Approximation of trajectories of non-linear systems by iterates of systems with linear state dynamics

A modification of an iterative Picard process is proposed to approximate the output of a non-linear system by a concatenation of systems with linear state dynamics and non-linear outputs. A local uniform convergence result is given. The motivating example is a non-linear system that arises in surface plasmon resonance experiments to determine protein–protein interaction constants. We show with simulations that for this example the approximants converge not only locally but over the full time interval of interest in the application.     

Analysis of sources of dioxin contamination in sediments and soils using multivariate statistical methods and neural networks

Multivariate statistical methods and neuronal networks were used to evaluate the concentration dioxin patterns of a large data set (407 samples) in order to identify the dioxin sources of contaminated waters (sediment and suspended particulate matter samples). The evaluations indicated that a considerable proportion of the dioxin contamination of the river Elbe in the section between the Mulde tributary and the North Sea and their flood plains (soil samples) and the Port of Hamburg was caused by pollution originating from the Bitterfeld region, an industrial area of the former German Democratic Republic. The dioxin patterns of sediment samples from tributaries of the river Elbe in the Bitterfeld area itself are similar to dioxin patterns that can be attributed to metalworking processes. The dioxin patterns of the Hamburg inner city waters could be attributed to "incineration" dioxin sources, for example waste incineration plants. The results of cluster analysis applying different modes of distance measure and linkage compared well with neuronal networks. The number of clusters was determined based on the stability of the results of different cluster analyses and background information.     

Purification, characterization and functional cloning of inositol oxygenase from Cryptococcus

The enzyme inositol oxygenase (myo-inositol : oxygen oxidoreductase; E.C. 1.13.99.1) is a monooxygenase that converts inositol into glucuronic acid in the presence of molecular oxygen. This enzyme is integrated into a pathway leading to either degradation and energy production or the biosynthesis of precursors for polysaccharides. The enzyme was purified from the yeast Cryptococcus lactativorus by a five-step chromatography procedure. The purified enzyme shows a molecular mass of 37 kDa on SDS-PAGE, similar to the estimation of the size of the native enzyme determined by size exclusion chromatography. Peptides of the inositol oxygenase protein derived from a tryptic digest were sequenced de novo by nanoelectrospray tandem mass spectrometry. Using degenerate oligonucleotides, the corresponding gene was cloned from first strand cDNA. The open reading frame encodes a 315 amino acid polypeptide with a predicted molecular mass of 36.9 kDa. Inositol oxygenase is a single copy gene in C. lactativorus. It has close homologues in other fungi such as Cryptococcus neoformans and Neurospora crassa. Biochemical characterization of the enzyme showed a pH optimum of 6-6.5 and a temperature optimum of 30 degrees C. Myo-inositol is the only accepted substrate with a Km of ca. 5 mM. The enzyme contains a Fe-centre but the enzyme activity is resistant to KCN.     

State Space Realization of a Three-dimensional Image Set with Application to Noise Reduction of Fluorescent Microscopy Images of Cells

A method is presented to calculate state space realizations of a three-dimensional image set. It is based on interpreting the image set as the impulse response of a 3D separable system. As an application it is shown how this method, combined with approximation steps, including balanced model reduction, can be used to suppress noise in three-dimensional image sets. The approach was motivated by a practical problem in the analysis of three-dimensional fluorescent microscopy image data of fluorescently labelled cells. The method is illustrated by an analysis of simulated data and experimental data. The proposed approach can also be applied to a two-dimensional image in a straightforward way.Received July 9, 2003; Revised April 20, 2003; Accepted June 11, 2004; First online version published in December 2004     

Comparative Investigation of the Volatile Urinary Profiles in Different <Emphasis Type="Italic">Phodopus</Emphasis> Hamster Species

Stir bar sorptive extraction method was used for investigation of the urinary volatile profiles in male and female Phodopus campbelli and Phodopus sungorus hamsters. Additionally, female Phodopus roborowsky urinary profiles were characterized. A quantitative analytical approach allowed comparisons of 17 selected compounds in urine. Results showed that campbelli and sungorus species show similar urinary volatile profiles for males and females. Differences appeared only in concentrations. Several unique compounds, such as pyrazine derivatives, were found to be gender- and age-specific. P. roborowsky females exhibited a completely different urinary volatile profile from campbelli and sungorus females, featuring a unique set of substituted quinoxalines.     

Design error diagnosis in digital circuits with stuck-at fault model

In this paper we describe in detail a new method for the single gate-level design error diagnosis in combinational circuits. Distinctive features of the method are hierarchical approach (the localizing procedure starts at the macro level and finishes at the gate level), use of stuck-at fault model (it is mapped into design error domain only in the end), and design error diagnostic procedure that uses only test patterns generated by conventional gate-level stuck-at fault test pattern generators (ATPG). No special diagnostic tests are used because they are much more time consuming. Binary decision diagrams (BDD) are exploited for representing and localizing stuck-at faults on the higher signal path level. On the basis of detected faulty signal paths, suspected stuck-at faults at gate inputs are calculated, and then mapped into suspected design error(s). This method is enhanced compared to our previous work. It is applicable to redundant circuits and allows using incomplete tests for error diagnosis. Experimental data on ISCAS benchmark circuits shows the advantage of the proposed method compared to the known algorithms of design error diagnosis.     

Ductility and magnetism: An ab-initio study of NiAl–Fe and NiAl–Mn alloys

An ab-initio density functional approach is applied to study the magnetism related aspects of ductility for NiAl microalloyed by Fe and Mn. The generalised stacking fault energies of NiAl are calculated for slip interfaces in which some of Ni and Al atoms are replaced by Fe(Mn) atoms. By comparing spin-polarised and nonmagnetic calculation, it is shown that the local magnetic moment of Fe atom impedes a change of the preferred slip system from 〈001〉 to 〈111〉. The opposite effect is found for NiAl–Mn, in which 〈001〉 slip is significantly unfavored. The results are in general in excellent agreement with experimental observations, verifying improved ductility for NiAl–Fe. The differences in slip behavior are correlated with the changes of the local magnetic moments in 〈001〉 and 〈111〉 slipped NiAl.     

Hierarchical test generation for combinational circuits with real defects coverage

This paper deals with the automatic test pattern generation (ATPG) technique at the higher level using a functional fault model and defect-fault relationship in the form of a defect coverage table at the lower level. The paper contributes to test pattern generation (TPG) techniques taking into account physical defect localisation. A new parameter––probabilistic effectiveness of input patterns––has been used in the TPG technique with the goal of increasing real defect coverage. This parameter is based on probabilities of physical defects in digital cells which may occur in real integrated circuits. This improvement has been implemented into the existing DefGen ATPG system for combinational circuits.