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.

Spatio-temporal patterns of drought in North Xinjiang, China, 1961–2012 based on meteorological drought index

Drought, which is one of the most frequently occurring severe hazards with long time scales and cov- ering wide geographical areas, is a natural phenomenon resulting in significant economic losses in agriculture and industry. Drought is caused by an imbalance between the inputs of and the demand for water which is insufficient to meet the demands of human activities and the eco-environment. As a major arid and semi-arid area and an important agricultural region in Northwest China, North Xinjiang （NX） shows great vulnerability to drought. In this paper, the characteristics of inter-annual and seasonal drought were analyzed in terms of drought occurrence and drought coverage, by using the composite index of meteorological drought and the data of daily precipitation, air temperature, wind speed, relative humidity and sunshine duration from 38 meteorological stations during the period 1961-2012. Trend analysis, wavelet analysis and empirical orthogonal function were also applied to investigate change trend, period and regional characteristics, respectively. In NX, annual and seasonal drought occurrence and drought coverage all showed a decreasing trend that was most significant in winter （with rates of-0.26 month/10a and -15.46%, respectively）, and drought occurrence in spring and summer were more frequent than that in autumn and winter. Spatially, drought was severe in eastern regions but mild in western regions of NX. Annual and seasonal drought occurrence at 38 meteorological stations displayed decreasing trends and were most significant in ＂Shi- hezi-Urumqi-Changji＂, which can help to alleviate severe drought hazards for local agricultural production and improve human livelihood. NX can be approximately classified into three sub-regions （severe drought region, moder- ate drought region and mild drought region）, which were calculated from annual drought frequencies. The cross wavelet transform suggested that SOl （Southern Oscillation Index）, AOI （Arctic Oscillation Index）, AAOI （Antarctic Oscillation Index）, PAOI （Pacific/North American Oscillation Index） and NAOI （North Atlantic Oscillation Index） have significant correlation with the variation of drought occurrence in NX. To prevent and mitigate the occurrence of drought disasters in NX, agricultural and government managers should pay more attention to those drought events that occur in spring and summer.

Spatio-temporal Pattern and Driving Forces of Comprehensive Agricultural Productivity in Jilin Province,China

Improving comprehensive agricultural productivity is an important measure to realize agricultural modernization.Based on the data from Jilin Statistical Yearbook,this study analyzed the spatial and temporal characteristics of comprehensive agricultural productivity discrepancy in the main agricultural production areas of Jilin Province,China.The comprehensive agricultural productivity of 25 county-level administrative units were evaluated by a comprehensive index system based on five aspects which included 20 indicators from 2004 to 2017.The pattern of the discrepancy was analyzed by the spatial differentiation indices and spatial convergence theory.The results were as follows:1) the overall comprehensive agricultural productivity was in a ’W-type’ rising trend;2) the discrepancy was in’inverted W-type’ trend;3) the spatial distribution characteristics were mainly discrete plaque and ’inverted V-type’;4) the formation of differences was forced by a combination of internal and external driving forces.Our study demonstrates the effectiveness of rising agricultural productivity and the level of economic and social developments in different counties in Jilin Province.

Spatio-Temporal Patterns of Wildlife Road Mortality in Golestan National Park-North East of Iran

Nowadays, wildlife road mortality is acknowledged as a main source of threatening long-term survival of wildlife. This paper as the first to analysis wild life vehicle collisions in Iran, aims to reconstruct and interpret the spatio-temporal patterns of WVCs on Asiaei highway in Golestan National Park (GNP). With the collaboration of environmental protection department of GNP, we identified about 1900 WVC Records involving 34 different species of mammals, birds, reptiles and amphibians between 2004 and 2013. Mammals were involved in more than 50% of overall WVCs, among which wild boar (Sus scrofa), Golden Jackal (Canis aureus), Red Fox (Vulpes vulpes), hedgehog (Erinaceus concolor), stone marten (Martes foina) and porcupine (Hystrix indica) were involved in more than 90% of mammals’ mortalities;So, we focused on analyzing spatio-temporal pattern of vehicle collisions of these six mammal species. During the study period, these species have undergone 95% increase in road mortalities, averagely. Detailed temporal analyses exhibited an increasing trend of road mortalities from spring to summer and then a reducing one to late winter. It was shown that a large number of collisions occurred in holiday periods when recreational trips considerably increased the traffic volume of Asiaei highway. Preliminary inspection of spatial patterns using Kernel density analysis revealed six collision hotspots, mostly located in the road bends with densely forested land cover on both sides;the promenades along the road seemed to play a significant role too. Scale dependency analyses of collision patterns, demonstrated clustering pattern at micro scales less than 10 km, randomness at meso scales 10 - 20 km and both regularity and clustering at macro scales more than 20 km. This paper suggests that road mortality of common species in GNP is a momentous issue, which needs to be considered by relevant governmental and public organizations. We also emphasize that the analyses of spatial and temporal patterns of WVCs are fundamentals to plan for mitigate wildlife road mortality.

Spatio-temporal Pattern Characteristics of Relationship Between Urbanization and Economic Development at County Level in China

The relationship between China’s urbanization and economic development(RCUED) is an important concern nationwide. As important actors in regional strategy and policy, county-level regions have played an increasingly significant role in the development of China’s social economy. However, the existing research on the RCUED lacks the fine depiction of the county-level administrative units.Using 2000 and 2010 census data and the statistical analysis method, we uncovered the evolution characteristics of China’s urbanization and economic development and conducted a quantitative identification for the RCUED with improved methods using the quadrant map approach. In addition, we investigated the spatial correlation effect of the RCUED using the spatial autocorrelation analysis method. The results were as follows: 1) In general, a high degree of matching exists between China’s urbanization and economic development at the county level at the significance level of 0.01. The correlation coefficients between China’s urbanization and economic development in2000 and 2010 were 0.608 and 0.603, respectively. 2) A significant regional difference exists in the RCUED at the county level. Based on a comparative analysis of 2276 county units in China in the two years, we found that county units can be categorized as under-urbanized, basic coordination and over-urbanized in various areas. No situation was observed where urbanization seriously lagged behind the economic development level, so the levels of urbanization and economic development appear to be basically coordinated,and the coordination state may be gradually optimized over time. 3) Over time, the spatial dependency of the RCUED has weakened and the spatial heterogeneity has increased. Northeast China has always been an area characterized by over-urbanization. The number of county units classified as under-urbanized has begun to decline in eastern coastal urban agglomeration areas, while counties rich in resources have transformed from having point-shaped over-urbanization to plane-shaped under-urbanization along the northern border,and the number of over-urbanized county units has increased in the middle reaches of the Yangtze River. 4)’Lag-lag’ type and ’advance-advance’ type accounted for 68% of all counties in China, and these counties were shown to have obvious spatial differentiation characteristics.

Spatio-temporal patterns of land use change along the Bohai Rim in China during 1985-2005

Based on TM image data and other survey materials, this paper analyzed the spatiotemporal patterns of land use change in the Bohai Rim during 1985-2005. The findings of this study are summarized as follows： （1） Land use pattern changed dramatically during 1985-2005. Industrial and residential land in urban and rural areas increased by 643,946 hm2 of which urban construction land had the largest and fastest increase of 294,953 hm2 at an annual rate of 3.72%. （2） The outward migration of rural population did not prevent the expansion of residential land in rural areas by 184,869 hm2. This increase reveals that construction of rural residences makes seriously wasteful and inefficient use of land. （3） Arable land, woodland and grassland decreased at a rate of -0.02%, -0.12% and -1.32% annually, while unused land shrank by 157,444 hm^2 at an annual rate of -1.69%. （4） The change of land use types showed marked fluctuations over the two stages （1985-1995 and 1995-2005） In particular, arable land, woodland and unused land experienced an inversed trend of change. （5） There was a significant interaction between arable land and woodland, industrial construction land in urban and rural areas showed a net trend of increase during the earlier period, but only adjustment to its internal structure during the second period. The loss of arable land to the construction of factories, mines and residences took place mainly in the fringe areas of large and medium-sized cities, along the routes of major roads, as well as in the economically developed coastal areas in the east. Such changes are closely related to the spatial differentiation of the level of urbanization and industrialization in the region.

Spatio-temporal Pattern and Spatial Heterogeneity of Ecotones Based on Land Use Types of Southeastern Da Hinggan Mountains in China

Ecotones have received great attention due to its critical function in energy flux, species harbor, global carbon sequestration, and land-atmosphere interaction. This study investigated land use pattern and spatial heterogeneity of the ecotones among agricultural land, forest land, and grassland of the southeastern Da Hinggan Mountains in the northeastern China. The change of these delineated ecotones under different slopes and aridity conditions was analyzed by two landscape indices, edge density(ED) and core area percentage of landscape(CPL), to explore the inter-linkage between spatial structure of ecotones and socioeconomic development and land management. Specifically, the ecotones such as agriculture-forest(AF) ecotone, forest-grassland(FG) ecotone, and agriculture-forestgrassland(AFG) ecotone moved from the arid southeast to the humid northwest. The flat area with small slope is more edge-fragmented than the steep area since the ED decreases as the slope increases. The AF ecotone mostly found in the humid region is moving to more humid areas while the agriculture-grassland(AG) ecotone mostly found in the dry region is moving towards the drier region.

Investigating Spatio-Temporal Pattern of Relative Risk of Tuberculosis in Kenya Using Bayesian Hierarchical Approaches

Proper understanding of global distribution of infectious diseases is an important part of disease management and policy making. However, data are subject to complexities caused by heterogeneities across host classes and space-time epidemic processes. This paper seeks to suggest or propose Bayesian spatio-temporal model for modeling and mapping tuberculosis relative risks in space and time as well identify risks factors associated with the tuberculosis and counties in Kenya with high tuberculosis relative risks. In this paper, we used spatio-temporal Bayesian hierarchical models to study the pattern of tuberculosis relative risks in Kenya. The Markov Chain Monte Carlo method via WinBUGS and R packages were used for simulations and estimation of the parameter estimates. The best fitting model is selected using the Deviance Information Criterion proposed by Spiegelhalter and colleagues. Among the spatio-temporal models used, the Knorr-Held model with space-time interaction type III and IV fit the data well but type IV appears better than type III. Variation in tuberculosis risk is observed among Kenya counties and clustering among counties with high tuberculosis relative risks. The prevalence of HIV is identified as the determinant of TB. We found clustering and heterogeneity of TB risk among high rate counties and the overall tuberculosis risk is slightly decreasing from 2002-2009. We proposed that the Knorr-Held model with interaction type IV should be used to model and map Kenyan tuberculosis relative risks. Interaction of TB relative risk in space and time increases among rural counties that share boundaries with urban counties with high tuberculosis risk. This is due to the ability of models to borrow strength from neighboring counties, such that nearby counties have similar risk. Although the approaches are less than ideal, we hope that our study provide a useful stepping stone in the development of spatial and spatio-temporal methodology for the statistical analysis of risk from tuberculosis in Kenya.

Spatio-temporal pattern and influencing factors of border tourism efficiency in China

In recent years,tourism has emerged as a significant driver of economic development in China’s border regions.The study utilizes various methods,such as the super-efficiency SBM model,spatial variability,cold and hot spot analysis,and Geo-Detector approach,to measure and describe the spatial and temporal evolution patterns of land border tourism efficiency and its influencing factors.The findings reveal that the Dai autonomous prefecture of Xishuangbanna has the highest border tourism efficiency of 1.6207,while Ngari prefecture has the lowest tourism efficiency with a value of only 0.0365 at the prefecture level during the period 2010-2019.The southwest and northwest regions of China are high-and low-level agglomeration areas respectively,indicating varying levels of border tourism development.Additionally,the study identifies an upward trend in China’s border tourism efficiency from 2010-2019.The southwest region emerges as a hotspot and the most active region,while the northwest and northeast regions are considered cold spots with ample room for improvement.Furthermore,the density of transportation facilities,national vulnerability,cultural proximity,the number of border ports,and market opportunity are crucial factors influencing the spatial and temporal pattern of border tourism efficiency in China.

Spatio-temporal Pattern of Cropland Abandonment in Hilly and Mountainous Areas:A Case Study of Jiangxi,China

Cropland abandonment is common and widely distributed in hilly and mountainous areas.Clarifying the current situation and development of cropland abandonment can provide reference for the rational and classified management of cropland abandonment in hilly and mountainous areas.Taking Jiangxi Province as the study area,and using the Google Earth Engine and Landsat data,the scale and years of abandoned cropland from 2002 to 2020 were calculated by using the random forest classifier and rules for identifying cropland abandonment.The spatio-temporal pattern of cropland abandonment at the county level was analyzed.The results indicated that the overall accuracy of land use classification was over 90%.The cropland abandonment rate ranged from 3%to 5.5%from 2002 to 2020,while the cropland abandonment rate was highest in 2013 and showed a downward trend after 2017.Among the years,the area of first-time abandoned cropland was the largest in 2005.The distribution of the cropland abandonment rate was low in the middle and north,but high in the surrounding area and the south.A notable positive spatial correlation was observed in the cropland abandonment rate,with a gradual intensification of spatial clustering.The LISA cluster map revealed a significant north-south disparity,exhibiting an incremental trend over time in the characteristics of the“High-High”cluster in the Southeastern Mountainous Area and the“Low-Low”cluster in the Poyang Lake Hilly Plain in Jiangxi.The results of this study can provide data for extracting spatial information and analyzing the driving factors of cropland abandonment in hilly and mountainous areas,and they can also provide a basis for the development of policies for the utilization and classification management of abandoned cropland.

Spatio-temporal evolution analysis of landscape pattern and habitat quality in the Qinghai Province section of the Yellow River Basin from 2000 to 2022 based on InVEST model

Habitat quality is an important indicator for evaluating the quality of ecosystem.The Qinghai Province section of the Yellow River Basin plays an important role in the ecological protection of the upper reaches of the Yellow River Basin.To comprehensively analysis the alterations of habitat quality in the Qinghai Province section of the Yellow River Basin,this study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model to calculate the habitat quality index and analyze the spatio-temporal variation characteristics of habitat quality in the study area from 2000 to 2022,and calculated seven landscape pattern indices(number of patches,patch density,largest patch index(LPI),landscape shape index(LSI),contagion index(CONTAG),Shannon diversity index,and Shannon evenness index)to research the variation of landscape pattern in the study area.The results showed that the number of patches,patch density,LPI,LSI,Shannon diversity index,and Shannon evenness index increased from 2000 to 2022,while the CONTAG decreased,indicating that the landscape pattern in the Qinghai Province section of the Yellow River Basin changed in the direction of distribution fragmentation,shape complexity,and heterogeneity.The average value of the habitat quality index in the Qinghai Province section of the Yellow River Basin from 2000 to 2022 was 0.90.Based on the value of habitat quality index,we divided the level of habitat quality into five categories:lower(0.00-0.20),low(0.20-0.40),moderate(0.40-0.60),high(0.60-0.80),and higher(0.80-1.00).Most areas were at the higher habitat quality level.The lower habitat quality patches were mainly distributed in Longyang Gorge and Yellow River-Huangshui River Valley.From 2000 to 2022,the habitat quality in most areas was stable;the increase areas were mainly distributed in Guinan County,while the decrease areas were mainly distributed in Xining City,Maqen County,Xinghai County,Qumarleb County,and Darlag County.To show the extent of habitat quality variation,we calculated Sen index.The results showed that the higher habitat quality area had a decrease trending,while other categories had an increasing tendency,and the decreasing was faster than increasing.The research results provide scientific guidance for promoting ecological protection and high-quality development in the Qinghai Province section of the Yellow River Basin.

Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations

The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.

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.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.

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%.

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.

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 selfattention 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.

Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.