Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

The heterogeneous impact of China-Europe railway express on the efficiency of logistics industry in node cities

Purpose-The spatiotemporal compression effect of China-Europe Railway Express(CR-Express)can reduce the filow costs of resources between China's node cities.Additionally,it can break through the limitations of low-added-value marine products,significantly impacting the logistics industry efficiency.However,there are few literature verifying and analyzing its heterogeneity.This study explores the impact of CR-Express on the efficiency of logistics industry in node cities and analyzes the heterogeneity.Design/methodology/approach-First,this study uses panel data to measure the efficiency of node city logistics industry.Secondiy,this study discusses the impact of the opening of CR-Express on the efficiency of logistics industry in node cities based on the multi-period differential model.Finally,according to the node city difference,the sample city experimental group is grouped for heterogeneity analysis.Findings-The results show that CR-Express can promote the urban logistics industry efficiency,with an average effect of 4.55%.According to the urban characteristics classification,the heterogeneity analysis shows that the efficiency improvement effect of logistics industry in inland cities is more obvious.The improvement effect of node cities and central cities in central and western China is stronger,especially in the sample of megacities and type I big cities.Compared with non-value chain industrial products,the CR-Express has significant promotion effects on the logistics efficiency of the cities where main goods are value chain products.Originality/value-Under the background of double cycle development,this paper can provide a scientific basis for the investment benefit evaluation of CR-Express construction and the follow-up route planning.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Displacement Field Variable Modeling Method for Heterogeneous Materials in Wind Power Blade Core Plates

In order to study the mechanical properties of the heterogeneous core plate of the wind turbine blade,a modeling method of the core plate based on displacement field variables is proposed.Firstly,the wind turbine blade core plate was modeled according to the theory of modeling heterogeneous material characteristics.Secondly,the three-point bending finite element model of the wind turbine blade core plate was solved by the display dynamic equation to obtain the deformation pattern and force-deformation relationship of the core plate.Finally,the three-point bending static test was conducted to compare with the finite element analysis.The test results show that:the damage form of the wind turbine blade core plate includes elasticity,yield,and failure stages.The main failure modes are plastic deformation,core material collapse,and panel-core delamination.The failure load measured by the test is 1.59 kN,which is basically consistent with the load-displacement result obtained by the simulation,with a difference of only 1.9%,which verifies the validity and reliability of the model.It provides data references for wind turbine blade structure design.

Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models

Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,the site-to-fault distance and the site foundation properties.In the present study,the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an effi ciency-robustness balanced formula is proposed.For this purpose,a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship.In this approach,each model class(a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data.The one with the highest plausibility is robust since it possesses the optimal balance between the data fi tting capability and the sensitivity to noise.A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis.The optimal predictive formula is proposed based on this database.It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore,Joyner and Fumal(1993).

Study on Simulation of Foreshock Activity Properties before Strong Earthquake Using Heterogeneous Cellular Automata Models

Three different degrees of heterogeneous fault models are simulated by using 2-D random dynamic cellular automata models for analyzing macroscopic behaviors of seismic activity evolution influenced by heterogeneity of fault structures. The results show that the heterogeneities of fault structures can influence evolution properties of the foreshock activity and rupture process, such as the mediate heterogeneous and less heterogeneous structures, which show relatively higher ASR rates and more significant seismic gaps before main shocks. Besides, stress drop distribution ranges of the foreshock events when approaching a main shock show more homogenous (narrower) than that of the foreshock events far from a main shock. So the heterogeneity of fault structures plays an important role in strong earthquake preparation processes.

Rock physics modeling of heterogeneous carbonatereservoirs： porosity estimation and hydrocarbon detection

In heterogeneous natural gas reservoirs, gas is generally present as small patch-like pockets embedded in the water-saturated host matrix. This type of heterogeneity, alsocalled ＂patchy saturation＂, causes significant seismic velocity dispersion and attenuation. Toestablish the relation between seismic response and type of fluids, we designed a rock physicsmodel for carbonates. First, we performed CT scanning and analysis of the fluid distributionin the partially saturated rocks. Then, we predicted the quantitative relation between the waveresponse at different frequency ranges and the basic lithological properties and pore fluids.A rock physics template was constructed based on thin section analysis of pore structuresand seismic inversion. This approach was applied to the limestone gas reservoirs of the rightbank block of the Amu Darya River. Based on poststack wave impedance and prestack elasticparameter inversions, the seismic data were used to estimate rock porosity and gas saturation.The model results were in ~ood a^reement with the production regime of the wells.

A cellular automata model of epidemics of a heterogeneous susceptibility

In this paper we present a model with spatial heterogeneity based on cellular automata （CA）. In the model we consider the relevant heterogeneity of host （susceptible） mixing and the natural birth rate. We divide the susceptible population into three groups according to the immunity of each individual based on the classical susceptible-infectedremoved （SIR） epidemic models, and consider the spread of an infectious disease transmitted by direct contact among humans and vectors that have not an incubation period to become infectious. We test the local stability and instability of the disease-free equilibrium by the spectrum radii of Jacobian. The simulation shows that the structure of the nearest neighbour size of the cell （or the degree of the scale-free networks） plays a very important role in the spread properties of infectious disease. The positive equilibrium of the infections versus the neighbour size follows the third power law if an endemic equilibrium point exists. Finally, we analyse the feature of the infection waves for the homogeneity and heterogeneous cases respectively.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Experimental determination of grain density function of AZ91/SiC composite with different mass fractions of SiC and undercoolings using heterogeneous nucleation model

The grain density,Nv,in the solid state after solidification of AZ91/SiC composite is a function of maximum undercooling,ΔT,of a liquid alloy.This type of function depends on the characteristics of heterogeneous nucleation sites and number of SiC present in the alloy.The aim of this paper was selection of parameters for the model describing the relationship between the grain density of primary phase and undercooling.This model in connection with model of crystallisation,which is based on chemical elements diffusion and grain interface kinetics,can be used to predict casting quality and its microstructure.Nucleation models have parameters,which exact values are usually not known and sometimes even their physical meaning is under discussion.Those parameters can be obtained after mathematical analysis of the experimental data.The composites with 0,1,2,3 and 4wt.% of SiC particles were prepared.The AZ91 alloy was a matrix of the composite reinforcement SiC particles.This composite was cast to prepare four different thickness plates.They were taken from the region near to the thermocouple,to analyze the undercooling for different composites and thickness plates and its influence on the grain size.The microstructure and thermal analysis gave set of values that connect mass fraction of SiC particles,and undercooling with grain size.These values were used to approximate nucleation model adjustment parameters.Obtained model can be very useful in modelling composites microstructure.

A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model using Model Regionalization

Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Explicit Correlation Model of Multi-source Constraints for Re-design Parts with Complex Curved Surface

In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories： surface re-design complex curved surface parts with multi-source constraints（PRCS） and surface unique complex curved surface parts with pure geometric constraints（PUCS）. A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical paws performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.

User growth and penetration modeling for converged heterogeneous network

To evaluate the trail potential of converged heterogeneous network （CHN） market, the logistic method for adoption modeling of CHN is used. User growth ＆ penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation, commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.

Heterogeneous traffic flow modeling with drivers’ timid and aggressive characteristics

The driver’s characteristics(e.g.,timid and aggressive)has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous.In the real traffic environment,the drivers are distinct due to a variety of factors such as personality characteristics.To better reflect the reality,we introduce the penetration rate to describe the degree of drivers’heterogeneity(i.e.,timid and aggressive),and proceed to propose a generalized heterogeneous car-following model with different driver’s characteristics.Through the linear stability analysis,the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method.The impacts of the penetration rate of drivers with low intensity,the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation.Results show that higher penetration of aggressive drivers contributes to traffic flow stability.The average value has a great impact on the stability of traffic flow,whereas the impact of the standard deviation is trivial.

Influence of formation heterogeneity on foam flooding performance using 2D and 3D models:an experimental study

The formation heterogeneity is considered as one of the major factors limiting the application of foam flooding.In this paper,influences of formation properties,such as permeability,permeability distribution,interlayer,sedimentary rhythm and 3D heterogeneity,on the mobility control capability and oil displacement efficiency of foam flooding,were systematically investigated using 2D homogeneous and 2D/3D heterogeneous models under 120°C and salinity of 20×10~4 mg/L.The flow resistance of foam was promoted as the permeability increased,which thus resulted in a considerable oil recovery behavior.In the scenario of the vertical heterogeneous formations,it was observed that the permeability of the high-permeable layer was crucial to foam mobility control,and the positive rhythm appeared favorable to improve the foam flooding performance.The additional oil recovery increased to about 40%.The interlayer was favorable for the increases in mobility reduction factor and oil recovery of foam flooding when the low permeability ratio was involved.For the 3D heterogeneous formations,foam could efficiently adjust the areal and vertical heterogeneity through mobility control and gravity segregation,and thus enhancing the oil recovery to 11%–14%.The results derived from this work may provide some insight for the field test designs of foam flooding.

3D Geological Modeling with Multi-source Data Integration in Polymetallic Region:A Case Study of Luanchuan,Henan Province,China

The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.