A study on transition of iron from active into passive state

An investigation was carried out on the transition of an iron electrode from active to passive state in a sulphuric acid solution. It was found that the active-passive transition was an auto-catalytic process in which a pre-passive film grew on the electrode surface. The growing pre-passive film had a fractal edge whose dimension was affected by the applied passivating potential and the presence of chlorides in the solution. Applying a more positive passivating potential led to a faster active-passive transition and resulted in a more irregular pre-passive film. If chlorides were introduced into the sulphuric acid solution, the active-passive transition became more rapid and the pre-passive film more irregular. Apart from the influence on the growth of the pre-passive film, the presence of chlorides in the passivating solution was found to deteriorate the stability of the final passive film. All these phenomena can be understood if the passivating iron electrode is regarded as a dissipative system. To explain these results, a fractal pre-film model is proposed in this paper. (C) 2004 Elsevier Ltd. All rights reserved.

Fractal PBG assisted co-planar waveguides

In general, conventional electromagnetic bandgap (PBGs) with uniform distribution show spurious ripples in pass-band and poor stop-band responses. This paper presents a detailed investigation in terms of pass-band and stop-band characteristics of uniplanar transmission line loaded with fractal shape PBGs. (c) 2005 Wiley Periodicals, Inc.

Fractal analysis as a tool for studying specialization in neuronal structure: The study of the evolution of the primate cerebral cortex and human intellect

We review recent findings that, using fractal analysis, have demonstrated systematic regional and species differences in the branching complexity of neocortical pyramidal neurons. In particular, attention is focused on how fractal analysis is being applied to the study of specialization in pyramidal cell structure during the evolution of the primate cerebral cortex. These studies reveal variation in pyramidal cell phenotype that cannot be attributed solely to increasing brain volume. Moreover, the results of these studies suggest that the primate cerebral cortex is composed of neurons of different structural complexity. There is growing evidence to suggest that regional and species differences in neuronal structure influence function at both the cellular and circuit levels. These data challenge the prevailing dogma for cortical uniformity.

The time dimension of neural network models

This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

Innovation and regional absorptive capacity:the labour market dimension

In 2003, Eurostat published an 'experimental' dataset on regional innovation levels derived from the Second Community Innovation Survey. This dataset, part of the European Innovation Scoreboard, also contains a range of regional labour market indicators. In this paper, we report an exploratory analysis of this data, focussing on how the labour market characteristics of regions shape regions' absorptive capacity (RACAP) and their ability to assimilate knowledge from public and externally conducted R&D. In particular, we aim to establish whether labour market aspects of RACAP are more important for innovation in prosperous or lagging regions of the European Union (EU). © Springer-Verlag 2006.

Coherent propagation and energy transfer in low-dimension nonlinear arrays

We present a theory of coherent propagation and energy or power transfer in a low-dimension array of coupled nonlinear waveguides. It is demonstrated that in the array with nonequal cores (e.g., with the central core) stable steady-state coherent multicore propagation is possible only in the nonlinear regime, with a power-controlled phase matching. The developed theory of energy or power transfer in nonlinear discrete systems is rather generic and has a range of potential applications including both high-power fiber lasers and ultrahigh-capacity optical communication systems. © 2012 American Physical Society.

Hopping transport on a fractal: ac conductivity of porous silicon

We have measured the frequency dependence of the conductivity and the dielectric constant of various samples of porous Si in the regime 1 Hz-100 kHz at different temperatures. The conductivity data exhibit a strong frequency dependence. When normalized to the dc conductivity, our data obey a universal scaling law, with a well-defined crossover, in which the real part of the conductivity sigma' changes from an sqrt(omega) dependence to being proportional to omega. We explain this in terms of activated hopping in a fractal network. The low-frequency regime is governed by the fractal properties of porous Si, whereas the high-frequency dispersion comes from a broad distribution of activation energies. Calculations using the effective-medium approximation for activated hopping on a percolating lattice give fair agreement with the data.