Biomedical subjects

W von der Linden

Publications and source records attributed to W von der Linden.

3 recordsLinked to original sources

PubMed2001

Simulations on infinite-size lattices.

We introduce a Monte Carlo method, as a modification of existing cluster algorithms, which allows simulations directly on systems of infinite size, and for quantum models also at beta = infinity. All two-point functions can be obtained, including dynamical information. When the number of iterations is increased, correlation functions at larger distances become available. Limits q-->0 and omega-->0 can be approached directly. As examples we calculate spectra for the d = 2 Ising model and for Heisenberg quantum spin ladders with two and four legs.

Journal Article↗

PubMed2001

Analysis of multicomponent mass spectra applying Bayesian probability theory.

In this paper we develop a method for the decomposition of mass spectra of gas mixtures, together with the relevant calibration measurements. The method is based on Bayesian probability theory. Given a set of spectra, the algorithm returns the relative concentrations and the associated margin of confidence for each component of the mixture. In addition to the concentrations, such a data set enables the derivation of improved values of the cracking coefficients of all contributing species, even for those components for which the set does not contain a calibration measurement. This latter feature also allows one to analyze mixtures that contain radicals in addition to stable molecules. As an example, we analyze and discuss the mass spectra obtained from the pyrolysis of azomethane, which contain the radical CH3 apart from nitrogen and C1- and C2-hydrocarbons.

Azo Compounds↗

PubMed1999

Signal and background separation.

In many areas of research the measured spectra consist of a collection of "peaks"--the sought-for signals--which sit on top of an unknown background. The subtraction of the background in a spectrum has been the subject of many investigations and different techniques, varying from filtering to fitting polynomial functions, have been developed. These techniques yield results that are not always satisfactory and often even misleading. Based upon the rules of probability theory, we derive a formalism to separate the background from the signal part of a spectrum in a rigorous and self-consistent manner. We compare the results of the probabilistic approach to those obtained by two commonly used methods in an analysis of particle induced x-ray emission spectra.

Journal Article↗