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The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution: Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters. • Noise is modeled as a GSTM distribution. • Sliding window uses multiple measurements to resist dynamic outliers. • VB method and MHE framework combine for joint inference of unknown parameters. • The superiority of the proposed filter has been confirmed through simulations. [ABSTRACT FROM AUTHOR]

The Kalman filter is a linear optimal estimator when it can accurately receive measurements and the noise obeys a Gaussian distribution. However, the measurement noise may have heavy-tailed characteristics because of outlier interference. Actually, real-world measurement information may have one-step randomly delayed measurements (ORDMs) with unknown latency probability (LP) due to exogenous disturbances. Moreover, systems are usually not linear. To overcome the influence of the state estimator of nonlinear systems from heavy-tailed measurement noise (HMN) and the unknown LP, this paper focuses on the robust Gaussian approximate (GA) filter and smoother for the above issues. First, the one-step predicted probability density function (PDF) is modeled as a Gaussian distribution, and the likelihood PDF is modeled as a Normal–Gamma–Beta (NGBM) distribution. Furthermore, novel robust Gaussian approximate filter and smoother are proposed based on the variational Bayesian technique. They provide general estimation frameworks for estimating state of nonlinear systems with HMN and ORDMs, and different nonlinear estimators can be implemented using different numerical techniques. In addition, the computational complexity of the proposed algorithms is calculated. Finally, taking a univariate nonstationary growth model, a passive ranging problem, and target tracking as examples, the effectiveness and superiority of the proposed robust algorithms contrasted with the existing algorithms are shown. The outcomes show that the proposed methods have higher estimation precision; however, the computational burden is marginally increased. • We design the novel filter and smoother to suppress the effects from heavy-tailed measurement noise and unknown latency probability. • The variational Bayesian approach is introduced to jointly approximate estimate the state and parameters. • The computational complexity of the proposed algorithms is analyzed. • Three simulation examples are utilized to verify the effectiveness and excellence of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach for state estimation in linear systems impacted by measurement outliers. By analyzing measurement information within a time window, the method enhances measurement utilization efficiency, leading to more accurate identification and mitigation of outliers. Noise is modeled as a Multivariate Laplace (ML) distribution, which effectively avoids the complexity of estimating the degrees of freedom (DOF) parameter. Moreover, the integration of the Variational Bayesian (VB) method with Moving Horizon Estimation (MHE) enables joint inference of unknown parameters, increasing the flexibility and accuracy of the noise model while improving the system's robustness against outliers. Simulation results show that the proposed algorithm outperforms existing methods in both effectiveness and robustness when handling outliers. • A robust KF based on MHE is introduced, analyzing measurement over time to resist outliers. • Noise is characterized by a multivariate Laplace distribution. • The MHE is integrated with the variational Bayesian method for joint parameter estimation. • The algorithm's effectiveness was validated through target tracking simulations. [ABSTRACT FROM AUTHOR]

• This work constructed a multi-channel Vision Transformer model, which is more suitable for multi-channel EEG data compared to traditional Vision Transformer models. • In the preprocessing stage, this work used Stockwell transform to extract the time-frequency features of EEG signals and compressed the time-frequency feature matrix to reduce computational load. • In this work, evaluations were conducted using two databases. In addition to the public CHB-MIT database, the SH-SDU database was collected from a clinical hospital to comprehensively assess the performance of the designed model in this study. • In terms of data augmentation, this work proposed a novel data augmentation method called random segmentation and random combination, which can expand the data for pre-ictal periods. • In this study, two evaluation criteria were used, including segment-based evaluation criteria and event-based evaluation criteria. The event-based evaluation criteria provide more valuable references for clinical applications. Automatic seizure prediction is crucial for developing a new therapy for patients suffering from medically intractable epilepsy, possessing important clinical application value. In order to solve the problems of overfitting due to the complexity of deep learning based seizure prediction networks and the susceptibility of EEG features to noise contamination, an automatic seizure prediction model based on Stockwell Transform (S-transform) and Multi-Channel Vision Transformer (MViT) is proposed in this work. The time-frequency representation of multi-channel electroencephalography (EEG) signals is obtained by using S-Transform. These time-frequency spectrograms are then compressed and sent into the MViT model for further spatial feature extraction and identification of preictal EEG state. The designed MViT model is lightweight, ensuring efficient feature discriminating ability even with a minimal number of network layers. In view of the persistence of EEG activities, K-of-N strategy is employed to further augment the predictive performance. Experiments on the CHB-MIT database and the SH-SDU clinical database yield segment-based accuracy of 97.57 % and 95.88 % respectively, demonstrating the superiority and potential of the proposed method in seizure prediction. [ABSTRACT FROM AUTHOR]

Inorganic coagulants are essential agents that can facilitate the sludge deep dewatering, but comprehensive comparative research of commonly used coagulants is lacking. The systematic comparison of six coagulants, including Ferric Chloride (FeCl 3), Polymeric Ferrous Sulfate (PFS), Ferrous Sulfate (Fe 2 (SO 4) 3), Aluminium Chloride (AlCl 3), Aluminium Sulfate (Al 2 (SO 4) 3), and Polymeric Aluminium Chloride (PAC) regarding their performance in conditioning sludge dewatering, physicochemical properties, the risk of heavy metals migration in sludge cake and the mechanism of sludge dewatering, was investigated. Within a dosage range of 20% dry solids (DS), the PFS and FeCl 3 showed a better dewatering effect among the six coagulants, and the water content (Wc) of the sludge cake decreased from 81.2%±1.1% to 57.7%±1.0% and 59.2%±1.6%, respectively. There was a correlation between the pH, zeta potential, viscosity, and bound water content of the sludge and the Wc of the sludge cake during dewatering conditioned by six coagulants. However, there was no correlation between sludge particle size and Wc of the sludge cake. The PFS and FeCl 3 reduced the protein content of tightly bound extracellular polymeric substances (TB-EPS) by 66.1% and 62.6%, respectively. Moreover, the iron coagulants significantly reduced the content of As, Cu, Ni, and Zn in the sludge cake, where the risk levels of As and Ni were largely weakened. The economic analysis shows that iron coagulants were more valuable than aluminium coagulants for engineering applications. Meanwhile, the dewatering mechanisms of the two types of coagulants were revealed. This study provides significant insights into the application of coagulants in engineering practice. [Display omitted] • PFS and FeCl 3 exhibited better dewatering performance among the six coagulants. • Most physicochemical properties of the sludge were correlated with sludge cake's water content. • Iron coagulants significantly reduced protein content in TB-EPS. • Iron coagulants largely weakened the toxicity of As and Ni in sludge cake. [ABSTRACT FROM AUTHOR]

What is the role of exosomal lncRNAs and mRNAs profiles and their interaction networks in regulating the development of polycystic ovary syndrome (PCOS)? LncRNA microarray was used to analyse the expression profiles of lncRNA and mRNA in follicular fluid exosomes from three patients with polycystic ovary syndrome (PCOS) and three control women. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to explore biological functions of exosomal mRNAs in PCOS. Six PCOS-related genes were selected, and coding-non-coding gene co-expression (CNC) networks and competing endogenous RNA (ceRNA) networks analysis were combined to reveal lncRNA–miRNA–mRNA interaction networks. A total of 5373 differentially expressed exosomal lncRNAs and 3381 differentially expressed exosomal mRNAs were identified (fold change ≥2 and P < 0.05). Gene ontology analysis indicated that 14 terms of biological process were related to reproductive development. KEGG pathway analysis revealed PCOS-related pathways, such as MAPK signalling pathway and some inflammation-related signalling pathways. Interaction networks of lncRNAs, miRNAs and six PCOS-related genes (IRS1, CYP11A1, BMP6, FSHR, WNT4 and CYP19A1) were constructed. The CNC networks and ceRNA networks analysis uncovered some novel PCOS-related exosomal lncRNA-miRNA-mRNA regulatory networks. Differential expression profiles of exosomal lncRNAs and mRNAs were identified, and some PCOS-related biological processes and regulatory networks were indicated. LncRNAs and mRNAs in follicular fluid exosomes may play important roles in the follicular development of PCOS. [ABSTRACT FROM AUTHOR]