Data-driven set-point learning control with ESO and RBFNN for nonlinear batch processes subject to nonrepetitive uncertainties.

This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design.

Event-Triggered Adaptive Neural Control for MIMO Nonlinear Systems With Rate-Dependent Hysteresis and Full-State Constraints via Command Filter.

This article presents an event-triggered adaptive NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.

LQR approach to robust stabilization of state space systems with matched uncertainties.

This note shows an elegant relationship between the quadratic optimal control and robust stabilization for linear time-invariant (LTI) systems, where the former control can robustly stabilize the latter system, provided that the matched uncertainty is bounded. Through reviewing the relevant literature, some common mistakes in regard to this relationship are found. The correct results are obtained and proved in both frequency and time domains. The results are applicable to both single- and multi-input cases. They are significant as the simple LQR design for the nominal system can be utilized to directly solve-with no further effort-the complex robust stabilization problem for a class of linear uncertain systems.

Emerging effects of non-coding RNA in vascular endothelial cells during strokes.

Vascular and neurological damage are the typical outcomes of ischemic strokes. Vascular endothelial cells (VECs), a substantial component of the blood-brain barrier (BBB), are necessary for normal cerebrovascular physiology. During an ischemic stroke (IS), changes in the brain endothelium can lead to a BBB rupture, inflammation, and vasogenic brain edema, and VECs are essential for neurotrophic effects and angiogenesis. Non-coding RNAs (nc-RNAs) are endogenous molecules, and brain ischemia quickly changes the expression patterns of several non-coding RNA types, such as microRNA (miRNA/miR), long non-coding RNA (lncRNA), and circular RNA (circRNA). Furthermore, vascular endothelium-associated nc-RNAs are important mediators in the maintenance of healthy cerebrovascular function. In order to better understand how VECs are regulated epigenetically during an IS, in this review, we attempted to assemble the molecular functions of nc-RNAs that are linked with VECs during an IS.

Hierarchical Passivity Criterion for Delayed Neural Networks via A General Delay-Product-Type Lyapunov-Krasovskii Functional.

This article is concerned with passivity analysis of neural networks with a time-varying delay. Several techniques in the domain are improved to establish the new passivity criterion with less conservatism. First, a Lyapunov-Krasovskii functional (LKF) is constructed with two general delay-product-type terms which contain any chosen degree of polynomials in time-varying delay. Second, a general convexity lemma without conservatism is developed to address the positive-definiteness of the LKF and the negative-definiteness of its time-derivative. Then, with these improved results, a hierarchical passivity criterion of less conservatism is obtained for neural networks with a time-varying delay, whose size and conservatism vary with the maximal degree of the time-varying delay polynomial in the LKF. It is shown that the conservatism of the passivity criterion does not always reduce as the degree of the time-varying delay polynomial increases. Finally, a numerical example is given to illustrate the proposed criterion and benchmark against the existing results.

Event-Triggered Adaptive Neural Network Tracking Control for Uncertain Systems With Unknown Input Saturation Based on Command Filters.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

Temporal alterations in pericytes at the acute phase of ischemia/reperfusion in the mouse brain.

Pericytes, as the mural cells surrounding the microvasculature, play a critical role in the regulation of microcirculation; however, how these cells respond to ischemic stroke remains unclear. To determine the temporal alterations in pericytes after ischemia/reperfusion, we used the 1-hour middle cerebral artery occlusion model, which was examined at 2, 12, and 24 hours after reperfusion. Our results showed that in the reperfused regions, the cerebral blood flow decreased and the infarct volume increased with time. Furthermore, the pericytes in the infarct regions contracted and acted on the vascular endothelial cells within 24 hours after reperfusion. These effects may result in incomplete microcirculation reperfusion and a gradual worsening trend with time in the acute phase. These findings provide strong evidence for explaining the "no-reflow" phenomenon that occurs after recanalization in clinical practice.

Analysis and prediction of COVID-19 epidemic in South Africa.

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.

Stability Analysis for Delayed Neural Networks via a Novel Negative-Definiteness Determination Method.

The stability of neural networks with a time-varying delay is studied in this article. First, a relaxed Lyapunov-Krasovskii functional (LKF) is presented, in which the positive-definiteness requirement of the augmented quadratic term and the delay-product-type terms are set free, and two double integral states are augmented into the single integral terms at the same time. Second, a new negative-definiteness determination method is put forward for quadratic functions by utilizing Taylor's formula and the interval-decomposition approach. This method encompasses the previous negative-definiteness determination approaches and has less conservatism. Finally, the proposed LKF and the negative-definiteness determination method are applied to the stability analysis of neural networks with a time-varying delay, whose advantages are shown by two numerical examples.

Adaptive Finite-Time Containment Control of Uncertain Multiple Manipulator Systems.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering errors caused by the used FTCFs are removed by using the error compensation mechanism (ECM). The proposed virtual control signal, the control torque, and the adaptive updating law can guarantee the set tracking errors converge to an adjustable neighborhood of the origin in finite time in the presence of uncertain parameters. Because the virtual control signal and ECM only use the local information, the established method is completely distributed. Two simulation examples are given to show the effectiveness of the proposed scheme.

Polynomial Lyapunov Functions for Synchronization of Nonlinearly Coupled Complex Networks.

In this article, we search for polynomial Lyapunov functions beyond the quadratic form to investigate the synchronization problems of nonlinearly coupled complex networks. First, with a relaxed assumption than the quadratic condition, a synchronization criterion is established for nonlinearly coupled networks with asymmetric coupling matrices. Compared with the existing synchronization criteria, our results are less conservative and have a wider application. Second, the synchronization problem for polynomial networks is characterized as the sum-of-squares (SOS) optimization one. In this way, polynomial Lyapunov functions can be obtained efficiently with SOS programming tools. Furthermore, it is shown that the local synchronization of certain nonpolynomial networks can also be analyzed by using the SOS optimization method through the Taylor series expansion. Finally, three numerical examples are presented to verify the effectiveness and less conservatism of our analytical results.

Optimization and Developmental Validation of 38-plex InDel Panel for Ancestry Inference.

OBJECTIVES: The previously established 38-plex InDel system was optimized and its performance was validated according to the Scientific Working Group on DNA Analysis Method (SWGDAM) application guidelines. The ancestry inference accuracy of individuals from East Asian, European, African and mixed populations was verified. METHODS: DNA standard sample 9947A was used as the template to establish the optimal amplification conditions by adjusting primer balance, Mg2+ final concentration and optimizing PCR thermal cycle parameters and amplification volume. The allelic dropout, nonspecific amplification and whether the origin of the inferred samples matched the known information were compared to evaluate the performance of this system. RESULTS: The optimal dosage of this system was 0.125-2 ng DNA template. The results of InDel typing were accurate, the amplification equilibrium was good, and the species specificity was good. This system showed certain tolerance to DNA samples including the inhibitor such as hemoglobin (≤80 µmol/L), indigo (≤40 mmol/L), calcium ion (≤1.0 mmol/L), and humic acid (≤90 ng/µL). The system enabled the direct amplification of DNA from saliva and blood on filter paper, and the results of ethnic inference were accurate. The system successfully detected the mixed DNA sample from two individuals. The test results of the system for common biological materials in practical cases were accurate. CONCLUSIONS: The results of the 38-plex InDel system are accurate and reliable, and the performance of the system meets the requirement of the SWGDAM guidelines. This system can accurately differentiate the ancestry origins of individuals from African, European, East Asian, and Eurasian populations and can be implemented in forensic practice.

Spinocerebellar ataxia type 3 with dopamine-responsive dystonia: A case report.

BACKGROUND: Spinocerebellar ataxia type 3 (SCA3) is a rare neurodegenerative disease with high genetic heterogeneity. SCA3 mainly manifests as progressive cerebellar ataxia accompanied by paralysis of extraocular muscles, dysphagia, lingual fibrillation, pyramidal tract sign, and extrapyramidal system sign. However, it rarely has clinical manifestations similar to Parkinson-like symptoms, and is even rarer in patients sensitive to dopamine. We report a patient initially diagnosed with dopamine-responsive dystonia who was ultimately diagnosed with SCA3 by genetic testing, which was completely different from the initial diagnosis. CASE SUMMARY: A 40-year-old Chinese woman was admitted to hospital due to severe inflexibility. At the beginning of the disease, she presented with anxiety and sleep disorder. At the later stage, she presented with gait disorder, which was similar to Parkinson's disease. Her medical history was unremarkable, but her mother, grandmother, and uncle all had similar illnesses and died due to inability to take care of themselves and related complications. Laboratory and imaging examinations showed no abnormalities, but electromyography and electroencephalography revealed delayed somatosensory evoked potentials and slow background rhythm, respectively. Her symptoms fluctuated during the daytime, and we initially diagnosed her with dopamine-responsive dystonia. After treatment with low-dose levodopa, the patient's symptoms were significantly improved, but the final genetic diagnosis was SCA3. CONCLUSION: SCA3 has various clinical phenotypes and needs to be differentiated from Parkinson's syndrome and dopamine-responsive dystonia.

Identification of tRNA-derived Fragments and Their Potential Roles in Atherosclerosis.

OBJECTIVE: Atherosclerosis (AS), a chronic inflammatory disease, is the basis of cardiovascular disease (CVD). Although the treatment has been greatly improved, AS still imposes a large burden on human health and the medical system, and we still need to further study its pathogenesis. As a novel biomolecule, transfer RNA-derived fragments (tRFs) play a key role in the progression of various disease. However, whether tRFs contribute to atherosclerosis pathogenesis remains unexplored. METHODS: With deep sequencing technology, the change of tRFs expression profiles in patients with AS compared to healthy control group was identified. The accuracy of the sequencing data was validated using RT qPCR. Subsequently, we predicted the potential target genes of tRFs by online miRNA target prediction algorithms. The potential functions of tRFs were evaluated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. RESULTS: There were 13 tRFs differentially expressed between patients with AS and healthy controls, of which 2 were up-regulated and 11 were down-regulated. Validation by RT-qPCR analysis confirmed the sequencing results, and tRF-Gly-GCC-009 was highly up-regulated in the AS group based on the results of sequencing which was confirmed by RT-qPCR analysis. Furthermore, GO enrichment and KEGG pathway analyses indicated that 10 signaling pathways were related to tRF-Gly-GCC-009. These pathways might be physiopathological fundamentals of AS, mainly involving in Apelin signaling, Notch signaling and calcium signaling. CONCLUSION: The results of our study provide important novel insight into the underlying pathogenesis and demonstrate that tRFs might be potential biomarkers and therapeutic targets for AS in the future.

A survey on attack detection, estimation and control of industrial cyber-physical systems.

Cyber-physical systems (CPSs) are complex systems that involve technologies such as control, communication, and computing. Nowadays, CPSs have a wide range of applications in smart cities, smart grids, smart manufacturing and intelligent transportation. However, with integration of industrial control systems with modern communication technologies, CPSs would be inevitably exposed to increasing security threats, which could lead to severe degradation of the system performance and even destruction of CPSs. This paper presents a survey on recent advances on security issues of industrial cyber-physical systems (ICPSs). We specifically discuss two typical kinds of attacks, i.e., Denial-of-Service (DoS) attack and Deception attack, and present recent results in terms of attack detection, estimation, and control of ICPSs. Classifications of current studies are analyzed and summarized based on different system modeling and analysis methods. In addition, advantages and disadvantage of various methodologies are also discussed. Finally, the paper concludes with some potential future research directions on secure ICPSs.

Reachable Set Estimation for Discrete-Time Markovian Jump Neural Networks With Generally Incomplete Transition Probabilities.

This paper is concerned with the problem of reachable set estimation for discrete-time Markovian jump neural networks with generally incomplete transition probabilities (TPs). This kind of TP may be exactly known, merely known with lower and upper bounds, or unknown. The aim of this paper is to derive a precise reachable set description for the considered system via the Lyapunov-Krasovskii functional (LKF) approach. By constructing an augmented LKF, using an equivalent transformation method to deal with the unknown TPs and utilizing the extended reciprocally convex matrix inequality, and the free matrix weighting approach to estimate the forward difference of the constructed LKF, several sufficient conditions that guarantee the existence of an ellipsoidal reachable set are established. Finally, a numerical example with simulation results is given to demonstrate the effectiveness and superiority of the proposed results.

Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered Backstepping.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal. In addition, the input saturation problem is dealt with by introducing the auxiliary system. Overall, it is shown that the designed controller drives the output tracking error to the desired neighborhood of the origin at a finite time and all the signals in the closed-loop system are bounded at a finite time. Two simulation examples are given to demonstrate the control effectiveness.

Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis.

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.

Adaptive control of a class of strict feedback nonlinear systems under replay attacks.

The adaptive control of a class of strict-feedback nonlinear system under replay attack is investigated in this paper. Durations of each attack and the resting time after each attack are analyzed and their explicit bounds are presented to ensure closed-loop stability. Two scenarios are considered. In the first scenario, it is shown that if the duration of each attack is less than a given constant, asymptotical convergence of system output is still preserved. The second scenario shows that if the resting time of each attack meets certain condition after each arbitrarily long duration of attack, closed-loop boundedness is still preserved. This shows that the system controlled under our proposed adaptive controller will not be broken down even in the presence of replay attacks. Simulation results are given to illustrate the effectiveness of the control schemes.