On exponential stability analysis for neural networks with time-varying delays and general activation functions

This paper is concerned with the exponential stability analysis for a class of cellular neural networks with both interval time-varying delays and general activation functions. The boundedness assumption of the activation function is not required. The limitation on the derivative of time delay being less than one is relaxed and the lower bound of time-varying delay is not restricted to be zero. A new Lyapunov-Krasovskii functional involving more information on the state variables is established to derive a novel exponential stability criterion. The obtained condition shows potential advantages over the existing ones since no useful item is ignored throughout the estimate of upper bound of the derivative of Lyapunov functional. Finally, three numerical examples are included to illustrate the proposed design procedures and applications.     

An Enzyme-Loaded Metal–Organic Framework-Assisted Microfluidic Platform Enables Single-Cell Metabolite Analysis

Colonization of cancer cells at secondary sites, a decisive step in tumor metastasis, is strongly dependent on the formation of metastatic microenvironments regulated by intrinsic single-cell metabolism traits. Herein, we report a single-cell microfluidic platform for high-throughput dynamic monitoring of tumor cell metabolites to evaluate tumor malignancy. This microfluidic device empowers efficient isolation of single cells (>99 %) in a squashed state similar to tumor extravasation, and employs enzyme-packaged metal–organic frameworks to catalyze tumor cell metabolites for visualization. The microfluidic evaluation was confirmed by in vivo assays, suggesting that the platform allowed predicting the tumorigenicity of captured tumor cells and screening metabolic inhibitors as anti-metastatic drugs. Furthermore, the platform efficiently detected various aggressive cancer cells in unprocessed whole blood samples with high sensitivity, showing potential for clinical application.     

Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.     

Finite turns and the regular closure of linear context-free languages

Turn bounded pushdown automata with different conditions for beginning a new turn are investigated. Their relationships with closures of the linear context-free languages under regular operations are studied. For example, automata with an unbounded number of turns that have to empty their pushdown store up to the initial symbol in order to start a new turn are characterized by the regular closure of the linear languages. Automata that additionally have to re-enter the initial state are (almost) characterized by the Kleene star closure of the linear languages. For both a bounded and an unbounded number of turns, requiring to empty the pushdown store is a strictly stronger condition than requiring to re-enter the initial state. Several new language families are obtained which form a double-stranded hierarchy. Closure properties of these families under AFL operations are derived. The regular closure of the linear languages share the strong closure properties of the context-free languages, i.e., the family is a full AFL. Interestingly, three natural new language families are not closed under intersection with regular languages and inverse homomorphism. Finally, an algorithm is presented parsing languages from the new families in quadratic time.     

Paclitaxel‐Loaded Stabilizer‐Free Poly(D,L‐lactide‐co‐glycolide) Nanoparticles Conjugated with Quantum Dots for Reversion of Anti‐Cancer Drug Resistance and Cancer Cellular Imaging

We prepared the PLGA‐loaded anti‐cancer drug and coated it with quantum dots to make it a dual‐function nanoparticles, and analyzed its potential use in cellular imaging and curing cancers. Two cancer cell lines, paclitaxel‐sensitive KB and paclitaxel‐resistant KB paclitaxel‐50 cervical carcinoma cells, were the relativistic models for analysis of the cytotoxicity of free paclitaxel and paclitaxel‐loaded PLGA conjugated with quantum‐dot nanoparticles. The paclitaxel‐loaded PLGA conjugated with quantum dots nanoparticles were significantly more cytotoxic than the free paclitaxel drug in paclitaxel‐resistant KB paclitaxel‐50 cells. This might have been because the cancer cells developed multi‐drug resistance (MDR), which hampered the action of free paclitaxel by pumping its molecules to extracellular areas. Addition of verapamil, a P‐glycoprotein inhibitor, reversed the MDR mechanism and significantly reduced KB paclitaxel‐50 cell viability. As a result, KB paclitaxel‐50 was highly associated with MDR on the cell membrane. The cytotoxicity results indicated that PLGA nanoparticles served as drug carriers and protected the drugs from MDR‐accelerated efflux. Combined quantum dots with PLGA nanoparticles allowed additional functionality for cellular imaging.     

Decoupling and recoupling of detonation waves associated with sudden expansion

Detonation propagation behavior associated with sudden expansions has been investigated both experimentally and numerically. Different mechanisms, from sustained propagation to detonation failure and reinitiation including shock and flame front decoupling and recoupling have been observed with the schlieren technique. The shock-induced flame propagation has been modeled with two-step chemistry and structured two-dimensional CFD on arbitrary geometries. The results of the numerical simulations show good correspondence to the variety of phenomena observed in experiments. Thus the numerical simulation can be used to study detonation propagation in complex geometries. It provides a tool for the design of safety devices and aids experimental investigations. Received 4 August 1995 / Accepted 25 April 1996     

Insight into the physics of foam densification via numerical simulation

Foamed materials are increasingly finding application in engineering systems on account of their unique properties. The basic mechanics which gives rise to these properties is well established, they are the result of collapsing the foam microstructure. Despite a basic understanding, the relationship between the details of foam microstructure and foam bulk response is generally unknown. With continued advances in computational power, many researchers have turned to numerical simulation to gain insight into the relationship between foam microstructure and bulk properties. However, numerical simulation of foam microscale deformation is a very challenging computational task and, to date, simulations over the full range of bulk deformations in which these materials operate have not been reported. Here a particle technique is demonstrated to be well-suited for this computational challenge, permitting simulation of the compression of foam microstructures to full densification. Computations on idealized foam microstructures are in agreement with engineering guidelines and various experimental results. Dependencies on degree of microstructure regularity and material properties are demonstrated. A surprising amount of porosity is found in fully-densified foams. The presence of residual porosity can strongly influence dynamic material response and hence needs to be accounted for in bulk (average) constitutive models of these materials.     

Bi-functional optimization of actively cooled, pressurized hollow sandwich cylinders with prismatic cores

All metallic, hollow sandwich cylinders having ultralight two-dimensional (2D) prismatic cores are optimally designed for maximum thermo-mechanical performance at minimum mass. The heated cylinder is subjected to uniform internal pressure and actively cooled by forced air convection. The use of two different core topologies is exploited: square- and triangular-celled cores. The minimum mass design model is so defined that three failure modes are prevented: facesheet yielding, core member yielding, and core member buckling. The intersection-of-asymptotes method, in conjunction with the fin analogy model, is employed to build the optimization model for maximum heat transfer rate. A non-dimensional parameter is introduced to couple the two objectives—structural and thermal—in a single cost function. It is found that the geometry corresponding to maximum heat transfer rate is not unique, and square-celled core sandwich cylinders outperform those having triangular cells. The eight-layered sandwich cylinders with square cells have the best overall performance in comparison with other core topologies. Whilst a sandwich cylinder with shorter length is preferred for enhanced thermo-mechanical performance, the influence of the outer radius of the cylinder is rather weak.     

Thermodynamic Automaton Simulations of Fluid Flow and Diffusion in Porous Media

A thermodynamic automaton model of fluid flow in porous media is presented. The model is a nonrelativistic version of a Lorentz invariant lattice gas model constructed by Udey et al. (1998). In the previous model it was shown that the energy momentum tensor and the relativistic Boltzman equation can be rigorously derived from the collision and propagation rules. In the present paper we demonstrate that this nonrelativistic model can be used to accurately simulate well known results involving single phase flow and diffusion in porous media. The simulation results show that (1) one-phase flow simulations in porous media are consistent with Darcy's law; (2) the apparent diffusion coefficient decreases with a decrease in permeability; (3) small scale heterogeneity does not affect diffusion significantly in the cases considered.