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Institute
- Institut für Biochemie und Biologie (28)
- Institut für Physik und Astronomie (28)
- Institut für Geowissenschaften (24)
- Institut für Chemie (17)
- Hasso-Plattner-Institut für Digital Engineering GmbH (13)
- Institut für Ernährungswissenschaft (13)
- Institut für Umweltwissenschaften und Geographie (8)
- Department Psychologie (7)
- Extern (5)
- Fachgruppe Politik- & Verwaltungswissenschaft (5)
Noise is ubiquitous in nature and usually results in rich dynamics in stochastic systems such as oscillatory systems, which exist in such various fields as physics, biology and complex networks. The correlation and synchronization of two or many oscillators are widely studied topics in recent years.
In this thesis, we mainly investigate two problems, i.e., the stochastic bursting phenomenon in noisy excitable systems and synchronization in a three-dimensional Kuramoto model with noise. Stochastic bursting here refers to a sequence of coherent spike train, where each spike has random number of followers due to the combined effects of both time delay and noise. Synchronization, as a universal phenomenon in nonlinear dynamical systems, is well illustrated in the Kuramoto model, a prominent model in the description of collective motion.
In the first part of this thesis, an idealized point process, valid if the characteristic timescales in the problem are well separated, is used to describe statistical properties such as the power spectral density and the interspike interval distribution. We show how the main parameters of the point process, the spontaneous excitation rate, and the probability to induce a spike during the delay action can be calculated from the solutions of a stationary and a forced Fokker-Planck equation. We extend it to the delay-coupled case and derive analytically the statistics of the spikes in each neuron, the pairwise correlations between any two neurons, and the spectrum of the total output from the network.
In the second part, we investigate the three-dimensional noisy Kuramoto model, which can be used to describe the synchronization in a swarming model with helical trajectory. In the case without natural frequency, the Kuramoto model can be connected with the Vicsek model, which is widely studied in collective motion and swarming of active matter. We analyze the linear stability of the incoherent state and derive the critical coupling strength above which the incoherent state loses stability. In the limit of no natural frequency, an exact self-consistent equation of the mean field is derived and extended straightforward to any high-dimensional case.
This thesis focuses on the synthesis of novel functional materials based on plasmonic nanoparticles. Three systems with targeted surface modification and functionalization have been designed and synthesized, involving modified perylenediimide doped silica-coated silver nanowires, polydopamine or TiO2 coated gold-palladium nanorods and thiolated poly(ethylene glycol) (PEG-SH)/dodecanethiol (DDT) modified silver nanospheres. Their possible applications as plasmonic resonators, chiral sensors as well as photo-catalysts have been studied. In addition, the interaction between silver nanospheres and 2,3,5,6-Tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) molecules has also been investigated in detail.
In the first part of the thesis, surface modification on Ag nanowires (NWs) with optimized silica coating through a modified Stöber method has been firstly conducted, employing sodium hydroxide (NaOH) to replace ammonia solution (NH4OH). The coated silver nanowires with a smooth silica shell have been investigated by single-particle dark-field scattering spectroscopy, transmission electron microscopy and electron-energy loss spectroscopy to characterize the morphologies and structural components. The silica-coated silver nanowires can be further functionalized with fluorescent molecules in the silica shell via a facile one-step coating method. The as-synthesized nanowire is further coupled with a gold nanosphere by spin-coating for the application of the sub-diffractional chiral sensor for the first time. The exciton-plasmon-photon interconversion in the system eases the signal detection in the perfectly matched 1D nanostructure and contributes to the high contrast of the subwavelength chiral sensing for the polarized light.
In the second part of the thesis, dumbbell-shaped Au-Pd nanorods coated with a layer of polydopamine (PDA) or titanium dioxide (TiO2) have been constructed. The PDA- and TiO2- coated Au-Pd nanorods show a strong photothermal conversion performance under NIR illumination. Moreover, the catalytic performance of the particles has been investigated using the reduction of 4-nitrophenol (4-NP) as the model reaction. Under light irradiation, the PDA-coated Au-Pd nanorods exhibit a superior catalytic activity by increasing the reaction rate constant of 3 times. The Arrhenius-like behavior of the reaction with similar activation energies in the presence and absence of light irradiation indicates the photoheating effect to be the dominant mechanism of the reaction acceleration. Thus, we attribute the enhanced performance of the catalysis to the strong photothermal effect that is driven by the optical excitation of the gold surface plasmon as well as the synergy with the PDA layer.
In the third part, the kinetic study on the adsorption of 2,3,5,6-Tetrafluoro-7,7,8,8-tetracyanoquino-dimethane (F4TCNQ) on the surface of Ag nanoparticles (Ag NPs) in chloroform has been reported in detail. Based on the results obtained from the UV-vis-NIR absorption spectroscopy, cryogenic transmission electron microscopy (cryo-TEM), scanning nano-beam electron diffraction (NBED) and electron energy loss spectroscopy (EELS), a two-step interaction kinetics has been proposed for the Ag NPs and F4TCNQ molecules. It includes the first step of electron transfer from Ag NPs to F4TCNQ indicated by the ionization of F4TCNQ, and the second step of the formation of Ag-F4TCNQ complex. The whole process has been followed via UV-vis-NIR absorption spectroscopy, which reveals distinct kinetics at two stages: the instantaneous ionization and the long-term complex formation. The kinetics and the influence of the molar ratio of Ag NPs/F4TCNQ molecules on the interaction between Ag NPs and F4TCNQ molecules in the organic solution are reported herein for the first time. Furthermore, the control experiment with silica-coated Ag NPs indicates that the charge transfer at the surface between Ag NPs and F4TCNQ molecules has been prohibited by a silica layer of 18 nm.
The galactic interstellar medium is magnetized and turbulent. The magnetic field and turbulence play important roles in many astrophysical mechanisms, including cosmic ray transport, star formation, etc. Therefore, measurements of magnetic field and turbulence information are crucial for the proper interpretation of astronomical observations. Nonetheless, the magnetic field observation is quite challenging, especially, there is not universal magnetic tracer for diffuse medium. Moreover, the modelling of turbulence can be oversimplified due to the lack of observational tools to diagnose the plasma properties of the turbulence in the galactic interstellar medium. The studies presented in this thesis have addressed these challenges by bridging the theoretical studies of magnetic field and turbulence with numerical simulations and observations.
The following research are presented in this thesis. The first observational evidence of the novel magnetic tracer, ground state alignment (GSA), is discovered, revealing the three-dimensional magnetic field as well as 2 orders of magnitude higher precision comparing to previous observational study in the stellar atmosphere of the post-AGB 89 Herculis. Moreover, the application of GSA in the sub-millimeter fine-structure lines is comprehensively studied for different elements and with magnetohydrodynamic simulations. Furthermore, the influence of GSA effect on the spectroscopy is analyzed and it is found that measurable variation will be produced on the spectral line intensity and the line ratio without accounting for the optical pumping process or magnetic field.
Additionally, a novel method to measure plasma modes in the interstellar medium, Signatures from Polarization Analysis (SPA), is proposed and applied to real observations. Magneto-sonic modes are discovered in different types of interstellar medium. An explanation is provided for the long-standing mystery, the origin of γ-ray enhanced emission “Cygnus Cocoon”, based on the comparison between the outcome of SPA and multi-waveband observational data. These novel methods have strong potentials for broader observational applications and will play crucial roles in future multi-wavelength astronomy.
This thesis focuses on the study of marked Gibbs point processes, in particular presenting some results on their existence and uniqueness, with ideas and techniques drawn from different areas of statistical mechanics: the entropy method from large deviations theory, cluster expansion and the Kirkwood--Salsburg equations, the Dobrushin contraction principle and disagreement percolation.
We first present an existence result for infinite-volume marked Gibbs point processes. More precisely, we use the so-called entropy method (and large-deviation tools) to construct marked Gibbs point processes in R^d under quite general assumptions. In particular, the random marks belong to a general normed space S and are not bounded. Moreover, we allow for interaction functionals that may be unbounded and whose range is finite but random. The entropy method relies on showing that a family of finite-volume Gibbs point processes belongs to sequentially compact entropy level sets, and is therefore tight.
We then present infinite-dimensional Langevin diffusions, that we put in interaction via a Gibbsian description. In this setting, we are able to adapt the general result above to show the existence of the associated infinite-volume measure. We also study its correlation functions via cluster expansion techniques, and obtain the uniqueness of the Gibbs process for all inverse temperatures β and activities z below a certain threshold. This method relies in first showing that the correlation functions of the process satisfy a so-called Ruelle bound, and then using it to solve a fixed point problem in an appropriate Banach space. The uniqueness domain we obtain consists then of the model parameters z and β for which such a problem has exactly one solution.
Finally, we explore further the question of uniqueness of infinite-volume Gibbs point processes on R^d, in the unmarked setting. We present, in the context of repulsive interactions with a hard-core component, a novel approach to uniqueness by applying the discrete Dobrushin criterion to the continuum framework. We first fix a discretisation parameter a>0 and then study the behaviour of the uniqueness domain as a goes to 0. With this technique we are able to obtain explicit thresholds for the parameters z and β, which we then compare to existing results coming from the different methods of cluster expansion and disagreement percolation.
Throughout this thesis, we illustrate our theoretical results with various examples both from classical statistical mechanics and stochastic geometry.
Contributions to the theoretical analysis of the algorithms with adversarial and dependent data
(2021)
In this work I present the concentration inequalities of Bernstein's type for the norms of Banach-valued random sums under a general functional weak-dependency assumption (the so-called $\cC-$mixing). The latter is then used to prove, in the asymptotic framework, excess risk upper bounds of the regularised Hilbert valued statistical learning rules under the τ-mixing assumption on the underlying training sample. These results (of the batch statistical setting) are then supplemented with the regret analysis over the classes of Sobolev balls of the type of kernel ridge regression algorithm in the setting of online nonparametric regression with arbitrary data sequences. Here, in particular, a question of robustness of the kernel-based forecaster is investigated. Afterwards, in the framework of sequential learning, the multi-armed bandit problem under $\cC-$mixing assumption on the arm's outputs is considered and the complete regret analysis of a version of Improved UCB algorithm is given. Lastly, probabilistic inequalities of the first part are extended to the case of deviations (both of Azuma-Hoeffding's and of Burkholder's type) to the partial sums of real-valued weakly dependent random fields (under the type of projective dependence condition).
Shape-Memory effects of thermoplatic multiblock copolymers with overlapping thermal transitions
(2021)
With ongoing anthropogenic global warming, some of the most vulnerable components of the Earth system might become unstable and undergo a critical transition. These subsystems are the so-called tipping elements. They are believed to exhibit threshold behaviour and would, if triggered, result in severe consequences for the biosphere and human societies. Furthermore, it has been shown that climate tipping elements are not isolated entities, but interact across the entire Earth system. Therefore, this thesis aims at mapping out the potential for tipping events and feedbacks in the Earth system mainly by the use of complex dynamical systems and network science approaches, but partially also by more detailed process-based models of the Earth system.
In the first part of this thesis, the theoretical foundations are laid by the investigation of networks of interacting tipping elements. For this purpose, the conditions for the emergence of global cascades are analysed against the structure of paradigmatic network types such as Erdös-Rényi, Barabási-Albert, Watts-Strogatz and explicitly spatially embedded networks. Furthermore, micro-scale structures are detected that are decisive for the transition of local to global cascades. These so-called motifs link the micro- to the macro-scale in the network of tipping elements. Alongside a model description paper, all these results are entered into the Python software package PyCascades, which is publicly available on github.
In the second part of this dissertation, the tipping element framework is first applied to components of the Earth system such as the cryosphere and to parts of the biosphere. Afterwards it is applied to a set of interacting climate tipping elements on a global scale. Using the Earth system Model of Intermediate Complexity (EMIC) CLIMBER-2, the temperature feedbacks are quantified, which would arise if some of the large cryosphere elements disintegrate over a long span of time. The cryosphere components that are investigated are the Arctic summer sea ice, the mountain glaciers, the Greenland and the West Antarctic Ice Sheets. The committed temperature increase, in case the ice masses disintegrate, is on the order of an additional half a degree on a global average (0.39-0.46 °C), while local to regional additional temperature increases can exceed 5 °C. This means that, once tipping has begun, additional reinforcing feedbacks are able to increase global warming and with that the risk of further tipping events.
This is also the case in the Amazon rainforest, whose parts are dependent on each other via the so-called moisture-recycling feedback. In this thesis, the importance of drought-induced tipping events in the Amazon rainforest is investigated in detail. Despite the Amazon rainforest is assumed to be adapted to past environmental conditions, it is found that tipping events sharply increase if the drought conditions become too intense in a too short amount of time, outpacing the adaptive capacity of the Amazon rainforest. In these cases, the frequency of tipping cascades also increases to 50% (or above) of all tipping events. In the model that was developed in this study, the southeastern region of the Amazon basin is hit hardest by the simulated drought patterns. This is also the region that already nowadays suffers a lot from extensive human-induced changes due to large-scale deforestation, cattle ranching or infrastructure projects.
Moreover, on the larger Earth system wide scale, a network of conceptualised climate tipping elements is constructed in this dissertation making use of a large literature review, expert knowledge and topological properties of the tipping elements. In global warming scenarios, tipping cascades are detected even under modest scenarios of climate change, limiting global warming to 2 °C above pre-industrial levels. In addition, the structural roles of the climate tipping elements in the network are revealed. While the large ice sheets on Greenland and Antarctica are the initiators of tipping cascades, the Atlantic Meridional Overturning Circulation (AMOC) acts as the transmitter of cascades. Furthermore, in our conceptual climate tipping element model, it is found that the ice sheets are of particular importance for the stability of the entire system of investigated climate tipping elements.
In the last part of this thesis, the results from the temperature feedback study with the EMIC CLIMBER-2 are combined with the conceptual model of climate tipping elements. There, it is observed that the likelihood of further tipping events slightly increases due to the temperature feedbacks even if no further CO$_2$ would be added to the atmosphere.
Although the developed network model is of conceptual nature, it is possible with this work for the first time to quantify the risk of tipping events between interacting components of the Earth system under global warming scenarios, by allowing for dynamic temperature feedbacks at the same time.
3D point clouds are a universal and discrete digital representation of three-dimensional objects and environments. For geospatial applications, 3D point clouds have become a fundamental type of raw data acquired and generated using various methods and techniques. In particular, 3D point clouds serve as raw data for creating digital twins of the built environment.
This thesis concentrates on the research and development of concepts, methods, and techniques for preprocessing, semantically enriching, analyzing, and visualizing 3D point clouds for applications around transport infrastructure. It introduces a collection of preprocessing techniques that aim to harmonize raw 3D point cloud data, such as point density reduction and scan profile detection. Metrics such as, e.g., local density, verticality, and planarity are calculated for later use. One of the key contributions tackles the problem of analyzing and deriving semantic information in 3D point clouds. Three different approaches are investigated: a geometric analysis, a machine learning approach operating on synthetically generated 2D images, and a machine learning approach operating on 3D point clouds without intermediate representation.
In the first application case, 2D image classification is applied and evaluated for mobile mapping data focusing on road networks to derive road marking vector data. The second application case investigates how 3D point clouds can be merged with ground-penetrating radar data for a combined visualization and to automatically identify atypical areas in the data. For example, the approach detects pavement regions with developing potholes. The third application case explores the combination of a 3D environment based on 3D point clouds with panoramic imagery to improve visual representation and the detection of 3D objects such as traffic signs.
The presented methods were implemented and tested based on software frameworks for 3D point clouds and 3D visualization. In particular, modules for metric computation, classification procedures, and visualization techniques were integrated into a modular pipeline-based C++ research framework for geospatial data processing, extended by Python machine learning scripts. All visualization and analysis techniques scale to large real-world datasets such as road networks of entire cities or railroad networks.
The thesis shows that some use cases allow taking advantage of established image vision methods to analyze images rendered from mobile mapping data efficiently. The two presented semantic classification methods working directly on 3D point clouds are use case independent and show similar overall accuracy when compared to each other. While the geometry-based method requires less computation time, the machine learning-based method supports arbitrary semantic classes but requires training the network with ground truth data. Both methods can be used in combination to gradually build this ground truth with manual corrections via a respective annotation tool.
This thesis contributes results for IT system engineering of applications, systems, and services that require spatial digital twins of transport infrastructure such as road networks and railroad networks based on 3D point clouds as raw data. It demonstrates the feasibility of fully automated data flows that map captured 3D point clouds to semantically classified models. This provides a key component for seamlessly integrated spatial digital twins in IT solutions that require up-to-date, object-based, and semantically enriched information about the built environment.
The scapula plays a significant role in efficient shoulder movement. Thus, alterations from typical scapular motion during upper limb movements are thought to be associated with shoulder pathologies. However, a clear understanding of the relationship is not yet obtained.. Scapular alterations may only represent physiological variability as their occurrence can appear equally as frequent in individuals with and without shoulder disorders. Evaluation of scapular motion during increased load might be a beneficial approach to detect clinically relevant alterations. However, functional motion adaptations in response to maximum effort upper extremity loading has not been established yet. Therefore, the overall purpose of this research project was to give further insight in physiological adaptations of scapular kinematics and their underlying scapular muscle activity in response to high demanding shoulder movements in healthy asymptomatic individuals. Prior to the investigation of the effect of various load situation, the reproducibility of scapular kinematics and scapular muscle activity were evaluated under maximum effort arm movements. Healthy asymptomatic adults performed unloaded and maximal loaded concentric and eccentric isokinetic shoulder flexion and extension movements in the scapular plane while scapular kinematics and scapular muscle activity were simultaneously assessed. A 3D motion capture system (infra-red cameras & reflective markers) was utilized to track scapular and humerus motion in relation to the thorax. 3D scapular position angles were given for arm raising and lowering between humerus positions of 20° and 120° flexion. To further characterize the scapular pattern, the scapular motion extent and scapulohumeral rhythm (ratio of scapular and humerus motion extent) were determined. Muscle activity of the upper and lower trapezius and the serratus anterior were assessed with surface electromyography. Amplitudes were calculated for the whole ROM and four equidistant movement phases. Reliability was characterized by overall moderate to good reproducibility across the load conditions. Irrespective of applied load, scapular kinematics followed a motion pattern of continuous upward rotation, posterior tilt and external rotation during arm elevation and a continuous downward rotation, anterior tilt and internal rotation during arm lowering. However, kinematics were altered between maximal loaded and unloaded conditions showing increased upward rotation, reduced posterior tilt and external rotation. Further, the scapulohumeral rhythm was decreased and scapular motion extent increased under maximal loaded movements. Muscle activity during maximum effort were of greater magnitude and differed in their pattern in comparison to the continuous increase and decrease of activity during unloaded shoulder flexion and extension. Relationships between scapular kinematics and their underlying scapular muscle activity could only be identified for a few isolated combinations, whereas the majority showed no associations. Scapular kinematics and scapular muscle activity pattern alter according to the applied load. Alterations between the load conditions comply in magnitude and partially in direction with differences seen between symptomatic and asymptomatic individuals. Even though long-term effects of identified adaptations in response to maximum load are so far unclear, deviations from typical scapular motion or muscle activation should not per se be seen as indicators of shoulder impairment. However, evaluation of alterations in scapular motion and activation in response to maximum effort may have the potential to identify individuals that are unable to cope with increased upper limb demands. Findings further challenge the understanding of scapular motion and stabilization by the trapezius and serratus anterior muscles, as clear relationships between the underlying scapular muscle activity and scapular kinematics were neither observed during unloaded nor maximal loaded shoulder movements.