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%.

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.

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.

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.

Prevalence and Demographic Distributions of Pre-Eclampsia among Pregnant Women at Ho Teaching Hospital

Background: In Sub-Saharan Africa, pre-eclampsia remains a major health problem contributing to high rates of maternal mortality. Despite this condition having adverse effects on maternal and child health, its prevalence and associated risk factors are still significant, especially in developing countries including Ghana. This study aimed to assess the prevalence and demographic distributions associated with pre-eclampsia among pregnant women at the Ho Teaching Hospital. Methods: A facility-based retrospective study was conducted by reviewing available data or hospital records of pregnant mothers admitted to the labor and maternity wards from January 2018 to December 2020. All pregnant women who were diagnosed with pre-eclampsia within this period were included in the study. The data were collected using a structured checklist. Results: 5609 data on pregnant women from 2018 to 2020 were recorded. Out of the 5609 data recorded, 314 pre-eclampsia cases were recorded giving an overall prevalence of 5.6%. The yearly prevalence for 2018, 2019, and 2020 were 4.6%, 5.6%, and 6.6%, respectively. The most recorded pre-eclampsia cases were seen among women within the age group of 18 - 24 years. The data showed that 112 (35.7%) of the pregnant women who had pre-eclampsia were nulliparous. Pre-eclampsia-associated maternal and fetal complications were;preterm delivery 221 (70.4%), intrauterine fetal death 62 (19.7%), eclampsia 9 (2.9%), HELLP syndrome 5 (1.6%) and maternal death 17 (5.4%). Associated factors of pre-eclampsia were parity, level of education, and occupation (p ≤ 0.05). Conclusion: The findings of this study showed a rising trend in the incidence of pre-eclampsia over the years at the Ho Teaching Hospital. Parity, level of education, and occupation were found to be associated with developing pre-eclampsia.

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.

Bivariate Analysis of Pollutants Monthly Maxima in Mexico City Using Extreme Value Distributions and Copula

In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metropolitan area. In order to obtain the joint distributions a copula will be considered. Since we are analyzing the monthly maxima, the extreme value distributions of Weibull and Fréchet are taken into account. Using these two distributions as marginal distributions in the copula a Bayesian inference was made in order to estimate the parameters of both distributions and also the association parameters appearing in the copula model. The pollutants taken into account are ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter with diameters smaller than 10 and 2.5 microns obtained from the Mexico City monitoring network. The estimation was performed by taking samples of the parameters generated through a Markov chain Monte Carlo algorithm implemented using the software OpenBugs. Once the algorithm is implemented it is applied to the pairs of pollutants where one of the coordinates of the pair is ozone and the other varies on the set of the remaining pollutants. Depending on the pollutant and the region where they were collected, different results were obtained. Hence, in some cases we have that the best model is that where we have a Fréchet distribution as the marginal distribution for the measurements of both pollutants and in others the most suitable model is the one assuming a Fréchet for ozone and a Weibull for the other pollutant. Results show that, in the present case, the estimated association parameter is a good representation to the correlation parameters between the pair of pollutants analyzed. Additionally, it is a straightforward task to obtain these correlation parameters from the corresponding association parameters.

Using Pearson’s System of Curves to Approximate the Distributions of the Difference between Two Correlated Estimates of Signal-to-Noise Ratios: The Cases of Bivariate Normal and Bivariate Lognormal Distributions

Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference between two correlated SNRs when the readings are from bivariate normal and bivariate lognormal distribution. We use the Pearsons system of curves to approximate the difference between the two estimates and use the bootstrap methods to validate the approximate distributions of the statistic of interest. Methods: The paper uses the delta method to find the first four central moments, and hence the skewness and kurtosis which are important in the determination of the parameters of the Pearsons distribution. Results: The approach is illustrated in two examples;one from veterinary microbiology and food safety data and the other on data from clinical medicine. We derived the four central moments of the target statistics, together with the bootstrap method to evaluate the parameters of Pearsons distribution. The fitted Pearsons curves of Types I and II were recommended based on the available data. The R-codes are also provided to be readily used by the readers.

Spatiotemporal Distributions of Bacterioplankton Communities in the Qiantang River(Hangzhou Section),China

The spatial and temporal distribution of bacterioplankton communities plays a vital role in understanding the ecological dynamics and health of aquatic ecosystems.In this study,we conducted a comprehensive investigation of the bacterioplankton communities in the Qiantang River(Hangzhou section).Water samples were collected quarterly from seven sites over a one-year period.Physical and chemical parameters,including dissolved oxygen(DO),water temperature(WT),chemical oxygen demand(COD),nitrite(NO_(2)^(-)),active phosphate(PO_(4)^(3-))and other indices were determined.In this study,theαdiversity,βdiversity and abundance differences of bacterial communities were investigated using 16S rRNA high-throughput sequencing analysis.The spatial and temporal distribution characteristics and main driving factors of the bacterioplankton community structure and diversity were discussed.The results showed that a total of 57 phyla were detected in the bacterioplankton community,among which Proteobacteria and Actinomycetes were the two dominant groups with the highest relative abundance.The results of PCoA based on Bray-Curtis distance showed that the sampling season had a slightly greater effect on the changes in bacterioplankton community structure in the Qiantang River.Mantel and partial Mantel test showed that environmental variables(Mantel r=0.6739,P<0.0001;partial Mantel r=0.507,P=0.0001)were more important than geographical distance(Mantel r=0.5322,P<0.001;partial Mantel r=0.1563,P=0.001).The distance attenuation model showed that there was significant distance attenuation in all four seasons,and the maximum limit of bacterial community diffusion was found in spring.RDA analysis showed that nine environmental factors,including COD,WT and DO,significantly affected community distribution(P<0.05).This study provides valuable insights into the spatial and temporal distribution characteristics of bacterioplankton communities,shedding light on their ecological roles in the Qiantang River.The information obtained can guide future research on the interactions between bacterioplankton and their environment,enabling the development of effective conservation strategies and sustainable management practices for aquatic ecosystems.

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.

Research on the Spatio-Temporal Evolution and Driving Forces of Green Spaces in the Central Urban Area of Zunyi City

Green space,as a medium for carrying out urban functions and guiding urban development,is becoming a scarce resource along with the urbanization process and the intensification of environmental problems.In the face of the spatial mismatch between high demand and low supply,it is of great significance to clarify the evolution mechanism of green space to undertake national spatial planning,protect the natural strategic resources in the urban fringe area,and promote the sustainable development of the“three living spaces.”The study focuses on the Zunyi City Center,selecting the 20 years of rapid development following its establishment as a city as the study period.It explores the dynamic evolution of green space and the main driving forces during different periods using remote-sensing image data.The study shows that from 2003 to 2023,the total scale of green space has an obvious decreasing trend along with the expansion of the urban built-up area.A large amount of arable land is being converted to construction land,resulting in a sudden decrease in arable land area.In the past 10 years,the comprehensive land use dynamics have accelerated.Still,the spatial difference has gradually narrowed,indicating that the overall development intensity of Zunyi City’s central urban area has increased.There is a gradual spread of the trend to the hilly areas.The limiting effect of the mountainous natural environment on the city’s development has gradually diminished under the superposition of external factors,such as economic development,industrial technological upgrading,and policy orientation so the importance of the effective protection and rational utilization of urban green space has become more prominent.

Spatio-temporal Evolution and Optimization of Landscape Ecological Risk in Karst Mountainous Areas

[Objective]The ecological vulnerability and landscape ecological risk of karst mountainous areas have increased as a result of enhanced disturbance of natural resources by human activities.This paper aimed to explore the characteristics of ecological risk evolution under different landscape patterns in the region,with a view to providing reference for land classification protection,sustainable use of resources and regional ecological risk optimization in karst mountainous areas.[Method]Taking Huangping County,a typical karst mountainous area,as an example,eight evaluation factors of natural and landscape patterns were selected to construct a landscape ecological risk evaluation model,to quantitatively explore the spatio-temporal evolution of landscape ecological risk and the trend of risk level transfer in the study area from^(2)010-2018,and to reveal the complex relationship between ecological risk and topography in karst mountainous areas.[Result]①From 2010 to 2018,land use types changed to different degrees,with the most amount of woodland transferred out(1627.37 hm^(2))and the most amount of construction land transferred in(1303.93 hm^(2));a total of 3552.31 hm^(2) of land was transferred,with a change ratio of 2.13%,and there was a significant conversion between construction land,arable land,and woodland.②From 2010 to 2018,the landscape ecological risk in the study area changed significantly,and the landscape ecological risk index decreased from 0.3441 to 0.1733,showing an upward and then downward trend;the landscape ecological risk of the whole region was dominated by low-risk and lower-risk zones,and the ecological risk level generally shifted from a high level to a low level,and the ecological environment was improved.③There was a negative correlation between ecological risk and topographic position,and high-risk zones were mainly distributed among low topographic zones;with the change of time,the advantage of risk level for the selection of topography was gradually weakened,and the influence of anthropogenic factors on the ecological risk of the landscape was becoming more and more prominent.[Conclusion]This paper can provide theoretical basis for land use optimization and ecological protection in karst mountainous areas.

The Influence of Climate Change and Variability on Spatio-Temporal Rainfall and Temperature Distribution in Zanzibar

Climate change has resulted in serious social-economic ramifications and extremely catastrophic weather events in the world, Tanzania and Zanzibar in particular, with adaptation being the only option to reduce impacts. The study focuses on the influence of climate change and variability on spatio-temporal rainfall and temperature variability and distribution in Zanzibar. The station observation datasets of rainfall, Tmax and Tmin acquired from Tanzania Meteorological Authority (TMA) and the Coordinated Regional Climate Downscaling Experiment program (CORDEX) projected datasets from the Regional climate model HIRHAM5 under driving model ICHEC-EC-EARH, for the three periods of 1991-2020 used as baseline (HS), 2021-2050 as near future (NF) and 2051-2080 far future (FF), under two representative concentration pathways (RCP) of 4.5 and 8.5, were used. The long-term observed Tmax and Tmin were used to produce time series for observing the nature and trends, while the observed rainfall data was used for understanding wet and dry periods, trends and slope (at p ≤ 0.05) using the Standardized Precipitation Index (SPI) and the Mann Kendall test (MK). Moreover, the Quantum Geographic Information System (QGIS) under the Inverse Distance Weighting (IDW) interpolation techniques were used for mapping the three decades of 1991-2000 (hereafter D1), 2001-2010 (hereafter D2) and 2011-2020 (hereafter D3) to analyze periodical spatial rainfall distribution in Zanzibar. As for the projected datasets the Climate Data Operator Commands (CDO), python scripts and Grid analysis and Display System (GrADS) soft-wares were used to process and display the results of the projected datasets of rainfall, Tmax and Tmin for the HS, NF and FF, respectively. The results show that the observed Tmax increased by the rates of 0.035℃ yr-1 and 0.0169℃ yr-1, while the Tmin was increased by a rate of 0.064℃ yr-1 and 0.104℃ yr-1 for Unguja and Pemba, respectively. The temporal distribution of wetness and dryness indices showed a climate shift from near normal to moderate wet during 2005 at Zanzibar Airport, while normal to moderately dry conditions, were observed in Pemba at Matangatuani. The decadal rainfall variability and distributions revealed higher rainfall intensity with an increasing trend and good spatial distribution in D3 from March to May (MAM) and October to December (OND). The projected results for Tmax during MAM and OND depicted higher values ranging from 1.7℃ - 1.8℃ to 1.9℃ - 2.0℃ and 1.5℃ to 2.0℃ in FF compared to NF under both RCPs. Also, higher Tmin values of 1.12℃ - 1.16℃ was projected in FF for MAM and OND under both RCPs. Besides, the rainfall projection generally revealed increased rainfall intensity in the range of 0 - 25 mm for Pemba and declined rainfall in the range of 25 - 50 mm in Unguja under both RCPs in perspectives of both NF and FF. Conclusively the study has shown that the undergoing climate change has posed a significant impact on both rainfall and temperature spatial and temporal distributions in Zanzibar (Unguja and Pemba), with Unguja being projected to have higher rainfall deficits while increasing rainfall strengths in Pemba. Thus, the study calls for more studies and formulation of effective adaptation, strategies and resilience mechanisms to combat the projected climate change impacts especially in the agricultural sector, water and food security.

Simulation of the spatio-temporal evolution of the electron energy distribution function in a pulsed hollow-cathode discharge

This article presents the 2D simulation results of a nanosecond pulsed hollow cathode discharge obtained through a combination of fluid and kinetic models.The spatio-temporal evolution of the electron energy distribution function(EEDF)of the plasma column and electrical characteristics of the nanosecond pulsed hollow cathode discharge at a gas pressure of 5 Torr are studied.The results show that the discharge development starts with the formation of an ionization front at the anode surface.The ionization front splits into two parts in the cathode cavity while propagating along its lateral surfaces.The ionization front formation leads to an increase in the fast isotropic EEDF component at its front,as well as in the anisotropic EEDF component.The accelerated electrons enter the cathode cavity,which significantly contributes to the formation of the highenergy EEDF component and EEDF anisotropy.

Spatio-temporal distribution of net primary productivity along the northeast China transect and its response to climatic change

An improved Carnegie Ames Stanford Approach model （CASA model） was used to estimate the net primary productivity （NPP） of the Northeast China Transect （NECT） every month from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that the change tendency of NPP spatial distribution in NECT is quite similar to that of precipitation and their spatial correlation coefficient is up to 0.84 （P 〈 0.01）. The inter-annual variation of NPP in NECT is mainly affected by the change of the aestival NPP every year, which accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 （P 〈 0.01）. The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. The NPP in summer （June to August） accounts for 65.9% of the annual NPP and is the lowest in winter. Recent climate changes have enhanced plant growth in NECT. The mean NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC·m^-2·a^-1, and the relative trend is 1.17%, which owns mainly to the increasing temperature.

Analysis on Spatio-temporal Distribution of Lightning in Dalian Area of China between 2007 and 2008

The cloud-to-ground lightning data between 2007 and 2008 were collected by lightning detection and location system,which was composed of four lightning detectors in four different sites of Dalian area.The spatio-temporal distribution of cloud-to-ground lightning in surrounding areas of Dalian was analyzed from several aspects of polarity distribution,diurnal variation,lightning intensity and lightning density.The results showed that the number of negative lightning accounted for 93.9% of the total number of lightning,and its average lightning intensity was 27.99 kA.The number of positive lightning accounted for 6.1% of the total number of lightning,and its average lightning intensity was 35.56 kA.The diurnal variation of lightning frequency showed an obvious structure of two peaks (17:00-18:00 and 04:00-05:00) and two valleys (09:00-10:00 and 00:00-01:00).The number of lightning between May and September was 91.5% of the annual number,and the lightning occurred the most frequently between June and August.Most of positive and negative lightning was at the intensity of 15-35 kA,80.0% lower than 40 kA,and 99.3% lower than 100 kA.The lightning density had obvious regional differences in distribution,high in the Liaodong Bay and the Dalian Bay and low in inland areas.Therefore,coastal areas should attract more attention in lightning disaster defense in the surrounding areas of Dalian.

Implementing geostatistical analysis to study spatio-temporal distribution patterns of swimming crabs(Portunus trituberculatus)

This study attempted to compare the performance of local polynomial interpolation,inverse distance weighted interpolation,and ordinary kriging in studying distribution patterns of swimming crabs.Cross-validation was used to select the optimum method to get distribution results,and kriging was used for making spatial variability analysis.Data were collected from 87 sampling stations in November of 2015(autumn)and February(winter),May(spring)and August(summer)of 2016.Results indicate that swimming crabs widely distributed in autumn and summer:in the summer,they were more spatially independent,and resources in each sampling station varied a lot;in the winter and spring,the abundance of crabs was much lower,but the individual crab size was bigger,and they showed the patchy and more concentrative distribution pattern,which means they were more spatially dependent.Distribution patterns were in accordance with ecological migration features of swimming crabs,which were affected by the changing marine environment.This study could infer that it is applicable to study crab fishery or even other crustacean species using geostatistical analysis.It not only helps practitioners have a better understanding of how swimming crabs migrate from season to season,but also assists researchers in carrying out a more comprehensive assessment of the fishery.Therefore,it may facilitate advancing the implementation in the pilot quota management program of swimming crabs in northern Zhejiang fishing grounds.

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.

Relationship between spatio-temporal evolution of soil pH and geological environment/surface cover in the eastern Nenjiang River Basin of Northeast China during the past 30 years

To illuminate the spatio-temporal variation characteristics and geochemical driving mechanism of soil pH in the Nenjiang River Basin,the National Multi-objective Regional Geochemical Survey data of topsoil,the Second National Soil Survey data and Normalized Difference Vegetation Index(NDVI)were analyzed.The areas of neutral and alkaline soil decreased by 21100 km^(2)and 30500 km^(2),respectively,while that of strongly alkaline,extremely alkaline,and strongly acidic soil increased by 19600 km^(2),18200 km^(2),and 15500 km^(2),respectively,during the past 30 years.NDVI decreased with the increase of soil pH when soil pH>8.0,and it was reversed when soil pH<5.0.There were significant differences in soil pH with various surface cover types,which showed an ascending order:Arbor<reed<maize<rice<high and medium-covered meadow<low-covered meadow<Puccinellia.The weathering products of minerals rich in K_(2)O,Na_(2)O,CaO,and MgO entered into the low plain and were enriched in different parts by water transportation and lake deposition,while Fe and Al remained in the low hilly areas,which was the geochemical driving mechanism.The results of this study will provide scientific basis for making scientific and rational decisions on soil acidification and salinization.