Quantitative assessment of the spatio-temporal correlations of seismic events induced by longwall coal mining

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.

Spatio-temporal correlation between human activity intensity and land surface temperature on the north slope of Tianshan Mountains

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.

EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption

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.

Hierarchical delineation and multivariate statistical discrimination of chemozones of the Cauvery Basin,south India:Implications on spatio-temporal scales of stratigraphic correlation

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.

Spatio-Temporal Correlation Analysis of Global Temperature Based on the Correlation Matrix Theory

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.

Influence of non-stationarity and auto-correlation of climatic records on spatio-temporal trend and seasonality analysis in a region with prevailing arid and semi-arid climate,Iran

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.

Gas monitoring data anomaly identification based on spatio-temporal correlativity analysis

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.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

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.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

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.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

Structural identification and antioxidative activity evaluation of flaxseed lignan macromolecules: structure-activity correlation

Flaxseed lignan macromolecules(FLM)are a class of important secondary metabolites in fl axseed,which have been widely concerned due to their biological and pharmacological properties,especially for their antioxidative activity.For the composition and structure of FLM,our results confirmed that ferulic acid glycoside(FerAG)was directly ester-linked with herbacetin diglucoside(HDG)or pinoresinol diglucoside(PDG),which might determine the beginning of FLM biosynthesis.Additionally,p-coumaric acid glycoside(CouAG)might determine the end of chain extension during FLM synthesis in fl axseed.FLM exhibited higher antioxidative activity in polar systems,as shown by its superior 1,1-diphenyl-2-picrylhydrazyl(DPPH)free radical scavenging capacity compared to the 2,2’-azinobis(3-ehtylbenzothiazolin-6-sulfnic acid)(ABTS)cation free radical scavenging capacity in non-polar systems.Moreover,the antioxidative activity of FLM was found to be highly dependent on its composition and structure.In particular,it was positively correlated with the number of phenolic hydroxyl groups(longer FLM chains)and inversely related to the steric hindrance at the ends(lower levels of FerAG and CouAG).These fi ndings verifi ed the potential application of FLM in nonpolar systems,particularly in functional food emulsions。

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

First results of turbulence investigation on Globus-M2 using radial correlation Doppler reflectometry

The first results of investigation of the turbulence structure using Doppler backscattering(DBS)on the Globus-M2 tokamak are presented.A one-channel DBS system with a variable probing frequency within the 18–26 GHz range was installed to investigate the edge plasma at normalized minor radiiρ=0.9–1.1.Radial correlation Doppler reflectometry was used to study the changes in turbulence eddies after the LH transition.Correlation analysis was applied to the phase derivative of complex in-phase and quadrature(IQ)signals of the DBS diagnostic as it contains information about the poloidal plasma rotation velocity.In L-mode,the radial correlation length L_(r)is estimated to be 3 cm and after transition to H-mode reduces to approximately 2 cm.Gyrokinetic modelling in a linear local approximation using code GENE indicates that the instability with positive growth rate at the normalized minor radiusρ=0.75 in L-mode and H-mode on Globus-M2 was the ion temperature gradient(ITG)mode.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

Study of baryon number transport dynamics and strangeness conservation effects using Ω-hadron correlations

In nuclear collisions at RHIC energies, an excess of Ω hyperons over ■ is observed, indicating that Ω has a net baryon number despite s and s quarks being produced in pairs. The baryon number in Ω may have been transported from the incident nuclei and/or produced in the baryon-pair production of Ω with other types of anti-hyperons such as Ξ. To investigate these two scenarios, we propose to measure the correlations between Ω and K and between Ω and anti-hyperons. We use two versions, the default and string-melting, of a multiphase transport(AMPT) model to illustrate the method for measuring the correlation and to demonstrate the general shape of the correlation. We present the Ω-hadron correlations from simulated Au+Au collisions at ■ =7.7 and 14.6 Ge V and discuss the dependence on the collision energy and on the hadronization scheme in these two AMPT versions. These correlations can be used to explore the mechanism of baryon number transport and the effects of baryon number and strangeness conservation on nuclear collisions.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.