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Purpose: Increased expression of leukocyte immunoglobulin-like receptor subfamily B member 2 (LILRB2) is associated with immune evasion in breast cancer (BC). The aim of this study to elucidate the role of LILRB2 in BC progression. Methods: LILRB2 expression in tumor tissues was detected by immunohistochemical staining. Human leukocyte antigen A (HLA-A) expression in BC cells was detected by Western blotting, and HLA-A ubiquitination was detected by immunoprecipitation and histidine pulldown assay. An in-situ tumor model was established in nude BALB/c mice to verify the role of LILRB2 in immune escape. Finally, the functions and potential mechanisms of LILRB2 in BC progression were explored using in silico data. Results: LILRB2 was upregulated in BC tissues and cells, and correlated positively with poor prognosis. LILRB2 promoted BC progression by downregulating HLA-A expression. Mechanistically, LILRB2 facilitates the ubiquitination and subsequent degradation of HLA-A by promoting the interaction between the ubiquitin ligase membrane-associated ring finger protein 9 (MARCH9) and HLA-A. In syngeneic graft mouse models, LILRB2-expressing BC cells evaded CD8 + T cells and inhibited the secretion of cytokines by the cytotoxic CD8 + T cells. Conclusion: LILRB2 downregulates HLA-A to promote immune evasion in BC cells and is a promising new target for BC treatment. [ABSTRACT FROM AUTHOR]

Introduction: Sepsis is a growing problem worldwide and associated with high mortality and morbidity. The early and accurate diagnosis and effective supportive therapy are critical for combating mortality. The aim of the study was to compare the kinetics of four biomarkers in plasma in patients admitted to ICU including sepsis and during antibiotics treatment. Methods: The biomarkers evaluated were HBP (Heparin-binding protein), HNL Dimer (Human Neutrophil Lipocalin), HNL Total and PCT (Procalcitonin). Plasma was obtained at admission to ICU and during follow-up at days 2 and 3. Antibiotic treatment was started or reviewed on admission to ICU. The results were compared to SOFA and KDIGO-scores and to survival. 277 patients admitted to ICU were included of which 30% had sepsis. The other groups were categorized as miscellaneous, other medical and trauma. Results: The plasma concentrations of all four biomarkers were highly elevated with the highest concentrations in sepsis patients. During the follow-up period HNL Dimer decreased already day 2 and further so day 3 (p<0.00001) in contrast to unchanged concentrations of the other three biomarkers. HNL Total showed the strongest relationships to the clinical scores (p<0.0001) and was by multiples regression analysis independently related to these scores. Conclusion: Our data supports and confirms our earlier findings of HNL Dimer being a novel and potentially useful clinical tool in antibiotic stewardship in sepsis. HNL Total reflects epithelial cell activity in the body and is an interesting biomarker for the management of organ failure in such patients. [ABSTRACT FROM AUTHOR]

This article focuses on a class of nonstrict feedback systems with input delay, state delays and time‐varying full‐state constraints by proposing an adaptive neural control scheme. To overcome the problems of all state variables effected by time‐varying constraints, the asymmetric time‐varying barrier Lyapunov functions are constructed. The influence of state delays and input delay is eliminated by employing suitable Lyapunov–Krasovskii functionals. Additionally, the process of controller design is based on backstepping method and the unknown functions can be approximated by radial basis function neural networks. Moreover, the problem of repeated differentiations for nonlinear components during controller design is hugely simplified by taking advantage of the dynamic surface control method. The boundness of all the closed‐loop signals can be ensured by the designed controller. Finally, two numerical simulations illustrate that the proposed adaptive neural control scheme is effective. [ABSTRACT FROM AUTHOR]

This paper addresses the preassigned-time control issue for state-constrained nonlinear systems subject to predefined performance indices (PPIs). In the recursive control design, a predefined performance barrier Lyapunov function is constructed to achieve the objectives, including the specified convergence time, tracking control indices, and compliance with state constraints. To enhance the negative definiteness of the stability function, a time-varying gain term and an additional sign-indefinite term are introduced in the control design. Through detailed stability analyses, the proposed control approach guarantees the preassigned time stability of the closed-loop and satisfies state constraints and PPIs. Two simulation results support the effectiveness and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: A previous report showed that the urine output of HPLBII-P in patients with diabetes mellitus and SARS-CoV-2 infection was increased as a sign of glomerular dysfunction. The aim of this report was to investigate the relation of the urine output of HPLBII-P to diabetes mellitus in two large community-based elderly populations, i.e., the ULSAM and PIVUS cohorts. Methods: HPLBII-P was measured by an ELISA in the urine of a community-based cohort of 839 men (ULSAM) collected at 77 years of age and in the urine of a community-based cohort of 75-year-old men, n = 387, and women, n = 401 (PIVUS). KIM-1, NGAL, and albumin were measured in urine and cathepsin S and cystatin C in serum. Results: HPLBII-P was significantly raised among males with diabetes in the ULSAM (p < 0.0001) and PIVUS cohorts (p ≤ 0.02), but not in the female cohort of PIVUS. In the female subpopulation of insulin-treated diabetes, HPLBII-P was raised (p = 0.02) as compared to women treated with oral antidiabetics only. In the ULSAM cohort, HPLBII-P was correlated to NGAL, KIM-1, and albumin in urine both in non-DM (all three biomarkers; p < 0.0001) and in DM (NGAL; p = 0.002, KIM-1; p = 0.02 and albumin; p = 0.01). Plasma glucose and HbA1c in blood showed correlations to U-HPLBII-P (r = 0.58, p < 0.001 and r = 0.42, p = 0.004, respectively). U-HPLBII-P and cathepsin S were correlated in the ULSAM group (r = 0.50, p < 0.001). No correlations were observed between U-HPLBII-P and serum creatinine or cystatin C. Conclusions: The urine measurement of HPLBII-P has the potential to become a novel and useful biomarker in the monitoring of glomerular activity in diabetes mellitus. [ABSTRACT FROM AUTHOR]

Background: The human phospholipase B-II precursor (HPLBII-P) was originally purified from white blood cells but is also found in other cellular structures, such as kidney glomeruli and tubuli. The objective of this report was to investigate the relationship of HPLBII-P in urine to acute kidney injury in patients with COVID-19. Methods: Urine was collected at admission from 132 patients with COVID-19 admitted to the intensive care units (ICUs) because of respiratory failure. HPLBII-P was measured using a sensitive ELISA. For comparison, human neutrophil lipocalin (HNL) was measured in urine, using the ELISA configured with the monoclonal antibody 763/8F, as a sign of tubular affection in addition to routine biomarkers of kidney disease. Results: Overall, the concentrations of urinary HPLBII-P were almost 3-fold higher in patients with COVID-19 compared to healthy controls (p < 0.0001) and with significantly higher concentrations even in patients with COVID-19 without signs of acute kidney injury (AKI) (p < 0.001). HPLBII-P was further increased in patients with AKI (p < 0.02). HPLBII-P was significantly increased in patients with diabetes mellitus (p = 0.0008) and correlated to plasma glucose (r = 0.29, p = 0.001) and urine albumin concentrations (r = 0.55, p < 0.001). Conclusions: Urine concentrations of HPLBII-P are highly raised in the urine of patients with COVID-19 and relate to AKI and diabetes mellitus. HPLBII-P may reflect glomerular injury and/or increased glomerular cell activity in SARS-CoV-2 infections. [ABSTRACT FROM AUTHOR]

In this study, the decentralized model reference adaptive control (DMRAC) problem is tackled for a class of time‐varying delay interconnect systems that comprise unknown system matrices and unknown dead‐zone inputs. Two robust adaptive control methods are proposed for state tracking based on the moderate matching time‐varying delay nonlinear assumptions and the matching between the controlled system and reference model matrices, respectively. The control gain function is explicitly expressed, and it is applied to the adaptive law gains simultaneously. Moreover, a Lyapunov–Krasovskii functional with two integral functions is developed. Besides the properties of the type‐B Nussbaum function, the circumstance where the system parameters are fully unknown is considered. As indicated by the results, all signals in the closed‐loop system are bounded while fulfilling asymptotically tracked control objectives. The simulation example of this study verifies the effectiveness and feasibility of the proposed design method. [ABSTRACT FROM AUTHOR]

Exit events induced by noise from the attracting domain containing a stable fixed point are ubiquitous phenomena in physical systems, wherein mean exit time is an important quantity which has been widely used in engineering, physical, chemical and biological fields. In this work, we devise a deep learning method to compute the mean exit time for dynamical systems excited by weak Gaussian noise. More specifically, we first derive a complete group of ordinary differential equations governing the most probable path, the quasipotential, and the prefactor along the path via WKB approximation. Then a neural network architechture is proposed to solve it in terms of automatic differentiation. The results of numerical experiments show the effectiveness and accuracy of the algorithm and imply its potential applications to discover the mechanisms of rare events triggered by random fluctuations in practical models. [ABSTRACT FROM AUTHOR]

Photodetectors are widely used in various fields, such as industrial manufacturing and military defense. Researchers have recently sought a photodetector that combines high polarization sensitivity and a robust optical response. As an anisotropic semiconductor material, SnS holds potential for photodetection across the visible light spectrum. This study employs first-principles density functional theory (DFT) along with the non-equilibrium Green's function (NEGF) method to theoretically investigate the optoelectronic properties of the SnS monolayer in two device orientations: Armchair and Zigzag. It is found that the maximum photocurrent values between the two orientations are small at zero bias voltage, and stable photocurrent can be obtained by adding bias voltage. We examine the maximum photocurrent variation under linearly polarized light irradiation within a small bias voltage range (0.1 to 1.0 V), found for the maximum photoresponse of monolayer SnS to be large and stable at photon energy of 2.4 and 3.2 eV, and analyze the underlying mechanism of photoresponse, employing energy band and density of state diagrams. Additionally, we have calculated the extinction ratio of the SnS monolayer, confirming its strong polarization sensitivity. Finally, by subjecting the device to biaxial strain, we significantly speculate to enhance its asymmetry, leading to a substantial increase in photocurrent at zero bias. A compressive strain of - 6% notably increases the photocurrent. These findings offer valuable theoretical insights for the design of SnS monolayers as photodetectors. [ABSTRACT FROM AUTHOR]

In this study, the fixed-time stabilization problem of stochastic high-order nonlinear systems with output constraint and high-order and low-order nonlinearities is addressed. A new coordinate transformation is employed to directly convert output-constrained stochastic systems into an equivalent unconstrained form. By fully extracting the characteristics of system nonlinearities and using the stochastic fixed-time stability theorem, a new design and analysis method is constructed to guarantee that the trivial solution of the closed-loop system is stochastically fixed-time stable while fulfilling the output constraint. [ABSTRACT FROM AUTHOR]

Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the "when→what→where", a.k.a. "time→activity→location" decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers' destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL. [ABSTRACT FROM AUTHOR]

In this paper, we solve the output feedback control problem of stochastic high-order planar nonlinear systems with output constraint and stochastic integral input-to-state stability (SiISS) inverse dynamics. By employing a key coordinate transformation, a stochastic nonlinear system with output constraint and SiISS inverse dynamics is converted into an unconstrained system. By skillfully constructing an observer and adopting SiISS small-gain conditions, we develop a new output feedback control design and analysis method, and prove that all the closed-system signals are bounded almost surely, the output constraint is not violated almost surely, and the equilibrium point of the closed-loop system is stochastically asymptotically stable. [ABSTRACT FROM AUTHOR]

This paper investigates the stability problem for sampled-data systems by adopting a refined semi-looped-functional, which is with the following two improvements. Firstly, the new functional term is with a new integral vector η0, which contains sampling information of the systems and associates two commonly used vectors. Secondly, the vector η0 is combined into various zero equations for processing the functional, especially where a new equation is derived from η0. Based on the refined functional, further stability results for sampled-data systems are obtained. And the effectiveness of the results is numerically verified through two examples at the end. [ABSTRACT FROM AUTHOR]

This article studies the adaptive state‐feedback control problem of output‐constrained stochastic high‐order nonlinear systems with stochastic integral input‐to‐state stability (SiISS) inverse dynamics. A key nonlinear transformation function is constructed to convert the original output‐constrained stochastic nonlinear system into an equivalent form without any output constraint. By subtly using the SiISS small‐gain condition and fully extracting the characteristics of system nonlinearities, two new control design and analysis methods are developed to guarantee that the closed‐loop system has an almost surely unique solution, all the closed‐loop signals are bounded almost surely, and the equilibrium point is stable in probability without the violation of output constraint. A simulation result is provided to show the effectiveness of this control method. [ABSTRACT FROM AUTHOR]

This article addresses the problem of adaptive tracking control for stochastic nonlinear systems with asymmetric full state constraints. Different from the traditional log‐type or tan‐type barrier Lyapunov functions (BLFs), a new asymmetric BLF is proposed in this article. Then, a state feedback controller is constructed with backstepping method so that all signals of the closed‐system are bounded and the asymmetric state constraint is well kept all the time. Without changing the structure of the controller, the design and the analysis for constrained and unconstrained systems are unified. Finally, the effectiveness of proposed control strategy has been demonstrated by simulations of numerical example and the single‐link robot arm model. [ABSTRACT FROM AUTHOR]

The aim of the study was to observe the safety and efficacy of anlotinib (ANL) alone or combined with S-1 in the first-line treatment of advanced hepatocellular carcinoma (HCC). Fifty-four patients with untreated advanced HCC who could not be resected were randomly divided into the ANL group (n = 27) and ANL+S-1 group (n = 27). The ANL group was given 10 mg ANL orally once a day for 14 consecutive days, stopped for 1 week, and repeated every 21 days. The ANL+S-1 group was given 10 mg ANL once a day orally and 40 mg S-1 twice a day orally for 14 consecutive days, stopped for 1 week, repeated every 21 days. All patients were treated until the disease progressed or toxicity became unacceptable. For patients who could not tolerate adverse reactions, the ANL dose should be reduced to 8 mg per day. CT or MRI was reviewed every 6 weeks to evaluate the efficacy. A total of 44 patients were included in the results analysis, including 22 patients in the ANL group and 22 patients in the ANL+S-1 group. In the ANL group, the objective response rate (ORR) was 4.5% (1/22), the disease control rate (DCR) was 77.3% (17/22), the median progression-free survival (PFS) was 4.2 months (95% CI: 3.6–6.0) and the median overall survival (mOS) was 7.0 months (95% CI: 6.3–9.0). In the ANL+S-1 group, the ORR was 18.2% (4/22), the DCR was 59.1% (13/22), the median PFS was 4.0 months (95% CI: 3.6–5.4) and the mOS was 6.0 months (95% CI: 5.5–7.4). There was no significant difference in ORR (p = 0.345) or DCR (p = 0.195) between the two groups. Adverse reactions were mainly hypertension, anorexia, fatigue, liver transaminase heightened and hand and foot skin reaction. ANL monotherapy was effective in the treatment of advanced HCC, and adverse reactions have been able to tolerated. [ABSTRACT FROM AUTHOR]

In this paper, a control scheme for stochastic predefined-time stabilization of continuous-time stochastic Itô systems is proposed. Compared with stochastic finite-time or fixed-time stabilization, the proposed control scheme for stochastic predefined-time stabilization allows the upper bound of the mean value of the settling-time function to lie below an arbitrarily given positive value. Some Lyapunov-type results for predefined-time stabilization of general stochastic Itô systems are presented. Moreover, a state feedback control scheme is designed for a class of stochastic nonlinear systems in strict-feedback form. Two simulation examples are provided to show the usefulness of the proposed stochastic predefined-time stabilization. [ABSTRACT FROM AUTHOR]

The higher education system refers to the organisational structure of higher education institutions and the staff and infrastructure needed to provide postsecondary education. To better develop a country or region's higher education system, administrators need to have a handle on the current state of the system, which requires regular and realistic assessments of the quality and sustainability of higher education. Thus, this study constructed a quality-sustainability model (QSM) for national higher education. Nine countries with developed higher education and 13 indicators were selected to reference higher education quality and sustainability globally. Principal component analysis (PCA) was used to downgrade these 13 indicators and extract the factor coefficient score matrixes. Of these, four principal components were used for further analysis. Each sub-indicator is assigned weights by the entropy weighting method (EWM) to obtain a quantifiable QSM. The model innovatively includes indicators such as "academic integrity" and is applied experimentally to data from nine countries to analyse the strengths and weaknesses of their higher education systems. The study found that each country's education system has different strengths, and by comparing and summarising them, the findings can guide the development of future-oriented higher education. This study has made some development recommendations based on the model by combining multidisciplinary theories. The study enriches existing methods for assessing the quality of higher education and identifies the weaknesses and directions for the development of higher education in some developed countries. [ABSTRACT FROM AUTHOR]

The existing data-driven identification methods for hybrid dynamical systems such as sparse optimization are usually limited to parameter identification for coefficients of pre-defined candidate functions or composition of prescribed function forms, which depend on the prior knowledge of the dynamical models. In this work, we propose a novel data-driven framework to discover the hybrid dynamical systems from time series data, without any prior knowledge required of the systems. More specifically, we devise a dual-loop algorithm to peel off the data subject to each subsystem of the hybrid dynamical system. Then, we approximate the subsystems by iteratively training several residual networks and estimate the transition rules by training a fully connected neural network. Several prototypical examples are presented to demonstrate the effectiveness and accuracy of our method for hybrid models with various dimensions and structures. This method appears to be an effective tool for learning the evolutionary governing laws of hybrid dynamical systems from available data sets with wide applications. [ABSTRACT FROM AUTHOR]

This paper is devoted to solving the global fixed-time event-triggered stabilization problem for a class of nonstrict-feedback stochastic systems with full state constraints. Firstly, the nonlinear state constrained system is converted into a novel constrained free system with the help of nonlinear mappings, which eliminates the feasibility conditions of virtual control signals while satisfying the state constraints. Secondly, a lemma about the variable separation technique is put forward to deal with unknown nonlinear terms in stochastic systems, which avoids the restrictive growth assumption that unknown functions need to be satisfied. Then, a fixed-time event-triggered control strategy based on the symbol function technique is designed by combining the backstepping method with the fixed-time stability theory of stochastic systems. It is proved that all closed-loop signals are globally fixed-time stable in probability. Finally, a numerical example and a single-link robot system are simulated to verify the availability and feasibility of the developed approach. [ABSTRACT FROM AUTHOR]

In this article, the event‐triggered‐based adaptive finite‐time secure control problem is considered for nonlinear cyber‐physical systems (CPSs) in the presence of unknown sensor and actuator deception attacks. Due to the existence of unknown deception attacks, the system states are unavailable such that the conventional backstepping cannot be applicable. To solve this problem, a novel coordinate transformation is introduced based on the compromised system states. Then the adaptive controller combined with the triggering mechanism is designed by using adding a power integrator technique. Besides, based on the finite‐time stability theorem, an adaptive switching law is proposed to regulate the dynamic controller parameter such that the unknown deception attacks can be effectively compensated. It is shown that the finite‐time stability can be guaranteed for the nonlinear CPSs and Zeno behavior can be avoided. Finally, a simulation example is provided to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article studies the robust consensus problem for uncertain multiagent systems under distributed proportional‐integral (PI) protocols. For both continuous‐time and discrete‐time multiagent systems, we allow additive channel uncertainties that are stable and bounded in ℋ∞ norm. By exploiting the generalized network complementary sensitivity function (GNCSF), necessary and sufficient conditions under which the robust consensus with distributed PI protocols can be guaranteed are derived. For the continuous‐time case, it is shown that the robust consensus can be reached if and only if the uncertainty radius and the ℋ∞ norm of the GNCSF satisfy certain constraint. For the discrete‐time case, it is shown that the robust consensus can be preserved under both the constraints on the parameters of the PI protocols and the relationship of the uncertainty radius and the ℋ∞ norm of the GNCSF. Finally, numerical examples are provided to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This article studies the finite-time composite antidisturbance control problem for nonhomogeneous Markovian jump systems (MJSs) subject to multiple disturbances via the Takagi–Sugeno (T–S) fuzzy control scheme. The designed two-gains disturbance observer (DO) and the considered MJSs are supposed to switch the different Markovian chains, which are depicted by a hidden Markovian model. Herein, an asynchronous DO design is explored for the first time, providing new insight into the construction of asynchronous DO. First, the $H_\infty /L_{2}-L_\infty$ finite-time boundedness of the augmented system is guaranteed. Second, based on the Finsler lemma, the linearization issue in calculating observer/controller gain is addressed. Finally, two illustrations are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the nonfragile guaranteed cost (GC) control problem of discrete-time impulsive switched Takagi–Sugeno (T–S) fuzzy systems with input quantization and asynchronous switching. The model-dependent dynamic quantizers are applied to obtain the quantized input signal. To deeply study the GC performance analysis and GC control problems in the presence of quantization, asynchronous switching, and impulses, a novel piecewise cost function containing the quantized input instead of normal input is applied in this article. By using the mode-dependent average dwell time approach and introducing a class of Lyapunov-like functions allowing to increase during the asynchronous period, new sufficient conditions are established to guarantee the asymptotical stability with the GC performance index for the impulsive switched T–S fuzzy systems with quantized control input and asynchronous switching. Then, new design conditions about the nonfragile GC controllers and dynamic quantizers of impulsive switched T–S fuzzy systems are obtained in the form of linear matrix inequalities. Finally, a numerical example and a practical example are provided. [ABSTRACT FROM AUTHOR]

Practical stability analysis for delayed systems is discussed. Weak practical stability conditions are derived by using Halanay’s inequality and the Lyapunov method. The results are applied to address the problem of practical stability of a power system over a delay-induced communication network. An event-detection-based control scheme is proposed to reduce the communication burdens. Based on the obtained practical stability conditions and the proposed event-detection scheme, sufficient practical stability conditions for load frequency control of a networked power system are given. Furthermore, a new design approach to the event-detection-based controller is presented. Finally, a numerical simulation is given to show the effectiveness and advantage of the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed strongly convex constrained optimization problem in the non-Euclidean sense, where the bit rate of the considered communication between nodes is assumed to be limited, and the communication topology is represented by a time-varying directed graph. By considering the limitation of communication capacity, the quantization technique is applied in the process of exchanging information over the network. Then a distributed quantized mirror descent (DQMD) algorithm, which uses the Bregman divergence and time-varying quantizers, is developed for the strongly convex optimization under a convex constraint set. The convergence of the developed algorithm is also analyzed. It is shown that the nodes’ state errors are bounded by some terms related to the quantization resolutions, and the sublinear upper-bounds can be guaranteed by choosing appropriate quantization resolutions. Finally, a distributed ridge regression is provided as an example to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the fixed-time synchronization problem of a class of delayed complex-valued inertial neural networks (CVINNs). The analysis does not rely on the traditional reduced-order transformation, but constructing Lyapunov functions directly focused on the original system. Based on the direct method and the separation method, different control strategies are proposed, under which the addressed CVINNs can achieve synchronization perfectly in a fixed time. The corresponding synchronization criteria in terms of matrix inequalities are derived, which are more concise and easier to verify than algebraic inequalities conditions, and the estimation of the settling times. The direct method makes full use of some innovative inequalities in the complex field; the exponential parameters in the designed controllers are independent. Finally, in order to fully support the theoretical results, based on two typical activation functions, the proposed theoretical results are numerically validated and compared. [ABSTRACT FROM AUTHOR]

In this article, the hidden Markov model (HMM)-based fuzzy control problem is addressed for slow sampling model nonlinear Markov jump singularly perturbed systems (SPSs), in which the general transition and mode detection information issue is considered. The general information issue is formulated as the one with not only the transition probabilities (TPs) and the mode detection probabilities (MDPs) being partly known but also with the certain estimation errors existing in the known elements of them. This formulation covers the cases with both the TPs and the MDPs being fully known, or one of them being fully known but another being partly known, or both them being partly known but without the certain estimation errors, which were considered in some previous literature. By utilizing the HMM with general information, some strictly stochastic dissipativity analysis criteria are derived for the slow sampling model nonlinear Markov jump SPSs. In addition, a unified HMM-based fuzzy controller design methodology is established for slow sampling model nonlinear Markov jump SPSs such that a fuzzy controller can be designed depending on whether the fast dynamics of the systems are available or not. A numerical example and a tunnel diode circuit are finally used to illustrate the validity of the obtained results. [ABSTRACT FROM AUTHOR]

The concept of quasi-potential plays a central role in understanding the mechanisms of rare events and characterizing the statistics of transition behaviors in stochastic dynamics. Despite its significance, the computation of quasi-potential is a challenging problem with limited existing techniques. In this paper, we devise a machine learning method to compute the quasi-potential of stochastic dynamical systems. More specifically, we first derive the Hamilton-Jacobi equation satisfied by quasi-potential via WKB approximation for two important classes of stochastic processes: diffusion processes and continuous-time jump Markov processes. Then we design a neural network with automatic differentiation technique to compute the quasi-potential based on the Hamilton-Jacobi equation. Two prototypical examples are presented to demonstrate the efficacy and accuracy of our method for stochastic differential equations with additive and multiplicative Gaussian noise and continuous-time jump Markov processes. This approach provides an effective tool in exploring the internal mechanisms of rare events triggered by random perturbations in various scientific fields. [ABSTRACT FROM AUTHOR]

During the past decades, much attention has been paid to the stability problem of linear time‐delay systems. In order to obtain tractable stability conditions shown in the linear matrix inequality form, a great number of remarkable results have been reported in the literature. This article first gives a survey of inequality techniques recently developed to estimate integral quadratic terms and reciprocally convex combination terms arising in the estimation of the time derivative of a Lyapunov–Krasovskii functional candidate. Emphases are specially placed on the evolution of various integral and matrix inequalities and the relationships among them. Second, several stability conditions are obtained and carefully compared to illustrate the effectiveness of different inequality techniques on reducing the conservatism. Finally, the quadratic negative‐definiteness lemmas are insightfully reviewed and meanwhile, a simple method to calculate matrix coefficients of a quadratic matrix‐valued function is presented. [ABSTRACT FROM AUTHOR]

The quantized guaranteed cost static output feedback control problem is investigated for a class of discrete-time nonlinear networked control systems in this article. In this article, the Takagi–Sugeno fuzzy model is put to use for the representation of considered nonlinear networked control systems, where local nonlinear models instead of local linear models are used in the Takagi–Sugeno fuzzy model. Two different dynamic quantizers are applied to quantize the control input and measurement output, respectively. Different from some previous work, a novel guaranteed cost performance function including the quantized control input is used in this article. Through using the $S$ -procedure and introducing some auxiliary scalars, sufficient conditions for the design of guaranteed cost static output feedback controller and dynamic quantizers are obtained in the form of linear matrix inequalities. Finally, the applicability of the proposed method is illustrated through the application in nonlinear mass-spring-damper mechanical system. [ABSTRACT FROM AUTHOR]

This article studies the output regulation problem (ORP) for nonlinear systems based on prescribed performance control (PPC). The items with the partial derivative of the virtual controller are combined together by using backstepping, and then the fuzzy logic systems (FLSs) are used to approximate these combined items, so that the designed virtual controller does not have the partial derivative of the previous virtual controllers. Therefore, this method not only reduces the calculation burden in the backstepping method, but also avoids the disadvantages of dynamic surface control (DSC). Finally, a function $\Theta $ is constructed such that the overall performance (dynamic performance and steady-state performance) of the tracking error (OPTE) is constrained by PP functions. The proposed control algorithm ensures that all the signals are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error achieves the PPC. Simulation examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive neural control scheme is proposed for a class of multi-input multi-output nonlinear systems in pure-feedback form with unmodeled dynamics and full state constraints. Radial basis function neural networks are employed to approximate and compensate for the unknown nonlinear continuous functions. By introducing nonlinear symmetric mapping, the full state-constrained tracking control problem of the multi-input multi-output pure-feedback system is transformed into a novel equivalent unconstrained one. For the transformed systems, a dynamic surface control method is applied to remove the difficulties for the multiple explosion of complexity problem. The use of Nussbaum-type function removes the need for any assumption on the function of control gain. By combining variable separation technique and the function's monotonously increasing property, the restrictive assumption of the dynamic disturbances caused by unmodeled dynamics is relaxed. One advantage is that only one adjustable parameter is used in the controller design. It is proved that all the closed-loop signals remain semi-globally uniformly ultimately bounded with good tracking performance, while the system states never violate the constraints. [ABSTRACT FROM AUTHOR]

Singularly perturbed systems have found widespread applications in practice. The existing results on singularly perturbed systems mainly focused on the Lyapunov asymptotic stability, which are unable to deal with the cases that the system states cannot exceed a given threshold during a fixed time interval. This article addresses the finite-time fuzzy control issue for discrete-time nonlinear singularly perturbed systems with input constraints. The aim is to guarantee the boundedness of the states of singularly perturbed systems during a finite-time interval. Based on the matrix inequality technique, some conditions are established to guarantee the finite-time boundedness of the fuzzy singularly perturbed systems, where the singularly perturbed parameter is independent so as to avoid the ill-conditioned problem caused by the small singularly perturbed parameter. The gains of the finite-time fuzzy controller can be obtained by solving some singularly perturbed parameter independent linear matrix inequalities. Finally, the proposed finite-time fuzzy controller design approach for nonlinear singularly perturbed systems is illustrated via the Van der Pol circuit. [ABSTRACT FROM AUTHOR]

This article addresses the problem of finite-time almost sure stability, where the system is with Markov jump parameters and delayed inputs. With using Gronwall’s inequality and strong law of large numbers for Markov chains, finite-time bounded criteria and finite-time stability criteria are derived by Lyapunov–Krasovskii functional method. Moreover, a finite-time stability criterion in form of linear matrix inequality is given to design the controller gain. The obtained results are applied to finite-time sampled-data control of a truck-trailer system and finite-time control of a Markov jump mass-spring model with delayed inputs. [ABSTRACT FROM AUTHOR]

This work addresses the nonfragile $H_{\infty }$ controller design problem for a class of discrete-time uncertain nonlinear systems. The norm-bounded uncertainty is contained in the nonlinear plant, which is described by the well-known Takagi–Sugeno (T–S) fuzzy model. The controller gain perturbation is also considered. When the input signal is transmitted from the controller to the system through the digital communication channels, it will be quantized by a static quantizer. The main attention is to design the nonfragile $H_{\infty }$ state-feedback controller for the closed-loop quantized uncertain T–S fuzzy system. By introducing some auxiliary scalars and the fuzzy basis-dependent Lyapunov function approach, sufficient conditions are established in the form of linear matrix inequalities (LMIs). The construction for the nonfragile $H_{\infty }$ controller can be completed by solving these LMIs. In the end, the applicability and effectiveness of the proposed method have been illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

In this article, we extend the adaptive tracking control to more general nonlinear systems with multiple uncertainties, including output constraint, input delay, unknown parameter, and external disturbances for the first time. Without any growth assumptions, the adaptive backstepping technique is combined with the parameter separation technique to solve the parametric nonlinearities while the related results need to apply restrictive assumptions or use an approximation-based scheme to deal with them. To alleviate the serious uncertainties caused by output constraint and input delay, the barrier Lyapunov function (BLF) and the Pade approximation method are employed in a unified framework when the control coefficient is known. Under the case of the unknown control coefficient, the Nussbaum gain technique is further combined to compensate it. Then, under both cases, universal adaptive state-feedback control strategies merged with rigorous stability analysis are proposed, respectively, which guarantees all the signals are uniformly ultimately bounded. In addition, the reference signal tracks the system output into a compact set of the origin and the constraint of output is not violated. Finally, the proposed controllers are applied to an inverted pendulum system, which demonstrates the designed controller is effective. [ABSTRACT FROM AUTHOR]

This work addresses the problem of aperiodically sampled control for the networked Takagi–Sugeno (T–S) fuzzy systems, where the aperiodically sampled input is generated by a periodic sampler and an event-triggered mechanism (ETM). The purpose of ETM is used to reduce the computational and communication burdens. For guaranteeing controller robustness, the practical stability of T–S fuzzy systems is considered by using the Lyapunov method and linear matrix inequality (LMI) technique. As one of the most powerful inequalities for deriving stability criteria using LMIs, Jensen’s inequality has recently been improved by various authors for the stability analysis of delayed systems. However, these results are conservative to obtain lower bounds for integrals with an exponential term. Inspired by this, improved integral inequalities are derived in this work, and they are applied to obtain practical stability criteria for aperiodically sampled control. Finally, a numerical example on flight control of a helicopter is given to illustrate the effectiveness of the obtained practical stability criteria. Furthermore, the effectiveness of the improved Jensen inequalities on the exponential stability criteria is illustrated by numerical comparisons. [ABSTRACT FROM AUTHOR]

Objectives: The alteration of vimentin-containing cells (VCCs) proliferation, differentiation, and migration in the brain stem of amyotrophic lateral sclerosis (ALS)-like transgenic mice (Tg(SOD1*G93A)1Gur mice) (TG mice) and wild-type mice (WT mice) at the different disease stages of TG mice was studied in this study. The aims of this study were to investigate the change features of proliferation, differentiation, and migration of endogenous neural precursor cells (NPCs) and to explore the potential effects of NPCs on restoring degenerated neurons in ALS. Methods: The proliferation, differentiation, and migration of VCCs in both different anatomic regions and neural cells of brain stem at the different stages including pre-onset (60–70 days), onset (90–100 days), and progression (120–130 days) stages of TG mice and in WT mice (control) were examined using the immunofluorescence technology. Results: VCCs were mainly distributed in the around (peripheral) central canal (CC) and the nuclei of brain stem in adult WT mice. VCCs proliferated and differentiated into astrocytes and directionally migrated from the around CC to the nuclei of brain stem, and then to the ventral part of damaged regions in brain stem at the pre-onset, onset, and progression stages of TG mice. Conclusions: The data suggest that NPCs widely distributed in the brain stem of adult TG mice can differentiate into astrocytes and migrate into damaged brain regions. This response might be a potential mechanism to repair degenerated motor neurons and restore dysfunctional neural circuitry in ALS. [ABSTRACT FROM AUTHOR]

This article focuses on the multiagent consensus disturbance rejection (MCDR) for groups of general linear individuals by using the distributed event-based adaptive control technique. Based on designing disturbance observers, event-driven control rules involving controllers and actuator updating rules are provided for networks of multiple individuals without and with input uncertainties; the updating rules rely on the state-dependent factor, the exponential decay threshold, and a tuning parameter. For both scenarios, the global information of network topologies is not required in the design and continuous communications between agents can be avoided. [ABSTRACT FROM AUTHOR]

This article studies the rendezvous problem of linear multiagent systems by parallel event-triggered connectivity-preserving control strategies. There are two distinguished features of our design. First, the event-triggered control laws can not only guarantee the convergence of the tracking error as existing event-triggered consensus control strategies but also have the additional ability to maintain the connectivity of the time-varying and position-dependent communication network as rendezvous control laws. Second, by combining the potential function technique, output regulation theory, and adaptive control technique, an event-triggered observer is applied to estimate both the leader’s system matrix and trajectory, which can work in parallel with the connectivity-preserving event-triggered controller. The executive time instants for the observer and the controller are asynchronous and generated by different triggering functions based on their own locally available measurement errors. [ABSTRACT FROM AUTHOR]

This technical correspondence studies the consensus problem for second-order multiagent systems under network topologies with a directed spanning tree. Consensus analysis for systems with the distributed delayed proportional–integral (PI)-type controller is given. Crossing directions of the characteristic roots can be identified by a sufficient condition. If the rightward crossing condition holds, the delay margin can be obtained to guarantee that the systems reach consensus if and only if the time delay is less than the critical value. Otherwise, it is possible that the systems switch from consensus to nonconsensus and back to the consensus as the delay increases. Simulation examples are provided to demonstrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Nerve growth factor (NGF) is a protective factor of neural cells; the possible relationship between the NGF and the pathogenesis of amyotrophic lateral sclerosis (ALS) hasn't been completely known. In this study, we observed and analyzed the expression and distribution of NGF, as well as the possible relationship between the NGF expression and distribution and the neural cell death in both SOD1 wild-type (WT) and Tg(SOD1*G93A)1Gur (TG) mice applying the fluorescence immunohistochemistry method. The results showed that the expression and distribution of NGF in the anterior horn (AH), the lateral horn (LH), and the surrounding central canal (CC) significantly increased at the supper early stage of ALS (Pre-onset stage) and the early stage (Onset stage), but the NGF expression and distribution in the AH, the LH, and the surrounding CC significantly reduced at the progression stage. The astrocyte, neuron, and oligodendrocyte produced the NGF and the neural precursor cells (NPCs) produced the NGF. The neural cell death gradually increased accompanying with the reduction of NGF expression and distribution. Our data suggested that the NGF was a protective factor of neural cells, because the neural cells in the AH, the LH, and the surrounding CC produced more NGF at the supper early and early stage of ALS; moreover, the NPCs produced the NGF. It implied that the NGF exerted the protective effect of neural cells, prevented from the neural cell death and aroused the potential of self-repair in the development of ALS. [ABSTRACT FROM AUTHOR]