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This article considers the parameter estimation problems for the controlled autoregressive systems interfered by moving average noises. A recursive extended gradient algorithm with penalty term is proposed by using the penalty criterion function. By introducing three fictitious output variables, the original system can be decomposed into three subsystems based on the hierarchical principle. The hierarchical recursive extended gradient algorithm with penalty term is then proposed to achieve the parameter estimation. Finally, the experimental results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Taxanes are the basic components of breast cancer chemotherapy. Nanoparticle albumin‐bound paclitaxel (nab‐paclitaxel) shows improved antitumor effects because of more targeted delivery. However, the effects of nab‐paclitaxel have not been systematically studied in patients with metastatic breast cancer (MBC) pretreated with taxanes. Considering the limited treatment options for MBC, this study retrospectively evaluated the clinical efficacy and adverse effects of nab‐paclitaxel in patients with taxane‐pretreated MBC.Patients who had previously received taxanes and subsequently received nab‐paclitaxel chemotherapy for MBC at Jiangsu Cancer Hospital between October 2014 and April 2022 were included for analysis. The primary end point was progression‐free survival (PFS), and the secondary end points were the objective response rate (ORR), disease control rate (DCR), clinical benefit rate (CBR), and side effects.A total of 236 female patients with MBC were included. The median PFS was 7.20 months (95% confidence interval [CI], 6.63–7.80 months), and the ORR, DCR, and CBR were 29.55% (95% CI, 23.50%–35.60%), 83.64% (95% CI, 78.70%–88.60%), and 56.36% (95% CI, 49.80%–63.00%), respectively. Following nab‐paclitaxel treatment, the median PFS of patients who were sensitive to taxanes during previous treatments was significantly longer than that of patients who were resistant to taxanes (7.57 months vs. 4.43 months, p < .001). The most common adverse events were sensory neuropathy (89.83%), neutropenia (48.73%), leukopenia (46.61%), and anemia (35.59%).Nab‐paclitaxel demonstrated clinical activity in taxane‐pretreated patients with MBC. This beneficial effect was observed both in patients who were sensitive and resistant to taxanes during previous treatments. These results suggest nab‐paclitaxel as the preferred chemotherapy regimen in patients with MBC, regardless of their sensitivity to taxanes during previous treatments. [ABSTRACT FROM AUTHOR]

This article investigates the identification issue of the bilinear system in the presence of the impulsive noise. The bilinear system based on the observer canonical form is translated into a regressive form, and a bilinear state observer is established to estimate the state variables. To overcome the effects of the impulsive noise to parameter estimation, the proposed algorithms employ a generalized continuous mixed p$$ p $$‐norm cost function, which can generate an adjustable gain that control the proportions of the error norms without resorting to a priori knowledge of the noise. Moreover, a sliding window is designed to update the dynamical data by removing the oldest data and adding the newest measurement data. An numerical example exhibits that the proposed algorithms can reduce the impact of the impulsive noise to parameter estimation and improve the parameter estimation accuracy compared with the conventional algorithms. [ABSTRACT FROM AUTHOR]

For multiphase batch process, the characteristics of process data under various batches differ. Consequently, the soft sensor model built for a particular working condition is inapplicable to other working conditions. Besides, each batch can be divided into several phases whose characteristics are probably different. To address the above problems, a soft sensor model based on phase division and transfer learning strategy is proposed. First, transfer learning strategy is adopted to construct a soft sensor model applicable to various working conditions. Specifically, geodesic flow kernel based on linear local tangent space alignment (LLTSA-GFK) algorithm is designed. By projecting process data to the common manifold subspace, the distribution difference of data between various batches is reduced and the performance of the soft sensor model is enhanced. In addition, sequence-based fuzzy clustering and just-in-time learning (JITL) are adopted to solve the multistage characteristic for batch process. The root-mean-square error (RMSE), coefficient of determination ( R 2 ) , and mean absolute error (MAE) are adopted to compare the conventional soft sensing approach (i.e., partial least-square regression based on JITL, support vector regression, and back propagation neural network) with the proposed approach. The superiority of the proposed model is verified by a fed-batch penicillin fermentation process. [ABSTRACT FROM AUTHOR]

Methods for the detection and quantification of food allergens in complex matrices are necessary to ensure compliance with labeling regulations and assess the effectiveness of food allergen preventive controls. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) has emerged as an orthogonal technique in complement to immunochemical-based assays. However, the absence of established guidelines for MS-based quantification of allergens in food has limited harmonization among the method development community. In this study, different quantification strategies were evaluated using a previously developed multiplexed LC-MS/MS method for the detection of egg, milk, and peanut. Peptide performance criteria (retention time, signal-to-noise ratio, and ion ratio tolerance) were established and quantification approaches using varying calibrants, internal standards, background matrices, and calibration curve preparation schemes were systematically evaluated to refine the previous method for routine laboratory use. A matrix-matched calibration curve using allergen ingredients as calibrants and stable isotope–labeled peptides as internal standards provided the most accurate quantitative results. The strategy was further verified with commercially available reference materials and allowed for the confident detection and quantification of food allergens. This work highlights the need for transparency in calibration strategy and peptide performance requirements for effective evaluation of mass spectrometric methods for the quantification of food allergens. [ABSTRACT FROM AUTHOR]

Wastewater treatment processes (WWTPs) are complex nonlinear systems with strong nonlinearity among many treatment units and disturbances including weather conditions, uncertainties, and time-varying dynamics. This paper studies the multi-objective and multi-units linear active disturbance rejection control for WWTPs to lower the overall cost index and enhance the effluent quality index simultaneously. A multi-objective optimization method using the multi-strategy mutation and the adaptive mechanism is employed to dynamically optimize set points. Then, multiple-units linear active disturbance rejection controllers are designed to achieve tracking control for dissolved oxygen concentration (including 3-5 units) and nitrate level with unmeasurable disturbances based on the optimal set points. The comparison experiments tested on the benchmark simulation model No. 1 demonstrate that the proposed method can improve the tracking performance and achieve a significant improvement in reducing the overall cost index and the effluent quality index. [ABSTRACT FROM AUTHOR]

As a semi-supervised machine learning strategy, active learning has recently been introduced into the soft sensing area for the performance enhancement and the save of human efforts. Active learning is capable to automatically select the most informative unlabeled samples for labeling, thus the costs related to human annotation can be reduced. Instead of randomly labeling data samples, in this paper, we employ kernel approximate linear dependence (ALD) to evaluate each unlabeled data samples, and the data samples with large evaluation values are labeled for model updating. Comparative study results show that the ALD based active learning strategy driven soft sensor obtains better prediction performance than the random selection strategy driven soft sensor. [ABSTRACT FROM AUTHOR]

The joint parameter and time-delay estimation problems for a class of nonlinear multirate time-delay system with uncertain output delays are addressed in this paper. The practical process typically has time-delay properties and the process data are often multirate, sampled with output data inevitably corrupted by uncertain delays. The linear parameter varying (LPV) finite impulse response (FIR) multirate time-delay model is initially built to describe the considered system. The problems of over-parameterization and the existence of both continuous model parameters and discrete time-delays have made the conventional maximum likelihood difficult to solve the considered problems. In order to handle these problems, the joint parameter and time-delay estimation for the LPV FIR multirate time-delay model are formulated in the expectation-maximization scheme, and the algorithm to estimate the model parameters and time-delays is derived, simultaneously based on multirate process data. The efficacy of the proposed method is verified through a numerical simulation and a practical chemical plant. [ABSTRACT FROM AUTHOR]

The parameter estimation problem for multi‐input multi‐output Hammerstein systems is considered. For the Hammerstein model to be identified, its dynamic time‐invariant subsystem is described by a controlled autoregressive model with a communication delay. The modified Kalman filter (MKF) algorithm is derived to estimate the unknown intermediate variables in the system and the MKF‐based recursive least squares (LS) algorithm is presented to estimate all the unknown parameters. Furthermore, the hierarchical identification is adopted to decompose the system into two fictitious subsystems: one containing the unknown parameters in the non‐linear block and the other containing the unknown parameters in the linear subsystem. Then an MKF‐based hierarchical LS algorithm is derived. The convergence analysis shows the performance of the presented algorithms. The numerical simulation results indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

Apart from strong nonlinearity and time-varying behaviors in industrial processes, the hidden time-delay information, which is unfortunately overlooked in most existing modeling methods, should also be taken into account in soft sensor modeling. In view of this, a novel soft sensor, referred to as local time-delay reconstruction based moving window time difference Gaussian process regression (LTR-MWTDGPR), is proposed in this paper. To deal with the time-delay, a fuzzy curve analysis based local time-delay parameter extraction procedure is performed along with a strategy of a moving window, which simultaneously captures the process time-varying feature. Then the local window training dataset and new query sample are reconstructed according to the time-delay parameter set at the next sampling instant. Afterwards, the time difference Gaussian process regression is employed to handle the drifting feature of local reconstructed dataset. The effectiveness and accuracy of the proposed LTR-MWTDGPR approach in predicting quality variables are verified through a real sulfur recovery unit and an industrial debutanizer column. [ABSTRACT FROM AUTHOR]

Process nonlinearity, multiple operating modes and time-varying characteristics often deteriorate the prediction performance of process models. In this article, a multi-mode moving-window Gaussian process regression (MWGPR) based approach for ARX modeling is proposed to effectively capture process nonlinearity or switching dynamics. The Gaussian mixture model (GMM) is first introduced to separate the data into different operating modes. Then the MWGPR strategy is applied to identify the local ARX model. Just-in-time learning (JITL) and dual updating are applied for more effective tracking of process dynamics. A simulation of a continuous fermentation process and a pilot scale experiment are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with theidentification problems of thelinear parameter varying (LPV) system with missing output in the presenceof the time-delay. A multiple-model approach is adopted. Local modelsvarying from one operating point to another are first described byfinite impulse response (FIR) models. To handle missing output andtime-delay, the expectation-maximization (EM) algorithm is utilizedto estimate the unknown parameters and the time-delay simultaneously.Output Error (OE) models are widely used in controller design. Therefore,the auxiliary model principle is employed to recover the OE modelsbased on the initially identified FIR models. The EM algorithm isthen used again to refine the unknown parameters of the OE modelswith the complete data set to obtain the final global model. Simulationexamples are presented to demonstrate the performance of the proposedmethod. [ABSTRACT FROM AUTHOR]

Abstract: This letter presents an iterative estimation algorithm for modeling a class of output nonlinear systems. The basic idea is to derive an estimation model and to solve an optimization problem using the gradient search. The proposed iterative numerical algorithm can estimate the parameters of a class of Wiener nonlinear systems from input–output measurement data. The proposed algorithm has faster convergence rates compared with the stochastic gradient algorithm. The numerical simulation results indicate that the proposed algorithm works well. [Copyright &y& Elsevier]

Summary: This article studies the parameter identification problem for bilinear state space models with time‐varying time delays. Considering the correlation of time delays, the Markov chain switching mechanism is adopted to model the delay sequence. Based on the observer canonical form, the bilinear state space model is transformed into a regressive form. A bilinear state observer is designed to estimate the state variables. Under the variational Bayesian scheme, the system parameters, discrete delays, and the Markov transition probabilities are identified by using the measurement data. A numerical example and a continuous stirred tank reactor simulation are employed to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Bacterial production of gaseous hydrocarbons such as ethylene and methane affects soil environments and atmospheric climate. We demonstrate that biogenic methane and ethylene from terrestrial and freshwater bacteria are directly produced by a previously unknown methionine biosynthesis pathway. This pathway, present in numerous species, uses a nitrogenase-like reductase that is distinct from known nitrogenases and nitrogenase-like reductases and specifically functions in CÐS bond breakage to reduce ubiquitous and appreciable volatile organic sulfur compounds such as dimethyl sulfide and (2-methylthio)ethanol. Liberated methanethiol serves as the immediate precursor to methionine, while ethylene or methane is released into the environment. Anaerobic ethylene production by this pathway apparently explains the long-standing observation of ethylene accumulation in oxygen-depleted soils. Methane production reveals an additional bacterial pathway distinct from archaeal methanogenesis. [ABSTRACT FROM AUTHOR]

This paper investigates the online learning problem of Lagrangian ε -twin support vector regression (L- ε -TSVR), with the goal of presenting incremental implementations. First, to solve the problem that the existing L- ε -TSVR cannot efficiently update the model under incremental scenarios, an incremental Lagrangian ε -twin support vector regression (IL- ε -TSVR) based on the semi-smooth Newton method is proposed. By utilizing the matrix inverse theorems to update the Hessian matrices incrementally, IL- ε -TSVR lowers the time complexity and expedites the training process. However, when solving the problem of nonlinear case, the training speed of IL- ε -TSVR rapidly decreases with the increasing size of the kernel matrix. Therefore, an incremental reduced Lagrangian ε -twin support vector regression (IRL- ε -TSVR) is presented. IRL- ε -TSVR employs the reduced technique to restrict the size of the inverse matrix at the cost of slightly lower the prediction accuracy. Next, to lighten the prediction accuracy loss caused by parameters reduction, a novel regularization term is introduced to replace the original one, and an improved incremental reduced Lagrangian ε -twin support vector regression (IIRL- ε -TSVR) is designed. The results on UCI benchmark datasets show that IL- ε -TSVR can effectively address the linear regression problem under incremental scenarios and obtain almost the same generalization capability as offline learning. Moreover, IRL- ε -TSVR and IIRL- ε -TSVR can reduce training time of nonlinear regression model and obtain sparse solution, and their generalization capabilities are close to those of offline ones. Particularly, the proposed algorithms can implement fast incremental learning of large-scale data. [ABSTRACT FROM AUTHOR]

Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly save human resources and improve performance under complex industrial conditions. Ensemble learning based soft sensors succeed in capturing complex nonlinearities, frequent dynamic changes, as well as time-varying characteristics in industrial processes. However, local model regions under traditional ensemble modelling methods are highly dependent on labeled data samples and, hence, their prediction accuracy might get affected when labeled samples are limited. A novel active learning (AL) framework upon the ensemble Gaussian process regression (GPR) model is proposed for smart soft sensor design in order to overcome this drawback. Firstly, to iteratively select the most informative unlabeled samples for labeling with hierarchical sampling based AL strategy, to then apply Gaussian mixture model (GMM) technique to autonomously identify operation phases, to further construct local GPR models without human involvement, and finally to integrate the base predictors by applying the Bayesian fusion strategy. Comparative studies for the penicillin fermentation process demonstrate the reliability and superiority of the recommended smart soft sensing. The cost of human annotation can be dramatically reduced by at least half while the prediction performance simultaneously keeps high. [ABSTRACT FROM AUTHOR]

Digital twins are a significant way to achieve fault detection of various smart manufacturing, which provide a new paradigm for complex industrial process monitoring. Wastewater treatment processes play a crucial role in water recycling, its failures may cause risks of adverse environmental impacts. This paper studies the digital twins fault detection framework based on the convolutional autoencoder for wastewater treatment processes monitoring. The designed digital twins fault detection framework can simulate the sludge bulking failure and the toxic impact failure conditions in the virtual space to construct the simulation data with continuous updating through wastewater data. The simulation data is divided into rate of change information sub-block, original sub-block, and cumulative information sub-block using the multi-block modeling strategy to fully explore the hidden information. Further, the sliding window method is utilized to resample the reconstructed sub-blocks to enhance the effects of the detection performance. Bayesian fusion is adopted, and the final decision is made based on the fused statistical value and the control limit. The comparison experiments tested on the digital twins fault detection framework demonstrate the superiority and feasibility of detection performance. [ABSTRACT FROM AUTHOR]

This paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time delay. Both the process and the measurement noises are considered in the system. On the basis of the observable canonical state space form and the key term separation, a pseudolinear regressive identification model is obtained. For the unknown states in the information vector, the Kalman filter is used to search for the optimal state estimates. A Kalman filter-based least squares iterative and a recursive least squares algorithms are proposed. Extending the information vector to include the latest information terms, which are missed for the time delay, the Kalman filter-based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time delay, parameters, and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper considers parameter estimation for linear parameter varying (LPV) systems with randomly missing output data. The multi-model LPV model is adopted and the identification problem is formulated under the scheme of the generalized expectation–maximization (GEM) algorithm. In order to deal with the missing output data, the local models are firstly taken to have the finite impulse response (FIR) model structure. To alleviate potential overparameterization problem, a prior on FIR model coefficients is imposed and the GEM algorithm is modified to derive the maximum a posterior (MAP) estimates of the multi-mode LPV FIR model parameters. Since the FIR model is not suitable for general control applications, a multi-mode LPV output error (OE) model is then identified by applying the GEM algorithm to the same identification data set with parameters initialized based on the estimated FIR models. One simulation example and two experiments are presented to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Many basic laws of physics or chemistry can be written in the form of differential equations. With the development of digital signals and computer technology, the research on discrete models has received more and more attention. The estimates of the unknown coefficients in the discretized difference equation can be obtained by optimizing certain criterion functions. In modern control theory, the state-space model transforms high-order differential equations into first-order differential equations by introducing intermediate state variables. In this paper, the parameter estimation problem for linear difference equation systems with uncertain noise is developed. By transforming system equations into state-space models and on the basis of the considered priors of the noise and parameters, a variational Bayesian iterative estimation algorithm is derived from the observation data to obtain the parameter estimates. The unknown states involved in the variational Bayesian algorithm are updated by the Kalman filter. A numerical simulation example is given to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]