Mathematical basis,correlativity and selection of seismicity parameters

Using the analytic expression of seismicity parameters, the dependency and correlativity between the statisticalparameters and seismic frequency or seismic intensity are discussed. The statistical parameters are divided into twokinds. One kind is the regional seismicity parameter, 17 parameters are analyzed in this paper. The other kind isthe seismicity distribution parameter. They are the distribution parameters of time, space and magnitude. The existent base and rationality of distribution parameters depend on the application of distribution model. We analyzeand draw an analogy between the natural probability, Poisson, Weibull distributions and multi-f racial analyticformula in time, space and magnitude. And some examples are given in this paper. The P value and H value of aftershock sequence attenuation, the U value and F value of eanhquake swarm sequence and the entropy of information are discussed preliminarily. Another method about analyzing relationships among time-series curves aregiven. The resemblance relativity degree R,.,, and the relativity degree G,.,, of relative change slope can be usedas the determining values. At last, some preliminary ideas about sifting and using for seismicity parameters are advanced in this paper.

Study on correlativity among capacity dimension D_0,informationd imension D_1,algorithmic complexityC

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Study on the correlativity of earthquake's impact on cities

Introduction With rapid development and advancement of economy and society, lots of city groups or city belts with ex-tra-large cities as their centers have been formed in China. The regions these city groups lie in usually havewell-developed economy, dense population, and are regional politics and culture centers. Some groups lie in theregions with high level of earthquake activity, such as the Surrounding Capital City Group with the centers of Bei-jing and Tianjin. Once a large earthquake occurs, its influence will spread to very extensive region and its disasterwill be tremendous too. So earthquake resistance and disaster mitigation of city group will be very significant issue.The cities in a group have close distance with each other; they can carry out unified preparation for disaster as onewhole and reduce the heavy load of single city before an earthquake, and have an advantage of prompt mutual-aidafter an earthquake because of close distance. It is especially significant to mitigate the lose of lives. One importantprecondition is that all the cities in one group cannot be exposed to the same level of destroy during one earth-quake. So the division of city group in the region with dense cities distribution shall be very significant to theemergent mutual-aid in early time after a large earthquake. For this goal, the characteristics and correlativity ofearthquake′s impact on cities in one group need to clearly be considered. The cities with similar features and strongcorrelativity of historical earthquake influence have large chance to suffer same level destroy during the futurestrong earthquake and are disadvantage to provide mutual-aid and shall not be divided into one group. ……

Generalized Correlativity of Median Filtering Operator on Signals

The generalized correlativity of input signal and output signal of a stack filtering operator is defined and used for numerously measuring these filtering operators's behavior in removing noise in signals. We show that under the criterion of the generalized correlativity, of stack filtering operators the median filtering operator is optimal, which implies that this filtering operator possesses better filtering behavior than the others.

Study on correlativity among some seismological prediction indexes related to earthquake frequency or energy

Correlation among various seismological prediction indexes related to earthquake frequency N or energy E for describing seismicity changes is studied by seismic data of 11 seismic zones (areas) in the Sichuan-Yunnan region. A statistical test for correlativity shows that seismicity indexes obtained from transformation of earthquake frequency N or energy E have correlation to a certain extent while they are used to describe seismicity changes. If the transformation is linear in all cases, the correlation coefficient is equal to 1. The smaller the transformation deviation from linearity, the bigger the correlation coefficient is.

Genetic parameter evaluation for growth and cold resistance traits of the giant freshwater prawn Macrobrachium rosenbergii

The giant freshwater prawn Macrobrachium rosenbergii distributed from tropical to subtropical regions,is a warm-water species,and its survival temperature is 14-35°C,which greatly limits its culture cycle and culture area in China.Therefore,it is urgent to cultivate a new high quality,high yield variety with improved cold-resistance,but the genetic parameters for cold-resistance traits are unknown in M.rosenbergii.In this study,the cold-resistance of adult M.rosenbergii populations was tested using the indoor artificial cooling method.Individuals were selected from 139 families of Shufeng G3 generation and cultured for 200 days.A linear mixed model was constructed by ASReml-R to evaluate the genetic parameters of the cold-resistance trait(cooling degree hours,CDH)and growth traits(body weight,BW,and body length,BL)based on the restricted maximum likelihood(REML)method.The results show that the heritability of CDH was low(0.12±0.05),while the growth traits(BW and BL)had low to moderate heritability,with 0.20±0.06 for BW and 0.06±0.04 for BL.The phenotypic and genetic correlation between BW and BL was significantly positive,but significantly negative phenotypic and genetic correlations were detected between CDH and BW and between CDH and BL.Furthermore,the analysis of the differences between cold-resistance and phenotypic traits showed that the female reproductive status,exoskeleton hardness and claw number of adult prawns had a great influence on the cold-resistance of M.rosenbergii(P<0.05),indicating that adults with claws and hard exoskeletons are preferred as parents in subsequent breeding selection.The present results can be attributed to the selection and breeding of a new cold-resistant variety of M.rosenbergii.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

Temperature-sensing array using the metal-to-insulator transition of Nd_(x)Sm_(1-x)NiO_(3)

Rare-earth nickelates(RENiO_(3))show widely tunable metal-to-insulator transition(MIT)properties with ignorable variations in lattice constants and small latent heat across the critical temperature(TMIT).Particularly,it is worth noting that compared with the more commonly investigated vanadium oxides,the MIT of RENiO_(3)is less abrupt but usually across a wider range of temperatures.This sheds light on their alternative applications as negative temperature coefficient resistance(NTCR)thermistors with high sensitivity compared with the current NTCR thermistors,other than their expected use as critical temperature resistance thermistors.In this work,we demonstrate the NTCR thermistor functionality for using the adjustable MIT of Nd_(x)Sm_(1-x)NiO_(3)within 200–400 K,which displays larger magnitudes of NTCR(e.g.,more than 7%/K)that is unattainable in traditional NTCR thermistor materials.The temperature dependence of resistance(R–T)shows sharp variation during the MIT of Nd_(x)Sm_(1-x)NiO_(3)with no hysteresis via decreasing the Nd content(e.g.,x≤0.8),and such a R–T tendency can be linearized by introducing an optimum parallel resistor.The sensitive range of temperature can be further extended to 210–360 K by combining a series of Nd_(x)Sm_(1-x)NiO_(3)with eight rare-earth co-occupation ratios as an array,with a high magnitude of NTCR(e.g.,7%–14%/K)covering the entire range of temperatures.

Quantum correlations and entanglement in coupled optomechanical resonators with photon hopping via Gaussian interferometric power analysis

Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation.Essentially,for quantum systems prepared in pure states,it is difficult to differentiate between quantum entanglement and quantum correlation.Nonetheless,this indistinguishability is no longer holds for mixed states.To contribute to a better understanding of this differentiation,we have explored a simple model for both generating and measuring these quantum correlations.Our study concerns two macroscopic mechanical resonators placed in separate Fabry–Pérot cavities,coupled through the photon hopping process.this system offers a comprehensively way to investigate and quantify quantum correlations beyond entanglement between these mechanical modes.The key ingredient in analyzing quantum correlation in this system is the global covariance matrix.It forms the basis for computing two essential metrics:the logarithmic negativity(E_(N)^(m))and the Gaussian interferometric power(P_(G)^(m)).These metrics provide the tools to measure the degree of quantum entanglement and quantum correlations,respectively.Our study reveals that the Gaussian interferometric power(P_(G)^(m))proves to be a more suitable metric for characterizing quantum correlations among the mechanical modes in an optomechanical quantum system,particularly in scenarios featuring resilient photon hopping.

Differences and similarities in radial growth of Betula species to climate change

Betula platyphylla and Betula costata are important species in mixed broadleaved-Korean pine(Pinus koraiensis)forests.However,the specific ways in which their growth is affected by warm temperatures and drought remain unclear.To address this issue,60 and 62 tree-ring cores of B.platyphylla and B.costata were collected in Yichun,China.Using dendrochronological methods,the response and adaptation of these species to climate change were examined.A“hysteresis effect”was found in the rings of both species,linked to May–September moisture conditions of the previous year.Radial growth of B.costata was positively correlated with the standardized precipitation-evapotranspiration index(SPEI),the precipitation from September to October of the previous year,and the relative humidity in October of the previous year.Growth of B.costata is primarily restricted by moisture conditions from September to October.In contrast,B.platyphylla growth is mainly limited by minimum temperatures in May–June of both the previous and current years.After droughts,B.platyphylla had a faster recovery rate compared to B.costata.In the context of rising temperatures since 1980,the correlation between B.platyphylla growth and monthly SPEI became positive and strengthened over time,while the growth of B.costata showed no conspicuous change.Our findings suggest that the growth of B.platyphylla is already affected by warming temperatures,whereas B.costata may become limited if warming continues or intensifies.Climate change could disrupt the succession of these species,possibly accelerating the succession of pioneer species.The results of this research are of great significance for understanding how the growth changes of birch species under warming and drying conditions,and contribute to understanding the structural adaptation of mixed broadleaved-Korean pine(Pinus koraiensis)forests under climate change.

Correlation Study on Grain Size Characteristics and Geotechnical Properties of Surface Sediments in Qingdao Offshore Area

The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.

Evaluation and Influencing Factors on Particle Agglomeration in RAP

Asphalt extraction test and scanning electron microscopy(SEM) were used for analysis of agglomerations of reclaimed asphalt pavement(RAP) particles. In order to quantify the agglomeration degree of RAP, the fineness modulus ratio(FMR) and the percentage loss index(PLI) were proposed. In addition, grey correlation analysis was conducted to discuss the relationship between particle agglomerations and RAP size,asphalt content(AC), and surface area. Two indexes indicate that the agglomeration degree increases in general as the RAP size reduces. This can be attributed to that particles are prone to agglomeration in the case of higher AC. Based on the SEM images and the material composition of RAP, the particle agglomeration in RAP can be classified into weak agglomeration and strong agglomeration. Grey correlation analysis shows that AC is the crucial factor affecting the agglomeration degree and RAP variability. In order to produce consistent and stable reclaimed mixtures, disposal measures of RAP are suggested to lower the AC of RAP.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

Roughness characterization and shearing dislocation failure for rock-backfill interface

Shearing dislocation is a common failure type for rock–backfill interfaces because of backfill sedimentation and rock strata movement in backfill mining goaf.This paper designed a test method for rock–backfill shearing dislocation.Using digital image techno-logy and three-dimensional(3D)laser morphology scanning techniques,a set of 3D models with rough joint surfaces was established.Further,the mechanical behavior of rock–backfill shearing dislocation was investigated using a direct shear test.The effects of interface roughness on the shear–displacement curve and failure characteristics of rock–backfill specimens were considered.The 3D fractal dimen-sion,profile line joint roughness coefficient(JRC),profile line two-dimensional fractal dimension,and the surface curvature of the frac-tures were obtained.The correlation characterization of surface roughness was then analyzed,and the shear strength could be measured and calculated using JRC.The results showed the following:there were three failure threshold value points in rock–backfill shearing dis-location:30%–50%displacement before the peak,70%–90%displacement before the peak,and 100%displacement before the peak to post-peak,which could be a sign for rock–backfill shearing dislocation failure.The surface JRC could be used to judge the rock–backfill shearing dislocation failure,including post-peak sliding,uniform variations,and gradient change,corresponding to rock–backfill disloca-tion failure on the field site.The research reveals the damage mechanism for rock–backfill complexes based on the free joint surface,fills the gap of existing shearing theoretical systems for isomerism complexes,and provides a theoretical basis for the prevention and control of possible disasters in backfill mining.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

FROM WAVE FUNCTIONS TO TAU-FUNCTIONS FOR THE VOLTERRA LATTICE HIERARCHY

For an arbitrary solution to the Volterra lattice hierarchy,the logarithmic derivatives of the tau-function of the solution can be computed by the matrix-resolvent method.In this paper,we define a pair of wave functions of the solution and use them to give an expression of the matrix resolvent;based on this we obtain a new formula for the k-point functions for the Volterra lattice hierarchy in terms of wave functions.As an application,we give an explicit formula of k-point functions for the even GUE(Gaussian Unitary Ensemble)correlators.

Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China

Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.