Multiple resonances with time delays and enhancement by non-Gaussian noise in Newman-Watts networks of Hodgkin-Huxley neurons

In this paper, we study the effect of time delay on the spiking activity in Newman-Watts small-world networks of Hodgkin-Huxley neurons with non-Gaussian noise, and investigate how the non-Gaussian noise affects the delay-induced behaviors. It was found that, as the delay increases, the neuron spiking intermittently performs the most ordered and synchronized behavior when the delay lengths are integer multiples of the spiking periods, which shows multiple temporal resonances and spatial synchronizations, and reveals that the locking between the delay lengths and the spiking periods might be the mechanism behind the behaviors. It was also found that the delay-optimized spiking behaviors could be enhanced when non-Gaussian noise's deviation from the Gaussian noise is appropriate. These results show that time delay and non-Gaussian noise would cooperate to play more constructive and efficient roles in the information processing of neural networks.     

Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

A new incrementally growing neural network model, called the growing fuzzy topology ART (GFTART) model, is proposed based on integrating the conventional fuzzy ART model with the incremental topology-preserving mechanism of the growing cell structure (GCS) model. This is in addition, to a new training algorithm, called the push-pull learning algorithm. The proposed GFTART model has two purposes: First, to reduce the proliferation of incrementally generated nodes in the F2 layer by the conventional fuzzy ART model based on replacing each F2 node with a GCS. Second, to enhance the class-dependent clustering representation ability of the GCS model by including the categorization property of the conventional fuzzy ART model. In addition, the proposed push-pull training algorithm enhances the cluster discriminating property and partially improves the forgetting problem of the training algorithm in the GCS model.     

Development of high throughput microfluidic cell culture chip for perfusion 3-dimensional cell culture-based chemosensitivity assay

This study reports a microfluidic cell culture chip encompassing 36 microbioreactors for high throughput perfusion 3-dimensional (3D) cell culture-based chemosensitivity assays. Its advantages include the capability for multiplexed medium delivery, and the function for both efficient and high throughput micro-scale 3D culture construct preparation and loading. The results showed that the proposed medium pumping mechanism was able to provide a uniform pumping rates ranging from 1.2 to 3.9 μl h⁻¹. In addition, the simple cell/hydrogel loading scheme has been proven to be able to carry out 3D cell culture construct preparation and loading precisely and efficiently. Furthermore, a chemosensitivity assay was successfully demonstrated using the proposed cell culture chip. The results obtained were also compared with the same evaluation based on a conventional 2D monolayer cell culture. It can be concluded that the choice of cell culture format can result in different chemosensitivity evaluation results. Overall, because of the nature of miniaturized perfusion 3D cell culture, the cell culture chip not only can provide stable, well-defined and more biologically relevant culture environments, but it also features low consumption of research resources. All these traits are found particularly useful for high-precision and high-throughput 3D cell culture-based assays.     

Pumping with electroosmosis of the second kind in mesoporous skeletons

This paper presents the use of mesoporous silica skeletons as substrates for electroosmotic (EO) micropumps. Mesoporous silica skeletons have bimodal pore size distributions consisting of macropores and cation-permselective mesopores. These materials have the potential for high flow rate per power because the cation-permselective mesopores can generate an induced charge layer (ICL) and electroosmosis of the second kind (EO-2) under high applied electric fields. The diffuse charge layers induced by the electric field result in an EO-2 flow rate that increases quadratically with increasing electric field. In contrast, the flow rate of the more common electroosmosis of the first kind (EO-1) is linearly proportional to electric field. Here, we investigate the impact of finite pressure loads on the EO-2 flow rate with experiments and an engineering model to evaluate the potential of mesoporous skeletons for micropumping applications. Our results include analyses of maximum flow rate, maximum pressure, and flow rate with intermediate pressure loads. The results indicate the existence of a critical pressure load at which reverse pressure-driven flow significantly diminishes the EO-2 flow. We also investigate the scaling of flow rate per power with respect to substrate thickness and area, demonstrating significant increases in flow rate per power with thinner substrates and favorable scaling for miniaturization of EO-2 pumps.     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

Ordered mesoporous Pd/SnO2 synthesized by a nanocasting route for high hydrogen sensing performance

Ordered mesoporous SnO₂ and mesoporous Pd/SnO₂ have been successfully synthesized via nanocasting method using the hexagonal mesoporous SBA-15 as template. Two different procedures, impregnation technique and direct synthesis, were utilized for the doping of Pd in the mesoporous SnO₂. The results of small angle X-ray diffraction (SAXD), nitrogen adsorption–desorption and transmission electron microscopy (TEM) demonstrate that the SnO₂ and Pd/SnO₂ display ordered mesoporous structures and high surface areas. Wide angle X-ray diffraction (WAXD) and X-ray photoelectron spectroscopy (XPS) reveal tetragonal structure of SnO₂ and the existence of Pd element. The sensing properties of mesoporous SnO₂ and mesoporous Pd/SnO₂ for H₂ were detected. The sensor utilizing mesoporous Pd/SnO₂ via direct synthesis method exhibits excellent response and recovery behavior and much higher sensitivity to H₂, compared to those using mesoporous SnO₂ and mesoporous Pd/SnO₂ via impregnation technique. It is believed that its high gas sensing performance is derived from the large surface area, high activity and well dispersion of Pd additive, as well as high porosity, which lead to highly effective surface interaction between the target gas molecules and the surface active sites.     

Structural and properties of nanocrystalline WO3/TiO2-based humidity sensors elements prepared by high energy activation

Nanocrystalline WO₃/TiO₂-based powders have been prepared by the high energy activation method with WO₃ concentration ranging from 1 to 10 mol%. The samples were thermal treated in a microwave oven at 600 °C for 20 min and their structural and micro-structural characteristics were evaluated by X-ray diffraction, Raman spectroscopy, EXAFS measurements at the Ti K-edge, and transmission electron microscopy. Nitrogen adsorption isotherms and H₂ Temperature Programmed Reduction were also carried out for physical characterization. The crystallite and particle mean sizes ranged from 30 to 40 nm and from 100 to 190 nm, respectively. Good sensor response was obtained for samples with at least 5 mol% WO₃ activated for at least 80 min. Ceramics heat-treated in microwave oven for 20 min have shown similar sensor response as those prepared in conventional oven for 120 min, which is highly cost effective. These results indicate that WO₃/TiO₂ ceramics can be used as a humidity sensor element.     

Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p.     

Suppressing intersample behavior in iterative learning control

Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.