Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains...Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.展开更多
As the evolution of mobile technology, mobile devices have become an essential tool in people＇s daily life. Moreover, with the rapid growth of Internet and mobile networks, people can easily access various services p...As the evolution of mobile technology, mobile devices have become an essential tool in people＇s daily life. Moreover, with the rapid growth of Internet and mobile networks, people can easily access various services provided by mobile platforms. Many services can be executed on the mobile devices with various mobile applications launched to mobile platforms. People can choose what they like to install in their mobile devices and hence make their life more convenient, entertaining, and productive. However, there are too many mobile applications for users to choose. The goal of this research is to propose a methodology which can recommend top-N lists for mobile applications. A comment correlation matrix is proposed. Furthermore, a recommendation algorithm for mobile applications based on user comments and key attributes is built. With the proposed method, it outperforms Google play and is closer to user real feelings.展开更多
The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix...The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.展开更多
Coutsourides derived an ad hoc nuisance paratmeter removal test for testing equality of two multiple correlation matrices of two independent p variate normal populations under the assumption that a sample of size ...Coutsourides derived an ad hoc nuisance paratmeter removal test for testing equality of two multiple correlation matrices of two independent p variate normal populations under the assumption that a sample of size n is available from each population. This paper presents a likelihood ratio test criterion for testing equality of K multiple correlation matrices and extends the results to the testing of equality of K partial correlation matrices.展开更多
The room temperature brittleness has been a long standing problem in bulk metallic glasses realm.This has seriously limited the application potential of metallic glasses and their composites.The elastic deformation be...The room temperature brittleness has been a long standing problem in bulk metallic glasses realm.This has seriously limited the application potential of metallic glasses and their composites.The elastic deformation behaviors of metallic glass matrix composites are closely related to their plastic deformation states.The elastic deformation behaviors of Cu48-xZr48Al4Nbx（x=0,3at.%）metallic glass matrix composites（MGMCs）with different crystallization degrees were investigated using an in-situ digital image correlation（DIC）technique during tensile process.With decreasing crystallization degree,MGMC exhibits obvious elastic deformation ability and an increased tensile fracture strength.The notable tensile elasticity is attributed to the larger shear strain heterogeneity emerging on the surface of the sample.This finding has implications for the development of MGMCs with excellent tensile properties.展开更多
Objectives: The objective is to analyze the interaction of the correlation structure and values of the regressor variables in the estimation of a linear model when there is a constant, possibly negative, intra-class c...Objectives: The objective is to analyze the interaction of the correlation structure and values of the regressor variables in the estimation of a linear model when there is a constant, possibly negative, intra-class correlation of residual errors and the group sizes are equal. Specifically: 1) How does the variance of the generalized least squares (GLS) estimator (GLSE) depend on the regressor values? 2) What is the bias in estimated variances when ordinary least squares (OLS) estimator is used? 3) In what cases are OLS and GLS equivalent. 4) How can the best linear unbiased estimator (BLUE) be constructed when the covariance matrix is singular? The purpose is to make general matrix results understandable. Results: The effects of the regressor values can be expressed in terms of the intra-class correlations of the regressors. If the intra-class correlation of residuals is large, then it is beneficial to have small intra-class correlations of the regressors, and vice versa. The algebraic presentation of GLS shows how the GLSE gives different weight to the between-group effects and the within-group effects, in what cases OLSE is equal to GLSE, and how BLUE can be constructed when the residual covariance matrix is singular. Different situations arise when the intra-class correlations of the regressors get their extreme values or intermediate values. The derivations lead to BLUE combining OLS and GLS weighting in an estimator, which can be obtained also using general matrix theory. It is indicated how the analysis can be generalized to non-equal group sizes. The analysis gives insight to models where between-group effects and within-group effects are used as separate regressors.展开更多
The Niger Delta region of Nigeria is heavily impacted by petroleum explora-tion,refining activities,and industrial emissions,contributing to widespread aerial petro-pollutant contamination.This study investigates the ...The Niger Delta region of Nigeria is heavily impacted by petroleum explora-tion,refining activities,and industrial emissions,contributing to widespread aerial petro-pollutant contamination.This study investigates the hydrochemi-cal dispersion and geospatial correlation of atmospheric petroleum hydrocar-bons(TPH)deposited through rainfall within selected locations in Rivers State,Nigeria.Thirty-four rainwater samples were collected from seven distinct sub-regional study locations:Obigbo,Komkom,Obiama,Okoloma,Egberu,Umu Agbai,and Obete.Hydrochemical analyses were conducted using gas chroma-tography-flame ionization detection(GC-FID)to quantify total petroleum hy-drocarbons(TPH),with detailed compositional profiling of aliphatic hydro-carbons(C8-C40)and polycyclic aromatic hydrocarbons(PAHs).The disper-sion analysis employed Hexbin density mapping,Contour visualization,and spatial interpolation techniques to delineate pollution hotspots,revealing sig-nificant contamination gradients across the study region.The correlation ma-trix assessed interrelationships between hydrocarbon fractions and geographic positioning,identifying strong positive correlations(r>0.9)between TPH and total aliphatic hydrocarbons(TAH),suggesting transportation and industrial emissions as primary sources.PAHs exhibited localized concentration spikes,particularly near gas flaring zones and commercial hubs,implicating fossil fuel combustion,industrial activities,and long-range pollutant transport as domi-nant contamination mechanisms.Geospatial analysis indicates higher hydro-carbon deposition in the Western and Northern regions,with Obigbo and Okoloma experiencing the most significant contamination.The study high-lights rainfall as a key vector for atmospheric petrochemical deposition,with implications for water quality,ecosystem health,and human exposure risks.These findings emphasize the need for stricter environmental monitoring,reg-ulatory enforcement of industrial emissions,and strategic efforts to mitigate hydrocarbon pollution in petroleum-producing regions.展开更多
For the feature of complex weapon manufacturing on internet,a coupling model is proposed.By using the model,the correlation between manufacturing cells in an extended manufacturing organization can be evaluated quanti...For the feature of complex weapon manufacturing on internet,a coupling model is proposed.By using the model,the correlation between manufacturing cells in an extended manufacturing organization can be evaluated quantitatively,so an appropriate control plan is determined.A strategy to improve and reduce the coupling relationship of the organization is studied.A correlation matrix of extended tasks is built to analyze the relationship between sub-tasks and manufacturing resources.An optimization method for manufacturing resource configuration is presented based on the coupling model.Finally,a software system for analyzing coupling model about manufacturing organization on internet is developed,and the result shows that the coupling model is effective.展开更多
A T equivalent high frequency heterojunction bipolar transistor (HBT) noise model is reported.This model is derived from Hawkins noise model commonly used in Si BJT.The main modifications include the influence of th...A T equivalent high frequency heterojunction bipolar transistor (HBT) noise model is reported.This model is derived from Hawkins noise model commonly used in Si BJT.The main modifications include the influence of the ideality factor,emitter resistance,intrinsic base collector capacitance,extrinsic base collector capacitance and other parasitic elements of HBT represented in equivalent circuit topology.In order to calculate accurate noise parameters from the equivalent circuit,the noise correlation matrix method is used to avoid any simplifications generated in circuit transformations and complex noise measurements.The analysis of the influence of the equivalent circuit elements on the minimum noise figure is reported,the results of analysis agree well with the physics explanations.By means of the formulae derived from device physics of HBT,the influence of device parameters on the minimum noise figure is also represented.展开更多
Three-dimensional fluorescence excitation–emission matrix（EEM） coupled with parallel factor analysis（PARAFAC） was performed for a total of 18 water samples taken from three water sources（two lakes and one wastew...Three-dimensional fluorescence excitation–emission matrix（EEM） coupled with parallel factor analysis（PARAFAC） was performed for a total of 18 water samples taken from three water sources（two lakes and one wastewater treatment plant（WWTP） secondary effluent）,with the purpose of identifying the major ultrafiltration（UF） membrane foulants in different water sources. Three fluorescent components（C1, C2 and C3） were identified,which represented terrestrially derived humic-like substances（C1）, microbially derived humic-like substances（C2）, and protein-like substances（C3）. The correlations between the different fluorescent components and UF membrane fouling were analyzed. It was shown that for the WWTP secondary effluent, all three components（C1, C2 and C3） made a considerable contribution to the irreversible and total fouling of the UF membrane.However, for the two lakes, only the C3 exhibited a strong correlation with membrane fouling, indicating that the protein-like substances were the major membrane foulants in the lake waters. Significant attachment of C1, C2 and C3 to the UF membrane was also confirmed by mass balance analyses for the WWTP secondary effluent; while the attachment of C1 and C2 was shown to be negligible for the two lakes. The results may provide basic formation for developing suitable fouling control strategies for sustainable UF processes.展开更多
This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The spec...This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.展开更多
The great success of US commercial shale gas exploitation stimulates the shale gas development in China,subsequently,the corresponding supporting policies were issued in the 12th Five-Year Plan.But from the experience...The great success of US commercial shale gas exploitation stimulates the shale gas development in China,subsequently,the corresponding supporting policies were issued in the 12th Five-Year Plan.But from the experience in the US shale gas development,we know that the resulted environmental threats are always an unavoidable issue,but no uniform and standard evaluation system has yet been set up in China.The comprehensive environment refers to the combination of natural ecological environment and external macro-environment.In view of this,we conducted a series of studies on how to set up a comprehensive environmental impact assessment system as well as the related evaluation methodology and models.First,we made an in-depth investigation into shale gas development procedures and any possible environmental impacts,and then compared,screened and modified environmental impact assessment methods for shale gas development.Also,we established an evaluating system and assessment models according to different status of the above two types of environment:the correlation matrix method was employed to assess the impacts on natural ecological environment and the optimization distance method was modified to evaluate the impacts on external macro-environment.Finally,we substitute the two subindexes into the comprehensive environmental impact assessment model and achieved the final numerical result of environmental impact assessment.This model can be used to evaluate if a shale gas project has any impact on environment,compare the impacts before and after a shale gas development project,or the impacts of different projects.展开更多
Highly symmetric quantum measurements,such as mutually unbiased measurements(MUMs)and general symmetric informationally complete positive-operator-valued measures(GSICPOVMs),play an important role in both foundational...Highly symmetric quantum measurements,such as mutually unbiased measurements(MUMs)and general symmetric informationally complete positive-operator-valued measures(GSICPOVMs),play an important role in both foundational and practical aspects of quantum information theory.Recently,a broad class of symmetric measurements were introduced[K Siudzińska,(2022)Phys.Rev.A 105,042209],which can be viewed as a common generalization of MUMs and GSIC-POVMs.In this work,the role of these symmetric measurements in entanglement detection is studied.More specifically,based on the correlation matrices defined via(informationally complete)symmetric measurements,a separability criterion for arbitrary dimensional bipartite systems is proposed.It is shown that the criterion is stronger than the method provided by Siudzińska,meanwhile,it can unify several popular separability criteria based on MUMs or GSIC-POVMs.Furthermore,using these(informationally complete)symmetric measurements,two efficient criteria are presented to detect the entanglement of tripartite quantum states.The detection power and advantages of these criteria are illustrated through several examples.展开更多
In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix.The underlying population is allowed to have a general dependence structure.The result no longer...In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix.The underlying population is allowed to have a general dependence structure.The result no longer follows the generalized Marcenko-Pastur law,which is brand new.It is the first result on rank correlation matrices with dependence.As applications,we study Kendall’s rank correlation matrix for multivariate normal distributions with a general covariance matrix.From these results,we further gain insights into Kendall’s rank correlation matrix and its connections with the sample covariance/correlation matrix.展开更多
Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models ca...Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.展开更多
Fault detection caused by single event effect( SEE) in system was studied,and an improved fault detection algorithm by fusing multi-information entropy for detecting soft error was proposed based on multi-objective de...Fault detection caused by single event effect( SEE) in system was studied,and an improved fault detection algorithm by fusing multi-information entropy for detecting soft error was proposed based on multi-objective detection approach and classification management method. In the improved fault detection algorithm, the analysis model of posteriori information with corresponding multi-fault alternative detection points was formulated through correlation information matrix, and the maximum incremental information entropy was chosen as the classification principle for the optimal detection points. A system design example was given to prove the rationality and feasibility of this algorithm.This fault detection algorithm can achieve the purpose of fault detection and resource configuration with high efficiency.展开更多
The aim of the present study is to assess the water quality along the Rosetta branch of the Nile River, Egypt. The study area extends from upstream of the EI-Rahawy drain to the end of the branch. The correlation matr...The aim of the present study is to assess the water quality along the Rosetta branch of the Nile River, Egypt. The study area extends from upstream of the EI-Rahawy drain to the end of the branch. The correlation matrix was performed to help identify the nature of correlations between the different parameters. The WQI （water quality index） was calculated seasonally at different points along the Rosetta branch to provide a simple indicator of water quality at these points. The results of WQI calculations showed that the fecal coliform is the main cause of poor water quality along the Rosetta branch. A statistical analysis was also performed using a two-way ANOVA （analysis of variance） to identify the significant sources of water pollution and to determine the impact of the parameters on a mass loading. A significant difference was observed between the impacts of the pollution sources on the water quality. Also, a significant difference was observed between the impacts of each parameter in the mass loading. The results showed that the E1-Rahawy, Tala and Sabal drains are the major sources for water quality degradation along the Rosetta branch and that the effect of the EI-Tahrir and the Zawyet El-Baher drains on the water quality is not significant.展开更多
Recently, the COVID-19 emerged in China and propagated around all the world has threatened millions of people and affected most countries and governments at several sides such as economical, educational, tourism, heal...Recently, the COVID-19 emerged in China and propagated around all the world has threatened millions of people and affected most countries and governments at several sides such as economical, educational, tourism, healthcare, etc. Indeed, one of the most important challenges that directly affect the people is the psychological side due to the harsh policies imposed by public authorities in most countries. In this paper, we propose a framework called CRISE that allows studying and understanding the psychological effect of COVID-19 during the lockdown period. Mainly, CRISE consists of four data stages: Collection, tRansformation, reductIon, and cluStEring. The first stage collects data from more than 2000 participants through a questionnaire containing attributes related to psychological effect before and during the lockdown. The second stage aims to preprocess the data before performing the study stage. The third stage proposes a model that finds the similarities among the attributes, based on the correlation matrix, to reduce its number. Finally, the fourth stage introduces a new version of Kmeans algorithm, called as Jaccard-based Kmeans (JKmeans), that allows to group participants having similar psychological situation in the same cluster for a later analysis. We show the effectiveness of CRISE in terms of clustering accuracy and understanding the psychological effect of COVID-19.展开更多
River Iyiudene is a vital distributary resource in Abakaliki, southeastern Nigeria and conveys an abundant amount of sediments to provincial and residual ecosystems. Although the importance of the river cannot be over...River Iyiudene is a vital distributary resource in Abakaliki, southeastern Nigeria and conveys an abundant amount of sediments to provincial and residual ecosystems. Although the importance of the river cannot be overemphasized, the geochemistry of its stream sediments is less investigated. Twenty (20) stream sediment samples were taken at the centre of the river channels to represent the entire drainage area well and avoid collapsed bank materials. The stream sediment samples were used to determine the dispersion, contamination status and sources of heavy metal concentrations. Total elemental digestion accompanied this with the use of aqua regia, an admixture of Hydrochloric acid (HCl) and Nitric acid (HNO<sub>3</sub>) in the ratio of 3:1 using the atomic absorption spectrometer (AAS). The heavy metal concentration levels in River Iyiudene were low compared with sediments from Imo River, Gulf of California, Upper continental crust, Average shale and surface horizons, excluding Cd, which showed high concentration levels than the other reference studies. The results delineated a wide contrast in the concentration levels of the heavy metals, with the mean contents in the order Zn > Cu > Pb > Cd > Ni > As. The pollution evaluation utilizing the Effect range low (ERL), Effect range median (ERM), single pollution index, and geo-accumulation index revealed Cd contamination. This study indicates that the heavy metals were sourced from the natural geological background of the river basin and possibly from agricultural runoff and atmospheric pollutants.展开更多
The present investigation is conducted to study the year wise (2011to 2018) changes of water hyacinth (Eichhornia crassipes) cover atSantragachi Lake a Wetland under National Wetland ConservationProgramme of India. Fu...The present investigation is conducted to study the year wise (2011to 2018) changes of water hyacinth (Eichhornia crassipes) cover atSantragachi Lake a Wetland under National Wetland ConservationProgramme of India. Further the relationship between water hyacinthcover and the most abundant migratory waterbirds of Satragachi, LesserWhistling Teal (LWT;Dendrocygna javanica) is assessed because this birdspecies is prefer depending on water hyacinth mat for their roosting. Thestudy comprises of eight satellite images procured from Google earth (2011to 2018) to explore this relationship. A marked decline in the number ofLWT at Santragachi wetland is observed in the year of 2017 and 2018. Itis very interesting fact that from 2017-2018, the water hyacinth mat of thiswetland is almost cleared before winter and the result of cluster analysissupports this fact. Significant positive correlation is also observed withinLWT number and water hyacinth cover area (r = 0.7481 at p< 0.05) alongwith the total perimeter (r = 0.8648 at p< 0.05) of the water hyacinthislands at Santragachi wetland. However, open water area is also neededfor diving, swimming, food searching for the LWT and other waterbirds.Therefore, more study is needed to optimize the clearing operations,focused on optimizing the shape and size of water hyacinth islands forproper management of the waterbirds habitat.展开更多
基金supported by the Beijing Natural Science Foundation(5252014)the National Natural Science Foundation of China(62303063)。
文摘Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.
文摘As the evolution of mobile technology, mobile devices have become an essential tool in people＇s daily life. Moreover, with the rapid growth of Internet and mobile networks, people can easily access various services provided by mobile platforms. Many services can be executed on the mobile devices with various mobile applications launched to mobile platforms. People can choose what they like to install in their mobile devices and hence make their life more convenient, entertaining, and productive. However, there are too many mobile applications for users to choose. The goal of this research is to propose a methodology which can recommend top-N lists for mobile applications. A comment correlation matrix is proposed. Furthermore, a recommendation algorithm for mobile applications based on user comments and key attributes is built. With the proposed method, it outperforms Google play and is closer to user real feelings.
文摘The correlation matrix, which is widely used in eigenvalue decomposition （EVD） or singular value decomposition （SVD）, usually can be denoted by R = E[yiy＇i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum （DSSS） signal＇s signature sequence estimation. The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.
文摘Coutsourides derived an ad hoc nuisance paratmeter removal test for testing equality of two multiple correlation matrices of two independent p variate normal populations under the assumption that a sample of size n is available from each population. This paper presents a likelihood ratio test criterion for testing equality of K multiple correlation matrices and extends the results to the testing of equality of K partial correlation matrices.
基金the financial support by the National Natural Science Foundation of China(51371078,51671067)
文摘The room temperature brittleness has been a long standing problem in bulk metallic glasses realm.This has seriously limited the application potential of metallic glasses and their composites.The elastic deformation behaviors of metallic glass matrix composites are closely related to their plastic deformation states.The elastic deformation behaviors of Cu48-xZr48Al4Nbx（x=0,3at.%）metallic glass matrix composites（MGMCs）with different crystallization degrees were investigated using an in-situ digital image correlation（DIC）technique during tensile process.With decreasing crystallization degree,MGMC exhibits obvious elastic deformation ability and an increased tensile fracture strength.The notable tensile elasticity is attributed to the larger shear strain heterogeneity emerging on the surface of the sample.This finding has implications for the development of MGMCs with excellent tensile properties.
文摘Objectives: The objective is to analyze the interaction of the correlation structure and values of the regressor variables in the estimation of a linear model when there is a constant, possibly negative, intra-class correlation of residual errors and the group sizes are equal. Specifically: 1) How does the variance of the generalized least squares (GLS) estimator (GLSE) depend on the regressor values? 2) What is the bias in estimated variances when ordinary least squares (OLS) estimator is used? 3) In what cases are OLS and GLS equivalent. 4) How can the best linear unbiased estimator (BLUE) be constructed when the covariance matrix is singular? The purpose is to make general matrix results understandable. Results: The effects of the regressor values can be expressed in terms of the intra-class correlations of the regressors. If the intra-class correlation of residuals is large, then it is beneficial to have small intra-class correlations of the regressors, and vice versa. The algebraic presentation of GLS shows how the GLSE gives different weight to the between-group effects and the within-group effects, in what cases OLSE is equal to GLSE, and how BLUE can be constructed when the residual covariance matrix is singular. Different situations arise when the intra-class correlations of the regressors get their extreme values or intermediate values. The derivations lead to BLUE combining OLS and GLS weighting in an estimator, which can be obtained also using general matrix theory. It is indicated how the analysis can be generalized to non-equal group sizes. The analysis gives insight to models where between-group effects and within-group effects are used as separate regressors.
文摘The Niger Delta region of Nigeria is heavily impacted by petroleum explora-tion,refining activities,and industrial emissions,contributing to widespread aerial petro-pollutant contamination.This study investigates the hydrochemi-cal dispersion and geospatial correlation of atmospheric petroleum hydrocar-bons(TPH)deposited through rainfall within selected locations in Rivers State,Nigeria.Thirty-four rainwater samples were collected from seven distinct sub-regional study locations:Obigbo,Komkom,Obiama,Okoloma,Egberu,Umu Agbai,and Obete.Hydrochemical analyses were conducted using gas chroma-tography-flame ionization detection(GC-FID)to quantify total petroleum hy-drocarbons(TPH),with detailed compositional profiling of aliphatic hydro-carbons(C8-C40)and polycyclic aromatic hydrocarbons(PAHs).The disper-sion analysis employed Hexbin density mapping,Contour visualization,and spatial interpolation techniques to delineate pollution hotspots,revealing sig-nificant contamination gradients across the study region.The correlation ma-trix assessed interrelationships between hydrocarbon fractions and geographic positioning,identifying strong positive correlations(r>0.9)between TPH and total aliphatic hydrocarbons(TAH),suggesting transportation and industrial emissions as primary sources.PAHs exhibited localized concentration spikes,particularly near gas flaring zones and commercial hubs,implicating fossil fuel combustion,industrial activities,and long-range pollutant transport as domi-nant contamination mechanisms.Geospatial analysis indicates higher hydro-carbon deposition in the Western and Northern regions,with Obigbo and Okoloma experiencing the most significant contamination.The study high-lights rainfall as a key vector for atmospheric petrochemical deposition,with implications for water quality,ecosystem health,and human exposure risks.These findings emphasize the need for stricter environmental monitoring,reg-ulatory enforcement of industrial emissions,and strategic efforts to mitigate hydrocarbon pollution in petroleum-producing regions.
基金Supported by the National Defense Industrial Technology Development Program of China~~
文摘For the feature of complex weapon manufacturing on internet,a coupling model is proposed.By using the model,the correlation between manufacturing cells in an extended manufacturing organization can be evaluated quantitatively,so an appropriate control plan is determined.A strategy to improve and reduce the coupling relationship of the organization is studied.A correlation matrix of extended tasks is built to analyze the relationship between sub-tasks and manufacturing resources.An optimization method for manufacturing resource configuration is presented based on the coupling model.Finally,a software system for analyzing coupling model about manufacturing organization on internet is developed,and the result shows that the coupling model is effective.
文摘A T equivalent high frequency heterojunction bipolar transistor (HBT) noise model is reported.This model is derived from Hawkins noise model commonly used in Si BJT.The main modifications include the influence of the ideality factor,emitter resistance,intrinsic base collector capacitance,extrinsic base collector capacitance and other parasitic elements of HBT represented in equivalent circuit topology.In order to calculate accurate noise parameters from the equivalent circuit,the noise correlation matrix method is used to avoid any simplifications generated in circuit transformations and complex noise measurements.The analysis of the influence of the equivalent circuit elements on the minimum noise figure is reported,the results of analysis agree well with the physics explanations.By means of the formulae derived from device physics of HBT,the influence of device parameters on the minimum noise figure is also represented.
基金supported by the National Natural Science Foundation of China(No.51208140)the National Water Pollution Control and Treatment Science and Technology Major Project of China(No.2012ZX07201002)
文摘Three-dimensional fluorescence excitation–emission matrix（EEM） coupled with parallel factor analysis（PARAFAC） was performed for a total of 18 water samples taken from three water sources（two lakes and one wastewater treatment plant（WWTP） secondary effluent）,with the purpose of identifying the major ultrafiltration（UF） membrane foulants in different water sources. Three fluorescent components（C1, C2 and C3） were identified,which represented terrestrially derived humic-like substances（C1）, microbially derived humic-like substances（C2）, and protein-like substances（C3）. The correlations between the different fluorescent components and UF membrane fouling were analyzed. It was shown that for the WWTP secondary effluent, all three components（C1, C2 and C3） made a considerable contribution to the irreversible and total fouling of the UF membrane.However, for the two lakes, only the C3 exhibited a strong correlation with membrane fouling, indicating that the protein-like substances were the major membrane foulants in the lake waters. Significant attachment of C1, C2 and C3 to the UF membrane was also confirmed by mass balance analyses for the WWTP secondary effluent; while the attachment of C1 and C2 was shown to be negligible for the two lakes. The results may provide basic formation for developing suitable fouling control strategies for sustainable UF processes.
基金Supported by the National Natural Science Foundation of China(No.60496311)
文摘This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR. Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.
文摘The great success of US commercial shale gas exploitation stimulates the shale gas development in China,subsequently,the corresponding supporting policies were issued in the 12th Five-Year Plan.But from the experience in the US shale gas development,we know that the resulted environmental threats are always an unavoidable issue,but no uniform and standard evaluation system has yet been set up in China.The comprehensive environment refers to the combination of natural ecological environment and external macro-environment.In view of this,we conducted a series of studies on how to set up a comprehensive environmental impact assessment system as well as the related evaluation methodology and models.First,we made an in-depth investigation into shale gas development procedures and any possible environmental impacts,and then compared,screened and modified environmental impact assessment methods for shale gas development.Also,we established an evaluating system and assessment models according to different status of the above two types of environment:the correlation matrix method was employed to assess the impacts on natural ecological environment and the optimization distance method was modified to evaluate the impacts on external macro-environment.Finally,we substitute the two subindexes into the comprehensive environmental impact assessment model and achieved the final numerical result of environmental impact assessment.This model can be used to evaluate if a shale gas project has any impact on environment,compare the impacts before and after a shale gas development project,or the impacts of different projects.
基金supported by the National Key R&D Program of China,Grant No.2020YFA0712700the National Natural Science Foundation of China,Grant Nos.11875317 and 61833010
文摘Highly symmetric quantum measurements,such as mutually unbiased measurements(MUMs)and general symmetric informationally complete positive-operator-valued measures(GSICPOVMs),play an important role in both foundational and practical aspects of quantum information theory.Recently,a broad class of symmetric measurements were introduced[K Siudzińska,(2022)Phys.Rev.A 105,042209],which can be viewed as a common generalization of MUMs and GSIC-POVMs.In this work,the role of these symmetric measurements in entanglement detection is studied.More specifically,based on the correlation matrices defined via(informationally complete)symmetric measurements,a separability criterion for arbitrary dimensional bipartite systems is proposed.It is shown that the criterion is stronger than the method provided by Siudzińska,meanwhile,it can unify several popular separability criteria based on MUMs or GSIC-POVMs.Furthermore,using these(informationally complete)symmetric measurements,two efficient criteria are presented to detect the entanglement of tripartite quantum states.The detection power and advantages of these criteria are illustrated through several examples.
基金supported by National Natural Science Foundation of China(Grant Nos.12031005 and 12101292)supported by National Natural Science Foundation of China(Grant No.12031005),supported by National Natural Science Foundation of China(Grant No.12171099)Natural Science Foundation of Shanghai(Grant No.21ZR1432900)。
文摘In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix.The underlying population is allowed to have a general dependence structure.The result no longer follows the generalized Marcenko-Pastur law,which is brand new.It is the first result on rank correlation matrices with dependence.As applications,we study Kendall’s rank correlation matrix for multivariate normal distributions with a general covariance matrix.From these results,we further gain insights into Kendall’s rank correlation matrix and its connections with the sample covariance/correlation matrix.
基金the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/180/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4270206DSR01).
文摘Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.
文摘Fault detection caused by single event effect( SEE) in system was studied,and an improved fault detection algorithm by fusing multi-information entropy for detecting soft error was proposed based on multi-objective detection approach and classification management method. In the improved fault detection algorithm, the analysis model of posteriori information with corresponding multi-fault alternative detection points was formulated through correlation information matrix, and the maximum incremental information entropy was chosen as the classification principle for the optimal detection points. A system design example was given to prove the rationality and feasibility of this algorithm.This fault detection algorithm can achieve the purpose of fault detection and resource configuration with high efficiency.
文摘The aim of the present study is to assess the water quality along the Rosetta branch of the Nile River, Egypt. The study area extends from upstream of the EI-Rahawy drain to the end of the branch. The correlation matrix was performed to help identify the nature of correlations between the different parameters. The WQI （water quality index） was calculated seasonally at different points along the Rosetta branch to provide a simple indicator of water quality at these points. The results of WQI calculations showed that the fecal coliform is the main cause of poor water quality along the Rosetta branch. A statistical analysis was also performed using a two-way ANOVA （analysis of variance） to identify the significant sources of water pollution and to determine the impact of the parameters on a mass loading. A significant difference was observed between the impacts of the pollution sources on the water quality. Also, a significant difference was observed between the impacts of each parameter in the mass loading. The results showed that the E1-Rahawy, Tala and Sabal drains are the major sources for water quality degradation along the Rosetta branch and that the effect of the EI-Tahrir and the Zawyet El-Baher drains on the water quality is not significant.
文摘Recently, the COVID-19 emerged in China and propagated around all the world has threatened millions of people and affected most countries and governments at several sides such as economical, educational, tourism, healthcare, etc. Indeed, one of the most important challenges that directly affect the people is the psychological side due to the harsh policies imposed by public authorities in most countries. In this paper, we propose a framework called CRISE that allows studying and understanding the psychological effect of COVID-19 during the lockdown period. Mainly, CRISE consists of four data stages: Collection, tRansformation, reductIon, and cluStEring. The first stage collects data from more than 2000 participants through a questionnaire containing attributes related to psychological effect before and during the lockdown. The second stage aims to preprocess the data before performing the study stage. The third stage proposes a model that finds the similarities among the attributes, based on the correlation matrix, to reduce its number. Finally, the fourth stage introduces a new version of Kmeans algorithm, called as Jaccard-based Kmeans (JKmeans), that allows to group participants having similar psychological situation in the same cluster for a later analysis. We show the effectiveness of CRISE in terms of clustering accuracy and understanding the psychological effect of COVID-19.
文摘River Iyiudene is a vital distributary resource in Abakaliki, southeastern Nigeria and conveys an abundant amount of sediments to provincial and residual ecosystems. Although the importance of the river cannot be overemphasized, the geochemistry of its stream sediments is less investigated. Twenty (20) stream sediment samples were taken at the centre of the river channels to represent the entire drainage area well and avoid collapsed bank materials. The stream sediment samples were used to determine the dispersion, contamination status and sources of heavy metal concentrations. Total elemental digestion accompanied this with the use of aqua regia, an admixture of Hydrochloric acid (HCl) and Nitric acid (HNO<sub>3</sub>) in the ratio of 3:1 using the atomic absorption spectrometer (AAS). The heavy metal concentration levels in River Iyiudene were low compared with sediments from Imo River, Gulf of California, Upper continental crust, Average shale and surface horizons, excluding Cd, which showed high concentration levels than the other reference studies. The results delineated a wide contrast in the concentration levels of the heavy metals, with the mean contents in the order Zn > Cu > Pb > Cd > Ni > As. The pollution evaluation utilizing the Effect range low (ERL), Effect range median (ERM), single pollution index, and geo-accumulation index revealed Cd contamination. This study indicates that the heavy metals were sourced from the natural geological background of the river basin and possibly from agricultural runoff and atmospheric pollutants.
文摘The present investigation is conducted to study the year wise (2011to 2018) changes of water hyacinth (Eichhornia crassipes) cover atSantragachi Lake a Wetland under National Wetland ConservationProgramme of India. Further the relationship between water hyacinthcover and the most abundant migratory waterbirds of Satragachi, LesserWhistling Teal (LWT;Dendrocygna javanica) is assessed because this birdspecies is prefer depending on water hyacinth mat for their roosting. Thestudy comprises of eight satellite images procured from Google earth (2011to 2018) to explore this relationship. A marked decline in the number ofLWT at Santragachi wetland is observed in the year of 2017 and 2018. Itis very interesting fact that from 2017-2018, the water hyacinth mat of thiswetland is almost cleared before winter and the result of cluster analysissupports this fact. Significant positive correlation is also observed withinLWT number and water hyacinth cover area (r = 0.7481 at p< 0.05) alongwith the total perimeter (r = 0.8648 at p< 0.05) of the water hyacinthislands at Santragachi wetland. However, open water area is also neededfor diving, swimming, food searching for the LWT and other waterbirds.Therefore, more study is needed to optimize the clearing operations,focused on optimizing the shape and size of water hyacinth islands forproper management of the waterbirds habitat.
