRC immune cell-related signature(MCICRS)that can be used to predict the clinical outcomes of MSS CRC patients receiving immunotherapy was developed and validated in our in-house cohort.Compared with VEGFRi alone,the combination of anti-PD-1 antibody and VEGFRi exhibited a prolonged survival benefit(median progression-free survival:4.4 vs.2.0 months,P=0.0024;median overall survival:10.2 vs.5.2 months,P=0.0038)and a similar adverse event incidence.Through single-cell and spatial transcriptomic analysis,we determined ten MSS CRC-enriched immune cell types and their spatial distribution,including naive CD4+T,regulatory CD4+T,CD4+Th17,exhausted CD8+T,cytotoxic CD8+T,proliferated CD8+T,natural killer(NK)cells,plasma,and classical and intermediate monocytes.Based on a systemic meta-analysis and ten machine learning algorithms,we obtained MCICRS,an independent risk factor for the prognosis of MSS CRC patients.Further analyses demonstrated that the low-MCICRS group presented a higher immune cell infiltration and immune-related pathway activation,and hence a significant relation with the superior efficacy of pan-cancer immunotherapy.More importantly,the predictive value of MCICRS in MSS CRC patients receiving immunotherapy was also validated with an in-house cohort.Anti-PD-1 antibody combined with VEGFRi presented an improved clinical benefit in MSS CRC with manageable toxicity.MCICRS could serve as a robust and promising tool to predict clinical outcomes for individual MSS CRC patients receiving immunotherapy.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supported by the National Key R&D Program of China(No.2022YFC3003502).
文摘The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.
基金funded by theMinistry of Science and Higher Education of Russia,R&D project number FEFS-2026-0003.
文摘Urban environmental quality research is crucial,as cities become competitive centers concentrating human talent,industrial activity,and financial resources,contributing significantly to national economies.Municipal and government priorities include retaining residents,preventing skilled worker outflow,and meeting the evolving needs of urban populations.The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk.Using advanced geoinformatics,GIS techniques,and an expert knowledge base,the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize the Scenario-based Urban Environment Quality Index across 87,905 standardized unit areas.The methodology incorporates comprehensive criteria aligned with existing urban planning frameworks and includes demographic targeting to address the city’s heterogeneous population.Validation against expert evaluations demonstrates high accuracy and consistency,while dynamicmodeling capabilities facilitate monitoring the effects of planned urban development initiatives.This approach bridges a critical gap in urban planning by providing granular,data-driven insights that reflect residents’real needs and spatial inequalities.The tool greatly benefits municipal authorities by enabling evidence-based prioritization of interventions,fostering inclusive and sustainable urban growth,and enhancing transparency and participatory governance.Its implementation as a no-code/low-code QGIS plugin ensures wide accessibility and practical application in strategic urban development,marking a significant advancement in urban environment quality assessment science and practice.
基金funded in part by the Rapid Response Fund of the AI for Collective Intelligence(AI4CI)hub,a UKRI National AI Research Hub(grant ref EP/Y028392/1).
文摘We present a spatial analysis of Bitcoin-accepting merchants using BTC Map,a global crowdsourced dataset built on OpenStreetMap,to provide ground-level evidence on Bitcoin’s payment ecosystem.While prior research emphasizes macroeconomic drivers,our analysis of approximately 11,000 merchants shows that local adoption is more strongly shaped by community dynamics and sectoral niches.Acknowledging quality variance in crowdsourced data,we focus on verified regional clusters.We find a global concentration of adoption in the hospitality sector,localised clusters driven by grassroots initiatives rather than national policy and significant presence in alternative healthcare and IT services.These findings highlight the limits of top-down interventions such as El Salvador’s legal tender law and underscore the role of social networks in sustaining adoption.By contrasting spatial micro-level evidence with national studies,this work positions merchant data as a key lens for understanding Bitcoin’s evolving role as a medium of exchange.
基金supported in part by the Xi’an Jiaotong-Liverpool University(XJTLU)Research Development Fund(2024–2027)under Grant RDF-23-02-010supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515110732+5 种基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62071247supported in part by the Science and Technology Development Fund,Macao,China SAR under Grants 0087/2022/AFJ and 001/2024/SKLin part by the National Natural Science Foundation of China under Grant 62261160650in part by the Research Committee of University of Macao,Macao SAR,China under Grants MYRG-GRG2023-00116-FST-UMDF and MYRG2020-00095-FSTsupported in part by the NSFC under Grant 62261160576 and 62301148in part by the Fundamental Research Funds for the Central Universities under Grant 2242023K5003.
文摘Reconfigurable intelligent surface(RIS)is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic waves.In the prior literature,the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are deployed.In this paper we investigate a general double-RIS assisted multiple-input multiple-output(MIMO)wireless communication system under spatially correlated non line-of-sight propagation channels,where the cooperation of the double RISs is also considered.The design objective is to maximize the achievable ergodic rate based on full statistical channel state information(CSI).Specifically,we firstly present a closedform asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical physics.Then a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled design.To further reduce the signal processing overhead and lower the complexity for practical realization,a common-phase scheme is proposed to design the double RISs.Simulation results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna arrays.And the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and complexity.Furthermore,the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.
基金supported by the Natural Science Foundation of Beijing Municipality(No.8222004),Chinathe National Natural Science Foundation of China(No.51978019)+3 种基金the Natural Science Foundation of Henan Province(No.252300420445),Chinathe Doctoral Research Initiation Fund of Henan University of Science and Technology(No.4007/13480062),Chinathe Henan Postdoctoral Foundation(No.13554005),Chinathe Joint Fund of Science and Technology R&D Program of Henan Province(No.232103810082),China。
文摘Sandy cobble soil exhibits pronounced heterogeneity.The assessment of the uncertainty surrounding its properties is crucial for the analysis of settlement characteristics resulting from volume loss during shield tunnelling.In this study,a series of probabilistic analyses of surface and subsurface settlements was conducted considering the spatial variability of the friction angle and reference stiffness modulus,under different volumetric block proportions(Pv)and tunnel volume loss rates(ηt).The non-intrusive random finite difference method was used to investigate the probabilistic characteristics of maximum surface settlement,width of subsurface settlement trough,maximum subsurface settlement,and subsurface soil volume loss rate through Monte Carlo simulations.Additionally,a comparison between stochastic and deterministic analysis results is presented to underscore the significance of probabilistic analysis.Parametric analyses were subsequently conducted to investigate the impacts of the key input parameters in random fields on the settlement characteristics.The results indicate that scenarios with higher Pv or greaterηt result in a higher dispersion of stochastic analysis results.Neglecting the spatial variability of soil properties and relying solely on the mean values of material parameters for deterministic analysis may result in an underestimation of surface and subsurface settlements.From a probabilistic perspective,deterministic analysis alone may prove inadequate in accurately capturing the volumetric deformation mode of the soil above the tunnel crown,potentially affecting the prediction of subsurface settlement.
基金supported by the Collaborative Tackling Project of the Yangtze River Delta SciTech Innovation Community(Nos.2024CSJGG01503,2024CSJGG01500)Guangxi Key Research and Development Program(No.AB24010317)Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics(Jiangxi Police College)(No.2025JXJYKFJJ002).
文摘Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.
基金National Natural Science Foundation of China"Research on the transmission mechanism,welfare effect and path optimization of long-term care insurance embedded in the rural medical and health service system(No.:72274107)""Special Funds for Shandong Taishan Scholars Project(No.:tsqn202312226)"+1 种基金"Shandong Province Youth Entrepreneurship Science and Technology Support Plan for Higher Education Institutions(No.2022RW044)"Shandong University of Finance and Economics 2024 Experimental Teaching Reform Research Project(zb202401):"Research on the Construction of Health and Medical Big Data Experimental Teaching Courses","Typical Cases of Embedding Long-term Care Insurance into the Medical and Health Service System to Promote Chinese Modernization","Digital Intelligence Empowerment Cross-integration-Health and Medical Big Data"Course Construction,"Insurance"Smart Course Construction,Research on the Education Model of Modern Financial and Economic Colleges under the"One Construction and Three Chains"Integration Path,"12456"Postgraduate Joint Training Base Quality Improvement System Construction,and the Ministry of Education Degree Center Theme Case"Digital Empowerment of Healthy China Construction:Innovative Practice of Social Security Robots"Interim Results.
文摘With China being gradually transformed into an open society where population can flow freely,it deserves more attention that interregional population flow will bring about the interactive growth of insurance.Based on the traditional insurance growth theory,this paper focuses on the internal mechanism how interregional population flow can affect insurance growth,uses the provincial panel data from 2012 to 2015 to construct a flow spatial weighting matrix based on the interregional population flow scale,and sets up a spatial econometric model for empirical analysis.Results show that,if the population flow increases by 1 percentage point,the region's insurance industry will grow 0.0794 percentage points,and other regions'insurance will grow 0.184 percentage points,making the national insurance industry increase by 0.264 percentage points.,which is to say,the indirect effects of spatial knowledge spillover on insurance growth account for more than two thirds of the overall effects.This conclusion provides the policy enlightenment for promoting the interregional population flow,adjusting the product structure and marketing strategy in time by insurance companies,and promoting the balanced and coordinative development of the insurance industry in China.
基金supported by Mission No. 9 "Geological Environment and Hazards" (2019QZKK0900) of "The Second Tibetan Plateau Scientific Expedition and Research" projectNational Natural Science Foundation of China (No.42101087)
文摘Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.
基金Supported by Chongqing Municipal Key Projects for Technological Innovation and Application Development(cstc2019jscx-gksbX0092).
文摘[Objectives]To elucidate the spatial variation characteristics and fertility status of soil nutrients in small-scale mountain tea gardens and to inform precise fertilization and nutrient management practices in these tea gardens.[Methods]Based on soil nutrient data collected from 72 sampling points in the tea garden in 2021,which covers an area of approximately 2.4 km^(2),the spatial variation characteristics were analyzed using geostatistical methods.Spatial distribution maps of soil pH,total nitrogen,available phosphorus,and available potassium were generated employing the ordinary Kriging interpolation method in Surfer 23 software.Furthermore,a quantitative assessment of soil fertility was performed utilizing the fuzzy comprehensive evaluation method.[Results]The majority of the soil in the tea garden was acidic.The average values for pH,organic matter,total nitrogen,available phosphorus,and available potassium were 4.66,14.4 g/kg,0.9 g/kg,6.2 mg/kg,and 78.1 mg/kg,respectively.The pH exhibited the lowest coefficient of variation at 12.85%,indicating low variability.The coefficients of variation for organic matter,total nitrogen,and available potassium ranged from 31.94%to 49.88%,reflecting moderate variability.In contrast,the coefficient of variation for available phosphorus was 243.41%,indicating high variability.The distribution of soil pH and available phosphorus in the study area was relatively uniform.In contrast,total nitrogen content exhibited a spatial pattern characterized by higher concentrations in the western region and lower concentrations in the eastern region.Organic matter content displayed a spatial distribution pattern with lower values centrally and higher values along the periphery.The distribution of available potassium content was marked by several pronounced"elevations"and"depressions",with notably lower levels observed in the northeastern region of the garden.Total nitrogen and organic matter were the most significant contributors to the integrated fertility index(I_(IFI)),each with a weight value of 0.29,whereas pH had the lowest weight value of 0.14.The proportions of tea garden soils categorized under I_(IFI)grades I to V were 0.26%,69.55%,25.89%,4.30%,and 0.0022%,respectively.[Conclusions]It is recommended that the application of phosphorus fertilizer should be reduced in the study area,whereas the use of potassium fertilizer should be increased in the northeastern region.Additionally,the incorporation of organic and nitrogen fertilizers is advised to improve the soil s capacity for water and nutrient retention.
文摘This study analyzes the spatial accessibility of key services in Caen,France,focusing on how different transport modes(car,bicycle,and public transit)influence access to essential services across the urban and suburban landscape.Indeed,the introduction of traffic restrictions in towns with low emission zones encourages a detailed study,on a fine spatial scale,of the differences in accessibility between different modes of transport,for different services and for different journey times.Using spatial analysis techniques,we examine accessibility patterns in relation to services such as shops,healthcare,education,and tourism,highlighting significant disparities between transport modes.The findings reveal that car travel provides the highest accessibility across all service categories,particularly for healthcare and recreational services,while bicycle and public transit accessibility is more limited,especially in peripheral areas.A Principal Component Analysis(PCA)synthesizes the multimodal accessibility data,and hierarchical clustering identifies distinct patterns of accessibility using different transport modes across the city.The study further explores temporal trends in accessibility,showing how different modes perform over varying travel times.Based on these findings,we propose targeted policy interventions aimed at improving public transit,enhancing cycling infrastructure,decentralizing essential services,and promoting mixed-use urban development.Future research directions include examining socio-economic disparities,the impact of emerging mobility technologies,and the environmental implications of accessibility patterns.This research provides valuable insights for urban planners seeking to improve mobility equity and sustainability in urban areas.
文摘Based on the data of NDVI and meteorological factors in Siziwang Banner from 2000 to 2021,the temporal and spatial variation characteristics of NDVI in the grassland of Siziwang Banner and its responses to climate change were analyzed.The results show that the NDVI of grassland in Siziwang Banner tended to rise overall,with the average tendency rate of 0.05/10 a.The annual variation of NDVI was mainly driven by precipitation,and there was an extremely significant positive correlation between the two.During the growing season,temperature was positively correlated with NDVI in May,but then the correlation gradually turned negative.NDVI was generally positively correlated with precipitation,and there was a significant lag.
基金supported by Tianchi talent project(Granted No.51052401507)。
文摘The High Mountain Asia(HMA)is a prominent global mountain system characterized by an average altitude exceeding 4,000 m,intricate topography,and significant spatial variability in climatic conditions.Despite its importance,there has been a relative paucity of research focusing on the spatiotemporal variations of snow cover,key controlling factors,and variability within HMA sub-basins.This study aims to address this gap by extracting snow cover percentage(SCP)and snow cover days(SCD)data from MOD10A2 snow products,integrating these with precipitation(P)and temperature(T)data from ERA5.Our objective is to analyze the spatiotemporal distribution characteristics of snow cover and to use path analysis to elucidate the key climatic factors and spatial differences influencing snow cover changes.The findings indicate that,on a temporal scale,the overall SCP in HMA exhibited a declining trend from 2001 to 2021.Interannual variations in SCP across HMA sub-basins revealed a decreasing trend in the Pamir(PAM),Western Tibetan Plateau(WTS),Eastern Tibetan Plateau(ETS),Western Kunlun(WKL),Qilian Shan(QLS),and Himalaya(HDS)regions,while an increasing trend was observed in other areas.Spatially,22.97%of the HMA regions experienced an increase in SCD,primarily in the Western Himalaya(WHL),Central Himalaya(CHL),and Southeastern Xizang(SET)regions.Conversely,28.08%of the HMA regions showed a decrease in SCD,predominantly in the Eastern Himalaya(EHL),HDS,and WTS regions.Temperature(T)emerged as the primary influencing factor of SCD change in most HMA sub-basins.However,in the Eastern Kunlun(EKL)and WHL sub-basins,precipitation(P)was identified as the main driver of SCD change,affecting all elevation zones in these regions.Additionally,other climatic conditions can also impact snow cover beyond the primary controlling factor.
基金funded by the National Key R&D Program of China(Grant no.2022YFC2807504)the Marine S&T Fund of Shandong Province for Qingdao Marine Science and Technology Center(Grant no.2022QNLM030002-1)the Central Public-interest Scientific Institution Basal Research(Grant no.2023TD02).
文摘Antarctic krill(Euphausia superba),widely distributes around Antarctica,is a key species supporting the biodiversity of the Southern Ocean ecosystem.The Commission for the Conservation of Antarctic Marine Living Resources(CCAMLR)has thus managed the krill fishery according to a precautionary way.Currently,CCAMLR is making effort to develop a refined krill fishery management approach based on more solid science,which requires accurate predictions of krill distribution.To address this need,this study investigated the effects of algorithm and spatial resolution on the performance of Antarctic krill distribution modelling.We integrated acoustic data from 4 surveys conducted in the waters adjacent to the Antarctic Peninsula with 11 environmental variables characterizing krill prey conditions,water mass properties,and seafloor topography.These data were processed at 4 spatial resolutions(5,10,15,and 20 km)to fit distribution models using 4 algorithms:Random Forests(RF),Generalized Additive Models(GAM),Extreme Gradient Boosting(XGBoost),and Artificial Neural Networks(ANN).Model performance was assessed and compared in terms of goodness-of-fit and predictive accuracy.The results showed that RF achieved the highest predictive performance at most resolutions,whereas GAM performed best at the coarsest resolution(20 km).XGBoost closely following RF in accuracy and demonstrated robustness as evidenced by the highly consistent partial dependence curves across resolutions.In contrast,ANN exhibited limitations with smaller sample sizes,resulting in comparatively poorer predictive performance.The analysis revealed a trade-off whereby reducing spatial resolution improved model fit and mitigated zero-inflation at the expense of fine-scale information and overall predictive accuracy.Ensemble models,integrating RF,GAM,and XGBoost,are proposed as potential balanced solutions to improve predictive stability,offering a more robust scientific basis for the refinement of krill management.
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
基金supported by the National Natural Science Foundation of China(Grant Nos.12090052,U24A2014,and 12325405).
文摘Spatial transcriptomics technology provides novel insights into the spatial organization of gene expression during embryonic development.In this study,we propose a method that integrates analysis across both temporal and spatial dimensions to investigate spatial transcriptomics data from mouse embryos at different developmental stages.We quantified the spatial expression pattern of each gene at various stages by calculating its Moran’s I.Furthermore,by employing time-series clustering to identify dynamic co-expression modules,we identified several developmentally stage-specific regulatory gene modules.A key finding was the presence of distinct,stage-specific gene network modules across different developmental periods:Early modules focused on morphogenesis,mid-stage on organ development,and late-stage on neural and tissue maturation.Functional enrichment analysis further confirmed the core biological functions of each module.The dynamic,spatially-resolved gene expression model constructed in this study not only provides new biological insights into the programmed spatiotemporal reorganization of gene regulatory networks during embryonic development but also presents an effective approach for analyzing complex spatiotemporal omics data.This work provides a new perspective for understanding developmental biology,regenerative medicine,and related fields.
基金supported by Qingdao Key Medical and Health Discipline ProjectThe Intramural Research Program of the Affiliated Hospital of Qingdao University,No. 4910Qingdao West Coast New Area Science and Technology Project,No. 2020-55 (all to SW)。
文摘Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.
基金supported by the NASA(Grant No.80NSSC21K0403)USAID Kansas State University subcontract KSU-A20-0163-S035 with Michigan State University.
文摘Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System(RHEAS).This system,with a high-resolution of 0.05°,effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index(SMDI)-based agricultural drought monitoring.The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022.The SMDI,also provides a comprehensive understanding of regional variations in drought severity(S),duration(D),and frequency(F),through S-D-F analysis to identify key drought hotspots across Senegal.Findings reveal a distinct north-south gradient in drought conditions,with the northern and central Senegal experiencing more frequent and severe droughts.The study highlights that Senegal experiences frequent short-duration droughts with high severity,resulting in extensive spatial impact.Addition ally,increasing trends in drought severity and duration suggest evolving climate change effects.These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productiv ity.Specifically,the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice,as well as cash crops like peanuts.The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies,ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.
基金supported by the National Natural Science Foundation of China(No.81972012).
文摘Recent data suggest that vascular endothelial growth factor receptor inhibitor(VEGFRi)can enhance the anti-tumor activity of the anti-programmed cell death-1(anti-PD-1)antibody in colorectal cancer(CRC)with microsatellite stability(MSS).However,the comparison between this combination and standard third-line VEGFRi treatment is not performed,and reliable biomarkers are still lacking.We retrospectively enrolled MSS CRC patients receiving anti-PD-1 antibody plus VEGFRi(combination group,n=54)or VEGFRi alone(VEGFRi group,n=32),and their efficacy and safety were evaluated.We additionally examined the immune characteristics of the MSS CRC tumor microenvironment(TME)through single-cell and spatial transcriptomic data,and an MSS CRC immune cell-related signature(MCICRS)that can be used to predict the clinical outcomes of MSS CRC patients receiving immunotherapy was developed and validated in our in-house cohort.Compared with VEGFRi alone,the combination of anti-PD-1 antibody and VEGFRi exhibited a prolonged survival benefit(median progression-free survival:4.4 vs.2.0 months,P=0.0024;median overall survival:10.2 vs.5.2 months,P=0.0038)and a similar adverse event incidence.Through single-cell and spatial transcriptomic analysis,we determined ten MSS CRC-enriched immune cell types and their spatial distribution,including naive CD4+T,regulatory CD4+T,CD4+Th17,exhausted CD8+T,cytotoxic CD8+T,proliferated CD8+T,natural killer(NK)cells,plasma,and classical and intermediate monocytes.Based on a systemic meta-analysis and ten machine learning algorithms,we obtained MCICRS,an independent risk factor for the prognosis of MSS CRC patients.Further analyses demonstrated that the low-MCICRS group presented a higher immune cell infiltration and immune-related pathway activation,and hence a significant relation with the superior efficacy of pan-cancer immunotherapy.More importantly,the predictive value of MCICRS in MSS CRC patients receiving immunotherapy was also validated with an in-house cohort.Anti-PD-1 antibody combined with VEGFRi presented an improved clinical benefit in MSS CRC with manageable toxicity.MCICRS could serve as a robust and promising tool to predict clinical outcomes for individual MSS CRC patients receiving immunotherapy.
