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The length of the vegetation period (VP) plays a central role for the interannual variation of carbon fixation of terrestrial ecosystems. Observational data analysis has indicated that the length of the VP has increased in the last decades in the northern latitudes mainly due to an advancement of bud burst (BB). This phenomenon has been widely discussed in the context of Global Warming because phenology is correlated to temperatures. Analyzing the patterns of spring phenology over the last century in Southern Germany provided two main findings: - The strong advancement of spring phases especially in the decade before 1999 is not a singular event in the course of the 20th century. Similar trends were also observed in earlier decades. Distinct periods of varying trend behavior for important spring phases could be distinguished. - Marked differences in trend behavior between the early and late spring phases were detected. Early spring phases changed as regards the magnitude of their negative trends from strong negative trends between 1931 and 1948 to moderate negative trends between 1948 and 1984 and back to strong negative trends between 1984 and 1999. Late spring phases showed a different behavior. Negative trends between 1931 and 1948 are followed by marked positive trends between 1948 and 1984 and then strong negative trends between 1984 and 1999. This marked difference in trend development between early and late spring phases was also found all over Germany for the two periods 1951 to 1984 and 1984 to 1999. The dominating influence of temperature on spring phenology and its modifying effect on autumn phenology was confirmed in this thesis. However, - temperature functions determining spring phenology were not significantly correlated with a global annual CO2 signal which was taken as a proxy for a Global Warming pattern. - an index for large scale regional circulation patterns (NAO index) could only to a small part explain the observed phenological variability in spring. The observed different trend behavior of early and late spring phases is explained by the differing behavior of mean March and April temperatures. Mean March temperatures have increased on average over the 20th century accompanied by an increasing variation in the last 50 years. April temperatures, however, decreased between the end of the 1940s and the mid-1980s, followed by a marked warming after the mid-1980s. It can be concluded that the advancement of spring phenology in recent decades are part of multi-decadal fluctuations over the 20th century that vary with the species and the relevant seasonal temperatures. Because of these fluctuations a correlation with an observed Global Warming signal could not be found. On average all investigated spring phases advanced between 5 and 20 days between 1951 and 1999 for all Natural Regions in Germany. A marked difference be! tween late and early spring phases is due to the above mentioned differing behavior before and after the mid-1980s. Leaf coloring (LC) was delayed between 1951 and 1984 for all tree species. However, after 1984 LC was advanced. Length of the VP increased between 1951 and 1999 for all considered tree species by an average of ten days throughout Germany. It is predominately the change in spring phases which contributes to a change in the potentially absorbed radiation. Additionally, it is the late spring species that are relatively more favored by an advanced BB because they can additionally exploit longer days and higher temperatures per day advancement. To assess the relative change in potentially absorbed radiation among species, changes in both spring and autumn phenology have to be considered as well as where these changes are located in the year. For the detection of the marked difference between early and late spring phenology a new time series construction method was developed. This method allowed the derivation of reliable time series that spanned over 100 years and the construction of locally combined time series increasing the available data for model development. Apart from analyzed protocolling errors, microclimatic site influences, genetic variation and the observers were identified as sources of uncertainty of phenological observational data. It was concluded that 99% of all phenological observations at a certain site will vary within approximately 24 days around the parametric mean. This supports to the proposed 30-day rule to detect outliers. New phenology models that predict local BB from daily temperature time series were developed. These models were based on simple interactions between inhibitory and promotory agents that are assumed to control the developmental status of a plant. Apart from the fact that, in general, the new models fitted and predicted the observations better than classical models, the main modeling results were: - The bias of the classical models, i.e. overestimation of early observations and underestimation of late observations, could be reduced but not completely removed. - The different favored model structures for each species indicated that for the late spring phases photoperiod played a more dominant role than for early spring phases. - Chilling only plays a subordinate role for spring BB compared to temperatures directly preceding BB.

The primary focus on the present study was to identify early risk factors for infant aggression in a sample of high risk, low-income teenager mothers and their infants. Despite the amount of research on externalizing behavior, relatively little is known about its development in early childhood. Because chronically aggressive school-age children tend to be those who first display symptoms during preschool years, an examination of the early manifestations of aggressive behavior and the development of measurements for infants is needed. The present study explored a model of infant aggression development that emphasized infant aggression developing largely through the interaction of infant′s dispositional characteristics with their caregiving environment. The study addressed the following relations: (1) Maternal psychosocial functioning with reported and observed infant aggression and negative emotionality, (2) reported measurements of infant aggression and negative emotionality with observed infant measurements of infant aggression and negative emotionality, (3) infant negative emotionality and infant aggression, (4) infant emotion regulation with infant aggression and negative emotionality, (5) the interaction between emotion regulation and negative emotionality in relation to infant aggression, and (6) attachment classification with infant aggression and negative emotionality. Finally, the question of whether these six relations would differ by gender was also addressed. Maternal psychosocial functioning was assessed with self-reported measurements. Infant aggression, negative emotionality and emotion regulation were measured during two standardized assessments, the Strange Situation and the Bayley Scales of Infant Development Assessment and maternal reported with the Infant-Toddler Social and Emotional Assessment. Several interesting findings emerged. One of the main findings concerned maternal attribution and its possible role as a risk factor for later externalizing behaviors. That is, mothers, especially depressed and stressed mothers, tended to report higher levels of infant aggression and negative emotionality than was noted by more objective observers. This tendency was particularly evident in mothers with girl infants. Another important finding concerned emotion regulation. Even at this early age, clear differences in emotion regulation could be seen. Interestingly, infants with high negative emotionality and low emotion regulation were observed to be the most aggressive. Also significant relations emerged for infant negative emotionality and aggression and vise versa. Thus, for purposes of treatment and scientific study, the three constructs (emotion regulation, negative emotionality, and aggression) should be considered in combination. Investigating each alone may not prove fruitful in future examinations. Additionally, different emotion regulation behaviors were observed for girl and boy infants. Aggressive girls looked more at the environment, their toys and their mother, whereas aggressive boys looked less at the environment and their mother and explored their toys more, although looked at the toys less. Although difficult to interpret at this point, it is nonetheless interesting that gender differences exist at this young age in emotion regulatory behaviors. In conclusion, although preliminary, findings from the present study provide intriguing directions for future research. More studies need to conducted focusing on infant aggression, as well as longitudinal studies following the infants over time.

New polymers and low molecular compounds, suitable for organic light emitting devices and organic electronic applications, have been synthesised in this years in order to obtain electron transport characteristics compatible with requirements for applications in real plastic devices. However, despite of the technological importance and of the relevant progress in devices manufacture, fundamental physical properties of such class of materials are still not enough studied. In particular extensive presence of distributions of localised states inside the band gap has a deep impact on their electronic properties. Such presence of shallow traps as well as the influence of the sample preparation conditions on deep and shallow localised states have not been, until now, systematically explored. The thermal techniques are powerful tools in order to study localised levels in inorganic and organic materials. Thermally stimulated luminescence (TSL), thermally stimulated currents (TSC) and thermally stimulated depolarisation currents (TSDC) allow to deeply look to shallow and deep trap levels as well as they permit to study, in synergy with dielectric spectroscopy (DES), polarisation and depolarisation effects. We studied, by means of numerical simulations, the first and the second order kinetic equations characterised by negligible and strong re-trapping respectively. We included in the equations Gaussian, exponential and quasi-continuous distributions of localised states. The shapes of the theoretical peaks have been investigated by means of systematic variation of the two main parameters of the equations, i. e. the energy trap depth E and the frequency factor a and of the parameters regulating the distributions, in particular for a Gaussian distribution the distribution width s and the integration limits. The theoretical findings have been applied to experimental glow curves. Thin films of polymers and low molecular compounds. Polyphenylquinoxalines, trisphenylquinoxalines and oxadiazoles, studied because of their technological relevance, show complex thermograms, having several levels of localised states and depolarisation peaks. In particular well ordered films of an amphiphilic substituted 2-(p-nitrophenyl)-5-(p-undecylamidophenyl)-1,3,4-oxadiazole (NADPO) are characterised by rich TSL thermograms. A wide region of shallow traps, localised at Em = 4 meV, has been successfully fit by means of a first order kinetic equation having a Gaussian distribution of localised states. Two further peaks, having a different origin, have been characterised. The peaks at Tm = 221.5 K and Tm = 254.2 have activation energy of Em= 0.63 eV and Em = 0.66 eV, frequency factor s = 2.4x1012 s-1 and s = 1.85x1011 s-1, distribution width s = 0.045 eV and s = 0.088 eV respectively. Increasing the number of thermal cycle, a peak, probably connected with structural defects, appears at Tm = 197.7 K. The numerical analysis of this peak was performed by means of a first order equation containing a Gaussian distribution of traps. The activation energy of the trap level is centred at Em = 0.55 eV. The distribution is perfectly symmetric with a quite small width s = 0.028 eV. The frequency factor is s = 1.15 x 1012 s-1, resulting of the same order of magnitude of its neighbour peak at Tm = 221.5 K, having both, probably, the same origin. Furthermore the work demonstrates that the shape of the glow curves is strongly influenced by the excitation temperature and by the thermal cycles. For that reason Gaussian distributions of localised states can be confused with exponential distributions if the previous thermal history of the samples is not adequately considered.

Jets are highly collimated flows of matter. They are present in a large variety of astrophysical sources: young stars, stellar mass black holes (microquasars), galaxies with an active nucleus (AGN) and presumably also intense flashes of gamma-rays. In particular, the jets of microquasars, powered by accretion disks, are probably small-scale versions of the outflows from AGN. Beside observations of astrophysical jet sources, also theoretical considerations have shown that magnetic fields play an important role in jet formation, acceleration and collimation. Collimated jets seem to be systematically associated with the presence of an accretion disk around a star or a collapsed object. If the central object is a black hole, the surrounding accretion disk is the only possible location for a magnetic field generation. We are interested in the formation process of highly relativistic jets as observed from microquasars and AGN. We theoretically investigate the jet collimation region, whose physical dimensions are extremely tiny even compared to radio telescopes spatial resolution. Thus, for most of the jet sources, global theoretical models are, at the moment, the only possibility to gain information about the physical processes in the innermost jet region. For the first time, we determine the global two-dimensional field structure of stationary, axisymmetric, relativistic, strongly magnetized (force-free) jets collimating into an asymptotically cylindrical jet (taken as boundary condition) and anchored into a differentially rotating accretion disk. This approach allows for a direct connection between the accretion disk and the asymptotic collimated jet. Therefore, assuming that the foot points of the field lines are rotating with Keplerian speed, we are able to achieve a direct scaling of the jet magnetosphere in terms of the size of the central object. We find a close compatibility between the results of our model and radio observations of the M87 galaxy innermost jet. We also calculate the X-ray emission in the energy range 0.2--10.1\,keV from a microquasar relativistic jet close to its source of 5 solar masses. In order to do it, we apply the jet flow parameters (densities, velocities, temperatures of each volume element along the collimating jet) derived in the literature from the relativistic magnetohydrodynamic equations. We obtain theoretical thermal X-ray spectra of the innermost jet as composition of the spectral contributions of the single volume elements along the jet. Since relativistic effects as Doppler shift and Doppler boosting due to the motion of jets toward us might be important, we investigate how the spectra are affected by them considering different inclinations of the line of sight to the jet axis. Emission lines of highly ionized iron are clearly visible in our spectra, probably also observed in the Galactic microquasars GRS 1915+105 and XTE J1748-288. The Doppler shift of the emission lines is always evident. Due to the chosen geometry of the magnetohydrodynamic jet, the inner X-ray emitting part is not yet collimated. Ergo, depending on the viewing angle, the Doppler boosting does not play a major role in the total spectra. This is the first time that X-ray spectra have been calculated from the numerical solution of a magnetohydrodynamic jet.

Nonlinear multistable systems under the influence of noise exhibit a plethora of interesting dynamical properties. A medium noise level causes hopping between the metastable states. This attractorhopping process is characterized through laminar motion in the vicinity of the attractors and erratic motion taking place on chaotic saddles, which are embedded in the fractal basin boundary. This leads to noise-induced chaos. The investigation of the dissipative standard map showed the phenomenon of preference of attractors through the noise. It means, that some attractors get a larger probability of occurrence than in the noisefree system. For a certain noise level this prefernce achieves a maximum. Other attractors are occur less often. For sufficiently high noise they are completely extinguished. The complexity of the hopping process is examined for a model of two coupled logistic maps employing symbolic dynamics. With the variation of a parameter the topological entropy, which is used together with the Shannon entropy as a measure of complexity, rises sharply at a certain value. This increase is explained by a novel saddle merging bifurcation, which is mediated by a snapback repellor. Scaling laws of the average time spend on one of the formerly disconnected parts and of the fractal dimension of the connected saddle describe this bifurcation in more detail. If a chaotic saddle is embedded in the open neighborhood of the basin of attraction of a metastable state, the required escape energy is lowered. This enhancement of noise-induced escape is demonstrated for the Ikeda map, which models a laser system with time-delayed feedback. The result is gained using the theory of quasipotentials. This effect, as well as the two scaling laws for the saddle merging bifurcation, are of experimental relevance.

In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules - also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues: o The statistical learning theory framework for analyzing boosting methods. We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution. o How can boosting methods be related to mathematical optimization techniques? To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms. o How to make Boosting noise robust? One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness. o How to adapt boosting to regression problems? Boosting methods are originally designed for classification problems. To extend the boosting idea to regression problems, we use the previous convergence results and relations to semi-infinite programming to design boosting-like algorithms for regression problems. We show that these leveraging algorithms have desirable theoretical and practical properties. o Can boosting techniques be useful in practice? The presented theoretical results are guided by simulation results either to illustrate properties of the proposed algorithms or to show that they work well in practice. We report on successful applications in a non-intrusive power monitoring system, chaotic time series analysis and a drug discovery process. --- Anmerkung: Der Autor ist Träger des von der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam vergebenen Michelson-Preises für die beste Promotion des Jahres 2001/2002.

Subject of this work is the investigation of generic synchronization phenomena in interacting complex systems. These phenomena are observed, among all, in coupled deterministic chaotic systems. At very weak interactions between individual systems a transition to a weakly coherent behavior of the systems can take place. In coupled continuous time chaotic systems this transition manifests itself with the effect of phase synchronization, in coupled chaotic discrete time systems with the effect of non-vanishing macroscopic mean field. Transition to coherence in a chain of locally coupled oscillators described with phase equations is investigated with respect to the symmetries in the system. It is shown that the reversibility of the system caused by these symmetries results to non-trivial topological properties of trajectories so that the system constructed to be dissipative reveals in a whole parameter range quasi-Hamiltonian features, i.e. the phase volume is conserved on average and Lyapunov exponents come in symmetric pairs. Transition to coherence in an ensemble of globally coupled chaotic maps is described with the loss of stability of the disordered state. The method is to break the self-consistensy of the macroscopic field and to characterize the ensemble in analogy to an amplifier circuit with feedback with a complex linear transfer function. This theory is then generalized for several cases of theoretic interest.

Subject of this work is the investigation of universal scaling laws which are observed in coupled chaotic systems. Progress is made by replacing the chaotic fluctuations in the perturbation dynamics by stochastic processes. First, a continuous-time stochastic model for weakly coupled chaotic systems is introduced to study the scaling of the Lyapunov exponents with the coupling strength (coupling sensitivity of chaos). By means of the the Fokker-Planck equation scaling relations are derived, which are confirmed by results of numerical simulations. Next, the new effect of avoided crossing of Lyapunov exponents of weakly coupled disordered chaotic systems is described, which is qualitatively similar to the energy level repulsion in quantum systems. Using the scaling relations obtained for the coupling sensitivity of chaos, an asymptotic expression for the distribution function of small spacings between Lyapunov exponents is derived and compared with results of numerical simulations. Finally, the synchronization transition in strongly coupled spatially extended chaotic systems is shown to resemble a continuous phase transition, with the coupling strength and the synchronization error as control and order parameter, respectively. Using results of numerical simulations and theoretical considerations in terms of a multiplicative noise partial differential equation, the universality classes of the observed two types of transition are determined (Kardar-Parisi-Zhang equation with saturating term, directed percolation).

Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test against the discrete hidden Markov models, learned from a representative set of training sequences for each seismic event type of interest. A time period from July, 1st to July, 5th, 1998 of rapidly increasing seismic activity prior to the eruptive cycle between July, 10th and July, 19th, 1998 at Merapi volcano is selected for evaluating the performance of this classification approach. Three distinct types of seismic events according to the established classification scheme of the Volcanological Survey of Indonesia (VSI) have been observed during this time period. Shallow volcano-tectonic events VTB (h < 2.5 km), very shallow dome-growth related seismic events MP (h < 1 km) and seismic signals connected to rockfall activity originating from the active lava dome, termed Guguran. The special configuration of the digital seismic station network at Merapi volcano, a combination of small-aperture array deployments surrounding Merapi's summit region, allows the use of array methods to parametrize the continuously recorded seismic wavefield. The individual signal parameters are analyzed to determine their relevance for the discrimination of seismic event classes. For each of the three observed event types a set of DHMMs has been trained using a selected set of seismic events with varying signal to noise ratios and signal durations. Additionally, two sets of discrete hidden Markov models have been derived for the seismic noise, incorporating the fact, that the wavefield properties of the ambient vibrations differ considerably during working hours and night time. A total recognition accuracy of 67% is obtained. The mean false alarm (FA) rate can be given by 41 FA/class/day. However, variations in the recognition capabilities for the individual seismic event classes are significant. Shallow volcano-tectonic signals (VTB) show very distinct wavefield properties and (at least in the selected time period) a stable time pattern of wavefield attributes. The DHMM-based classification performs therefore best for VTB-type events, with almost 89% recognition accuracy and 2 FA/day. Seismic signals of the MP- and Guguran-classes are more difficult to detect and classify. Around 64% of MP-events and 74% of Guguran signals are recognized correctly. The average false alarm rate for MP-events is 87 FA/day, whereas for Guguran signals 33 FA/day are obtained. However, the majority of missed events and false alarms for both MP and Guguran events are due to confusion errors between these two event classes in the recognition process. The confusion of MP and Guguran events is interpreted as being a consequence of the selected parametrization approach for the continuous seismic data streams. The observed patterns of the analyzed wavefield attributes for MP and Guguran events show a significant amount of similarity, thus providing not sufficient discriminative information for the numerical classification. The similarity of wavefield parameters obtained for seismic events of MP and Guguran type reflect the commonly observed dominance of path effects on the seismic wave propagation in volcanic environments. The recognition rates obtained for the five-day period of increasing seismicity show, that the presented DHMM-based automatic classification system is a promising approach for the difficult task of classifying volcano-seismic signals. Compared to standard signal detection algorithms, the most significant advantage of the discussed technique is, that the entire seismogram is detected and classified in a single step.

Research on monolayers of amphiphilic lipids on aqueous solution is of basic importance in surface science. Due to the applicability of a variety of surface sensitive techniques, floating insoluble monolayers are very suitable model systems for the study of order, structure formation and material transport in two dimensions or the interactions of molecules at the interface with ions or molecules in the bulk (headword 'molecular recognition'). From the behavior of monolayers conclusions can be drawn on the properties of lipid layers on solid substrates or in biological membranes. This work deals with specific and fundamental interactions in monolayers both on the molecular and on the microscopic scale and with their relation to the lattice structure, morphology and thermodynamic behavior of monolayers at the air-water interface. As model system especially monolayers of long chain fatty acids are used, since there the molecular interactions can be gradually adjusted by varying the degree of dissociation by means of the suphase pH value. For manipulating the molecular interactions besides the subphase composition also temperature and monolayer composition are systematically varied. The change in the monolayer properties as a function of an external parameter is analyzed by means of isotherm and surface potential measurements, Brewster-angle microscopy, X-ray diffraction at grazing incidence and polarization modulated infrared reflection absorption spectroscopy. For this a quantitative measure for the molecular interactions and for the chain conformational order is derived from the X-ray data. The most interesting results of this work are the elucidation of the origin of regular polygonal and dendritic domain shapes, the various effects of cholesterol on molecular packing and lattice order of long chain amphiphiles, as well as the detection of an abrupt change in the head group bonding interactions, the chain conformational order and the phase transition pressure between tilted phases in fatty acid monolayers near pH 9. For the interpretation of the latter point a model of the head group bonding structure in fatty acid monolayers as a function of the pH value is developed.