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Modelling and simulation of light propagation in non-aged and aged step-index polymer optical fibres
(2004)
This thesis discusses theoretical and practical aspects of modelling of light propagation in non-aged and aged step-index polymer optical fibres (POFs). Special attention has been paid in describing optical characteristics of non-ideal fibres, scattering and attenuation, and in combining application-oriented and theoretical approaches. The precedence has been given to practical issues, but much effort has been also spent on the theoretical analysis of basic mechanisms governing light propagation in cylindrical waveguides. As a result a practically usable general POF model based on the raytracing approach has been developed and implemented. A systematic numerical optimisation of its parameters has been performed to obtain the best fit between simulated and measured optical characteristics of numerous non-aged and aged fibre samples. The model was verified by providing good agreement, especially for the non-aged fibres. The relations found between aging time and optimal values of model parameters contribute to a better understanding of the aging mechanisms of POFs.
Recurrence plots, a rather promising tool of data analysis, have been introduced by Eckman et al. in 1987. They visualise recurrences in phase space and give an overview about the system's dynamics. Two features have made the method rather popular. Firstly they are rather simple to compute and secondly they are putatively easy to interpret. However, the straightforward interpretation of recurrence plots for some systems yields rather surprising results. For example indications of low dimensional chaos have been reported for stock marked data, based on recurrence plots. In this work we exploit recurrences or ``naturally occurring analogues'' as they were termed by E. Lorenz, to obtain three key results. One of which is that the most striking structures which are found in recurrence plots are hinged to the correlation entropy and the correlation dimension of the underlying system. Even though an eventual embedding changes the structures in recurrence plots considerably these dynamical invariants can be estimated independently of the special parameters used for the computation. The second key result is that the attractor can be reconstructed from the recurrence plot. This means that it contains all topological information of the system under question in the limit of long time series. The graphical representation of the recurrences can also help to develop new algorithms and exploit specific structures. This feature has helped to obtain the third key result of this study. Based on recurrences to points which have the same ``recurrence structure'', it is possible to generate surrogates of the system which capture all relevant dynamical characteristics, such as entropies, dimensions and characteristic frequencies of the system. These so generated surrogates are shadowed by a trajectory of the system which starts at different initial conditions than the time series in question. They can be used then to test for complex synchronisation.
One of the most striking features of ecological systems is their ability to undergo sudden outbreaks in the population numbers of one or a small number of species. The similarity of outbreak characteristics, which is exhibited in totally different and unrelated (ecological) systems naturally leads to the question whether there are universal mechanisms underlying outbreak dynamics in Ecology. It will be shown into two case studies (dynamics of phytoplankton blooms under variable nutrients supply and spread of epidemics in networks of cities) that one explanation for the regular recurrence of outbreaks stems from the interaction of the natural systems with periodical variations of their environment. Natural aquatic systems like lakes offer very good examples for the annual recurrence of outbreaks in Ecology. The idea whether chaos is responsible for the irregular heights of outbreaks is central in the domain of ecological modeling. This question is investigated in the context of phytoplankton blooms. The dynamics of epidemics in networks of cities is a problem which offers many ecological and theoretical aspects. The coupling between the cities is introduced through their sizes and gives rise to a weighted network which topology is generated from the distribution of the city sizes. We examine the dynamics in this network and classified the different possible regimes. It could be shown that a single epidemiological model can be reduced to a one-dimensional map. We analyze in this context the dynamics in networks of weighted maps. The coupling is a saturation function which possess a parameter which can be interpreted as an effective temperature for the network. This parameter allows to vary continously the network topology from global coupling to hierarchical network. We perform bifurcation analysis of the global dynamics and succeed to construct an effective theory explaining very well the behavior of the system.
This thesis analyses synchronization phenomena occurring in large ensembles of interacting oscillatory units. In particular, the effects of nonisochronicity (frequency dependence on the oscillator's amplitude) on the macroscopic transition to synchronization are studied in detail. The new phenomena found (Anomalous Synchronization) are investigated in populations of oscillators as well as between oscillator's ensembles.
We calculate the additional carbon emissions as a result of the conversion of natural land in a process of urbanisation; and the change of carbon flows by “urbanised” ecosystems, when the atmospheric carbon is exported to the neighboring territories, from 1980 till 2050 for the eight regions of the world. As a scenario we use combined UN and demographic model′s prognoses for regional total and urban population growth. The calculations of urban areas dynamics are based on two models: the regression model and the Gamma-model. The urbanised area is sub-divided on built-up, „green“ (parks, etc.) and informal settlements (favelas) areas. The next step is to calculate the regional and world dynamics of carbon emission and export, and the annual total carbon balance. Both models give similar results with some quantitative differences. In the first model, the world annual emissions attain a maximum of 205 MtC/year between 2020-2030. Emissions will then slowly decrease. The maximum contributions are given by China and the Asia and Pacific regions. In the second model, world annual emissions increase to 1.25 GtC in 2005, beginning to decrease afterwards. If we compare the emission maximum with the annual emission caused by deforestation, 1.36GtC per year, then we can say that the role of urbanised territories (UT) is of a comparable magnitude. Regarding the world annual export of carbon by UT, we observe its monotonous growth by three times, from 24 MtC to 66 MtC in the first model, and from 249 MtC to 505 MtC in the second one. The latter, is therefore comparable to the amount of carbon transported by rivers into the ocean (196-537 MtC). By estimating the total balance we find that urbanisation shifts the total balance towards a “sink” state. The urbanisation is inhibited in the interval 2020-2030, and by 2050 the growth of urbanised areas would almost stop. Hence, the total emission of natural carbon at that stage will stabilise at the level of the 1980s (80 MtC per year). As estimated by the second model, the total balance, being almost constant until 2000, then starts to decrease at an almost constant rate. We can say that by the end of the XXI century, the total carbon balance will be equal to zero, when the exchange flows are fully balanced, and may even be negative, when the system begins to take up carbon from the atmosphere, i.e., becomes a “sink”.
My thesis is concerned with several new noise-induced phenomena in excitable neural models, especially those with FitzHugh-Nagumo dynamics. In these effects the fluctuations intrinsically present in any complex neural network play a constructive role and improve functionality. I report the occurrence of Vibrational Resonance in excitable systems. Both in an excitable electronic circuit and in the FitzHugh-Nagumo model, I show that an optimal amplitude of high-frequency driving enhances the response of an excitable system to a low-frequency signal. Additionally, the influence of additive noise and the interplay between Stochastic and Vibrational Resonance is analyzed. Further, I study systems which combine both oscillatory and excitable properties, and hence intrinsically possess two internal frequencies. I show that in such a system the effect of Stochastic Resonance can be amplified by an additional high-frequency signal which is in resonance with the oscillatory frequency. This amplification needs much lower noise intensities than for conventional Stochastic Resonance in excitable systems. I study frequency selectivity in noise-induced subthreshold signal processing in a system with many noise-supported stochastic attractors. I show that the response of the coupled elements at different noise levels can be significantly enhanced or reduced by forcing some elements into resonance with these new frequencies which correspond to appropriate phase-relations. A noise-induced phase transition to excitability is reported in oscillatory media with FitzHugh-Nagumo dynamics. This transition takes place via noise-induced stabilization of a deterministically unstable fixed point of the local dynamics, while the overall phase-space structure of the system is maintained. The joint action of coupling and noise leads to a different type of phase transition and results in a stabilization of the system. The resulting noise-induced regime is shown to display properties characteristic of excitable media, such as Stochastic Resonance and wave propagation. This effect thus allows the transmission of signals through an otherwise globally oscillating medium. In particular, these theoretical findings suggest a possible mechanism for suppressing undesirable global oscillations in neural networks (which are usually characteristic of abnormal medical conditions such as Parkinson′s disease or epilepsy), using the action of noise to restore excitability, which is the normal state of neuronal ensembles.
Independent component analysis (ICA) is a tool for statistical data analysis and signal processing that is able to decompose multivariate signals into their underlying source components. Although the classical ICA model is highly useful, there are many real-world applications that require powerful extensions of ICA. This thesis presents new methods that extend the functionality of ICA: (1) reliability and grouping of independent components with noise injection, (2) robust and overcomplete ICA with inlier detection, and (3) nonlinear ICA with kernel methods.
For recombinant production of proteins for structural and functional analyses, the E. coli expression system is the most widely used due to high yields and straightforward processing. However, particularly the expression of eukaryotic proteins in E. coli is often problematic, e.g. when the protein is not folded correctly and is deposited in insoluble inclusion bodies. In some cases it is favourable to analyse deletion constructs of a protein or an individual protein domain instead of the full-length protein. This implies the generation of a set of expression constructs that need to be characterised. In this work methods to optimise and evaluate in vitro folding of inclusion body proteins as well as high-throughput characterisation of expression constructs were developed. Transferring inclusion body proteins to their native state involves two steps: (a) solubilisation with a chaotropic reagent or a strong ionic detergent and (b) folding of the protein by removal of the chaotrop accompanied by the transfer into an appropriate buffer. The yield of natively folded protein is often substantially reduced due to aggregation or misfolding; it may, however, be improved by certain additives to the folding buffer. These additives need to be identified empirically. In this thesis a screening procedure for folding conditions was developed. To reduce the number of possible combinations of screening additives, empirical observations documented in the literature as well as well known properties of certain screening additives were considered. To decrease the amount of protein and work invested, the screen was miniaturised and automated using a pipetting robot. Twenty rapid dilution conditions for the denatured protein are tested and two conditions for folding of proteins using the detergent/cyclodextrin protein folding system of Rozema et al. (1996). 100 µg protein is used per condition. In addition, eight conditions can be tested for folding of His-tagged proteins (approx. 200 µg) immobilised on metal chelate resins. The screen was successfully applied to fold a human protein, the p22 subunit of dynactin that is expressed in inclusion bodies in E. coli. For p22 dynactin – as is the case for many proteins – there was no biological assay available to assess the success of the folding screen. Protein solubility can not be used as a stringent criterion because beside natively folded protein, soluble misfolded species and microaggregates may occur. This work evaluates methods to detect small amounts of natively folded protein after automated folding screening. Before folding screening with p22 dynactin, two model enzymes, bovine carbonic anhydrase II (CAB) and pig heart mitochondrial malate dehydrogenase, were used for evaluation. Recovered activity after refolding was correlated to different biophysical methods. 8-anilino-1-naphtalenesulfonic acid binding-experiments gave no useful information when refolding CAB, due to low sensitivity and because misfolded protein could not be readily distinguished from native protein. Tryptophan fluorescence spectra of refolded CAB were used to assess the success of refolding. The shift of the intensity maximum to a shorter wavelength, compared to the denaturant unfolded protein, as well as the fluorescence intensity correlated to recovered enzymatic activity. For both model enzymes, analytical hydrophobic interaction chromatography (HIC) was useful to identify refolded samples that contain active enzyme. Compactly folded, active enzyme eluted in a distinct peak in a decreasing ammonium sulfate gradient. The detection limit of analytical HIC was approx. 5 µg. In case of CAB, tryptophan fluorescence spectroscopy and analytical HIC showed that both methods in combination can be useful to rule out false positives or false negatives obtained with one method. These two methods were also useful to identify conditions for folding of p22 dynactin. However, tryptophan fluorescence spectroscopy can lead to false positives because in some cases spectra of soluble microaggregates are not well distinguishable from spectra of natively folded protein. In summary, a fast and reliable screening procedure was developed to make inclusion body proteins accessible to structural or functional analyses. In a separate project, 88 different E. coli expression constructs for 17 human protein domains that had been identified by sequence analysis were analysed using high-throughput purification and folding analysis in order to obtain candidates suitable for structural analysis. After 96 deep-well microplate expression and automated protein purification, solubly expressed protein domains were directly analysed using 1D ¹H-NMR spectroscopy. It was found that isolated methyl group signals below 0.5 ppm are particularly sensitive and reliable probes for folded protein. In addition – similar to the evaluation of a folding screen – analytical HIC proved to be an efficient tool for identifying constructs that yield compactly folded protein. Both methods, 1D ¹H-NMR spectroscopy and analytical HIC, provided complementary results. Six constructs, representing two domains, could be quickly identified as targets that are well suitable for structural analysis. The structure of one of these domains was solved recently by co-workers, the other structure was published by another group during this project.
Highly collimated, high velocity streams of hot plasma – the jets – are observed as a general phenomenon being found in a variety of astrophysical objects regarding their size and energy output. Known as jet sources are protostellar objects (T Tauri stars, embedded IR sources), galactic high energy sources ("microquasars"), and active galactic nuclei (extragalactic radio sources and quasars). Within the last two decades our knowledge regarding the processes involved in astro-physical jet formation has condensed in a kind of standard model. This is the scenario of a magnetohydrodynamically accelerated and collimated jet stream launched from the innermost part of an accretion disk close to the central object. Traditionally, the problem of jet formation is divided in two categories. One is the question how to collimate and accelerate an uncollimated low velocity disk wind into a jet. The second is the question how to initiate that outflow from a disk, i.e. how to turn accretion of matter into an ejection as a disk wind. My own work is mainly related to the first question, the collimation and acceleration process. Due to the complexity of both, the physical processes believed to be responsible for the jet launching and also the spatial configuration of the physical components of the jet source, the enigma of jet formation is not yet completely understood. On the theoretical side, there has been a substantial advancement during the last decade from purely station-ary models to time-dependent simulations lead by the vast increase of computer power. Observers, on the other hand, do not yet have the instruments at hand in order to spatially resolve observe the very jet origin. It can be expected that also the next years will yield a substantial improvement on both tracks of astrophysical research. Three-dimensional magnetohydrodynamic simu-lations will improve our understanding regarding the jet-disk interrelation and the time-dependent character of jet formation, the generation of the magnetic field in the jet source, and the interaction of the jet with the ambient medium. Another step will be the combina-tion of radiation transfer computations and magnetohydrodynamic simulations providing a direct link to the observations. At the same time, a new generation of telescopes (VLT, NGST) in combination with new instrumental techniques (IR-interferometry) will lead to a "quantum leap" in jet observation, as the resolution will then be sufficient in order to zoom into the innermost region of jet formation.
In a classical context, synchronization means adjustment of rhythms of self-sustained periodic oscillators due to their weak interaction. The history of synchronization goes back to the 17th century when the famous Dutch scientist Christiaan Huygens reported on his observation of synchronization of pendulum clocks: when two such clocks were put on a common support, their pendula moved in a perfect agreement. In rigorous terms, it means that due to coupling the clocks started to oscillate with identical frequencies and tightly related phases. Being, probably, the oldest scientifically studied nonlinear effect, synchronization was understood only in 1920-ies when E. V. Appleton and B. Van der Pol systematically - theoretically and experimentally - studied synchronization of triode generators. Since that the theory was well developed and found many applications. Nowadays it is well-known that certain systems, even rather simple ones, can exhibit chaotic behaviour. It means that their rhythms are irregular, and cannot be characterized only by one frequency. However, as is shown in the Habilitation work, one can extend the notion of phase for systems of this class as well and observe their synchronization, i.e., agreement of their (still irregular!) rhythms: due to very weak interaction there appear relations between the phases and average frequencies. This effect, called phase synchronization, was later confirmed in laboratory experiments of other scientific groups. Understanding of synchronization of irregular oscillators allowed us to address important problem of data analysis: how to reveal weak interaction between the systems if we cannot influence them, but can only passively observe, measuring some signals. This situation is very often encountered in biology, where synchronization phenomena appear on every level - from cells to macroscopic physiological systems; in normal states as well as in severe pathologies. With our methods we found that cardiovascular and respiratory systems in humans can adjust their rhythms; the strength of their interaction increases with maturation. Next, we used our algorithms to analyse brain activity of Parkinsonian patients. The results of this collaborative work with neuroscientists show that different brain areas synchronize just before the onset of pathological tremor. Morevoever, we succeeded in localization of brain areas responsible for tremor generation.