Realization of negative differential resistance and switching devices based on copper phthalocyanine by the control of evaporation rate

We have observed, respectively, a negative differential resistance (NDR) and switching conduction in current-voltage (I-V) characteristics of organic diodes based on copper phthalocyanine (CuPc) film sandwiched between indium-tin-oxide (ITO) and aluminum (Al) by controlling the evaporation rate. The NDR effect is repeatable which can be well, controlled by sweep rate and start voltage, and the switching exhibits write-once-read-many-times (WORM) memory characteristics. The traps in the organic layer and interfacial dipole have been used to explain the NDR effect and switching conduction. This opens up potential applications for CuPc organic semiconductor in low power memory and logic circuits.

Electrically-controlled near-infrared chiroptical switching of enantiomeric dinuclear ruthenium complexes

Enantiomerically pure dinuclear ruthenium complexes with 1,2-dicarbonylhydrazide as a bridging ligand are optically active in the visible and near infrared spectral regions depending on the oxidation states of the metal centers and are useful as an electrochemically driven near infrared chiroptical switch.

Column switching-back flushing technique for the analysis of aromatic compounds in gasoline

A simple method, based on the technique of capillary column switching-back flushing, has been developed for the detailed analysis of aromatic compounds in gasoline. The sample was first separated on a 30-m long OV-2330 polar precolumn and then backflushed onto a nonpolar analytical column. The early eluting components from the precolumn and the components of interest (aromatic compounds plus heavier compounds) eluting from the analytical column are all directed to the same flame ionization detection system through a T piece, which permits the quantitative analysis of aromatic hydrocarbons in gasoline by a normalization method using correcting factors. The switching time window of the method is +/-5 s, resulting in easier operation and higher reliability. The reproducibility of the quantitative analysis was less than or equal to3% RSD for real gasoline samples. (C) 2002 Elsevier Science B.V. All rights reserved.

Switching Between Discrete and Continuous Process Models to Predict Molecular Genetic Activity

Two kinds of process models have been used in programs that reason about change: Discrete and continuous models. We describe the design and implementation of a qualitative simulator, PEPTIDE, which uses both kinds of process models to predict the behavior of molecular energetic systems. The program uses a discrete process model to simulate both situations involving abrupt changes in quantities and the actions of small numbers of molecules. It uses a continuous process model to predict gradual changes in quantities. A novel technique, called aggregation, allows the simulator to switch between theses models through the recognition and summary of cycles. The flexibility of PEPTIDE's aggregator allows the program to detect cycles within cycles and predict the behavior of complex situations.

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably.

Construction of Neural Network Classification Expert Systems Using Switching Theory Algorithms

A new family of neural network architectures is presented. This family of architectures solves the problem of constructing and training minimal neural network classification expert systems by using switching theory. The primary insight that leads to the use of switching theory is that the problem of minimizing the number of rules and the number of IF statements (antecedents) per rule in a neural network expert system can be recast into the problem of minimizing the number of digital gates and the number of connections between digital gates in a Very Large Scale Integrated (VLSI) circuit. The rules that the neural network generates to perform a task are readily extractable from the network's weights and topology. Analysis and simulations on the Mushroom database illustrate the system's performance.

An energy efficient power controller switching methodology for an ambient healthcare network

A methodology for improved power controller switching in mobile Body Area Networks operating within the ambient healthcare environment is proposed. The work extends Anti-windup and Bumpless transfer results to provide a solution to the ambulatory networking problem that ensures sufficient biometric data can always be regenerated at the base station. The solution thereby guarantees satisfactory quality of service for healthcare providers. Compensation is provided for the nonlinear hardware constraints that are a typical feature of the type of network under consideration and graceful performance degradation in the face of hardware output power saturation is demonstrated, thus conserving network energy in an optimal fashion.

beta-Arrestin-mediated PDE4 cAMP phosphodiesterase recruitment regulates beta-adrenoceptor switching from Gs to Gi.

Phosphorylation of the beta(2) adrenoreceptor (beta(2)AR) by cAMP-activated protein kinase A (PKA) switches its predominant coupling from stimulatory guanine nucleotide regulatory protein (G(s)) to inhibitory guanine nucleotide regulatory protein (G(i)). beta-Arrestins recruit the cAMP-degrading PDE4 phosphodiesterases to the beta(2)AR, thus controlling PKA activity at the membrane. Here we investigate a role for PDE4 recruitment in regulating G protein switching by the beta(2)AR. In human embryonic kidney 293 cells overexpressing a recombinant beta(2)AR, stimulation with isoprenaline recruits beta-arrestins 1 and 2 as well as both PDE4D3 and PDE4D5 to the receptor and stimulates receptor phosphorylation by PKA. The PKA phosphorylation status of the beta(2)AR is enhanced markedly when cells are treated with the selective PDE4-inhibitor rolipram or when they are transfected with a catalytically inactive PDE4D mutant (PDE4D5-D556A) that competitively inhibits isoprenaline-stimulated recruitment of native PDE4 to the beta(2)AR. Rolipram and PDE4D5-D556A also enhance beta(2)AR-mediated activation of extracellular signal-regulated kinases ERK12. This is consistent with a switch in coupling of the receptor from G(s) to G(i), because the ERK12 activation is sensitive to both inhibitors of PKA (H89) and G(i) (pertussis toxin). In cardiac myocytes, the beta(2)AR also switches from G(s) to G(i) coupling. Treating primary cardiac myocytes with isoprenaline induces recruitment of PDE4D3 and PDE4D5 to membranes and activates ERK12. Rolipram robustly enhances this activation in a manner sensitive to both pertussis toxin and H89. Adenovirus-mediated expression of PDE4D5-D556A also potentiates ERK12 activation. Thus, receptor-stimulated beta-arrestin-mediated recruitment of PDE4 plays a central role in the regulation of G protein switching by the beta(2)AR in a physiological system, the cardiac myocyte.

Regularity of invariant densities for 1D systems with random switching

© 2015 IOP Publishing Ltd & London Mathematical Society.This is a detailed analysis of invariant measures for one-dimensional dynamical systems with random switching. In particular, we prove the smoothness of the invariant densities away from critical points and describe the asymptotics of the invariant densities at critical points.

Sensitivity to switching rates in stochastically switched ODEs

We consider a stochastic process driven by a linear ordinary differential equation whose right-hand side switches at exponential times between a collection of different matrices. We construct planar examples that switch between two matrices where the individual matrices and the average of the two matrices are all Hurwitz (all eigenvalues have strictly negative real part), but nonetheless the process goes to infinity at large time for certain values of the switching rate. We further construct examples in higher dimensions where again the two individual matrices and their averages are all Hurwitz, but the process has arbitrarily many transitions between going to zero and going to infinity at large time as the switching rate varies. In order to construct these examples, we first prove in general that if each of the individual matrices is Hurwitz, then the process goes to zero at large time for sufficiently slow switching rate and if the average matrix is Hurwitz, then the process goes to zero at large time for sufficiently fast switching rate. We also give simple conditions that ensure the process goes to zero at large time for all switching rates. © 2014 International Press.

Stochastic switching in infinite dimensions with applications to random parabolic PDE

© 2015 Society for Industrial and Applied Mathematics.We consider parabolic PDEs with randomly switching boundary conditions. In order to analyze these random PDEs, we consider more general stochastic hybrid systems and prove convergence to, and properties of, a stationary distribution. Applying these general results to the heat equation with randomly switching boundary conditions, we find explicit formulae for various statistics of the solution and obtain almost sure results about its regularity and structure. These results are of particular interest for biological applications as well as for their significant departure from behavior seen in PDEs forced by disparate Gaussian noise. Our general results also have applications to other types of stochastic hybrid systems, such as ODEs with randomly switching right-hand sides.

Characterizing the Switching Thresholds of Magnetophoretic Transistors.

The switching thresholds of magnetophoretic transistors for sorting cells in microfluidic environments are characterized. The transistor operating conditions require short 20-30 mA pulses of electrical current. By demonstrating both attractive and repulsive transistor modes, a single transistor architecture is used to implement the full write cycle for importing and exporting single cells in specified array sites.