Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in So...This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.展开更多
As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limi...As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.展开更多
Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing...Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.展开更多
Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing...Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.展开更多
Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode...Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.展开更多
Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to...Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.展开更多
Exploring the spatial evolution patterns of land use in creative urban tourism complexes provides theoretical and decision-making support to foster creative tourism projects.This study focuses on the Hangzhou Leisure ...Exploring the spatial evolution patterns of land use in creative urban tourism complexes provides theoretical and decision-making support to foster creative tourism projects.This study focuses on the Hangzhou Leisure Expo Garden as a case study,utilizing a land use change index model to analyze the spatial evolution characteristics and dynamic processes of creative urban tourism complexes,as well as to explore their spatial differentiation mechanisms.The analysis indicates that Hangzhou Leisure Expo Garden,initially a derelict industrial area dominated by production and residential land use,has evolved into a creative urban tourism complex with tourism comprehensive service land at its core,going through the pattern evolution processes of“constrained sprawl,”“intensive expansion,”and“random integration.”From the perspective of tourism human-land relationships,the formation of land use evolution patterns in creative urban tourism complexes results from various stakeholders(government,tourism enterprises,residents,tourists,etc.),as humanistic factors,continuously adapting to specific urban spaces,which are considered as geographical elements and have locational advantages and are oriented towards economic and social values.Based on the acquisition of stakeholder interests,the transformation of resource-disadvantaged areas into tourism advantage areas is facilitated,thereby achieving the re-creation of tourism creative space and promoting intensive spatial growth.展开更多
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
Strong sensitivity of satellite microwave remote sensing to the change of surface dielectric properties,as well as the insensitivity to air pollution and solar illumination effects,makes it very suitable for monitorin...Strong sensitivity of satellite microwave remote sensing to the change of surface dielectric properties,as well as the insensitivity to air pollution and solar illumination effects,makes it very suitable for monitoring freeze-thaw conditions.The freeze-thaw cycle changes in the Qinghai-Xizang Plateau have an important impact on the ecological environment and infrastructure.Based on the Scanning Multi-channel Microwave Radiometer(SMMR)and other sensors of microwave satellite,the freeze-thaw cycle data of permafrost in the Qinghai-Xizang Plateau in the past 40 years from 1981 to 2020 was obtained.The changes of soil freeze-thaw conditions in different seasons of 2020 and in the same season of 1990,2000,2010 and 2020 were compared,and the annual variation trend of soil freeze-thaw area in the four years was analyzed.Further,the linear regression analysis was carried out on the duration of soil freezing/thawing/transition and the interannual variation trend under different area conditions from 1981 to 2020.The results show that the freeze-thaw changes in different years are similar.In winter,it is mainly frozen for about 110 days.Spring and autumn are transitional periods,lasting for 170 days.In summer,it is mainly thawed for about 80 days.From 1981 to 2020,the freezing period and the average freezing area of the Qinghai-Xizang Plateau decreased at a rate of 0.22 days and 1986 km^(2) per year,respectively,while the thawing period and the average thawing area increased at a rate of 0.07 days and 3187 km^(2) per year,respectively.The research results provide important theoretical support for the ecological environment and permafrost protection of the Qinghai-Xizang Plateau.展开更多
Rural industrial development and ecological civilization transformation are crucial to China's comprehensive advancement of rural revitalization. However, many regions still face the issue of a conflict between ec...Rural industrial development and ecological civilization transformation are crucial to China's comprehensive advancement of rural revitalization. However, many regions still face the issue of a conflict between economic development and ecological protection. Symbiosis theory provides a new perspective for understanding the interactive relationship of rural industry and ecology(RIE). Jiangxi Province, one of China's first national pilot zones for ecological conservation, exemplifies rural areas' typical challenges in balancing industrial development and ecological protection, and has been selected as the study area. By integrating the characteristics of RIE with symbiosis theory, a comprehensive RIE assessment framework was constructed. The comprehensive model, spatial autocorrelation method, and symbiosis theory model were employed to address the spatio-temporal evolution characteristics of RIE, reveal the symbiotic relationship(SR) and the symbiosis types of RIE, and explore the path of symbiotic development between RIE. Results indicated that:(1) Since 2015, RIE has shown an upward trend, with regional differences in ecological development levels gradually shrinking. Significant spatial correlation and agglomeration characteristics exist, but a coordinated regional development pattern has not yet emerged.(2) Overall, the symbiosis degree(SD) between RIE showed a positive trend with narrowing gaps, the symbiosis coefficient(SC) of industry to ecology converged to 0.5 under a positive asymmetric mutualism(PAM) mode, suggesting that their relationship tended to be coordinated. Specifically, rural ecology grew increasingly influential on industry in most counties.(3) Rural areas were classified into different types led by industry-dominated PAM, and various optimization paths were proposed. Future efforts should promote the equalization of the interaction forces between RIE according to local conditions.展开更多
Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroim...Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.展开更多
The fluidity of coal-water slurry(CWS)is crucial for various industrial applications such as long-distance transportation,gasification,and combustion.However,there is currently a lack of rapid and accurate detection m...The fluidity of coal-water slurry(CWS)is crucial for various industrial applications such as long-distance transportation,gasification,and combustion.However,there is currently a lack of rapid and accurate detection methods for assessing CWS fluidity.This paper proposed a method for analyzing the fluidity using videos of CWS dripping processes.By integrating the temporal and spatial features of each frame in the video,a multi-cascade classifier for CWS fluidity is established.The classifier distinguishes between four levels(A,B,C,and D)based on the quality of fluidity.The preliminary classification of A and D is achieved through feature engineering and the XGBoost algorithm.Subsequently,convolutional neural networks(CNN)and long short-term memory(LSTM)are utilized to further differentiate between the B and C categories which are prone to confusion.Finally,through detailed comparative experiments,the paper demonstrates the step-by-step design process of the proposed method and the superiority of the final solution.The proposed method achieves an accuracy rate of over 90%in determining the fluidity of CWS,serving as a technical reference for future industrial applications.展开更多
In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot...In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot spot analysis,and Geo-Detector approach,to measure and describe the spatial and temporal evolution patterns of land border tourism efficiency and its influencing factors.The findings reveal that the Dai autonomous prefecture of Xishuangbanna has the highest border tourism efficiency of 1.6207,while Ngari prefecture has the lowest tourism efficiency with a value of only 0.0365 at the prefecture level during the period 2010-2019.The southwest and northwest regions of China are high-and low-level agglomeration areas respectively,indicating varying levels of border tourism development.Additionally,the study identifies an upward trend in China’s border tourism efficiency from 2010-2019.The southwest region emerges as a hotspot and the most active region,while the northwest and northeast regions are considered cold spots with ample room for improvement.Furthermore,the density of transportation facilities,national vulnerability,cultural proximity,the number of border ports,and market opportunity are crucial factors influencing the spatial and temporal pattern of border tourism efficiency in China.展开更多
Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanne...Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.展开更多
Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingda...Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work u...False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.展开更多
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
文摘This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.
基金supported by National Natural Science Foundation of China(Nos.62477026,62177029,61807020)Humanities and Social Sciences Research Program of the Ministry of Education of China(No.23YJAZH047)the Startup Foundation for Introducing Talent of Nanjing University of Posts and Communications under Grant NY222034.
文摘As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.
基金supported by the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y02)Yunnan Key Laboratory of Plateau Geographic Processes and Environmental Changes,Faculty of Geography,Yunnan Normal University(PGPEC2304)China Scholarship Council。
文摘Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.
基金National Natural Science Foundation of China,No.42230106,No.42171250State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04。
文摘Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.
基金support for this work was supported by Key Lab of Intelligent and Green Flexographic Printing under Grant ZBKT202301.
文摘Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.
基金supported by The Henan Province Science and Technology Research Project(242102211046)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25A520039)+1 种基金theNatural Science Foundation project of Zhongyuan Institute of Technology(K2025YB011)the Zhongyuan University of Technology Graduate Education and Teaching Reform Research Project(JG202424).
文摘Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.
文摘Exploring the spatial evolution patterns of land use in creative urban tourism complexes provides theoretical and decision-making support to foster creative tourism projects.This study focuses on the Hangzhou Leisure Expo Garden as a case study,utilizing a land use change index model to analyze the spatial evolution characteristics and dynamic processes of creative urban tourism complexes,as well as to explore their spatial differentiation mechanisms.The analysis indicates that Hangzhou Leisure Expo Garden,initially a derelict industrial area dominated by production and residential land use,has evolved into a creative urban tourism complex with tourism comprehensive service land at its core,going through the pattern evolution processes of“constrained sprawl,”“intensive expansion,”and“random integration.”From the perspective of tourism human-land relationships,the formation of land use evolution patterns in creative urban tourism complexes results from various stakeholders(government,tourism enterprises,residents,tourists,etc.),as humanistic factors,continuously adapting to specific urban spaces,which are considered as geographical elements and have locational advantages and are oriented towards economic and social values.Based on the acquisition of stakeholder interests,the transformation of resource-disadvantaged areas into tourism advantage areas is facilitated,thereby achieving the re-creation of tourism creative space and promoting intensive spatial growth.
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
基金National Natural Science foundation of China(No.42271432)Foundation of Shanxi Vocational University of Engineering Science and Technology(No.KJ 202426).
文摘Strong sensitivity of satellite microwave remote sensing to the change of surface dielectric properties,as well as the insensitivity to air pollution and solar illumination effects,makes it very suitable for monitoring freeze-thaw conditions.The freeze-thaw cycle changes in the Qinghai-Xizang Plateau have an important impact on the ecological environment and infrastructure.Based on the Scanning Multi-channel Microwave Radiometer(SMMR)and other sensors of microwave satellite,the freeze-thaw cycle data of permafrost in the Qinghai-Xizang Plateau in the past 40 years from 1981 to 2020 was obtained.The changes of soil freeze-thaw conditions in different seasons of 2020 and in the same season of 1990,2000,2010 and 2020 were compared,and the annual variation trend of soil freeze-thaw area in the four years was analyzed.Further,the linear regression analysis was carried out on the duration of soil freezing/thawing/transition and the interannual variation trend under different area conditions from 1981 to 2020.The results show that the freeze-thaw changes in different years are similar.In winter,it is mainly frozen for about 110 days.Spring and autumn are transitional periods,lasting for 170 days.In summer,it is mainly thawed for about 80 days.From 1981 to 2020,the freezing period and the average freezing area of the Qinghai-Xizang Plateau decreased at a rate of 0.22 days and 1986 km^(2) per year,respectively,while the thawing period and the average thawing area increased at a rate of 0.07 days and 3187 km^(2) per year,respectively.The research results provide important theoretical support for the ecological environment and permafrost protection of the Qinghai-Xizang Plateau.
基金supported by the National Natural Science Foundation of China (No.42361050,42201232)Humanities and Social Sciences Research Project of Jiangxi Colleges and Universities (No.JC24211)+1 种基金Science and Technology Research Project of Jiangxi Provincial Department of Education (No.GJJ2200553)Jiangxi provincial Social Science Foundation of China (No.23JL11)。
文摘Rural industrial development and ecological civilization transformation are crucial to China's comprehensive advancement of rural revitalization. However, many regions still face the issue of a conflict between economic development and ecological protection. Symbiosis theory provides a new perspective for understanding the interactive relationship of rural industry and ecology(RIE). Jiangxi Province, one of China's first national pilot zones for ecological conservation, exemplifies rural areas' typical challenges in balancing industrial development and ecological protection, and has been selected as the study area. By integrating the characteristics of RIE with symbiosis theory, a comprehensive RIE assessment framework was constructed. The comprehensive model, spatial autocorrelation method, and symbiosis theory model were employed to address the spatio-temporal evolution characteristics of RIE, reveal the symbiotic relationship(SR) and the symbiosis types of RIE, and explore the path of symbiotic development between RIE. Results indicated that:(1) Since 2015, RIE has shown an upward trend, with regional differences in ecological development levels gradually shrinking. Significant spatial correlation and agglomeration characteristics exist, but a coordinated regional development pattern has not yet emerged.(2) Overall, the symbiosis degree(SD) between RIE showed a positive trend with narrowing gaps, the symbiosis coefficient(SC) of industry to ecology converged to 0.5 under a positive asymmetric mutualism(PAM) mode, suggesting that their relationship tended to be coordinated. Specifically, rural ecology grew increasingly influential on industry in most counties.(3) Rural areas were classified into different types led by industry-dominated PAM, and various optimization paths were proposed. Future efforts should promote the equalization of the interaction forces between RIE according to local conditions.
文摘Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.
基金supported by the Youth Fund of the National Natural Science Foundation of China(No.52304311)the National Natural Science Foundation of China(No.52274282)the Postdoctoral Fellowship Program of CPSF(No.GZC20233016)。
文摘The fluidity of coal-water slurry(CWS)is crucial for various industrial applications such as long-distance transportation,gasification,and combustion.However,there is currently a lack of rapid and accurate detection methods for assessing CWS fluidity.This paper proposed a method for analyzing the fluidity using videos of CWS dripping processes.By integrating the temporal and spatial features of each frame in the video,a multi-cascade classifier for CWS fluidity is established.The classifier distinguishes between four levels(A,B,C,and D)based on the quality of fluidity.The preliminary classification of A and D is achieved through feature engineering and the XGBoost algorithm.Subsequently,convolutional neural networks(CNN)and long short-term memory(LSTM)are utilized to further differentiate between the B and C categories which are prone to confusion.Finally,through detailed comparative experiments,the paper demonstrates the step-by-step design process of the proposed method and the superiority of the final solution.The proposed method achieves an accuracy rate of over 90%in determining the fluidity of CWS,serving as a technical reference for future industrial applications.
基金National Natural Science Foundation of China,No.42201311Natural Science Foundation of Shandong Province,No.ZR2022QD132+1 种基金Fundamental Research Funds for the Central Universities,No.202013012Rural Revitalization Project of Ocean University of China,No.ZX2024007。
文摘In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot spot analysis,and Geo-Detector approach,to measure and describe the spatial and temporal evolution patterns of land border tourism efficiency and its influencing factors.The findings reveal that the Dai autonomous prefecture of Xishuangbanna has the highest border tourism efficiency of 1.6207,while Ngari prefecture has the lowest tourism efficiency with a value of only 0.0365 at the prefecture level during the period 2010-2019.The southwest and northwest regions of China are high-and low-level agglomeration areas respectively,indicating varying levels of border tourism development.Additionally,the study identifies an upward trend in China’s border tourism efficiency from 2010-2019.The southwest region emerges as a hotspot and the most active region,while the northwest and northeast regions are considered cold spots with ample room for improvement.Furthermore,the density of transportation facilities,national vulnerability,cultural proximity,the number of border ports,and market opportunity are crucial factors influencing the spatial and temporal pattern of border tourism efficiency in China.
基金the National Natural Science Foundation of China(NNSFC)(Grant Nos.72001213 and 72301292)the National Social Science Fund of China(Grant No.19BGL297)the Basic Research Program of Natural Science in Shaanxi Province(Grant No.2021JQ-369).
文摘Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.
基金supported by the Chinese Field Epidemiology Training Program,the Research and Development of Standards and Standardization of Nomenclature in the Field of Public Health-Research Project on the Development of the Disciplines of Public Health and Preventive Medicine[242402]the Shandong Medical and Health Science and Technology Development Plan[202112050731].
文摘Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
基金supported in part by the Research Fund of Guangxi Key Lab of Multi-Source Information Mining&Security(MIMS21-M-02).
文摘False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.
