In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the ...In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.展开更多
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 p...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.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.展开更多
In this study,we introduce the sequence space l^(μ)(p,Δ^(m)) with a fractional order μ.Furthermore,we give some topological properties of this space.Also we introduce α-,β-,andγ-duals of l^(μ)(p,Δ^(m)) and its...In this study,we introduce the sequence space l^(μ)(p,Δ^(m)) with a fractional order μ.Furthermore,we give some topological properties of this space.Also we introduce α-,β-,andγ-duals of l^(μ)(p,Δ^(m)) and its some matrix mappings.展开更多
The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast ...The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.展开更多
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hoch...In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hochschild homology is given.展开更多
This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def...This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.展开更多
The Ordos Basin is a large superimposed hydrocarbon-bearing basin in China,and further research on the sedimentary characteristics and sedimentary evolution of the sequence framework of target layers is of great theor...The Ordos Basin is a large superimposed hydrocarbon-bearing basin in China,and further research on the sedimentary characteristics and sedimentary evolution of the sequence framework of target layers is of great theoretical and practical significance for guiding oil and gas exploration.The sedimentary facies and sedimentary evolution of the high-resolution sequence framework of the Carboniferous Taiyuan Formation in the Hangjinqi area have been systematically analyzed for the first time by drilling,logging and seismic data.The results show that four types of sequence interfaces can be identified in the Taiyuan Formation:regional unconformity surfaces,scour surfaces,lithologic-lithofacies transformation surfaces and flooding surfaces.According to the sedimentary response caused by the upward and downward movements of the base level at different levels,the Taiyuan Formation can be divided into 2 long-term cycles(LSC_(1)-LSC_(2)),4 mid-term cycles(MSC_(1)-MSC4)and 7 short-term cycles(SSC_(1)-SSC7).The long-and mid-term cycles correspond to members T_(1)and T_(2)and layers T_(1)-1,T_(1-2),T_(2-1),and T_(2)-2,respectively.Long-term cycles are dominated by C_(1);mid-term cycles are dominated by C_(1)and C_(2),followed by A2;and short-term cycles are dominated by C_(1),C_(2),A1 and A2.Under the high-resolution sequence stratigraphic framework,the Hangjinqi area underwent a transformation of fan delta and tidal flat depositional systems during the Taiyuan Formation sedimentary period.In the MSC_(1)-MSC_(2)stage,owing to a large-scale paleocontinent,the fan delta sedimentary body,which was limited in scale and scope,developed only in the southeastern corner and gradually transitioned basinward to tidal flat facies.In the MSC3-MSC4 stage,as the paleocontinent continuously decreased and the sedimentary range expanded,fan-delta plain sedimentation began in the study area.Several braided distributary channels with poor connectivity developed on the fan-delta plain,and between them were floodplains and peat swamps.展开更多
The Fujian oyster(Crassostrea angulata) is an economically significant shellfish species distributed mainly along the Fujian coast, Southeast China. However, its genetic diversity and structure remain unclear. The mai...The Fujian oyster(Crassostrea angulata) is an economically significant shellfish species distributed mainly along the Fujian coast, Southeast China. However, its genetic diversity and structure remain unclear. The main distribution area of the C. angulata is located in Fujian, South China. In total, 420 C. angulata were collected from 14 natural habitats(populations) along the Fujian coast, and their genetic diversity and structure were analyzed in the mitochondrial COI and nuclear gene ITS2 sequences. Results reveal that all the 14 populations of C. angulata exhibited high levels of genetic diversity, with a total of 57(haplotype diversity: 0.811±0.016) and 124(haplotype diversity: 0.912±0.007) haplotypes revealed by COI and ITS2, respectively. Notably, significant intermediate level of genetic differentiations between the Ningde Zhujiang(ZJ) population(FS T by COI: 0.035–0.142, P<0.05;FS T by ITS2: 0.078–0.123, P<0.05) with other populations were observed for the first time, which is also supported by the results of molecular variance analysis(FC T by COI: 0.105, P<0.05;FC T by ITS2: 0.086, P<0.05) and the clustering of the ZJ population into distinct branches in the interpopulation genetic differentiation tree. Furthermore, the evolutionary tree and haplotype network analyses do not support the formation of a clear geographical genealogical structure among these 14 populations. In addition, the population dynamics analysis suggests that the C. angulata may have undergone expansion during the third ice age of the Pleistocene. These results provide a reference for the preservation and further genetic improvement of C. angulata.展开更多
Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to thei...Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyromagnetic ratios.A promising approach to achieving high nuclear spin polarization is transferring the polarization of electrons to nuclear spins.The nitrogen-vacancy(NV)center in diamond has emerged as a highly effective medium for this purpose,and various hyperpolarization protocols have been developed.Among these,the pulsed polarization(PulsePol)method has been extensively studied due to its robustness against static energy shifts of the electron spin.In this work,we present a novel polarization protocol and uncover a family of magic sequences for hyperpolarizing nuclear spins,with PulsePol emerging as a special case of our general approach.Notably,we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half𝜋pulse duration of the protocol,Rabi and detuning errors.This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields.Additionally,the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center.展开更多
We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties i...We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.展开更多
Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along...Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along Longmenshan central fault zone of NE direction and form a narrow strip with the length of 325 krn and the depth between several and 40 km. The depth profile （section of NW direction） vertical to the strike of aftershock zone （NE direction） shows anisomerous wedgy distribution characteristic of afiershock concentrated regions; it is related to the force form of the Longmenshan nappe tectonic belt. The stronger aftershocks could be divided into northern segment and southern segment apparently and the focal depths of strong aftershocks in the 50 km area between northern segment and southern segment are shallower. It seems like ＇to be going to rupture＇ segment. We also study focal mechanisms and segmentation of strong aftershocks. The principal compressive stress azimuth of aftershock area is WNW direction and the faulting types of aftershocks at southern and northern segment have the same proportion. Because afiershocks distribute on different secondary faults, their focal mechanisms present complex local tectonic stress field. The faulting of seven strong earthquakes on the Longmenshan central fault is mainly characterized by thrust with the component of right-lateral strike-slip. Meantime six strong aftershocks on the Longmenshan back-range fault and Qingchuan fault present strike-slip faulting. At last we discuss the complex segmentation rupture mechanism of the Wenchuan earthquake.展开更多
Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments...Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.展开更多
Extraction of moving objects is an important and fundamental research topic for many video applications. This paper addresses an unsupervised spatio-temporal segmentation scheme to extract moving objects from video se...Extraction of moving objects is an important and fundamental research topic for many video applications. This paper addresses an unsupervised spatio-temporal segmentation scheme to extract moving objects from video sequences.The temporal segmentation localizes moving objects by comparing the motion vector of each block in each frame with the corresponding global motion vector estimated by an outlier rejection(OR) based method.Furthermore,the temporal compensation utilizing the temporal coherence of moving objects is considered in the temporal segmentation to solve the temporarily stopping problem.The detected moving regions usually have discontinuous boundaries and some holes.These regions are then compensated in the spatial domain. In the spatial segmentation,the watershed algorithm considering the global information improves the accuracy of segmentation in the spatial domain.The modified mean filter is presented to suppress some minima.By using a fusion module,moving objects are extracted.Experiments on various sequences have successfully demonstrated the validity of the proposed scheme.展开更多
Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tan...Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.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
In industrial control systems,such as power transmission facilities and water treatment plants,Programmable Logic Controllers(PLCs)can work consistently and stably over long periods if there are no faults.Black-box id...In industrial control systems,such as power transmission facilities and water treatment plants,Programmable Logic Controllers(PLCs)can work consistently and stably over long periods if there are no faults.Black-box identification aims to automatically construct Petri net models with the help of I/O signals from PLC devices only.The main challenge is how to convert the infinitely long PLC signals into an event sequence,which is the foundation for subsequent modeling.The current algorithms are confronted with a number of challenges,including an exponential increase in the number of transitions,high time complexity,and susceptibility to noisy signals.To solve these problems,this paper proposes a new method for converting PLC signals into a transition sequence.The method is based on the principles of Boolean absorption law,which filters out noise information in the I/O signals.Then firing functions representing input–output causality are constructed from the filtered signals.Finally,the original signal sequence is traversed to generate a transition sequence.The experimental results show that these methods can rapidly identify a transition sequence.Compared to traditional methods,the proposed algorithms have polynomial time complexity.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
Based on the experimental results of casting thin section,low temperature nitrogen adsorption,high pressure mercury injection,nuclear magnetic resonance T2 spectrum,contact angle and oil-water interfacial tension,the ...Based on the experimental results of casting thin section,low temperature nitrogen adsorption,high pressure mercury injection,nuclear magnetic resonance T2 spectrum,contact angle and oil-water interfacial tension,the relationship between pore throat structure and crude oil mobility characteristics of full particle sequence reservoirs in the Lower Permian Fengcheng Formation of Mahu Sag,Junggar Basin,are revealed.(1)With the decrease of reservoir particle size,the volume of pores connected by large throats and the volume of large pores show a decreasing trend,and the distribution and peak ranges of throat and pore radius shift to smaller size in an orderly manner.The upper limits of throat radius,porosity and permeability of unconventional reservoirs in Fengcheng Formation are approximately 0.7μm,8%and 0.1×10^(−3)μm^(2),respectively.(2)As the reservoir particle size decreases,the distribution and peak ranges of pores hosting retained oil and movable oil are shifted to a smaller size in an orderly manner.With the increase of driving pressure,the amount of retained and movable oil of the larger particle reservoir samples shows a more obvious trend of decreasing and increasing,respectively.(3)With the increase of throat radius,the driving pressure of reservoir with different particle levels presents three stages,namely rapid decrease,slow decrease and stabilization.The oil driving pressures of various reservoirs and the differences of them decrease with the increase of temperature and obviously decrease with the increase of throat radius.According to the above experimental analysis,it is concluded that the deep shale oil of Fengcheng Formation in Mahu Sag has great potential for production under geological conditions.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.42075007)the Open Project of Provincial Key Laboratory for Computer Information Processing Technology under Grant KJS1935Soochow University,and the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.
基金supported by National Natural Science Foundation of China(Grant No.62073256)the Shaanxi Provincial Science and Technology Department(Grant No.2023-YBGY-342).
文摘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.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
文摘In this study,we introduce the sequence space l^(μ)(p,Δ^(m)) with a fractional order μ.Furthermore,we give some topological properties of this space.Also we introduce α-,β-,andγ-duals of l^(μ)(p,Δ^(m)) and its some matrix mappings.
基金supported by the National Natural Science Foundation of China(Nos.22479092 and 22078190)。
文摘The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
基金Supported by National Natural Science Foundation of China(Grant No.11971418).
文摘In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hochschild homology is given.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00249743).
文摘This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.
基金supported by the Fundamental Research Funds for the Liaoning Universities(Grant No.LJ202410166012).
文摘The Ordos Basin is a large superimposed hydrocarbon-bearing basin in China,and further research on the sedimentary characteristics and sedimentary evolution of the sequence framework of target layers is of great theoretical and practical significance for guiding oil and gas exploration.The sedimentary facies and sedimentary evolution of the high-resolution sequence framework of the Carboniferous Taiyuan Formation in the Hangjinqi area have been systematically analyzed for the first time by drilling,logging and seismic data.The results show that four types of sequence interfaces can be identified in the Taiyuan Formation:regional unconformity surfaces,scour surfaces,lithologic-lithofacies transformation surfaces and flooding surfaces.According to the sedimentary response caused by the upward and downward movements of the base level at different levels,the Taiyuan Formation can be divided into 2 long-term cycles(LSC_(1)-LSC_(2)),4 mid-term cycles(MSC_(1)-MSC4)and 7 short-term cycles(SSC_(1)-SSC7).The long-and mid-term cycles correspond to members T_(1)and T_(2)and layers T_(1)-1,T_(1-2),T_(2-1),and T_(2)-2,respectively.Long-term cycles are dominated by C_(1);mid-term cycles are dominated by C_(1)and C_(2),followed by A2;and short-term cycles are dominated by C_(1),C_(2),A1 and A2.Under the high-resolution sequence stratigraphic framework,the Hangjinqi area underwent a transformation of fan delta and tidal flat depositional systems during the Taiyuan Formation sedimentary period.In the MSC_(1)-MSC_(2)stage,owing to a large-scale paleocontinent,the fan delta sedimentary body,which was limited in scale and scope,developed only in the southeastern corner and gradually transitioned basinward to tidal flat facies.In the MSC3-MSC4 stage,as the paleocontinent continuously decreased and the sedimentary range expanded,fan-delta plain sedimentation began in the study area.Several braided distributary channels with poor connectivity developed on the fan-delta plain,and between them were floodplains and peat swamps.
基金Supported by the National Natural Science Foundation of China(No.32172979)the Natural Science Foundation of Fujian Province(No.2021J05159)the 2023 Special Program for Promoting High-Quality Development of Marine and Fishery Industry in Fujian Province(No.PJHYF-L-2023-2)。
文摘The Fujian oyster(Crassostrea angulata) is an economically significant shellfish species distributed mainly along the Fujian coast, Southeast China. However, its genetic diversity and structure remain unclear. The main distribution area of the C. angulata is located in Fujian, South China. In total, 420 C. angulata were collected from 14 natural habitats(populations) along the Fujian coast, and their genetic diversity and structure were analyzed in the mitochondrial COI and nuclear gene ITS2 sequences. Results reveal that all the 14 populations of C. angulata exhibited high levels of genetic diversity, with a total of 57(haplotype diversity: 0.811±0.016) and 124(haplotype diversity: 0.912±0.007) haplotypes revealed by COI and ITS2, respectively. Notably, significant intermediate level of genetic differentiations between the Ningde Zhujiang(ZJ) population(FS T by COI: 0.035–0.142, P<0.05;FS T by ITS2: 0.078–0.123, P<0.05) with other populations were observed for the first time, which is also supported by the results of molecular variance analysis(FC T by COI: 0.105, P<0.05;FC T by ITS2: 0.086, P<0.05) and the clustering of the ZJ population into distinct branches in the interpopulation genetic differentiation tree. Furthermore, the evolutionary tree and haplotype network analyses do not support the formation of a clear geographical genealogical structure among these 14 populations. In addition, the population dynamics analysis suggests that the C. angulata may have undergone expansion during the third ice age of the Pleistocene. These results provide a reference for the preservation and further genetic improvement of C. angulata.
基金supported by the National Natural Science Foundation of China (Grant Nos.12475012,62461160263 for P.W.,and 62276171 for H.L.)Quantum Science and Technology-National Science and Technology Major Project of China (Project No.2023ZD0300600 for P.W.)+3 种基金Guangdong Provincial Quantum Science Strategic Initiative (Grant Nos.GDZX240-3009 and GDZX2303005 for P.W.)Guangdong Basic and Applied Basic Research Foundation (Grant No.2024-A1515011938 for H.L.)Shenzhen Fundamental ResearchGeneral Project (Grant No.JCYJ20240813141503005 for H.L.)the Talents Introduction Foundation of Beijing Normal University (Grant No.310432106 for P.W.)。
文摘Hyperpolarization of nuclear spins is crucial for advancing nuclear magnetic resonance and quantum information technologies,as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyromagnetic ratios.A promising approach to achieving high nuclear spin polarization is transferring the polarization of electrons to nuclear spins.The nitrogen-vacancy(NV)center in diamond has emerged as a highly effective medium for this purpose,and various hyperpolarization protocols have been developed.Among these,the pulsed polarization(PulsePol)method has been extensively studied due to its robustness against static energy shifts of the electron spin.In this work,we present a novel polarization protocol and uncover a family of magic sequences for hyperpolarizing nuclear spins,with PulsePol emerging as a special case of our general approach.Notably,we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half𝜋pulse duration of the protocol,Rabi and detuning errors.This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields.Additionally,the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center.
文摘We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.
基金supported by National Key Basic Research 973bNational Scientific Technology Support Plan (2006BAC01B02-01-01).
文摘Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along Longmenshan central fault zone of NE direction and form a narrow strip with the length of 325 krn and the depth between several and 40 km. The depth profile （section of NW direction） vertical to the strike of aftershock zone （NE direction） shows anisomerous wedgy distribution characteristic of afiershock concentrated regions; it is related to the force form of the Longmenshan nappe tectonic belt. The stronger aftershocks could be divided into northern segment and southern segment apparently and the focal depths of strong aftershocks in the 50 km area between northern segment and southern segment are shallower. It seems like ＇to be going to rupture＇ segment. We also study focal mechanisms and segmentation of strong aftershocks. The principal compressive stress azimuth of aftershock area is WNW direction and the faulting types of aftershocks at southern and northern segment have the same proportion. Because afiershocks distribute on different secondary faults, their focal mechanisms present complex local tectonic stress field. The faulting of seven strong earthquakes on the Longmenshan central fault is mainly characterized by thrust with the component of right-lateral strike-slip. Meantime six strong aftershocks on the Longmenshan back-range fault and Qingchuan fault present strike-slip faulting. At last we discuss the complex segmentation rupture mechanism of the Wenchuan earthquake.
基金This work was supported by financial support from Universiti Sains Malaysia(USM)under FRGS grant number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.
基金the National Natural Science Foundationof China(No.60502034)
文摘Extraction of moving objects is an important and fundamental research topic for many video applications. This paper addresses an unsupervised spatio-temporal segmentation scheme to extract moving objects from video sequences.The temporal segmentation localizes moving objects by comparing the motion vector of each block in each frame with the corresponding global motion vector estimated by an outlier rejection(OR) based method.Furthermore,the temporal compensation utilizing the temporal coherence of moving objects is considered in the temporal segmentation to solve the temporarily stopping problem.The detected moving regions usually have discontinuous boundaries and some holes.These regions are then compensated in the spatial domain. In the spatial segmentation,the watershed algorithm considering the global information improves the accuracy of segmentation in the spatial domain.The modified mean filter is presented to suppress some minima.By using a fusion module,moving objects are extracted.Experiments on various sequences have successfully demonstrated the validity of the proposed scheme.
基金supported by National Natural Science of Foundation of China(No.10871026)
文摘Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.
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supported by the Science and Technology Planning Project of Fujian Province,China,under Grant No.2024H0014(2024H01010100).
文摘In industrial control systems,such as power transmission facilities and water treatment plants,Programmable Logic Controllers(PLCs)can work consistently and stably over long periods if there are no faults.Black-box identification aims to automatically construct Petri net models with the help of I/O signals from PLC devices only.The main challenge is how to convert the infinitely long PLC signals into an event sequence,which is the foundation for subsequent modeling.The current algorithms are confronted with a number of challenges,including an exponential increase in the number of transitions,high time complexity,and susceptibility to noisy signals.To solve these problems,this paper proposes a new method for converting PLC signals into a transition sequence.The method is based on the principles of Boolean absorption law,which filters out noise information in the I/O signals.Then firing functions representing input–output causality are constructed from the filtered signals.Finally,the original signal sequence is traversed to generate a transition sequence.The experimental results show that these methods can rapidly identify a transition sequence.Compared to traditional methods,the proposed algorithms have polynomial time complexity.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金Supported by Leading Talent Program of Autonomous Region(2022TSYCLJ0070)PetroChina Prospective and Basic Technological Project(2021DJ0108)Natural Science Foundation for Outstanding Young People in Shandong Province(ZR2022YQ30).
文摘Based on the experimental results of casting thin section,low temperature nitrogen adsorption,high pressure mercury injection,nuclear magnetic resonance T2 spectrum,contact angle and oil-water interfacial tension,the relationship between pore throat structure and crude oil mobility characteristics of full particle sequence reservoirs in the Lower Permian Fengcheng Formation of Mahu Sag,Junggar Basin,are revealed.(1)With the decrease of reservoir particle size,the volume of pores connected by large throats and the volume of large pores show a decreasing trend,and the distribution and peak ranges of throat and pore radius shift to smaller size in an orderly manner.The upper limits of throat radius,porosity and permeability of unconventional reservoirs in Fengcheng Formation are approximately 0.7μm,8%and 0.1×10^(−3)μm^(2),respectively.(2)As the reservoir particle size decreases,the distribution and peak ranges of pores hosting retained oil and movable oil are shifted to a smaller size in an orderly manner.With the increase of driving pressure,the amount of retained and movable oil of the larger particle reservoir samples shows a more obvious trend of decreasing and increasing,respectively.(3)With the increase of throat radius,the driving pressure of reservoir with different particle levels presents three stages,namely rapid decrease,slow decrease and stabilization.The oil driving pressures of various reservoirs and the differences of them decrease with the increase of temperature and obviously decrease with the increase of throat radius.According to the above experimental analysis,it is concluded that the deep shale oil of Fengcheng Formation in Mahu Sag has great potential for production under geological conditions.
