Effect of granular shape on radial segregation in a two-dimensional drum

Granular segregation is widely observed in nature and industry.Most research has focused on segregation caused by differences in the size and density of spherical grains.However,due to the fact that grains typically have different shapes,the focus is shifting towards shape segregation.In this study,experiments are conducted by mixing cubic and spherical grains.The results indicate that spherical grains gather at the center and cubic grains are distributed around them,and the degree of segregation is low.Through experiments,a structured analysis of local regions is conducted to explain the inability to form stable segregation patterns with obviously different geometric shapes.Further,through simulations,the reasons for the central and peripheral distributions are explained by comparing velocities and the number of collisions of the grains in the flow layer.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Segregation of Mg-6Gd alloy under natural convection:From macro solute distribution to micro dendrite growth

Segregation is a serious defect in alloy ingots which severely deteriorates materials performance.The segregation defect in Mg-6Gd alloy is studied by coupling macro thermal-solutal-convection transport and micro dendrite growth.The macroscopic fluid dynamics and mass transfer equations are resolved to forecast the segregation behavior under conditions of continuous temperature variation during the solidification process.The numerical model is validated by testing double-diffusive natural convection in a closed square cavity.A phase field model is then applied to simulate the micro dendrite growth,using macro undercooling and liquid flow velocity as boundary conditions.Results show that the multiscale segregation behavior,including macro solute distribution and micro dendritic morphology,is strongly dependent on the temperature condition and the liquid convection,which provides guidance for reducing and eliminating the segregation defect.

Effect of casting process on the inner-wall band segregation of high-strength antisulfur pipes

Controlling inner-wall band segregation is one of the difficulties in the production of high-strength antisulfur pipes.Comparative tests were carried out on different casting processes(superheat,mold electromagnetic stirring,end electromagnetic stirring,casting speed and soft reduction)for the smelting of high-strength antisulfur pipes.The microstructures of continuous-casting billets and hot-rolled or tempered pipes were analyzed using a metallographic microscope and scanning electron microscope.The mechanism and evolution law regarding the inner-wall band segregation of high-strength antisulfur pipes were studied,and the influence of different casting processes was explored.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Towards designing high mechanical performance low-alloyed wrought magnesium alloys via grain boundary segregation strategy:A review

Low-alloyed magnesium(Mg)alloys have emerged as one of the most promising candidates for lightweight materials.However,their further application potential has been hampered by limitations such as low strength,poor plasticity at room temperature,and unsatisfactory formability.To address these challenges,grain refinement and grain structure control have been identified as crucial factors to achieving high performance in low-alloyed Mg alloys.An effective way for regulating grain structure is through grain boundary(GB)segregation.This review presents a comprehensive summary of the distribution criteria of segregated atoms and the effects of solute segregation on grain size and growth in Mg alloys.The analysis encompasses both single element segregation and multi-element co-segregation behavior,considering coherent interfaces and incoherent interfaces.Furthermore,we introduce the high mechanical performance low-alloyed wrought Mg alloys that utilize GB segregation and analyze the potential impact mechanisms through which GB segregation influences materials properties.Drawing upon these studies,we propose strategies for the design of high mechanical performance Mg alloys with desirable properties,including high strength,excellent ductility,and good formability,achieved through the implementation of GB segregation.The findings of this review contribute to advancing the understanding of grain boundary engineering in Mg alloys and provide valuable insights for future alloy design and optimization.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

The occurrence,inheritance,and segregation of complex genomic structural variation in synthetic Brassica napus

"Synthetic"allopolyploids recreated by interspecific hybridization play an important role in providing novel genomic variation for crop improvement.Such synthetic allopolyploids often undergo rapid genomic structural variation(SV).However,how such SV arises,is inherited and fixed,and how it affects important traits,has rarely been comprehensively and quantitively studied in advanced generation synthetic lines.A better understanding of these processes will aid breeders in knowing how to best utilize synthetic allopolyploids in breeding programs.Here,we analyzed three genetic mapping populations(735 DH lines)derived from crosses between advanced synthetic and conventional Brassica napus(rapeseed)lines,using whole-genome sequencing to determine genome composition.We observed high tolerance of large structural variants,particularly toward the telomeres,and preferential selection for balanced homoeologous exchanges(duplication/deletion events between the A and C genomes resulting in retention of gene/chromosome dosage between homoeologous chromosome pairs),including stable events involving whole chromosomes("pseudoeuploidy").Given the experimental design(all three populations shared a common parent),we were able to observe that parental SV was regularly inherited,showed genetic hitchhiking effects on segregation,and was one of the major factors inducing adjacent novel and larger SV.Surprisingly,novel SV occurred at low frequencies with no significant impacts on observed fertility and yield-related traits in the advanced generation synthetic lines.However,incorporating genome-wide SV in linkage mapping explained significantly more genetic variance for traits.Our results provide a framework for detecting and understanding the occurrence and inheritance of genomic SV in breeding programs,and support the use of synthetic parents as an important source of novel trait variation.

No basalt accumulation and segregation atop the 660-km discontinuity beneath the Sea of Okhotsk

Recent seismic evidence shows that basalt accumulation is widespread in the mantle transition zone(MTZ),yet its ubiquity or sporadic nature remains uncertain.To investigate this phenomenon further,we characterized the velocity structure across the 660-km discontinuity that separates the upper mantle from the lower mantle beneath the Sea of Okhotsk by modeling the waveform of the S660P phase,a downgoing S wave converting into a P wave at the 660-km interface.These waves were excited by two regional>410-km-deep events and were recorded by stations in central Asia.Our findings showed no need to introduce velocity anomalies at the base of the MTZ to explain the S660P waveforms because the IASP91 model adequately reproduced the waveforms.This finding indicates that the basalt accumulation has not affected the bottom of the MTZ in the study area.Instead,this discontinuity is primarily controlled by temperature or water content variations,or both.Thus,we argue that the basalt accumulation at the base of the MTZ is sporadic,not ubiquitous,reflecting its heterogeneous distribution.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Microstructure,segregation and precipitate evolution in directionally solidified GH4742 superalloy

The evolution of microstructure,elemental segregation,and precipitation in GH4742 superalloy under a wide range of cooling rates was investigated using zonal melting liquid metal cooling(ZMLMC) experiments.Comparing various nickel-based superalloys,the primary dendrite spacing is significantly linearly correlated with G^(-1/2)V^(-1/4) at high cooling rates,where G and V are temperature gradient and drawing rate,respectively.As the cooling rate decreases,the primary dendrite spacing increases in a dispersive manner.The secondary dendrite arm spacing is significantly correlated with(GV)^(-0.4) for all cooling rate ranges.The degree of elemental segregation increases and then decreases as the cooling rate increases,which is due to the competition between solute counter-diffusion and dendrite tip subcooling.With increasing the solidification rate,the size of γ′,carbides,and non-metallic inclusions gradually decreases.The morphology of the γ′ precipitate changes from plume-like to cubic to spherical.The morphology of carbide changes from block to fine-strip then to Chinese-script.The morphology of carbide is controlled by both dendrite interstitial shape and element diffusion.The inclusions are mainly composite inclusions,which usually show the growth of Ti(C,N) with oxide as the heterogeneous nucleation center and carbide on the outer surface of the carbonitride.As the cooling rate increases,the number density of composite inclusions first increases and then decreases,which is closely related to the elemental segregation behavior.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Atomic-scale insights of indium segregation and its suppression by GaAs insertion layer in InGaAs/AlGaAs multiple quantum wells

The In segregation and its suppression in InGaAs/AlGaAs quantum well are investigated by using high-resolution x-ray diffraction(XRD)and photoluminescence(PL),combined with the state-of-the-art aberration corrected scanning transmission electron microscopy(Cs-STEM)techniques.To facility our study,we grow two multiple quantum wells(MQWs)samples,which are almost identical except that in sample B a thin GaAs layer is inserted in each of the InGaAs well and AlGaAs barrier layer comparing to pristine InGaAs/AlGaAs MQWs(sample A).Our study indeed shows the direct evidences that In segregation occurs in the InGaAs/AlGaAs interface,and the effect of the Ga As insertion layer on suppressing the segregation of In atoms is also demonstrated on the atomic-scale.Therefore,the atomic-scale insights are provided to understand the segregation behavior of In atoms and to unravel the underlying mechanism of the effect of GaAs insertion layer on the improvement of crystallinity,interface roughness,and further an enhanced optical performance of InGaAs/AlGaAs QWs.

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

Stretchable alkenamides terminated Ti_(3)C_(2)T_(x)MXenes to release strain for lattice-stable mixed-halide perovskite solar cells with suppressed halide segregation

Bandgap-tunable mixed-halide perovskite materials have attracted considerable interest because of their indispensability as top counterparts in tandem solar cells.However,the soft and disordered lattice always suffers from severe phase segregation under illumination,which is particularly susceptible to residual lattice strain.Herein,we report a strain regulation strategy by using alkenamides terminated Ti_(3)C_(2)T_(x)MXenes as an additive into perovskite precursor.Apart from the role of a template for grain growth to obtain high-quality films,the stretchable alkyl chain promotes lattice shrinkage or expansion to form an elastic grain boundary to eliminate the spatially distributed stain and shut down ion migration channels.As a result,the all-inorganic perovskite solar cells based on CsPbIBr_(2)and CsPbI_(2)Br halides achieve prolonged device stability under harsh conditions and the best power conversion efficiencies up to 11.06%and 14.30%,respectively.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Experimental investigation of the mixing efficiency via intensity of segregation along axial direction of a rotating bar reactor

As a significant index to evaluate the mixing efficiency,studying the concentration distribution is directly related to the intensity of segregation(I_(s)).In this work,the I_(s) of the mixture composed of NaCl solutionwater was investigated experimentally in a rotating bar reactor(RBR)by the conductivity method.The results showed that the mixing efficiency was improved along the axial direction from the bottom to the top in the RBR.The concentration distribution at the bottom section was more uneven,and I_(s) was higher compared with the top section,which decreased from 6.53×10^(-5)to 1.57×10^(-7).With the increase of rotational speed from 0 to 700 r·min^(-1),I_s at the bottom and top sections decreased from 4.27×10^(-3)to 7.10×10^(-5)and from 1.93×10^(-3)to 7.29×10^(-7),respectively.The increases flow rate of solution A,and the decreases of concentration of NaCl and flow rate of solution B gave rise to the reduction of I_(s),signifying an improved mixing efficiency.The results revealed that the conductivity method used in this paper has high efficiency and low cost to measure the I_(s),which indicates a promising prospect for estimating reactors'mixing performance.