Optimal inspection and perfect repair

Models are developed for decision making about monitoring andmaintenance of systems whose performance through time is describedby a general stochastic process. The system is monitored andpreventive and corrective maintenance actions are carried outin response to the observed system state. The decision processis simplified by using the maximum process as a decision variable.The models developed generalize age replacement models and othersimple maintenance strategies. The approach can deal with failuresthat prevent the system functioning further, and also failuresthat are defined by regulation or economic considerations. Attentionis restricted to perfect repair and inspection, but the structureprovides the framework for further developments.     

CNDO studies of Y-Ba-Cu-O superconductors II. Simple models of YBa2Cu3Ox

The electronic structure of YBa₂Cu₃O_x (x=6 and 7) is investigated using the CNDO molecular orbital method. Electronic structures of model clusters [Cu₃O₁₀]^–15, [Ba₈Cu₃O₁₀]⁺¹, [Y₈Cu₃O₁₀]⁺⁹ of the non-superconducting (x=6) and [Cu₃O₁₂]^–17, [Ba₈Cu₃O₁₂]^–1, [Y₈Cu₃O₁₂]⁺⁷ of the superconducting (x=7) phases are compared. Y and Ba layers cause a considerable electron density transfer from the central Cu(1) region.     

Slow-light enhanced optical detection in liquid-infiltrated photonic crystals

Slow-light enhanced optical detection in liquid-infiltrated photonic crystals is theoretically studied. Using a scattering-matrix approach and the Wigner–Smith delay time concept, we show that optical absorbance benefits both from slow-light phenomena as well as a high filling factor of the energy residing in the liquid. Utilizing strongly dispersive photonic crystal structures, we numerically demonstrate how liquid-infiltrated photonic crystals facilitate enhanced light–matter interactions, by potentially up to an order of magnitude. The proposed concept provides strong opportunities for improving existing miniaturized absorbance cells for optical detection in lab-on-a-chip systems.     

Signal classification through multifractal analysis and complex domain neural networks

This paper describes a system capable of classifying stochastic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.     

A balancing condition for weak limits of families of minimal surfaces

We prove a balancing condition for weak limits of families of embedded minimal surfaces of finite total curvature. We use it to prove compactness theorems for certain families of minimal surfaces.     

The emergence of a giant component in random subgraphs of pseudo‐random graphs

Let G be a d‐regular graph G on n vertices. Suppose that the adjacency matrix of G is such that the eigenvalue λ which is second largest in absolute value satisfies λ = o(d). Let G_p with p = α/d be obtained from G by including each edge of G independently with probability p. We show that if α < 1, then whp the maximum component size of G_p is O(log n) and if α > 1, then G_p contains a unique giant component of size Ω(n), with all other components of size O(log n). © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 2004