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Context: The accuracy of estimated breeding values (EBVs) is an important metric in genetic evaluation systems in Australia. With reduced costs for DNA genotyping due to advances in molecular technology, more and more animals have been genotyped for EBVs. The rapid increase in genotyped animals has grown beyond the capacity of the current genomic best linear unbiased prediction (GBLUP) method. Aims: This study aimed to implement and evaluate a new single-nucleotide polymorphism (SNP)–BLUP model for the computation of prediction error variances (PEVs) to accommodate the increasing number of genotyped animals in beef and sheep single-step genetic evaluations in Australia. Methods: First, the equivalence of PEV estimates obtained from both GBLUP and SNP-BLUP models was demonstrated. Second, the computing resources required by each model were compared. Third, within the SNP-BLUP model, the PEVs obtained from subsets of SNP were evaluated against those from the complete dataset. Fourth, the new model was tested in the Australian Merino sheep and Angus beef cattle datasets. Key results: The PEVs of genotyped animals calculated from the SNP–BLUP model were equivalent to the PEVs derived from the GBLUP model. The SNP–BLUP model used much less time than did the GBLUP model when the number of genotyped animals was larger than the number of SNPs. Within the SNP–BLUP model, the running time could be further reduced using a subset of SNPs makers, with high correlations (>0.97) observed between the PEVs obtained from the complete dataset and subsets. However, it is important to exercise caution when selecting the size of the subsets in the SNP–BLUP model, as reducing the subset size may result in an increase in the bias of the PEVs. Conclusions: The new SNP-BLUP model for PEV calculation for genotyped animals outperforms the current GBLUP model. A new accuracy program has been developed for the Australian genetic evaluation system which uses much less memory and time to compute accuracies. Implications: The new model has been implemented in routine sheep and beef genetic evaluation systems in Australia. This development ensures that the calculation of accuracies is sustainable, with increasing numbers of animals with genotypes. The accuracy of estimated breeding values is routinely provided to breeders as part of genetic evaluations in Australia. A new method to approximate accuracies using genomic information has been developed and is presented in this paper. The new model has been implemented in Australia's routine sheep and beef genetic evaluation systems. This development ensures that the calculation of accuracies is sustainable with increasing numbers of animals with genotypes. [ABSTRACT FROM AUTHOR]

Spectral-type subspace clustering algorithms have attracted wide attention because of their excellent performance displayed in a great deal of applications in machine learning domain. It is critical for spectral-type subspace clustering algorithms to obtain suitable coefficient matrices which could reflect the subspace structures of data sets. In this paper, we propose a latent block diagonal representation clustering algorithm (LBDR). For a data set, the goal of LBDR is to construct a block diagonal and dense coefficient matrix and settle the noise adaptively within the original data set by using dimension reduction technique concurrently. In brief, by seeking the solution of a joint optimization problem, LBDR is capable of finding a suitable coefficient matrix and a projection matrix. Furthermore, a series of experiments conducted on several benchmark databases show that LBDR dominates the related methods. [ABSTRACT FROM AUTHOR]

The existing graph-based RGBT tracking methods mainly focus on assigning a weight to each local image patch to suppress background influence in target bounding box, but the influences of background clutter might limit the improvement of tracking performance. To solve this problem, we propose a new algorithm, called cooperative low-rank graph model, to suppress background clutter. Specifically, the proposed feature decomposition module decomposes input dual-modal features into low-rank components and sparse noisy components, which could be used collaboratively by regularizing graph learning by combining modal weights. Besides, to avoid SVD (Singular Value Decomposition) operations we have designed an efficient solver based on ADMM (Alternating Direction Methods of Multipliers), which could factorize the low-rank matrix into two low-dimensional submatrices. Extensive experiments on four RGBT tracking benchmark data sets show that our method performs favorably against other state-of-the-art tracking algorithms, and achieves more robust tracking performance. [ABSTRACT FROM AUTHOR]

In coal mine engineering, numerical software is used to analyze the behavior of coal rock damage and fluid migration. The order of the coefficient matrix used in numerical calculations is increasing, and this increases the computation steps in obtaining the coefficient matrix solution. The storage and solution of the coefficient matrix are key factors influencing the efficiency of the numerical software. Therefore, to save storage space and reduce the computation steps, the coefficient matrix must be effectively compressed and stored. In this work, the structural characteristics of different coefficient matrices are analyzed in detail, and we find that for different computational regions, as long as the nodes are numbered according to certain rules, the corresponding coefficient matrices will have similar structural characteristics. The nonzero elements are symmetrically distributed in the diagonal band, and all the elements on both sides outside the band are zero. Based on this, the coefficient matrix is compressed by a pivoting scheme, and the compressed matrix is directly eliminated by dislocation Gaussian elimination. Thus, a compressed storage method that integrates the compression and solution of the coefficient matrix is established. The compressed storage and calculation module is incorporated into our self-developed simulation software COMBUSS-3D to simulate the evolution of the temperature field in the goaf of Luling Coal Mine. Compared with the conventional method, the compressed storage module can significantly improve the computing rate of the simulation, by approximately 80%. [ABSTRACT FROM AUTHOR]

Advances in information technology have harnessed the application of Quick Response (QR) codes in day-to-day activities, simplifying information exchange. QR codes are witnessed almost everywhere, on consumables, newspapers, information bulletins, etc. The simplicity of QR code creation and ease of scanning with free software have tremendously influenced their wide usage, and since QR codes place information on an object they are a tool for the IoT. Many healthcare IoT applications are deployed with QR codes for data-labeling and quick transfer of clinical data for rapid diagnosis. However, these codes can be duplicated and tampered with easily, attributed to open- source QR code generators and scanners. This paper presents a novel (n,n) secret-sharing scheme based on Nonnegative Matrix Factorization (NMF) for secured transfer of QR codes as multiple shares and their reconstruction with a regularized Super Resolution Convolutional Neural Network (SRCNN). This scheme is an alternative to the existing polynomial and visual cryptography-based schemes, exploiting NMF in part-based data representation and structural regularized SRCNN to capture the structural elements of the QR code in the super-resolved image. The experimental results and theoretical analyses show that the proposed method is a potential solution for secured exchange of QR codes with different error correction levels. The security of the proposed approach is evaluated with the difficulty in launching security attacks to recover and decode the secret QR code. The experimental results show that an adversary must try 258 additional combinations of shares and perform 3 × 288 additional computations, compared to a representative approach, to compromise the proposed system. [ABSTRACT FROM AUTHOR]

To improve the ability of the acoustic computed tomography (CT) temperature field reconstruction system for reconstructing various temperature distributions in the measured area, square and circular layouts of transducers were studied. In case of the square layout, the focus was on the placement of the transducers, and in case of the circular layout, the focus was on the division of grids. We proposed the coefficient matrix theory and indexes for studying and evaluating transducers layout. Four typical temperature field models were simulated and reconstructed. Simulation results showed that in a square layout, optimizing the position of transducers can result in better reconstruction quality. In a circular layout, a regular hexagonal grid division was better than an equal-area division, and better reconstruction quality was obtained by choosing the appropriate number of grids. For a small number of transducers, the circular layout of nine transducers with 27 acoustic rays and 91 regular hexagonal grids resulted in better reconstruction quality than the square and circular layout of eight transducers; in each reconstruction, the error was ≤ 5.07%. [ABSTRACT FROM AUTHOR]

Aiming at the poor prediction performance of the traditional GM(1,N) model, this paper proposes the GM(1,N) model with expression optimization: firstly, unknown parameters are introduced into the coefficient matrix of the traditional GM(1,N) model to obtain the model expression with unknown parameters: Then the average relative error function with unknown parameters is constructed: Finally, the particle swarm optimization algorithm is used to obtain the parameter column with the smallest average relative error. Taking the carbon emission of the logistics industry in Hubei Province as an example, this paper forecasts the carbon emission by using the traditional GM(1,N) model, GM(1, N) model with background value optimization, and GM(1, N) model with expression optimization. By comparing and analyzing the prediction results of the three models, it is concluded that GM(1,N) model with expression optimization has better prediction performance. [ABSTRACT FROM AUTHOR]

Surface location error (SLE) caused by the forced vibration is one of the main factors limiting machining accuracy in stable milling. This paper presents a novel scheme with high precision and high efficiency to predict SLE based on the Newton polynomial-Chebyshev nodes. The dynamic model embodying the forced and regenerative excitations is first derived from the simplified milling process. After describing the response of the system with the direct integration format, the Newton polynomial frame is chosen to spread the self-excited and forced-excited elements successively in each equidistant time interval to obtain the separate output of tool vibration displacements with a close form. Then, the coefficient matrix which is not affected by the change of time is established in each spindle speed location to calculate SLE. Three examples which have been verified by the experiments are introduced to capture the optimal approximated order with exhibiting the characteristic of the proposed methods. It is observed that the prediction accuracy of the high-order methods drop seriously for the absence of Runge effect. In order to avoid this unwanted phenomenon, the origin uniform time joints are modified by a nonlinear sampling technique based on Chebyshev nodes to establish the novel coefficient matrix with fewer calculated loads for SLE prediction, and a series of comparisons illuminate the proposed modified method performs satisfactorily. [ABSTRACT FROM AUTHOR]

The change of electrodes' conductivity is a crucial parameter during battery aging process, non-contact detection of battery electrodes' defects through conductivity reconstruction is an innovative technology. In this paper, the magnetic induction tomography (MIT) was applied to reconstruct the conductivity of electrodes, the simplified battery models with complete and broken electrodes were chosen as target A and target Brespectively, and an eight-channel MIT system was designed to measure the change of mutual induced voltage. A method of computing the forward problem called 'A·E' was adopted to compute the coefficient matrix for the forward problem and the frequency 12.665kHz was chosen as the excitation frequency. Based on the conductivity reconstruction images, relative errors of reconstructed conductivity and objective function, 'A·E' method exhibited advantages in terms of computation time and accuracy compared to coefficient sensitivity method with the simulation data. In order to test the accuracy of 'A·E' method, data measured by MIT system was applied in the inverse problem with four regularization parameter selection methods. L-curve criterion and generalized cross-validation criterion identified defects successfully as they were more sensitive to the change of conductivity. [ABSTRACT FROM AUTHOR]

In this article, a fast numerical approach is proposed for finding the solution of nonlinear delay differential equations by using hybrid Taylor and Block-pulse Functions (HTBPFs). Firstly, some features of hybrid functions which are a combination of Block-Pulse functions and Taylor polynomials on the interval are introduced [0; 1) . In this spectral approach, the operational matrices of stretch, derivation and coefficient matrices are utilized. Based on these piecewise functions, we transfer delay differential equations (DDEs) into a system of linear or nonlinear algebraic equations. Also, in this numerical approach, it is shown that these operational matrices are sparse which is an effective advantage of the fast implementation of numerical computation. Then, error analysis is done. Finally, three examples are solved to show that the new proposed approach is comparable with other methods of high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is a multidisciplinary damage detection technique that incorporates various mechanisms, of which guided waves (GWs) -based damage detection is one of the critical fields of research. To realize the complete process of SHM, research on GWs has concentrated on diagnosis and localization. GWs-based damage detection technology is widely used in plate structure, combining different sensor placement algorithms and equipment such as scanning laser doppler vibrometry (SLDV) to achieve accurate damage location. Simultaneously, developing the finite element method (FEM) allows for precise simulation in discontinuous media and complex sharp structures. Therefore, this paper combines the theoretical calculation approach of elastic wave propagation with the orthogonal matching pursuit (OMP) algorithm, using the excitation signal and signal reflected by damage as the input matrix and target matrix, respectively. Thus, the reflection-waves coefficient matrix (RCM) containing damage information could be obtained after calculation by the OMP algorithm. Besides, a prediction approach of reference reflected signal based on four-layers recurrent neural networks (RNNs) is proposed with experiments data to reduce the difficulty of expanding a dictionary matrix in a plate with multiple defects, which allows for predicting the damage reflection signal under different distances with 88.14% accuracy. And finally, considering the aluminum plate with hole damage as an example, the experiment results show that the proposed approaches are effective for the localization of single damage and muti-damage. Meanwhile, the prospects and limitations of the algorithm for precise localization are also addressed in the last. [ABSTRACT FROM AUTHOR]

• Second-order displacement fields at liquid–solid interface. • Solution of wave equations based on velocity potentials. • Comparison between complete solution and special solution of displacements. • Sharply mutation of displacements at certain angle. • Different effect of interface on the second-order displacements. Based on the second-order harmonic potential theory, the characteristics of the second-order harmonic field generated at the solid–liquid interface induced by P wave incidence are analyzed. A planar model of the solid–liquid interface is established to study the variation of the second-order displacement field versus the incident angles. The homogeneous solution coefficient matrix, refraction and reflection coefficient matrix are introduced. According to the boundary conditions and Lagrange's various parameters method, the second-order displacement field is obtained, and its dependence on the solid–liquid interface is investigated. The different effects of boundary on the tangential displacement and normal displacement are demonstrated. Numerical simulation shows that the complete solution varies slightly at the incident angle, and the tangential displacement and the normal displacement change sharply at a mutation angle θ ω due to the boundary effect. [ABSTRACT FROM AUTHOR]

The Hankel matrix of type B Narayana polynomials was proved to be totally positive by Wang and Zhu, and independently by Sokal. Pan and Zeng raised the problem of giving a planar network proof of this result. In this paper, we present such a proof by constructing a planar network allowing negative weights, applying the Lindström-Gessel-Viennot lemma and establishing an involution on the set of nonintersecting families of directed paths. • Providing a combinatorial proof of the Hankel total positivity of type B Narayana polynomials. • Allowing negative weights in the construction of planar networks. • Establishing an elegant sign-reversing involution on families of nonintersecting lattice paths. [ABSTRACT FROM AUTHOR]