Subspace decomposition-based correlation matrix multiplication

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.

Development of Comment Correlation Matrix for Mobile Application Recommendation

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.

On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence

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.

On Testing Equality of K Multiple Correlation Matrices

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.

Estimation of a Linear Model in Terms of Intra-Class Correlations of the Residual Error and the Regressors

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.

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

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.

CRISE: Toward Better Understanding of COVID-19 Psychological Impact during Lockdown

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.

Heavy Metal Dispersion in Stream Sediments in River Iyiudene, Abakaliki South-Eastern Nigeria: Source, Distribution Pattern, and Contamination Assessment

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 (HNO3) 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.

GIS Based Approach to Determine the Changes of Water Hyacinth (Eichhornia crassipes) Cover and Relation with Lesser Whistling Teal (Dendrocygna javanica) Assemblage at Santragachi Wetland, West Bengal

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.

Analyzing Field-Derived Arboreal Spectral Signatures

Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, ASD (Analytical Spectral Devices, Boulder, CO, USA) FieldSpec® Pro cover a spectral FR (Full Range) of 350 - 2500 nm exceeding spectral sensitivities of commonly used orbital platforms. The plausibility of deriving a spectral library of trees or forests within a training set is venerable. On the other hand, diagnostic spectral features between tree species or types are inherently difficult to ascertain from orbital platforms. This is so especially when the spectral library is applied to a demarcated region beyond the extents of training set. Basic suborbital limitations in detailed identification of trees and forests are presented in this study. We draw attention to spectral or temporal deficiencies and offer probable solutions depending on preferred or optimal spectral sensitivities. For example, Hyperion with 220 bands (400 - 2500 nm), one of the three primary instruments on the EO-1 spacecraft, has narrow bandwidths and covers the entire range of the spectral profiles collected for North Dakota tree species. With a 30 m spatial resolution, it is still useful in species identification in moderate stands of forest. Hyperion is a tasking satellite with limited passes over North Dakota (≈7% of total area) limiting its use as a platform of choice for statewide forest resource mapping.

Novel Fault Detection Optimization Algorithm for Single Event Effect system Based on Multi-information Entropy Fusion

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.

Elastic deformation behavior of CuZrAlNb metallic glass matrix composites with different crystallization degrees

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.

Separability criteria based on a class of symmetric measurements

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.

Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

MEAN-VARIANCE MODEL BASED ON FILTERS OF MINIMUM SPANNING TREE

This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree （MST） is proposed. Daily data of the 30 stocks of the Hang Seng Index （HSI） and Dow Jones Index （DJI） from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that （1） although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and （2） the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.

A novel approach for part family formation using K-means algorithm

The reconfigurable manufacturing system （RMS） is the next step in manufacturing, allowing the production of any quantity of highly customized and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system will be reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various recon figurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of RMS. The methodology carried out in two phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, agglomerative hier archical Kmeans algorithm is used for the parts family for mation resulting in an optimum set of part families for reconfigurable manufacturing system.

Identifying the major fluorescent components responsible for ultrafiltration membrane fouling in different water sources

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.