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.     

Prolonged sensitivity of immobilized thylakoid membranes in cross-linked matrix to atrazine

Freshly prepared pea thylakoid membranes were immobilized in bovine serum albumin-glutaraldehyde cross-linked matrix (BSA-GA matrix) and their stability under long term storage was analyzed by Pulse-Amplitude-Modulated (PAM) chlorophyll fluorescence and photosynthetic oxygen evolution measured by oxygen rate electrode. The thylakoid membranes stored at 4 °C showed prolonged stability in BSA-GA matrix and additional adsorption on nitrocellulose membrane filters gave them more stability. The sensitivity of the parameters of the oxygen evolution of thylakoid membranes to atrazine increased with immobilization. The half-inhibition time for oxygen evolution and quantum efficiency of photosynthesis could be prolonged to more than 15 days. These results suggest that the immobilized thylakoid membranes in BSA-GA matrix can be used as biological receptor in biosensors for a long period of time (up to 25 days) applying the proposed new method for atrazine detection by using polarographic oxygen rate electrode. This method is more sensitive, faster and easier to use than other methods for detection of herbicides based on determination of the photochemical activity of photosystem II.     

Evanescent wave sensor based on permanently bent single mode optical fiber

A novel refractive index sensing scheme based on evanescent wave interaction through locally and permanently bent single mode optical fibers is proposed. Local and permanent bends in single mode optical fibers enable significant power coupling between core and cladding modes. Order and number of excited cladding modes depend on bend features and determine the field profile at the output of the bent region. This in turn constitutes a simple mechanism to tailor the field distribution in single mode optical fibers useful for spatial light modulation. Moreover, since guided cladding modes are strongly influenced by the surrounding refractive index (SRI), the power transmitted at the output of the bent region as well as its dependence on the optical wavelength are strongly sensitive to the SRI opening new scenarios in sensing applications.     

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.     

Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.     

Effect of micro-electrode geometry on NO2 gas-sensing characteristics of one-dimensional tin dioxide nanostructure microsensors

Electrodes with micro-gaps are fabricated by using dc-sputtering and FIB techniques. SnO₂ nanowires are deposited on the micro-gap (1-30 μm) by suspension dropping method to fabricate a micro-gas sensor. The sensing ability of various SnO₂ micro-gap sensors is measured. A comparison between sensors reveals that the short-gap electrode has numerous advantages in terms of reliability, high sensitivity and detection of low concentrations of NO₂, while the large-gap electrode is relatively sensitive for high concentrations. Conductance measurements are carried out at different surface temperatures and NO₂ concentrations in order to investigate the effects that the gap size has on the overall sensor conductance. The results suggest that the interface between the electrode and sensitive layer has a very important role for the sensing mechanism of tin dioxide gas sensors.     

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.