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In the past decade, the augmented complex-valued least mean square adaptive filtering algorithm based on the minimum mean square error criterion and widely-linear model has attracted much attentions due to its simplicity and applicability to non-circular signals. However, the system noise with impulse characteristics will dramatically affect the convergence of the augmented complex-valued least mean square algorithm. Recently, a robust widely-linear complex-valued least mean M-estimate algorithm has been proposed, which significantly improves the robustness of the augmented complex-valued least mean square algorithm to impulsive noise. But it should be noted that the widely-linear complex-valued least mean M-estimate algorithm with fixed step size faces the trade-off between convergence speed and steady-state accuracy. To overcome this drawback, a variable step size widely-linear complex-valued least mean M-estimate algorithm is presented in this paper, in which the optimal step size at each iteration is obtained by maximizing the difference between the mean square deviations of the WL-CLMM algorithm at adjacent moments. In order to reveal the statistical convergence behavior of the proposed variable-step-size widely-linear complex-valued least mean M-estimate algorithm to better guide the parameter selection in practical applications. Theoretical transient and steady-state mean-square deviation convergence behaviors of the variable-step-size widely-linear complex-valued least mean M-estimate algorithm are analyzed and the corresponding mean-square deviation expressions are also derived. Computer simulations on system identification and stereophonic acoustic echo cancellation in impulsive noise environments confirm the validity of the analysis results and the performance improvement from the variable step size strategy. [ABSTRACT FROM AUTHOR]

The production process of the metallurgical industry generates a significant quantity of hazardous waste. At present, the common disposal method for metallurgical hazardous waste is landfilling, which synchronously leads to the leaching of toxic elements and the loss of valuable metals. This paper presents a comprehensive review of the research progress in the harmless treatment and resource utilization of stainless steel dust/sludge (including stainless steel dust and stainless steel pickling sludge) and aluminum ash (including primary aluminum ash and secondary aluminum dross), which serve as representative hazardous wastes in ferrous metallurgy and nonferrous metallurgy, respectively. Additionally, the general steps involved in the comprehensive utilization of metallurgical hazardous waste are summarized. Finally, this paper provides a prospective analysis on the future development and research trends of comprehensive utilization for metallurgical hazardous waste, aiming to offer a basis for the future harmless, high-value, resource-based treatment of metallurgical hazardous waste and the realization of industrial applications in China. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This study proposes a spline filtering algorithm-based generalized maximum correntropy criterion (GMCC), named the spline adaptive filter (SAF-)-GMCC algorithm. Compared with traditional spline algorithms, the SAF-GMCC can cope with impulsive interference effectively, because the GMCC has a low sensitivity to mutation signals. The GMCC-based variable step-size spline filtering algorithm (SAF-GMCC) is proposed to solve the limitation of the fixed step-size on the SAF-GMCC algorithm's performance and to improve the convergence rate and steady-state error performance. Combining these algorithms with the active noise control (ANC) model, this study proposes the filtered-c generalized maximum correntropy criterion (FcGMCC) and variable step-size filtered-c generalized maximum correntropy criterion (FcVGMCC) algorithms. Finally, the nonlinear system identification model simulates an experimental environment with impulsive interference. The SAF-GMCC and SAF-VGMCC algorithms offer better robustness than the existing algorithms. And the alpha-stable noise environment simulation with different impact strengths, in the ANC model verifies the FcGMCC and FcVGMCC algorithms' robustness in nonlinear and non-Gaussian noise environments. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

This paper presents a robust distributed adaptive algorithm called diffusion recursive least M-estimate (DRLM), which enhances the robustness of the diffusion recursive least square (DRLS) algorithm against impulsive noise by incorporating the strong resistance to impulsive noise of the modified Huber function (MHF). However, the tracking speed of the proposed DRLM algorithm is not favorable in scenario where the network parameter vector of interest changes abruptly. To overcome this drawback, a new variable forgetting factor (VFF) strategy is devised and the VFF-DRLM algorithm is obtained. Then, the asymptotic unbiasedness and steady-state network mean square deviation (NMSD) performance of the DRLM algorithm are analyzed. Finally, the accuracy of the steady-state analysis result of the DRLM algorithm and the superiority of the proposed algorithms compared to other competing algorithms are demonstrated by extensive numerical simulations. [ABSTRACT FROM AUTHOR]

The distributed estimation performance of multitask diffusion affine projection (AP) algorithm (MD-APA) will be greatly reduced under the impulsive noise interference. To overcome this defect, a robust MD-APA is derived by using M-estimate function (MD-APM) to resist the impulsive noise interference. The mean performance, mean square performance and steady-state performance of MD-APM algorithm are studied, and the convergence range of the step-size and the theoretical steady-state MSD are obtained. In addition, the computational complexity of MD-APM algorithm is analyzed in detail. Simulation experiments show that the proposed MD-APM algorithm has better estimation performance compared with MD-APA and MD-APSA under the impulsive noise interference, and its theoretical steady-state mean square deviation (MSD) can provide accurate prediction. [ABSTRACT FROM AUTHOR]

An adaptive least mean M-estimate algorithm for censored regression (CR-LMM) is presented for the robust parameter estimation of the censored regression system. To correct the bias produced by censored observation, the estimated error derived from the probit regression model is employed to construct an M-estimate cost function. It can expel the adverse impact of the impulsive noise and is solved by the unconstrained optimization method. Furthermore, the robust variable step-size (VSS) strategy, which is also predicted on the robust cost function, is also utilized to improve the convergence performance of the proposed CR-LMM algorithm, i.e., convergence speed and steady-state mean square deviation. The condition which guarantees the CR-LMM algorithm stability is obtained by analyzing the convergence in the mean and mean-square sense, and the theoretical steady-state result is also derived. Computer simulations in system identification scenarios are carried out to demonstrate that the proposed algorithms are superior to the existing algorithms in the impulsive environment with different background noise and the theoretical results are verified. [ABSTRACT FROM AUTHOR]

Traditional functional linked neural networks (FLNNs) impose a significant computational burden due to their input expansion, primarily stemming from the utilization of digital filters. This paper presents a Laguerre FLNNs filter for nonlinear active noise control (NANC) systems. By employing the truncated Laguerre series, the presented filter achieves effective approximation of long primary paths with a reduced filter length. Moreover, we develop adaptive algorithms rooted in information-theoretic learning (ITL) within the framework of the Laguerre-FLNNs NANC model. Using the ITL criterions, a Laguerre filtered-s maximum correntropy criterion (LFsMCC) algorithm is derived and a Laguerre filtered-s quantized minimum error entropy criterion (LFsQMEE) algorithm is proposed by minimizing Renyi's quadratic entropy. To reduce the computation cost, an online vector quantization method is utilized to improve the LFsQMEE. This technique selectively quantizes the error vectors, reducing them to a smaller subset of samples within the codebook. Moreover, an enhanced LFsQMEE with a fiducial point is introduced. The steady-state performance and the computational complexity are analyzed. Theoretical analysis is validated through simulations and the control performance of the proposed model and algorithms is tested in experiments with both simulated and real paths. [ABSTRACT FROM AUTHOR]

In this work, we study the combination of adaptive filters in colored noise environments. First, a combination framework using delayed weights is introduced to tackle the colored noise. Based on this, delayed convex and affine combinations of two LMS filters are developed, resulting in the so-called Dcvx-LMS and Daff-LMS algorithms. Then, the convergence behaviors of the two algorithms are investigated using standard mean-square deviation analysis. In addition, to speed up the convergence and reduce the computational complexity, we propose delayed combination with periodic feedback, delayed combined-step-size and block implementation methods. Finally, simulation results demonstrate the superiority of our algorithms over previously reported techniques in the presence of colored noise. [ABSTRACT FROM AUTHOR]

In this paper, a generalized maximum correntropy normalized subband adaptive filter (GMCNSAF) algorithm is proposed for enhancing the convergence behavior of generalized maximum correntropy criterion algorithm under correlated signals. To promote the convergence behavior of GMCNSAF when identifying the sparse system, the polynomial zero attraction constraint is incorporated into it to yield the PZAGMCNSAF algorithm. Furthermore, to alleviate the conflicting requirements with regard to convergence rate and steady-state misalignment, a variable step-size scheme is applied into the PZAGMCNSAF, obtaining the VSSPZAGMCNSAF algorithm. Also, we analyze the convergence condition of GMCNSAF and give the step-size bound. Simulations prove that the proposed algorithms obtain superior performance for sparse system identification and echo cancelation scenario under impulsive interference as compared to related algorithms. [ABSTRACT FROM AUTHOR]

When the distributed nodes are contaminated by the different types of noises, including Gaussian and generalized Gaussian distribution noises, the diffusion distributed least mean square algorithm suffers severely from performance degradation. To address this problem, this paper proposes background and impulsive noise indicators to identify the background noise non-Gaussianity and detect the impulsive noise, respectively. Then, according to the results of noise indicators, different distributed algorithms are selected for distributed in-network identification where different nodes share different noise types. The proposed algorithm is called distributed conditional selection algorithm, which is superior to the existing diffusion-based algorithms due to its combining the advantages of the related algorithms. Simulations with non-impulsive and impulsive interference confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Constrained least mean square (CLMS) algorithm is the most popular constrained adaptive filtering algorithm due to its simple structure and easy implementation. However, its convergence slows down when the input signal is colored. To address this issue, this paper firstly introduces the normalized subband adaptive filter (NSAF) into the constrained filtering problem and derives a constrained NSAF (CNSAF) algorithm using the Lagrange multiplier method. Benefiting from the good decorrelation capability of the NSAF, the proposed CNSAF algorithm significantly improves the convergence performance of the CLMS algorithm under colored inputs. Then, the mean and mean-square stability of the CNSAF algorithm is analyzed, and the theoretical models to characterize the transient and steady-state mean square deviation (MSD) behaviors of the CNSAF algorithm are derived utilizing the Kronecker product property and vectorization method. Further to extend the CNSAF algorithm to the problem of sparse system identification, a sparse version of the CNSAF algorithm (S-CNSAF) is derived. Finally, the validity of the derived theoretical MSD prediction models and the superiority of the proposed algorithms are confirmed by extensive computer simulations on system identification with colored inputs. [ABSTRACT FROM AUTHOR]

Recently, the kernel adaptive filtering algorithm with the generalized maximum correntropy criterion (GMCC) has gained attention due to its excellent nonlinear system modeling ability and robustness to non-Gaussian noise. However, since the default center of the GMCC is located at zero, it hampers its performance in the non-zero mean noise environment. To overcome the problem, this paper proposes a kernel adaptive filtering algorithm based on the GMCC with variable center (GMCC-VC) called generalized kernel maximum correntropy criterion with variable center (GKMCC-VC). In addition, the online vector quantization (VQ) method is introduced to the GKMCC-VC algorithm for suppressing the increasing network size. The probability of divergence, the mean-square steady-state behavior, and the convergence condition of the GKMCC-VC algorithm are also obtained under some assumptions. The potential relationship between the GMCC-VC and the generalized minimum error entropy (GMEE) criteria is discussed in the paper. Finally, the simulation results verify the superiority of the proposed algorithm in modeling nonlinear systems and the correctness of the theoretical model. • This paper proposes the GKMCC-VC algorithm for non-zero mean and non-Gaussian noise. • The steady-state performance of the proposed algorithm is analyzed. • Simulation results show the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The TLS-APL algorithm is proposed by using the gradient descent method and the unconstrained optimization method, which solves the problem of overall performance degradation of the APL algorithm in the EIV model. • By using some common assumptions, the local extremum of the TLS-APL algorithm is analyzed. Therefore, the expected parameter is the optimal value of the iterative estimation and fully demonstrates the correctness of the iterative update. • The mean, mean square, and steady-state MSD performance of the TLS-APL algorithm is derived by using the Kronecker product and vec operator respectively. In addition, the fitting effects of theoretical steady-state MSD curves and actual steady-state MSD curves under different parameters are analyzed, which proves the rationality of theoretical derivation. • The VSS method is presented from the maximum decrease of MSD from the current iteration to the next iteration. Compared to the TLS-APL algorithm with a fixed step size, the VSS-TLS-APL algorithm with the VSS method has both fast convergence speed and low steady-state error. In the errors-in-variables (EIV) model, the steady state characteristics of the affine-projection-like (APL) algorithm are poor. Hence, the total least squares APL (TLS-APL) algorithm different from the bias-compensated is proposed, which can reduce the adverse influence of input and output noise on weight update. Then, the local extremum of the TLS-APL algorithm is verified, that is, the expected parameter is the optimal value of the iterative estimation. Moreover, the mean, mean square, and steady-state mean square deviation (MSD) performance of the proposed TLS-APL algorithm are deduced by using the Kronecker product and vec operator, respectively. To balance the convergence characteristics and steady-state characteristics, the step size optimization of the TLS-APL algorithm, namely the variable step size TLS-APL (VSS-TLS-APL) algorithm, is derived from the analysis of MSD. Considering the existence of gradient errors, the fitting effects of theoretical steady-state MSD curves and actual steady-state MSD curves under different parameters are analyzed separately, which proves the rationality of theoretical derivation. In addition, the VSS-TLS-APL algorithm with optimized step size is compared with the TLS-APL algorithm with fixed step size in the application of system identification, and the effect of the variable step size (VSS) is verified. The experiments of system identification and acoustic echo cancelation prove that the VSS-TLS-APL algorithm is superior to the existing known algorithms. [ABSTRACT FROM AUTHOR]

Due to its low computational burden, the affine-projection-like (APL) adaptive filtering algorithm has been extensively studied for colored signal input. Recently, a robust APL algorithm was designed by adopting the M-estimate cost function in impulsive noise environment; however, its convergence rate is very slow for sparse system identification. This paper proposed a proportionate APL M-estimate (PAPLM) algorithm, which is derived by using the proportionate matrix to heighten the convergence rate. To maintain good steady-state performance of the PAPLM algorithm, a time-varying parameter PAPLM (TV-PAPLM) algorithm is proposed, which uses a modified exponential function to adjust the time-varying parameter according to the ratio of the mean square score function to the system noise variance. Moreover, the steady-state excess mean-square error performance of PAPLM algorithm is analyzed and obtained in detail. Simulation results reveal that the proposed PAPLM and TV-PAPLM algorithms achieve fast convergence rate and good steady-state performance in sparse system identification. [ABSTRACT FROM AUTHOR]

The generalized variable step-size diffusion continuous mixed p-norm (GVSS-DCMPN) algorithm is proposed in this paper, which is derived based on the improved continuous mixed p-norm (CMPN) strategy. In detail, a linear function is designed for the CMPN strategy. The proposed GVSS-DCMPN algorithm capable of exploiting various error norms to obtain performance improvement in non-Gaussian noise environment can be viewed as the generalization of the traditional p-norm-based algorithms in the sense of continuous errors. In particular, the GVSS-DCMPN algorithm transforms into the VSS-DCMPN algorithm when the slope of the linear function is set to 0. The computational complexity as well as the mean convergence is analyzed in the paper. Simulation results over the diffusion network show that the proposed algorithm achieves performance gain over some existing diffusion algorithms. [ABSTRACT FROM AUTHOR]

The filtered-s maximum correntropy criterion (FsMCC) algorithm was developed for nonlinear active noise control (NANC) in impulsive noise environments with the use of functional linked artificial neural networks (FLANNs) structure. However, the kernel-width of the FsMCC is constant, which is dull and cannot self-regulate to achieve both the fast convergence and low steady-state error performance. For better convergence ability, in this paper, a fractional-order gradient ascent filtered-s maximum correntropy criterion algorithm (FGA-FsMCC) is presented for multi-channel NANC system. The fractional lower-order moments supersede the non-existent second-order moments. To come up with a more reliable algorithm, by merging two kernels with complementary width, the fractional-order gradient ascent filtered-s mixture correntropy criterion (FGA-FsMMCC) was presented. The computational complexity and convergence conditions are analyzed. To verify the robustness of proposed algorithms, simulations are carried out to discuss parameters and control effects of algorithms for alpha-stable noise in different nonlinear environments. • Design multi-channel nonlinear active noise control system with functional linked artificial neural networks. • Proposing a fractional-order gradient ascent filtered-s maximum correntropy criterion algorithm. • Defining the maximum mixture correntropy criterion. • Deriving a fractional-order gradient ascent filtered-s maximum mixture correntropy criterion algorithm. • Computational complexity analysis of proposed algorithms. [ABSTRACT FROM AUTHOR]

• A novel generalized hyperbolic secant function with l p -norm algorithm is proposed for nonlinear ANC system. • An improved FsGHSFP (IFsGHSFP) algorithm is developed to address the problem of tuning the parameter. • A convex combination algorithm of IFsGHSFP is suggested to strike a balance between convergence and misalignment. In nonlinear active noise control (ANC) system where the primary noise obeys non-Gaussian distribution, the traditional filter-s least mean square (FsLMS) algorithm is hardly satisfactory. In order to surmount this deficiency, a novel generalized hyperbolic secant function with l p norm (FsGHSFP) algorithm is proposed for nonlinear ANC system, which designs a robust optimization criterion utilizing the generalized hyperbolic secant distribution function. The FsGHSFP algorithm has robust noise attenuation capability for non-Gaussian noise, but suffers from the problem of tuning the parameter. To address this problem, an improved FsGHSFP (IFsGHSFP) algorithm is developed by taking advantage of the fact that continuous mixed norm does not need to adjust norm parameter. Moreover, in order to strike a balance between convergence and misalignment, thereby further enhancing the robustness of the ANC system, a convex combination algorithm of IFsGHSFP is suggested. The robustness mechanism of the proposed algorithms is analyzed, as well as stability conditions and computational complexity. It is verified by simulations that the proposed algorithms accelerate the convergence speed and improve the noise attenuation performance. In addition, the experimental results further validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Multi-view clustering has been a significant research problem in unsupervised clustering in recent years and has important applications in computer vision, data mining and other fields. To explore more clustering information from different views and improve performance, researchers have developed a variety of multi-view clustering algorithms by applying the same L 1 or L 2 , 1 norm constraints for different views, which essentially assumes that the errors satisfy the characteristics described by the corresponding norms. It is clear that such assumption is not always satisfied in the real-world datasets. Therefore, the performance of existing algorithms can decrease under the complex noise that may be contained in the data. To solve the above problem, we introduce an information theoretic learning-based metric into a multi-view model to address noise. First, we propose a new mixture correntropy (MC) metric. Then, we propose a MC based robust multi-view K-means clustering (MC-RMVKM) method by applying the MC to an existing model. Furthermore, an efficient iterative algorithm is derived to solve the joint MC-RMVKM learning problem. Extensive results on real-world multi-view data show that the MC-based algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have optimal performance when the regularization parameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. • We provide the analysis of the transient and steady-state behaviors of the proposed algorithm. • Numerical simulations illustrate the superior performance of the VRP-WL-CNLMS algorithm and the validity of the theoretical analysis. To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have optimal performance when the regularization parameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. The proposed algorithm can adaptively change the regularized parameter by exploiting a time-varying parameter that is obtained via making the power of noise-free a posteriori error minimum. A proper estimated method is provided to compute the power of the measured noise when the noise is unknown, and the moving-average method is employed to update the regularized parameter for avoiding large fluctuations. Then we provide the analysis of the transient and steady-state (TAS) behaviors of the proposed algorithm. Simulation results with different input signals illustrate that the VRP-WL-CNLMS algorithm has better advantages than other algorithms, and verify the theoretical validity of TAS analysis of the proposed algorithm in the system identification (SI) environment. Finally, the experimental results of wind prediction show that the predicted value of the proposed algorithm has a smaller error value with the original signal than the WL-CNLMS algorithm and can predict the signal better that can support the superiority of the proposed algorithm as well. [ABSTRACT FROM AUTHOR]

The Unscented Kalman filter (UKF) based on maximum correntropy criterion (MCC) is robust to heavy-tailed non-Gaussian noise. However, the approximate linear measurement equation obtained by statistical linearization technique may be not accurate enough since it only uses prior information. In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods. Different from the existing UKF algorithms, we only need to perform one-time unscented transformation in each filtering cycle, reducing the execution time of algorithm. Then a nonlinear enhancement model is constructed to handle predictions and observations simultaneously, which will be included in the cost function of robust MCC. In the process of iterative solution, thanks to the latest iteration values which are used to update the measurement information, RS-IMCC-UKF has more accurate results compared with traditional filters. In order to avoid non-positive definite characteristic in covariance matrix and the inverse operation in ill-conditioned matrix, the hyperbolic QR decomposition and Moore–Penrose pseudo-inversion are introduced to improve the numerical stability of the algorithm. Finally, a target tracking is modeled to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

• We first propose the constrained recursive least M-estimate (CRLM) algorithm by introducing the MHF into the cost function of CRLS, which is robust to impulsive noise. Further, an enhanced version of the CRLM algorithm, namely RCRLM algorithm was also derived to eliminate the round-off error caused by long input data sequence. • The computational complexity analysis of the proposed algorithms is given and compared with some other constrained algorithms. • Based on some simplified assumptions, we analyze the mean and mean square stability and theoretical transient and steady state behaviors under contaminated-Gaussian (CG) noise model. • Finally, the validity of theoretical analysis results is proved by linear system identification simulation. Further, the application of beamforming also shows the superior performance of the proposed RCRLM algorithm. Recently, the constrained adaptive filtering algorithms with strong robustness to non-Gaussian noise have been widely studied. Among them, the robust constrained least mean M-estimate (CLMM) algorithm has significant performance in the background of impulse noise base on the well anti-impulse noise characteristic of the M-estimate function. However, there is an irreconcilable contradiction between the steady-state error and the convergence rate of CLMM. To solve this contradiction, this paper introduces the modified Huber function (MHF) into the constrained recursive least squares (CRLS) algorithm and develops the constrained recursive least M-estimate (CRLM) algorithm, which fully combines the superior convergence performance of CRLS and the anti-impulse noise characteristic of the MHF. We furthermore propose an enhanced version of the CRLM, namely robust CRLM (RCRLM), which is robust to round-off error. Then, We analyze the mean and mean square stability and transient, steady-state NMSD of the proposed RCRLM algorithm under some simplified assumptions. Simulation results in various non-Gaussian noise scenarios show that the proposed RCRLM is superior to some existing constraint algorithms in terms of convergence speed, steady-state error and tracking ability, and the theoretical analysis results are also verified. [ABSTRACT FROM AUTHOR]

To solve the problem that the performance of maximum total correntropy (MTC) algorithm is limited by kernel width, a more flexible algorithm called maximum mixture total correntropy (MMTC) is proposed in this brief, which is derived by combining the mixture correntropy (MC) and total least square (TLS) method. Then, the local mean and mean square performance of the MMTC algorithm are analyzed in detail under some simplifying assumptions, the stable step size range and theoretical steady-state MSD expression are also given. Numerical simulations exhibit that the MMTC performs better than MTC for the parameter estimation of the error-in-variable (EIV) model in the background of impulse noise, the validity of the theoretical analysis is also verified. [ABSTRACT FROM AUTHOR]

• In this paper, we analyze the transient performance of the geometric algebra based least mean M-estimate (GA-LMM) filtering algorithm in detail under some simplifying assumptions and give the step size range that ensure the mean square stability of the GA-LMM. Further, to eliminate the constraint of the constant step size on the performance of the GA-LMM, a novel variable step-size algorithm called VSS-GA-LMM is designed and the optimal step size is obtained by maximizing the difference of mean square deviation (MSD) between successive iterations, which effectively balances the contradiction between convergence rate and steady-state error. • The validity of the transient performance analysis about GA-LMM and the advantages of the GA-LMM and VSS-GA-LMM algorithms over other existing GA based algorithms are confirmed through numerical simulations. In this paper, the transient performance of the geometric algebra based least mean M-estimate (GA-LMM) filtering algorithm is analyzed in detail under some simplifying assumptions. Further, the variable step-size variant VSS-GA-LMM is designed to eliminate the constraint of the constant step size on the performance of the GA-LMM and the optimal step size is obtained by maximizing the difference of mean square deviation (MSD) between successive iterations, which effectively balances the contradiction between convergence rate and steady-state error. Finally, numerical simulations are presented to verify the validity of the theoretical analysis of the GA-LMM and the advantages of the GA-LMM and VSS-GA-LMM algorithms. [ABSTRACT FROM AUTHOR]

Background: Carcass traits are crucial indicators of meat production efficiency. However, the molecular regulatory mechanisms associated with these traits remain unclear. Results: In this study, we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms. Based on association analyses with the elastic net (EN) model, we identified 12 candidate genes (AMY1A, AP3B2, CEBPG, EEF2, EIF4EBP1, FGFR1, FOXD3, GOLM1, LOC107052698, PABPC1, SERPINB6 and TBC1D16) for 4 carcass-related traits, namely live weight, dressed weight, eviscerated weight, and breast muscle weight. SERPINB6 was identified as the only overlapping gene by 3 analyses, EN model analysis, weighted gene co-expression network analysis and differential expression analysis. Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts. Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression. Furthermore, a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3′UTR of SERPINB6. Conclusions: Collectively, our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation. Additionally, the downstream variant rs317934171 regulates SERPINB6 expression. These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits. [ABSTRACT FROM AUTHOR]

This paper proposes a Siamese motion-aware Spatio-temporal network (SiamMAST) for video action recognition. The SiamMAST is designed based on the fusion of four features via processing video frames: spatial features, temporal features, spatial dynamic features, and temporal dynamic features of a moving target. The SiamMAST comprises AlexNets as the backbone, LSTMs, and the spatial motion-awareness and temporal motion-awareness sub-modules. RGB images are fed into the network, where AlexNets extract spatial features. Further, they are fed into LSTMs to generate temporal features. Additionally, spatial motion-awareness and temporal motion-awareness sub-modules are proposed to capture spatial and temporal dynamic features. Finally, all features are fused and fed into the classification layer. The final recognition result is produced by averaging the test label probabilities across a fixed number of RGB frames and selecting the label of the highest probability. The whole network is trained offline using an end-to-end approach with large-scale image datasets using the standard SGD algorithm with back-propagation. The proposed network is evaluated on two challenging datasets UCF101 (93.53%) and HMDB51 (69.36%). The experiments have demonstrated the effectiveness and efficiency of our proposed SiamMAST. [ABSTRACT FROM AUTHOR]

Many traditional Chinese medicines (TCMs) have been confirmed to have antibacterial activities. However, very few substances have been found to be active against Gram-negative bacteria. This study aimed to identify antimicrobial activity substances against Gram-negative bacteria from fourteen TCMs. Fourteen TCMs with antibacterial potential were chosen for quantitative extraction and antibacterial activity assay, and the plant with the highest activity against Escherichia coli was selected to construct the component-target network. The following virtual screening and enzyme inhibition experiments were performed to analyse the antibacterial mechanisms of the compounds from Galla chinensis. The chemical constituents of Galla chinensis were identified by chemical fingerprinting. 1, 2, 3, 4, 6-Penta-O-galloyl-β-D-glucose (PGG) from Galla chinensis exhibited significant inhibition activity against adenylyl transferase (ATase) of E. coli and antibacterial activity against E. coli. Meanwhile, PGG was identified in the Galla chinensis ethanol extract as the abundant ingredient with a high content of 1.95% (w/w). PGG enriched in Galla chinensis is a promising natural antibiotic with the mode of action inhibiting ATase activity. To our knowledge, this is the first study attributing the antibacterial activity of PGG to its affinity with ATase. [ABSTRACT FROM AUTHOR]

Telocytes (TCs) are distinctive interstitial cells due to their characteristic structures and heterogeneity. They are suggested to participate in tissue repair/regeneration. TCs have been identified in many organs of various mammals. However, data on TCs in lower animals are still very limited. In this work, TCs were identified in the myocardium of the bullfrog (Rana catesbeiana) by light and transmission electron microscopy (TEM). The structural relationships between TCs and neighbouring cell types were measured using the ImageJ (FiJi) morphometric software. TCs with slender Tps (telepodes) were located around cardiomyocytes (CMC). TEM revealed TCs with long Tps in the stroma between CMC. The homocellular tight junctions were observed between the Tps. The Tps were also very close to the neighbouring CMC. The distance between Tps and CMC was 0.15 ± 0.08 μm. Notably, Tps were observed to adhere to the periphery of the satellite cells. The Tps and the satellite cells established heterocellular structural connections by tight junctions. Additionally, Tps were frequently observed in close proximity to mast cells (MCs). The distance between the Tps and the MCs was 0.19 ± 0.09 μm. These results confirmed that TCs are present in the myocardium of the bullfrog, and that TCs established structural relationships with neighbouring cell types, including satellite cells and MCs. These findings provide the anatomical evidence to support the note that TCs are involved in tissue regeneration. [ABSTRACT FROM AUTHOR]

Nanoscale air channel transistors (NACTs) have received significant attention due to their remarkable high‐frequency performance and high switching speed, which is enabled by the ballistic transport of electrons in sub‐100 nm air channels. Despite these advantages, NACTs are still limited by low currents and instability compared to solid‐state devices. GaN, with its low electron affinity, strong thermal and chemical stability, and high breakdown electric field, presents an appealing candidate as a field emission material. Here, a vertical GaN nanoscale air channel diode (NACD) with a 50 nm air channel is reported, fabricated by low‐cost IC‐compatible manufacturing technologies on a 2‐inch sapphire wafer. The device boasts a record field emission current of 11 mA at 10 V in the air and exhibits outstanding stability during cyclic, long‐term, and pulsed voltage testing. Additionally, it displays fast switching characteristics and good repeatability with a response time of fewer than 10 ns. Moreover, the temperature‐dependent performance of the device can guide the design of GaN NACTs for applications in extreme conditions. The research holds great promise for large current NACTs and will speed up their practical implementation. [ABSTRACT FROM AUTHOR]

Multiple strokes are the main factors affecting the safe and stable operation of power equipment. As the core lightning protection equipment, arresters are also frequently thermally collapsed due to multiple strokes. This paper investigates thermal effects of polymer-housed metal-oxide surge arrester (PMOSA) under multiple strokes. The comparative experiments of PMOSA lightning impulse are carried out through multiple strokes of experiment platform, including continuous lightning stroke and multiple strokes. Furthermore, the temperature distribution of the housing surface is obtained by infrared imager for thermal effect analysis. Subsequently, some typical DC parameters are tested to explore the association of impulse aging and thermal effects of the arrester under multiple strokes. Finally, a thermal-electric coupling model (TECM) is constructed to investigate the influence of some factors on the temperature of PMOSA from the perspective of materials and structure. The research in this paper can provide a theory reference and brief data support for improving the heat dissipation performance of arresters under multiple strokes based on structural optimization design and material modification. [ABSTRACT FROM AUTHOR]

A new testing method (oscillating wave method) has been proposed as a solution to the problem of short maintenance time in the traction power supply system, which could complete tests of winding and insulation via one connection and improve the maintenance efficiency of the transformer. Through the method being proved highly sensitive to winding faults, the variation under different winding states, however, remains unclear. This article aims to analyze oscillating wave signatures in the occurrence of axial displacement (AD). First, the modeling method will be proposed that can simulate oscillating wave signatures in different winding states. Then, the time-domain feature and the new proposed frequency-domain feature are applied to analyze the fault extent and location of AD fault. The results show that this model accurately simulates oscillating wave signatures. Moreover, the time-domain feature [Euclidean distance (ED)] is closely related to fault extent. The combined use of the time-domain feature (ED) and the frequency-domain feature (W-Energy entropy in Z-Score) is effective in the identification of fault location. These features, therefore, can provide the reference for winding state evaluation. [ABSTRACT FROM AUTHOR]

B GaN Nanoscale Air Channel Diodes b In article number 2206385 by Mo Li and co-workers, a vertical GaN nanodiode with a 50 nm air channel is reported, fabricated using IC-compatible technologies, with a record field emission current of 11 mA@10 V. Notably, the device displays outstanding stability and fast switching characteristics with a sub-10 ns response time. Additionally, the temperature-dependent performance can guide the design of nano-air-channel-devices for applications in extreme conditions. Fast Response GaN Nanoscale Air Channel Diodes with Highly Stable 10 mA Output Current toward Wafer-Scale Fabrication (Adv. Sci. 17/2023). [Extracted from the article]