Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechani...Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechanisms of climate change,actively adapting to climate change,pursuing rational development,and protecting the ecological environment.Taking the north slope of Tianshan Mountains,located in the arid area of northwestern China and extremely sensitive to climate change,as the research area,this study retrieves the surface temperature of the mountain based on MODIS data,while characterizing the intensity of human activities thereby data on the night light,population distribution and land use.The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed,and the spatio-temporal correlation between them was further explored.It is found that:(1)The average human activity intensity(0.11)in the research area has kept relatively low since this century,and the overall trend has been slowly rising in a stepwise manner(0.0024·a-1);in addition,the increase in human activity intensity has lagged behind that in construction land and population by 1-2 years.(2)The annual average surface temperature in the area is 7.18℃with a pronounced growth.The rate of change(0.02℃·a-1)is about 2.33 times that of the world.The striking boost in spring(0.068℃·a-1)contributes the most to the overall warming trend.Spatially,the surface temperature is low in the south and high in the north,due to the prominent influence of the underlying surface characteristics,such as elevation and vegetation coverage.(3)The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there,showing a strong distribution in the east section and a weak one in the west section.The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities,manifestations,and land-use changes.Vegetation-related human interventions,such as agriculture and forestry planting,urban greening,and afforestation,can effectively reduce the surface warming caused by human activities.This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development.展开更多
Rock failure process as a natural response to mining activities is associated with seismic events, which can pose a potential hazard to mine operators, equipment and infrastructures. Mining-induced seismicity has been...Rock failure process as a natural response to mining activities is associated with seismic events, which can pose a potential hazard to mine operators, equipment and infrastructures. Mining-induced seismicity has been found to be internally correlated in both time and space domains as a result of rock fracturing during progressive mining activities. Understanding the spatio-temporal(ST) correlation of mininginduced seismic events is an essential step to use seismic data for further analysis, such as rockburst prediction and caving assessment. However, there are no established methods to perform this critical task. Input parameters used for the prediction of seismic hazards, such as the time window of past data and effective prediction distance, are determined based on site-specific experience without statistical or physical reasons to support. Therefore, the accuracy of current seismic prediction methods is largely constrained, which can only be addressed by quantitively assessing the ST correlations of mininginduced seismicity. In this research, the ST correlation of seismic event energy collected from a study mine is quantitatively analysed using various statistical methods, including autocorrelation function(ACF), semivariogram and Moran’s I analysis. In addition, based on the integrated ST correlation assessment, seismic events are further classified into seven clusters, so as to assess the correlations within individual clusters. The correlation of seismic events is found to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process.展开更多
Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmiss...Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.展开更多
BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM ...BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM To explore the correlations among life satisfaction,pleasure levels,and negative emotions in patients with CRF.METHODS One hundred patients with CRF who received therapy at the First Affiliated Hospital of Jinzhou Medical University between December 2022 and February 2025 were included.The Depression,Anxiety,and Stress Scale(DASS-21),Satisfaction with Life Scale(SWLS),and Temporal Experience of Pleasure Scale(TEPS)were used to evaluate negative emotions,life satisfaction,and pleasure level,respectively.Pearson’s correlation coefficient analyzed the correlation between life satisfaction,pleasure level,and negative emotions.Linear regression analysis identified the factors affecting negative emotions.RESULTS The average DASS-21 score among patients with CRF was 51.90±2.30,with subscale scores of 17.90±1.50 for depression,18.53±1.18 for anxiety,and 15.47±2.36 for stress,all significantly higher than the domestic norm(P<0.05).The average SWLS score was 22.17±4.90.Correlation analysis revealed a negative correlation between the SWLS and total DASS-21 scores(P<0.05),but not with the individual depression,anxiety,or stress dimensions.The average TEPS score was 67.80±8.34.TEPS scores were negatively correlated with the DASS-21 score and the stress dimension(P<0.05),but not with depression or anxiety.Linear regression analysis showed that TEPS scores significantly influenced DASS-21 scores(P<0.05).CONCLUSION Patients with CRF experience high levels of negative emotions,which are negatively correlated with life satisfaction and pleasure.Furthermore,pleasure level had an impact on negative emotions.展开更多
Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,whi...Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.展开更多
Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spa...Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.展开更多
Success in locating oil pools in the Cauvery Basin,south India had been found to be based on the ability to delineate precisely the stratigraphic traps resulting from frequent sea level changes.However,recognition and...Success in locating oil pools in the Cauvery Basin,south India had been found to be based on the ability to delineate precisely the stratigraphic traps resulting from frequent sea level changes.However,recognition and delineation of them in terms of depositional units through conventional stratigraphic methods have been elusive owing to the limitations of such methods and lack of unified stratigraphic markers that could be traced at regional and basinal scale.This paper attempts to recognize depositional units in terms of chemozones,chronologic and lithostratigraphic units by assigning distinct geochemical signatures.Geochemical signatures were assigned through hierarchical delineation and discriminant function analysis.It is observed that individual depositional units could be recognized statistically with whole-rock geochemical composition.The strata under study show two second order chemozones comprising six major chemozones that in turn correspond to third order sea level cycles and minor chemozones at the scale of fourth order and/or further shorter sea level cycles.The geochemical signatures showed 100% distinctness between sample populations categorized according to chronostratigraphy and lithostratigraphy.The durations of these stratigraphic units range from 18 million years to less than a million years and indicate distinct geochemical compositional change at different time slices.By implication and also due to the close correspondence between sea level variations reported from this basin and global sea level cycles,it is suggested that recognition and correlation of individual depositional units with distal counterparts could be made accurately.Implication of these results is that stratigraphic units,at varying scales either temporally or spatially,could be assigned with unique geochemical signature,with which accurate prediction and correlation of similar units elsewhere is possible with measurable accuracy.展开更多
Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed ...Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed and compared.The results show that there are meaningful true correlations as well as correlation＂noises＂in the TCMs.The true correlations contain short range correlations（SRCs） among temperature series of neighboring grid points as well as long range correlations（LRCs） among temperature series of different regions,such as the El Nino area and the warm pool areas of the Pacific,the Indian Ocean,the Atlantic,etc.At different time scales,these two kinds of correlations show different features：at 1-10-day scale,SRCs are more important than LRCs;while at 15-day-or-more scale,the importance of SRCs and LRCs decreases and increases respectively,compared with the case of 1-10-day scale.It is found from the analyses of eigenvalues and eigenvectors of TCMs and corresponding RCMs that most correlation information is contained in several eigenvectors of TCMs with relatively larger eigenvalues,and the projections of global temperature series onto these eigenvectors are able to reflect the overall characteristics of global temperature changes to some extent.Besides,the correlation coefficients（CCs） of grid point temperature series show significant temporal and spatial variations.The average CCs over 1950-1956,1972-1977,and 1996-2000 are significantly higher than average while that over the periods 1978-1982 and 1991-1996 are opposite,suggesting a distinctive oscillation of quasi-10-20 yr.Spatially,the CCs at 1-and 15-day scales both show band-like zonal distributions;the zonally averaged CCs at 1-day scale display a better latitudinal symmetry,while they are relatively worse at 15-day scale because of sea-land contrast of the Northern and Southern Hemisphere.However,the meridionally averaged CCs at 15-day scale display a longitudinal quasi-symmetry.展开更多
Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes,especially in arid and semi-arid regi...Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes,especially in arid and semi-arid regions.In this study,various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables.The Mann-Kendall test was considered to identify the trend,while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series.Meanwhile,time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests.The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones,however,after eliminating the serial correlation factor,this increasing trend changes to an insignificant decreasing trend at a 95%confidence level.The seasonal mean air temperature trend suggested a significant increase in the majority of the stations.The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones.Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones;furthermore,most of the stations follow a decreasing trend for seasonal precipitation.Furthermore,spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming.Stationarity analysis indicated that the stationarity of climatic series influences on their trend;so that,the series which have significant trends are not static.The findings of this investigation can help planners and policy-makers in various fields related to climatic issues,implementing better management and planning strategies to adapt to climate change and variability over Iran.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
Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics o...Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.展开更多
Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the m...Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.展开更多
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
Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery...Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.展开更多
The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between suppl...The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.展开更多
Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(...Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.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
BACKGROUND Resilience is an individual’s ability and psychological rebound capacity to adapt well after experiencing adversity,trauma,etc.Patients with strong resilience can face illnesses actively.AIM To determine t...BACKGROUND Resilience is an individual’s ability and psychological rebound capacity to adapt well after experiencing adversity,trauma,etc.Patients with strong resilience can face illnesses actively.AIM To determine the association of resilience with coping styles and quality of life in patients with malignancies.METHODS This study included patients with malignant tumors who were hospitalized at Fuyang Hospital Affiliated to Anhui Medical University from March 2022 to March 2024.The Connor-Davidson Resilience Scale,Medical Coping Modes Questionnaire,Social Support Rating Scale,and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 were utilized to assess patients’resilience,coping styles,social support,and quality of life,respectively.Pearson correlation analysis was conducted to assess the correlations.RESULTS A total of 175 patients with malignant tumors demonstrated no marked difference in terms of age,education level,employment status,monthly household income,and disease staging(P<0.05).Further,patients with malignancies demonstrated scores of 17.49±1.20,17.27±1.46,and 11.19±1.29 points in terms of coping styles in confrontation,avoidance,and resignation dimensions,respectively.Subjective support,objective support,and support utilization scores in terms of social support were 10.67±1.80,11.26±2.08,and 9.24±1.14 points,respectively.The total resilience score and tenacity,self-improvement,and optimism dimension scores were positively correlatedwith the confrontation coping style score,whereas the total resilience score and tenacity and self-improvementscores were negatively associated with avoidance and resignation coping style scores(P<0.05).The total resiliencescore and the tenacity dimension score were positively associated with physical,role,cognitive,emotional,andsocial functions,as well as global health status(P<0.05),and were inversely related to fatigue,insomnia,andeconomic difficulties(P<0.05).CONCLUSIONThe resilience of patients with malignancies is positively associated with the confrontation dimension in the copingstyle,the total and various social support domain scores,and the overall quality of life.Clinical medical staff needto pay attention to the effect of medical coping styles and social support on the resilience level of patients withmalignancies to further improve their quality of life.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
The Fushan Depression is one of the petroliferous depressions in the Beibuwan Basin,South China Sea.Previous studies have preliminarily explored the origin and source of crude oils in some areas of this depression.Nev...The Fushan Depression is one of the petroliferous depressions in the Beibuwan Basin,South China Sea.Previous studies have preliminarily explored the origin and source of crude oils in some areas of this depression.Nevertheless,no systematic investigations on the classification and origin of oils and hy-drocarbon migration processes have been made for the entire petroleum system in this depression,which has significantly hindered the hydrocarbon exploration in the region.A total of 32 mudstone and 58 oil samples from the Fushan Depression were analyzed to definite the detailed oil-source correlation within the sequence and sedimentary framework.The organic matter of third member of Paleogene Liushagang Formation(Els(3))source rocks,both deltaic and lacustrine mudstone,are algal-dominated with high abundance of C_(23)tricyclic terpane and C_(30)4-methylsteranes.The deltaic source rocks occur-ring in the first member(Els_(1))and second member(Els_(2))of the Paleogene Liushagang Formation are characterized by high abundance of C_(19+20)tricyclic terpane and oleanane,reflecting a more terrestrial plants contribution.While lacustrine source rocks of Els_(1)and Els_(2)display the reduced input of terrige-nous organic matter with relatively low abundance of C 19+20 tricyclic terpane and oleanane.Three types of oils were identified by their biomarker compositions in this study.Most of the oils discovered in the Huachang and Bailian Els_(1)reservoir belong to group A and were derived from lacustrine source rocks of Els_(1)and Els_(2).Group B oils are found within the Els_(1)and Els_(2)reservoirs,showing a close relation to the deltaic source rocks of Els_(1)and Els_(2),respectively.Group C oils,occurring in the Els3 reservoirs,have a good affinity with the Els3 source rocks.The spatial distribution and accumulation of different groups of oils are mainly controlled by the sedimentary facies and specific structural conditions.The Els_(2)reservoir in the Yong'an area belonging to Group B oil,are adjacent to the source kitchen and could be considered as the favorable exploration area in the future.展开更多
基金National Natural Science Foundation of China(41461086)National Natural Science Foundation of China(41761108)。
文摘Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechanisms of climate change,actively adapting to climate change,pursuing rational development,and protecting the ecological environment.Taking the north slope of Tianshan Mountains,located in the arid area of northwestern China and extremely sensitive to climate change,as the research area,this study retrieves the surface temperature of the mountain based on MODIS data,while characterizing the intensity of human activities thereby data on the night light,population distribution and land use.The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed,and the spatio-temporal correlation between them was further explored.It is found that:(1)The average human activity intensity(0.11)in the research area has kept relatively low since this century,and the overall trend has been slowly rising in a stepwise manner(0.0024·a-1);in addition,the increase in human activity intensity has lagged behind that in construction land and population by 1-2 years.(2)The annual average surface temperature in the area is 7.18℃with a pronounced growth.The rate of change(0.02℃·a-1)is about 2.33 times that of the world.The striking boost in spring(0.068℃·a-1)contributes the most to the overall warming trend.Spatially,the surface temperature is low in the south and high in the north,due to the prominent influence of the underlying surface characteristics,such as elevation and vegetation coverage.(3)The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there,showing a strong distribution in the east section and a weak one in the west section.The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities,manifestations,and land-use changes.Vegetation-related human interventions,such as agriculture and forestry planting,urban greening,and afforestation,can effectively reduce the surface warming caused by human activities.This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development.
文摘Rock failure process as a natural response to mining activities is associated with seismic events, which can pose a potential hazard to mine operators, equipment and infrastructures. Mining-induced seismicity has been found to be internally correlated in both time and space domains as a result of rock fracturing during progressive mining activities. Understanding the spatio-temporal(ST) correlation of mininginduced seismic events is an essential step to use seismic data for further analysis, such as rockburst prediction and caving assessment. However, there are no established methods to perform this critical task. Input parameters used for the prediction of seismic hazards, such as the time window of past data and effective prediction distance, are determined based on site-specific experience without statistical or physical reasons to support. Therefore, the accuracy of current seismic prediction methods is largely constrained, which can only be addressed by quantitively assessing the ST correlations of mininginduced seismicity. In this research, the ST correlation of seismic event energy collected from a study mine is quantitatively analysed using various statistical methods, including autocorrelation function(ACF), semivariogram and Moran’s I analysis. In addition, based on the integrated ST correlation assessment, seismic events are further classified into seven clusters, so as to assess the correlations within individual clusters. The correlation of seismic events is found to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process.
文摘Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.
文摘BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM To explore the correlations among life satisfaction,pleasure levels,and negative emotions in patients with CRF.METHODS One hundred patients with CRF who received therapy at the First Affiliated Hospital of Jinzhou Medical University between December 2022 and February 2025 were included.The Depression,Anxiety,and Stress Scale(DASS-21),Satisfaction with Life Scale(SWLS),and Temporal Experience of Pleasure Scale(TEPS)were used to evaluate negative emotions,life satisfaction,and pleasure level,respectively.Pearson’s correlation coefficient analyzed the correlation between life satisfaction,pleasure level,and negative emotions.Linear regression analysis identified the factors affecting negative emotions.RESULTS The average DASS-21 score among patients with CRF was 51.90±2.30,with subscale scores of 17.90±1.50 for depression,18.53±1.18 for anxiety,and 15.47±2.36 for stress,all significantly higher than the domestic norm(P<0.05).The average SWLS score was 22.17±4.90.Correlation analysis revealed a negative correlation between the SWLS and total DASS-21 scores(P<0.05),but not with the individual depression,anxiety,or stress dimensions.The average TEPS score was 67.80±8.34.TEPS scores were negatively correlated with the DASS-21 score and the stress dimension(P<0.05),but not with depression or anxiety.Linear regression analysis showed that TEPS scores significantly influenced DASS-21 scores(P<0.05).CONCLUSION Patients with CRF experience high levels of negative emotions,which are negatively correlated with life satisfaction and pleasure.Furthermore,pleasure level had an impact on negative emotions.
基金This work was supported by the Science and Technology Project of China Southern Power Grid Corporation(ZBKJXM20180157)the National Natural Science Foundation of China(Grant Nos.61772456,61761136020).
文摘Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.
基金This work is funded by the National Natural Science Foundation of China[grant numbers 41830645 and 41625003].
文摘Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.
文摘Success in locating oil pools in the Cauvery Basin,south India had been found to be based on the ability to delineate precisely the stratigraphic traps resulting from frequent sea level changes.However,recognition and delineation of them in terms of depositional units through conventional stratigraphic methods have been elusive owing to the limitations of such methods and lack of unified stratigraphic markers that could be traced at regional and basinal scale.This paper attempts to recognize depositional units in terms of chemozones,chronologic and lithostratigraphic units by assigning distinct geochemical signatures.Geochemical signatures were assigned through hierarchical delineation and discriminant function analysis.It is observed that individual depositional units could be recognized statistically with whole-rock geochemical composition.The strata under study show two second order chemozones comprising six major chemozones that in turn correspond to third order sea level cycles and minor chemozones at the scale of fourth order and/or further shorter sea level cycles.The geochemical signatures showed 100% distinctness between sample populations categorized according to chronostratigraphy and lithostratigraphy.The durations of these stratigraphic units range from 18 million years to less than a million years and indicate distinct geochemical compositional change at different time slices.By implication and also due to the close correspondence between sea level variations reported from this basin and global sea level cycles,it is suggested that recognition and correlation of individual depositional units with distal counterparts could be made accurately.Implication of these results is that stratigraphic units,at varying scales either temporally or spatially,could be assigned with unique geochemical signature,with which accurate prediction and correlation of similar units elsewhere is possible with measurable accuracy.
基金Supported jointly by the National Natural Science Foundation of China under Grant Nos. 40930952, 40875040, and 40905034the National Basic Research Program of China under Grant No. 2006CB400503the National Science & Technology Support Program of China under Grant Nos. 2007BAC03A01 and 2007BAC29B01
文摘Based on the NCEP/NCAR reanalysis daily mean temperature data from 1948 to 2005 and random time series of the same size,temperature correlation matrixes（TCMs） and random correlation matrixes（RCMs） are constructed and compared.The results show that there are meaningful true correlations as well as correlation＂noises＂in the TCMs.The true correlations contain short range correlations（SRCs） among temperature series of neighboring grid points as well as long range correlations（LRCs） among temperature series of different regions,such as the El Nino area and the warm pool areas of the Pacific,the Indian Ocean,the Atlantic,etc.At different time scales,these two kinds of correlations show different features：at 1-10-day scale,SRCs are more important than LRCs;while at 15-day-or-more scale,the importance of SRCs and LRCs decreases and increases respectively,compared with the case of 1-10-day scale.It is found from the analyses of eigenvalues and eigenvectors of TCMs and corresponding RCMs that most correlation information is contained in several eigenvectors of TCMs with relatively larger eigenvalues,and the projections of global temperature series onto these eigenvectors are able to reflect the overall characteristics of global temperature changes to some extent.Besides,the correlation coefficients（CCs） of grid point temperature series show significant temporal and spatial variations.The average CCs over 1950-1956,1972-1977,and 1996-2000 are significantly higher than average while that over the periods 1978-1982 and 1991-1996 are opposite,suggesting a distinctive oscillation of quasi-10-20 yr.Spatially,the CCs at 1-and 15-day scales both show band-like zonal distributions;the zonally averaged CCs at 1-day scale display a better latitudinal symmetry,while they are relatively worse at 15-day scale because of sea-land contrast of the Northern and Southern Hemisphere.However,the meridionally averaged CCs at 15-day scale display a longitudinal quasi-symmetry.
文摘Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes,especially in arid and semi-arid regions.In this study,various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables.The Mann-Kendall test was considered to identify the trend,while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series.Meanwhile,time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests.The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones,however,after eliminating the serial correlation factor,this increasing trend changes to an insignificant decreasing trend at a 95%confidence level.The seasonal mean air temperature trend suggested a significant increase in the majority of the stations.The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones.Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones;furthermore,most of the stations follow a decreasing trend for seasonal precipitation.Furthermore,spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming.Stationarity analysis indicated that the stationarity of climatic series influences on their trend;so that,the series which have significant trends are not static.The findings of this investigation can help planners and policy-makers in various fields related to climatic issues,implementing better management and planning strategies to adapt to climate change and variability over Iran.
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Supported by the National Natural Science Foundation of China （40971275, 50811120111）
文摘Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.
文摘Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.
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.
基金the Science and Engineering Re-search Board(SERB),India for providing the financial assistance to support this work(Project No.SRG/2020/002449).
文摘Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.
基金supported by the National Key Research and Development Program of China[grant number 2017YFB0503601]。
文摘The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.
文摘Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.
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.
文摘BACKGROUND Resilience is an individual’s ability and psychological rebound capacity to adapt well after experiencing adversity,trauma,etc.Patients with strong resilience can face illnesses actively.AIM To determine the association of resilience with coping styles and quality of life in patients with malignancies.METHODS This study included patients with malignant tumors who were hospitalized at Fuyang Hospital Affiliated to Anhui Medical University from March 2022 to March 2024.The Connor-Davidson Resilience Scale,Medical Coping Modes Questionnaire,Social Support Rating Scale,and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 were utilized to assess patients’resilience,coping styles,social support,and quality of life,respectively.Pearson correlation analysis was conducted to assess the correlations.RESULTS A total of 175 patients with malignant tumors demonstrated no marked difference in terms of age,education level,employment status,monthly household income,and disease staging(P<0.05).Further,patients with malignancies demonstrated scores of 17.49±1.20,17.27±1.46,and 11.19±1.29 points in terms of coping styles in confrontation,avoidance,and resignation dimensions,respectively.Subjective support,objective support,and support utilization scores in terms of social support were 10.67±1.80,11.26±2.08,and 9.24±1.14 points,respectively.The total resilience score and tenacity,self-improvement,and optimism dimension scores were positively correlatedwith the confrontation coping style score,whereas the total resilience score and tenacity and self-improvementscores were negatively associated with avoidance and resignation coping style scores(P<0.05).The total resiliencescore and the tenacity dimension score were positively associated with physical,role,cognitive,emotional,andsocial functions,as well as global health status(P<0.05),and were inversely related to fatigue,insomnia,andeconomic difficulties(P<0.05).CONCLUSIONThe resilience of patients with malignancies is positively associated with the confrontation dimension in the copingstyle,the total and various social support domain scores,and the overall quality of life.Clinical medical staff needto pay attention to the effect of medical coping styles and social support on the resilience level of patients withmalignancies to further improve their quality of life.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金funded by the South Oil Exploration and Development Company of PetroChina(2021-HNYJ-010).
文摘The Fushan Depression is one of the petroliferous depressions in the Beibuwan Basin,South China Sea.Previous studies have preliminarily explored the origin and source of crude oils in some areas of this depression.Nevertheless,no systematic investigations on the classification and origin of oils and hy-drocarbon migration processes have been made for the entire petroleum system in this depression,which has significantly hindered the hydrocarbon exploration in the region.A total of 32 mudstone and 58 oil samples from the Fushan Depression were analyzed to definite the detailed oil-source correlation within the sequence and sedimentary framework.The organic matter of third member of Paleogene Liushagang Formation(Els(3))source rocks,both deltaic and lacustrine mudstone,are algal-dominated with high abundance of C_(23)tricyclic terpane and C_(30)4-methylsteranes.The deltaic source rocks occur-ring in the first member(Els_(1))and second member(Els_(2))of the Paleogene Liushagang Formation are characterized by high abundance of C_(19+20)tricyclic terpane and oleanane,reflecting a more terrestrial plants contribution.While lacustrine source rocks of Els_(1)and Els_(2)display the reduced input of terrige-nous organic matter with relatively low abundance of C 19+20 tricyclic terpane and oleanane.Three types of oils were identified by their biomarker compositions in this study.Most of the oils discovered in the Huachang and Bailian Els_(1)reservoir belong to group A and were derived from lacustrine source rocks of Els_(1)and Els_(2).Group B oils are found within the Els_(1)and Els_(2)reservoirs,showing a close relation to the deltaic source rocks of Els_(1)and Els_(2),respectively.Group C oils,occurring in the Els3 reservoirs,have a good affinity with the Els3 source rocks.The spatial distribution and accumulation of different groups of oils are mainly controlled by the sedimentary facies and specific structural conditions.The Els_(2)reservoir in the Yong'an area belonging to Group B oil,are adjacent to the source kitchen and could be considered as the favorable exploration area in the future.
