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An adaptive neural tracking control is investigated for a class of nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints and saturation input. First, the continuous differentiable saturation model is employed to ensure the input constraint, and a barrier Lyapunov function is designed to achieve the full-state constraint. Second, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown time-delay terms, and neural networks are employed to approximate the unknown nonlinearities. Finally, based on Lyapunov stability theory, an adaptive controller is proposed to guarantee that all the signals in the closed-loop system are 4-Moment (or 2-Moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin. Two examples are shown to further demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this study, the adaptive tracking control is investigated for a class of stochastic pure-feedback non-linear time-delay systems with output constraint and asymmetric input saturation non-linearity. First, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, and the barrier Lyapunov function is designed to cope with the output constraints. Then, the appropriate Lyapunov--Krasovskii functional and the property of hyperbolic tangent functions are used to address the effects of the unknown time-delay terms, and the neural network is employed to approximate the unknown non-linearities. At last, based on Lyapunov stability theory, a robust adaptive neural controller is proposed, which decreases the number of learning parameters and thus avoids the over-estimation problem. Under the designed neural controller, all the closed-loop signals are guaranteed to be 4-moment (or 2 moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin for bounded initial conditions. Two simulation examples are presented to further illustrate the effectiveness of the designed method. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural tracking control scheme for strict-feedback stochastic nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilising the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function neural networks approximation are used to handle unknown nonlinear functions and stochastic disturbances. At last, by using the common Lyapunov function method and the backstepping technique, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterisation, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded, and the prescribed tracking control performance are guaranteed under arbitrary switchings. Three examples are presented to further illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Large-scale growth of low-cost, efficient, and durable non-noble metal-based electrocatalysts for water splitting is crucial for future renewable energy systems. Atomic layer deposition (ALD) provides a promising route for depositing uniform thin coatings of electrocatalysts, which are useful in many technologies, including the splitting of water. In this communication, we report the growth of a NiO/Ni catalyst directly on carbon fiber paper by atomic layer deposition and report subsequent reduction and oxidation annealing treatments. The 10-20 nm NiO/Ni nanoparticle catalysts can reach a current density of 10 mA·cm-2 at an overpotential of 189 mV for hydrogen evolution reactions and 257 mV for oxygen evolution reactions with high stability. We further successfully achieved a water splitting current density of 10 mA·cm-2 at 1.78 V using a typical NiO/Ni coated carbon fiber paper two-electrode setup. The results suggest that nanoparticulate NiO/Ni is an active, stable, and noble-metal-free electrocatalyst, which facilitates a method for future water splitting applications. [ABSTRACT FROM AUTHOR]

In this study, the accuracy (precision and trueness) of digital impressions and the fitness of single crowns manufactured based on digital impressions were evaluated. #14-17 epoxy resin dentitions were made, while full-crown preparations of extracted natural teeth were embedded at #16. (1) To assess precision, deviations among repeated scan models made by intraoral scanner TRIOS and MHT and model scanner D700 and inEos were calculated through best-fit algorithm and three-dimensional (3D) comparison. Root mean square (RMS) and color-coded difference images were offered. (2) To assess trueness, micro computed tomography (micro-CT) was used to get the reference model (REF). Deviations between REF and repeated scan models (from (1)) were calculated. (3) To assess fitness, single crowns were manufactured based on TRIOS, MHT, D700 and inEos scan models. The adhesive gaps were evaluated under stereomicroscope after cross-sectioned. Digital impressions showed lower precision and better trueness. Except for MHT, the means of RMS for precision were lower than 10 µm. Digital impressions showed better internal fitness. Fitness of single crowns based on digital impressions was up to clinical standard. Digital impressions could be an alternative method for single crowns manufacturing. [ABSTRACT FROM AUTHOR]

Intermodulation and harmonics are important in frequency analysis of nonlinear systems. In neuron research, most investigations are taken in studying synchronization between the external stimuli and the output of neuron, but harmonics and intermodulation are often ignored. In this paper, harmonics and intermodulation of the subthreshold FitzHugh–Nagumo neuron are investigated and their magnitudes are used to predict frequency response of the neuron. Furthermore, through analyzing the magnitudes of harmonics, the intrinsic frequencies of the neuron could be identified. [ABSTRACT FROM AUTHOR]

The Weibull modulus of toughened ceramics was evaluated. The fracture of the toughened ceramics was assumed to be initiated by surface cracks induced during small-scale contacts such as grinding and polishing and an exponential function was selected to describe the R-curve behavior. Based on a brief theoretical analysis, a numerical simulation procedure was designed to predict the fracture strength for toughened ceramics with different R-curve characteristics. The Weibull modulus of each toughened ceramic was estimated and compared with that of the un-toughened base material. It was concluded that an increase in Weibull modulus can always result from toughening. The increase in Weibull modulus was found to be related directly to the relative crack tolerance, i.e., the ratio of the initial crack size to the critical crack size. This suggests that the improvement in crack stability due to toughening is the main reason for the increased Weibull modulus. [ABSTRACT FROM AUTHOR]