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.

Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

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.

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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.

Spatio-temporal variation and focal mechanism of the Wenchuan M_S8.0 earthquake sequence

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.

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

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.

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

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.

Mitochondrial targeting sequence of magnetoreceptor MagR:More than just targeting

Iron-sulfur clusters(ISC)are essential cofactors for proteins involved in various biological processes,such as electron transport,biosynthetic reactions,DNA repair,and gene expression regulation.ISC assembly protein IscA1(or MagR)is found within the mitochondria of most eukaryotes.Magnetoreceptor(MagR)is a highly conserved A-type iron and iron-sulfur cluster-binding protein,characterized by two distinct types of iron-sulfur clusters,[2Fe-2S]and[3Fe-4S],each conferring unique magnetic properties.MagR forms a rod-like polymer structure in complex with photoreceptive cryptochrome(Cry)and serves as a putative magnetoreceptor for retrieving geomagnetic information in animal navigation.Although the N-terminal sequences of MagR vary among species,their specific function remains unknown.In the present study,we found that the N-terminal sequences of pigeon MagR,previously thought to serve as a mitochondrial targeting signal(MTS),were not cleaved following mitochondrial entry but instead modulated the efficiency with which iron-sulfur clusters and irons are bound.Moreover,the N-terminal region of MagR was required for the formation of a stable MagR/Cry complex.Thus,the N-terminal sequences in pigeon MagR fulfil more important functional roles than just mitochondrial targeting.These results further extend our understanding of the function of MagR and provide new insights into the origin of magnetoreception from an evolutionary perspective.

The Application of Nicotiana benthamiana as a Transient Expression Host to Clone the Coding Sequences of Plant Genes

Coding sequences (CDS) are commonly used for transient gene expression, in yeast two-hybrid screening, to verify protein interactions and in prokaryotic gene expression studies. CDS are most commonly obtained using complementary DNA (cDNA) derived from messenger RNA (mRNA) extracted from plant tissues and generated by reverse transcription. However, some CDS are difficult to acquire through this process as they are expressed at extremely low levels or have specific spatial and/or temporal expression patterns in vivo. These challenges require the development of alternative CDS cloning technologies. In this study, we found that the genomic intron-containing gene coding sequences (gDNA) from Arabidopsis thaliana, Oryza sativa, Brassica napus, and Glycine max can be correctly transcribed and spliced into mRNA in Nicotiana benthamiana. In contrast, gDNAs from Triticum aestivum and Sorghum bicolor did not function correctly. In transient expression experiments, the target DNA sequence is driven by a constitutive promoter. Theoretically, a sufficient amount of mRNA can be extracted from the N. benthamiana leaves, making it conducive to the cloning of CDS target genes. Our data demonstrate that N. benthamiana can be used as an effective host for the cloning CDS of plant genes.

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.

Recent advances in spatio-temporally controllable systems for management of glioma

Malignant glioma remains one of the most aggressive intracranial tumors with devastating clinical outcomes despite the great advances in conventional treatment approaches,including surgery and chemotherapy.Spatio-temporally controllable approaches to glioma are now being actively investigated due to the preponderance,including spatio-temporal adjustability,minimally invasive,repetitive properties,etc.External stimuli can be readily controlled by adjusting the site and density of stimuli to exert the cytotoxic on glioma tissue and avoid undesired injury to normal tissues.It is worth noting that the removability of external stimuli allows for on-demand treatment,which effectively reduces the occurrence of side effects.In this review,we highlight recent advancements in drug delivery systems for spatio-temporally controllable treatments of glioma,focusing on the mechanisms and design principles of sensitizers utilized in these controllable therapies.Moreover,the potential challenges regarding spatio-temporally controllable therapy for glioma are also described,aiming to provide insights into future advancements in this field and their potential clinical applications.

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%.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Possible effects of selecting different station distributions in the optimal sequence estimation method

Since the inception of the optimal sequence estimation (OSE) method,various research teams have substantiated its efficacy as the optimal stacking technique for handling array data,leading to its successful application in numerous geoscience studies.Nevertheless,concerns persist regarding the potential impact of aliasing resulting from the choice of distinct station distributions on the outcomes derived from OSE.In this investigation,I employ theoretical deduction and experimental analysis to elucidate the reasons behind the immunity of the Y_(l'm')-related common signal obtained through OSE to variations in station distribution selection.The primary objective of OSE is also underscored,i.e.,to restore/strip a Y_(l'm')-related common periodic signal from various stations.Furthermore,I provide additional clarification that the‘Y_(l'm')-related common signal’and the‘Y_(l'm')-related equivalent excitation sequence’are distinct concepts.These analyses will facilitate the utilization of the OSE technique by other researchers in investigating intriguing geophysical phenomena and attaining sound explanations.

Pig pangenome graph reveals functional features of non‑reference sequences

Background The reliance on a solitary linear reference genome has imposed a significant constraint on our compre-hensive understanding of genetic variation in animals.This constraint is particularly pronounced for non-reference sequences(NRSs),which have not been extensively studied.Results In this study,we constructed a pig pangenome graph using 21 pig assemblies and identified 23,831 NRSs with a total length of 105 Mb.Our findings revealed that NRSs were more prevalent in breeds exhibiting greater genetic divergence from the reference genome.Furthermore,we observed that NRSs were rarely found within coding sequences,while NRS insertions were enriched in immune-related Gene Ontology terms.Notably,our investigation also unveiled a close association between novel genes and the immune capacity of pigs.We observed substantial differences in terms of frequencies of NRSs between Eastern and Western pigs,and the heat-resistant pigs exhibited a substantial number of NRS insertions in an 11.6 Mb interval on chromosome X.Additionally,we discovered a 665 bp insertion in the fourth intron of the TNFRSF19 gene that may be associated with the ability of heat tolerance in South-ern Chinese pigs.Conclusions Our findings demonstrate the potential of a graph genome approach to reveal important functional features of NRSs in pig populations.

Phylogenetic, phylogeographic and divergence time analysis of Anopheles subpictus species complex using ITS2 and COI sequences

Objective:To address the phylogenetic and phylogeographic relationship between different lineages of Anopheles(An.)subpictus species complex in most parts of the Asian continent by maximum utilization of Internal Transcriber Spacer 2(ITS2)and cytochrome C oxidase I(COI)sequences deposited at the GenBank.Methods:Seventy-five ITS2,210 COI and 26 concatenated sequences available in the NCBI database were used.Phylogenetic analysis was performed using Bayesian likelihood trees,whereas median-joining haplotype networks and time-scale divergence trees were generated for phylogeographic analysis.Genetic diversity indices and genetic differentiation were also calculated.Results:Two genetically divergent molecular forms of An.subpictus species complex corresponding to sibling species A and B are established.Species A evolved around 37-82 million years ago in Sri Lanka,India,and the Netherlands,and species B evolved around 22-79 million years ago in Sri Lanka,India,and Myanmar.Vietnam,Thailand,and Cambodia have two molecular forms:one is phylogenetically similar to species B.Other forms differ from species A and B and evolved recently in the above mentioned countries,Indonesia and the Philippines.Genetic subdivision among Sri Lanka,India,and the Netherlands is almost absent.A substantial genetic differentiation was obtained for some populations due to isolation by large geographical distances.Genetic diversity indices reveal the presence of a long-established stable mosquito population,at mutation-drift equilibrium,regardless of population fluctuations.Conclusions:An.subpictus species complex consists of more than two genetically divergent molecular forms.Species A is highly divergent from the rest.Sri Lanka and India contain only species A and B.

On power series statistical convergence and new uniform integrability of double sequences

In the present paper,we mostly focus on P_(p)^(2)-statistical convergence.We will look into the uniform integrability via the power series method and its characterizations for double sequences.Also,the notions of P_(p)^(2)-statistically Cauchy sequence,P_(p)^(2)-statistical boundedness and core for double sequences will be described in addition to these findings.

The Upper Jurassic sediments,Marib-Shabwa Basin,Yemen:Lithofacies aspects and sequence stratigraphic analysis

The present study is devoted to understanding the evolution of the Upper Jurassic Sab'atayn Formation in the Marib-Shabwa Basin,Yemen,through a sequence stratigraphic analysis based on integrating datasets of sedimentology,seismic sections,and well logs.The Sab'atayn Formation(Tithonian age)is represented by a series of clastic and evaporites that were deposited under fluvio-deltaic to prodeltaic settings.It is divided into four members including Yah(at the base),upwards to Seen,Alif,and Safir at the top.Two third-order depositional sequences were determined for the Tithonian succession which were separated by three sequence boundaries.These sequences were classified into their systems tracts signifying several sedimentation patterns of progradational,aggradational,and retrogradational parasequence sets.The first depositional sequence corresponds to the early-middle Tithonian Yah and Seen units that can be classified into lowstand,transgressive,and highstand systems tracts.The second sequence comprises the late Tithonian Alif unit that can be subdivided into transgressive and highstand systems tracts.The sandy deposits of the Alif Member(highstand deposits)represent the most productive hydrocarbon reservoir in the basin.The Upper Jurassic sediments in the study area were resulted from a combination of eustatic and tectonic effects.