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Faculty / Organisational entity
- Kaiserslautern - Fachbereich Mathematik (1052)
- Kaiserslautern - Fachbereich Informatik (751)
- Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik (297)
- Kaiserslautern - Fachbereich Physik (293)
- Fraunhofer (ITWM) (205)
- Kaiserslautern - Fachbereich Chemie (117)
- Kaiserslautern - Fachbereich Elektrotechnik und Informationstechnik (115)
- Kaiserslautern - Fachbereich Biologie (97)
- Kaiserslautern - Fachbereich Sozialwissenschaften (76)
- Kaiserslautern - Fachbereich Wirtschaftswissenschaften (36)
- Kaiserslautern - Fachbereich Bauingenieurwesen (28)
- Landau - Fachbereich Natur- und Umweltwissenschaften (26)
- Distance and Independent Studies Center (DISC) (15)
- Kaiserslautern - Fachbereich Raum- und Umweltplanung (15)
- Kaiserslautern - Fachbereich ARUBI (9)
- Landau - Fachbereich Psychologie (7)
- Kaiserslautern - Fachbereich Architektur (2)
- Landau - Fachbereich Erziehungswissenschaften (2)
Weak memory consistency models capture the outcomes of concurrent
programs that appear in practice and yet cannot be explained by thread
interleavings. Such outcomes pose two major challenges to formal
methods. First, establishing that a memory model satisfies its
intended properties (e.g., supports a certain compilation scheme) is
extremely error-prone: most proposed language models were initially
broken and required multiple iterations to achieve soundness. Second,
weak memory models make verification of concurrent programs much
harder, as a result of which there are no scalable verification
techniques beyond a few that target very simple models.
This thesis presents solutions to both of these problems.
First, it shows that the relevant metatheory of weak memory
models can be effectively decided (sparing years of manual proof
efforts), and presents Kater, a tool that can answer metatheoretic
queries in a matter of seconds. Second, it presents GenMC, the first
(and only) scalable stateless model checker that is parametric in the
choice of the memory model, often improving the prior state of the art
by orders of magnitude.
This thesis outlines the development of thermoplastic-graphite based plate heat exchangers from material screening to operation including performance evaluation and fouling investi-gations. Polypropylene and polyphenylene sulfide as matrix and graphite as filler were cho-sen as feedstock materials, as they possess a low density and excellent corrosion resistance at a comparatively low price.
For the purpose of material screening, custom-made polymer composite plates with a plate thickness of 1-2 mm and a filler content of up to 80 wt.% were investigated for their thermal and mechanical suitability with regard to their use in plate heat exchangers. Three-point flexural tests show that the loading of polypropylene with graphite leads to mechanical prop-erties that allow the composites to be applied as corrugated heat exchanger plates. The simu-lated maximum overpressure is greater than 7 bar, depending on the wall thickness. The thermal conductivity of the composites was increased by a factor of 12.5 compared to pure polypropylene, resulting in thermal conductivities of up to 2.74 W/mK.
The fabrication of the developed corrugated heat exchanger plates, with a thickness between 0.85 mm and 2.5 mm and a heat transfer surface area of 11.13·10-3 m² was carried out via processes that can be automized, namely extrusion and embossing. With the manufactured plate heat exchanger, overall heat transfer coefficients are determined over a wide range of operating conditions (Re = 200 - 1600), which are used to validate a plate heat exchanger model and consequently to compare the composites with conventional materials. The em-bossing, which seems to result in a shift of the internal graphite structure, leads to a further improvement of the thermal conductivity by 7-20 %, in addition to the impact of the filler. With low plate thicknesses, overall heat transfer coefficients of up to 1850 W/m²K could be obtained. Considering the low density of the manufactured thermal plates, this ensures com-parable performance with metallic materials over a wide range of process conditions (Re = 200 - 4000).
The fouling kinetics and amount of calcium sulfate and calcium carbonate, respectively, on different polypropylene/graphite composites in a flat plate heat exchanger and the developed chevron type plate heat exchanger are determined and compared to the reference material stainless steel. For a straight evaluation of the fouling susceptibility of the materials the for-mation of bubbles on the materials is considered by optical imaging or excluded by a degas-ser. The results are interpreted using surface free energy and roughness of the surfaces. The results show that if bubble formation is avoided, the polymer composites have a very low fouling tendency compared to stainless steel, which is attributed to the low surface free ener-gies of approximately 25 mN/m. This is particularly the case when turbulent flows are pre-sent, as is in plate heat exchangers or when sandblasted specimen are used. Sandblasting also continues to increase heat transfer compared to untreated samples by increasing thermal conductivity and creating local turbulences. Depending on the test conditions, the fouling resistance formed on the stainless steel surface is an order of magnitude greater than on the flat plate polymer composites. In addition, the fouling layers adhere only weakly to the com-posites, which indicates an easy cleaning in place after the formation of deposits. The fouling investigations in the plate heat exchanger reveal sensitivity to calcium sulfate fouling, how-ever, CFD simulations indicate that this is due to flow maldistribution and not the actual pol-ymer composite materials.
Plant-specific factors affecting short-range attraction and oviposition of European grapevine moths
(2024)
The spread of pests and pathogens is increasingly intensified by climate change and globalization. Two of the most serious insect pests threating European viticulture are the European grape berry moth, Eupoecilia ambiguella (Hübner) and the European grapevine moth Lobesia botrana (Denis & Schiffermüller). Larvae feed on fructiferous organs of grapevine Vitis vinifera, resulting in high yield and quality losses. Under the aspects of integrated pest management, insecticide measures are only reasonable when other control strategies become ineffective. In order to support the development of novel decision support system for the application of insecticides, the aim of this thesis was to decipher plant-specific factors, which affect the short-range attraction and oviposition of L. botrana and E. ambiguella.
The focus was set on the visual, volatile, tactile and gustatory stimuli provided by their host plant after settlement. The use of artificial surfaces as model plant showed that oviposition of both species is affected by the color, the shape and the texture of the oviposition site. To explain a susceptibility of certain grapevine cultivars and phenological stages of the berries to egg infestations, we analysed and compared the chemical composition of the epicuticular waxes of the berry surface as well as the volatile organic compounds emitted by the berries. Thereby it turned out that the attractiveness to wax extracts decreased during ripening of the berries, highlighting a preference of earlier phenological stages of the berries for oviposition. In addition, grapevine cultivars exhibited variations in their volatile composition. The principle components perceived by female’s antennae could not explain the differentiation between cultivars, suggesting volatiles do not trigger orientation to certain cultivars. Furthermore, a method was developed to measure real-time behavioural response of female moths to volatiles. The setup allowed to quantify the orientation to a volatile source as well as movements of the antennae and ovipositor. They could be linked to the olfactory and gustatory perception of volatiles during the evaluation of suitable host plants for oviposition. In addition, the risk of potential alternative host plants in the vicinity of the vineyard was investigated. This confirmed that L. botrana in particular prefers the stimuli provided by some plants to those of grapevine. Overall, the results suggest that during oviposition, volatiles emitted by the plants and the composition of the plant surface are the most important factors for host plant differentiation.
Recent research suggests that the common core of all aversive traits can be understood through the Dark Factor of Personality (D). Previously, the overlap among aversive traits has also been described as the low pole of HEXACO Honesty-Humility. Relying on longitudinal data and a range of theoretically derived outcome criteria, we test in four studies (total N > 2,500) whether and how D and low Honesty-Humility differ. Although the constructs shared around 66% of variance (meta-analytically aggregated across all studies), they longitudinally differently accounted for diverse aversive traits and showed theoretically meaningful and distinct associations to pretentiousness, distrust-related beliefs, and empathy. These results suggest that D and low Honesty-Humility are best understood as strongly overlapping, yet functionally different and nomologically distinct constructs.
Production, purification and analysis of novel peptide antibiotics from terrestrial cyanobacteria
(2024)
Cyanobacteria are a known source for bioactive compounds, of which several also show antibiotic activity. In regard to the growing number of multi-resistant pathogens, the search for novel antibiotic substances is of great importance and unexploited sources should be explored. So, this thesis initially dealt with the identification of productive strains, especially within the group of the terrestrial cyanobacteria, which are less well studied than marine and freshwater strains. Amongst these, Chroococcidiopsis cubana, an extremely desiccation and radiation tolerant, unicellular cyanobacterium was found to produce an extracellular antimicrobial metabolite effective against the Gram-positive indicator bacterium Micrococcus luteus as well as the pathogenic yeast Candida auris. However, as the sole identification of a productive cyanobacterium is not sufficient for further analysis and a future production scale-up, the second part of this thesis targeted the identification of compound synthesis prerequisites. As a result, a limitation of nitrogen was shown to be the production trigger, a finding that was used for the establishment of a continuous production system. The increased compound formation was then used for purification and analysis steps. As a second approach, in silico identified bacteriocin gene clusters from C. cubana were cloned and heterologously expressed in Escherichia coli. By this, the bacteriocin B135CC was identified as a strong bacteriolytic agent, active predominantly against the Gram-positive strains Staphylococcus aureus and Mycobacterium phlei. The peptide showed no cytotoxic effects against mouse neuroblastoma (N2a-) cells and a high temperature tolerance up to 60 °C. In order to facilitate the whole project, two standard protocols, specifically adapted for the work with cyanobacteria, were established. First, a method for a quick and easy in vivo vitality estimation of phototrophic cells and second, an approach for a high throughput determination of nitrate concentrations in microalgal cultures. Both methods greatly helped to proceed the main objectives of this work, the first one by simplifying the development of suitable cryopreservation protocols for individual cyanobacteria strains and the second one by accelerating the determination of the optimal nitrate concentration for the production of the antimicrobial compound from C. cubana. In the course of this cultivation optimization, the ability of cyanobacteria to utilize organic carbon sources for an accelerated cell growth was examined in greater detail. It could be shown that C. cubana reaches significantly higher growth rates when mixotrophically cultivated with fructose or glucose. Interestingly, this effect was even further enhanced when light intensity was decreased. Under these low-light conditions, phototrophically cultivated C. cubana cells showed a clearly decreased cell growth. This effect might be extremely useful for a quick and economic preparation of precultures.
The ability to sense and respond to different environmental conditions allows living organisms to adapt quickly to their surroundings. In order to use light as a source of information, plants, fungi, and bacteria employ phytochromes. With their ability to detect far-red and red light, phytochromes constitute a major photoreceptor family. Bacterial phytochromes (BphPs) are composed of an apo-phytochrome and an open-chain tetrapyrrole, the chromophore biliverdin IXα, which mediates the photosensory properties. Depending on the photoexcitation and the quality of the incident light, phytochromes interconvert between two photoconvertible parental states: the red light-absorbing Pr-form and the far-red light-absorbing Pfr-form. In contrast to prototypical phytochromes, with a thermal stable Pr ground state, there is a group of bacterial phytochromes that exhibit dark reversion from the Pr- to the Pfr-form. These special proteins are classified as bathy phytochromes and range across different classes of bacteria. Moreover, the majority of BphPs act as sensor histidine kinases in two-component regulatory systems. The light-triggered conformational change results in the autophosphorylation of the histidine kinase domain and the transphosphorylation of an associated response regulator, inducing a cellular response. Spectroscopic analysis utilizing homologously produced protein identified PaBphP, the histidine kinase of the human opportunistic pathogen Pseudomonas aeruginosa, as a bathy phytochrome. Intensive research on PaBphP revealed evidence that the interconversion between its physiological active and inactive states is influenced by light and darkness rather than far-red and red light. In order to conduct a comprehensive systematic analysis, further bacterial phytochromes were investigated regarding their biochemical and spectroscopic behavior, as well as their autokinase activity. In addition to PaBphP, this work employs the bathy phytochromes AtBphP2, AvBphP2, XccBphP from the non-photosynthetic plant pathogens Agrobacterium tumefaciens, Allorhizobium vitis, Xanthomonas campestris, as well as RtBphP2 from the soil bacterium Ramlibacter tataouinensis. All investigated BphPs displayed a bathy-typical behavior by developing a distinct Pr-form under far-red light conditions and undergoing dark reversion to their Pfr-form. Different Pr/Pfr-fractions can be identified among the BphP populations in varying natural light conditions, including red or blue light. The Pr-form is considered as the active form due to autophosphorylation activity in the heterologously produced phytochromes when exposed to light. In the absence of light, associated with the development of the Pfr-form, the phytochromes exhibited disabled or strongly reduced autokinase activity. Additionally, light-triggered phosphorylation was observed for the response regulator PaAlgB, which is linked to the phytochrome of P. aeruginosa. This study presents the first comparative investigation of numerous bathy phytochromes under identical conditions. The work addressed a gap in the literature by providing quantitative correlation between kinase activity and calculated Pr/Pfr-fractions obtained from spectroscopic measurements. The biological role of PaBphP was partially elucidated through phenotypic characterization employing P. aeruginosa mutant and overexpression strains. The generation of a functional model was possible by considering the postulated functions of the other phytochromes found in the literature. In summary, bathy BphPs are hypothesized to modulate bacterial virulence according to the circadian day/night rhythm of their hosts. The pathogens are believed to reduce their virulence during daylight hours to evade immune and defense reactions, while increasing their virulence during the evening and night, enabling more effective infections.
Functional structures as well as materials provided by nature have always been a great source of inspiration for new technologies. Adapting and improving the discovered concepts, however, demands a detailed understanding of their working principles, while employing natural materials for fabrication tasks requires suitable functionalization and modification.
In this thesis, the white scales of the beetle Cyphochilus are examined in order to reveal unknown aspects of their light transport properties. In addition, the monomer of the material they are made of is utilized for 3D microfabrication.
White beetle scales have been fascinating scientists for more than a decade because they display brilliant whiteness despite their small thickness and the low refractive index contrast. Their optical properties arise from highly efficient light scattering within the disordered intra-scale network structure.
To gain a better understanding of the scattering properties, several previous studies have investigated the light transport and its connection to the structural anisotropy with the aid of diffusion theory. While this framework allows to relate the light scattering to macroscopic transport properties, an accurate determination of the effective refractive index of the structure is required. Due to its simplicity, the Maxwell-Garnett mixing rule is frequently used for this task, although its constraint to particle and feature sizes much smaller than the wavelength is clearly violated for the scales.
To provide a correct calculation of the effective refractive index, here, finite-difference time-domain simulations are used to systematically examine the impact of size effects on the effective refractive index. Deploying this simulation approach, the Maxwell-Garnett mixing rule is shown to break down for large particles. In contrast, it is found that a quadratic polynomial function describes the effective refractive index in close approximation, while its coefficients can be obtained from an empirical linear function. As a result, a simple mixing rule is reported that unambiguously surpasses classical mixing rules when composite media containing large feature sizes are considered. This is important not only for the accurate description of white beetle scales, but also for other turbid media, such as biological tissues in opto-biomedical diagnostics.
Describing light transport by means of diffusion theory moreover neglects any coherent effects, such as interference. Hence, their impact on the generation of brilliant whiteness is currently unknown. To shed a light on their role, spatial- and time-resolved light scattering spectromicroscopy is applied to investigate the scales and a model structure of them based on disordered Bragg stacks. For both structures the occurrence of weakly localized photonic modes, i.e., closed scattering loops, is observed, which is further verified in accompanying simulations. As shown in this thesis, leakage from these random photonic modes contributes at least 20% to the overall reflected light. This reveals the importance of coherent effects for a complete description of the underlying light transport properties; an aspect that is entirely missing in the purely diffusive transport presumed so far. Identifying the importance of weak localization for the generation of brilliant whiteness paves the way to further enhance the design of efficient optical scattering media, an issue that recently drawn great attention.
Unlike their plant-based counterparts, rigid carbohydrates, such as chitin, are currently unavailable for 3D microfabrication via direct laser writing, despite their great significance in the animal kingdom for the construction of functional microstructures. To overcome this gap, the monomeric unit of chitin, N-acetyl-D-glucosamine, is here functionalized to serve as a photo-crosslinkable monomer in a non-hydrogel photoresist. Since all previous photoresists based on animal carbohydrates are in the form of hydrogel formulations, a new group of photoresists is established for direct laser writing.
Moreover, it is exhibited that the sensitization effect, previously used only in the context of UV curing, can be successfully transferred to direct laser writing to increase the maximum writing speed. This effect is based on the beneficial combination of two photoinitiators.
In this, one photoinitiator is an efficient crosslinking agent for the monomer used, but a rather poor two-photon absorber. The other photoinitiator (called sensitizer) possesses, conversely, a much higher two-photon absorption coefficient at the applied wavelength but is not well suited as a crosslinking agent. In combination, the energy absorbed by the sensitizer is passed to the photoinitiator, resulting in the formation of radicals needed to start the polymerization. As this greatly increases the rate at which the photoinitiator is radicalized, resists containing a photoinitiator and a sensitizer are shown to outperform resists containing only one of the components. Deploying the sensitization effect in direct laser writing therefore offers a simple way to individually tune the crosslinking ability and the two-photon absorption properties by combining existing compounds, compared to the costly chemical synthesis of novel, customized photoinitiators.
In contrast to motorbike tyres, whose friction during cornering has to be as high as possible, the desired effect in skiing is the opposite, that of low friction. The reduced friction between skis and ice or snow is made possible by a film of meltwater that forms as a function of friction power. To support this friction mechanism, skis are waxed with different waxes in both hobby and professional sports, depending on a variety of conditions. Waxes with fluorine additives show best performance in most conditions, corresponding to the lowest friction coefficients. However, for health and environmental reasons, the International Ski Federation (FIS) and the Biathlon Un-ion (IBU) have imposed a complete ban on fluorine additives at all FIS races and IBU events with effect from the 2023/2024 season. As a result, wax manufacturers are required to develop and extensively test fluorine-free waxes in order to remain competitive.
Traditional tests take place either indoors or outdoors in the field. Athletes, who complete a particular distance and whose time is measured, also note the impres-sions that the prepared skis provide to the skiers. The time and cost involved in nu-merous individual tests is a drawback, and the presence of only a single type of snow in the hall or field, air resistance, changing environmental conditions and var-iations in the athlete's movement, limit the depth of information. For the need of re-ducing the time-consuming procedure of indoor and outdoor tests, a tribometer of-fers a solution where friction measurements can be performed on a laboratory scale. Due to the consistent adjustable conditions such as temperature, speed and load applied to the friction partners, scientific studies can be carried out with reduced dis-turbance variables. At present, the tribometric results of laboratory instruments for predicting friction values do not translate into application in practice. The reasons for this are the compromises that have to be made in the design of the tribometers.
This work reviews the existing tribometers for their operating conditions and con-firms the need for a scientific method of characterising different waxes. In order to fill the gap between friction results obtained in laboratory tests which cannot yet be used in the selection of waxes, and traditional field tests, this thesis is dedicated to the methodical design and manufacture of a linear tribometer capable of measuring friction between a ski base made of UHMWPE (ultra high molecular weight polyeth-ylene) and an ice sample. The tribometer provides for the first time results that allow differentiating be-tween different modified waxes with regard to their running performance. Friction-influencing factors such as speed, temperature and the surface pressure below the ski base can be adjusted within the range relevant for ski sports. Furthermore, the laboratory-scale test stand, which is located in a cold chamber, is capable of ac-commodating not only typical ski jumping base lengths and widths, but also cross-country and alpine ski bases. To verify the tribometer, a ski base is treated with three waxes of different fluorine content and measured comparatively. With a minimum of 95% confidence, the friction differences between the tested waxes depending on their fluorine content is validated and proven at the end of this work.
Pervasive human impacts rapidly change freshwater biodiversity. Frequently recorded exceedances of regulatory acceptable thresholds by pesticide concentrations suggest that pesticide pollution is a relevant contributor to broad-scale trends in freshwater biodiversity. A more precise pre-release Ecological Risk Assessment (ERA) might increase its protectiveness, consequently reducing the likelihood of unacceptable effects on the environment. European ERA currently neglects possible differences in sensitivity between exposed ecosystems. If the taxonomic composition of assemblages would differ systematically among certain types of ecosystems, so might their sensitivity toward pesticides. In that case, a single regulatory threshold would be over- or underprotective.
In this thesis, we evaluate (1) whether the assemblage composition of macroinvertebrates, diatoms, fishes, and aquatic macrophytes differs systematically between the types of a European river typology system, and (2) whether these taxonomical differences engender differences in sensitivity toward pesticides. While a selection of ecoregions is available for Europe, only a single typology system that classifies individual river segments is available at this spatial scale - the Broad River Types (BRT).
In the first two papers of this thesis, we compiled and prepared large databases of macroinvertebrate (paper one), diatom, fish, and aquatic macrophyte (paper two) occurrences throughout Europe to evaluate whether assemblages are more similar within than among BRT types. Additionally, we compared its performance to that of different ecoregion systems. We employed multiple tests to evaluate the performances, two of which were also designed in the studies. All typology systems failed to reach common quality thresholds for the evaluated metrics for most taxa. Nonetheless, performance differed markedly between typology systems and taxa, with the BRT often performing worst. We showed that currently available, European freshwater typology systems are not well suited to capture differences in biotic communities and suggest several possible amelioration.
In the third study, we evaluated whether ecologically meaningful differences in sensitivity exist between BRT types. To this end, we predicted the sensitivity of macroinvertebrate assemblages across Europe toward Atrazine, copper, and Imidacloprid using a hierarchical species sensitivity distribution model. The predicted assemblage sensitives differed only marginally between BRT types. The largest difference between
median river type sensitivities was a factor of 2.6, which is far below the assessment factor suggested for such models (6), as well as the factor of variation commonly observed between toxicity tests of the same species-compound pair (7.5 for copper). Our results don’t support the notion that a type-specific ERA might improve the accuracy of thresholds. However, in addition to the taxonomic composition the bioavailability of chemicals, the interaction with other stressors, and the sensitivity of a given species might differ between river types.
Mechanistic disease spread models for different vector borne diseases have been studied from the 19th century. The relevance of mathematical modeling and numerical simulation of disease spread is increasing nowadays. This thesis focuses on the compartmental models of the vector-borne diseases that are also transmitted directly among humans. An example of such an arboviral disease that falls under this category is the Zika Virus disease. The study begins with a compartmental SIRUV model and its mathematical analysis. The non-trivial relationship between the basic reproduction number obtained through two methods have been discussed. The analytical results that are mathematically proven for this model are numerically verified. Another SIRUV model is presented by considering a different formulation of the model parameters and the newly obtained model is shown to be clearly incorporating the dependence on the ratio of mosquito population size to human population size in the disease spread. In order to incorporate the spatial as well as temporal dynamics of the disease spread, a meta-population model based on the SIRUV model was developed. The space domain under consideration are divided into patches which may denote mutually exclusive spatial entities like administrative areas, districts, provinces, cities, states or even countries. The research focused only on the short term movements or commuting behavior of humans across the patches. This is incorportated in the multi-patch meta-population model using a matrix of residence time fractions of humans in each patches. Mathematically simplified analytical results are deduced by which it is shown that, for an exemplary scenario that is numerically studied, the multi-patch model also admits the threshold properties that the single patch SIRUV model holds. The relevance of commuting behavior of humans in the disease spread has been presented using the numerical results from this model. The local and non-local commuting are incorporated into the meta-population model in a numerical example. Later, a PDE model is developed from the multi-patch model.
Cancer, a complex and multifaceted disease, continues to challenge the boundaries of biomedical research. In this dissertation, we explore the complexity of cancer genesis, employing multiscale modeling, abstract mathematical concepts such as stability analysis, and numerical simulations as powerful tools to decipher its underlying mechanisms. Through a series of comprehensive studies, we mainly investigate the cell cycle dynamics, the delicate balance between quiescence and proliferation, the impact of mutations, and the co-evolution of healthy and cancer stem cell lineages. The introductory chapter provides a comprehensive overview of cancer and the critical importance of understanding its underlying mechanisms. Additionally, it establishes the foundation by elucidating key definitions and presenting various modeling perspectives to address the cancer genesis. Next, cell cycle dynamics have been explored, revealing the temporal oscillatory dynamics that govern the progression of cells through the cell cycle.
The first half of the thesis investigates the cell cycle dynamics and evolution of cancer stem cell lineages by incorporating feedback regulation mechanisms. Thereby, the pivotal role of feedback loops in driving the expansion of cancer stem cells has been thoroughly studied, offering new perspectives on cancer progression. Furthermore, the mathematical rigor of the model has been addressed by deriving wellposedness conditions, thereby strengthening the reliability of our findings and conclusions. Then, expanding our modeling scope, we explore the interplay between quiescent and proliferating cell populations, shedding light on the importance of their equilibrium in cancer biology. The models developed in this context offer potential avenues for targeted cancer therapies, addressing perspective cell populations critical for cancer progression. The second half of the thesis focuses on multiscale modeling of proliferating and quiescent cell populations incorporating cell cycle dynamics and the extension thereof with mutation acquisition. Following rigorous mathematical analysis, the wellposedness of the proposed modeling frameworks have been studied along with steady-state solutions and stability criteria.
In a nutshell, this thesis represents a significant stride in our understanding of cancer genesis, providing a comprehensive view of the complex interplay between cell cycle dynamics, quiescence, proliferation, mutation acquisition, and cancer stem cells. The journey towards conquering cancer is far from over. However, this research provides valuable insights and directions for future investigation, bringing us closer to the ultimate goal of mitigating the impact of this formidable disease.
In this thesis, material removal mechanisms in grinding are investigated considering a gritworkpiece interaction as well as a grinding-wheel workpiece interaction. In grit-workpiece interaction in a micrometer scale, single grit scratch experiments were performed to investigate material removal mechanism in grinding namely rubbing, plowing, and cutting. Experiments performed were analyzed based on material removal, process forces and specific energy. A finite element model is developed to simulate a single-grit scratch process. As part of the development of the finite element scratch model a 2D and 3D model is developed. A 2D model is utilized to test
material parameters and test various mesh discretizational approaches. A 3D model undertaking the tested material parameters from the 2D model is developed and is tested against experimental results for various mesh discretization. The simulation model is validated based on process forces and ground topography from experiments. The model is also further scaled to simulate multiple grit-workpiece interaction validated against experimental results. As a final step, simulation models are developed to simulate material removal, due to the interaction of grinding wheel and workpiece. A developed virtual grinding wheel topographical model is employed to display
an approach, to upscale a grinding process from grit-workpiece interaction to wheel-workpiece
interaction. In conclusion, practical conclusions drawn and scope for future studies are derived
based on the developed simulation models.
Climate change will have severe consequences on Eastern Boundary Upwelling Systems (EBUS). They host the largest fisheries in the world supporting the life of millions of people due to their tremendous primary production. Therefore, it is of utmost importance to better understand predicted impacts like alternating upwelling intensities and light impediment on the structure and the trophic role of protistan plankton communities as they form the basis of the food web. Numerical models estimate the intensification of the frequency in eddy formation. These ocean features are of particular importance due to their influence on the distribution and diversity of plankton communities and the access to resources, which are still not well understood even to the present day. My PhD thesis entails two subjects conducted during large-scaled cooperation projects REEBUS (Role of Eddies in Eastern Boundary Upwelling Systems) and CUSCO (Coastal Upwelling System in a Changing Ocean).
Subject I of my study was conducted within the multidisciplinary framework REEBUS to investigate the influence of eddies on the biological carbon pump in the Canary Current System (CanCS). More specifically, the aim was to find out how mesoscale cyclonic eddies affect the regional diversity, structure, and trophic role of protistan plankton communities in a subtropical oligotrophic oceanic offshore region.
Samples were taken during the M156 and M160 cruises in the Atlantic Ocean around Cape Verde during July and December 2019, respectively. Three eddies with varying ages of emergence and three water layers (deep chlorophyll maximum DCM, right beneath the DCM and oxygen minimum zone OMZ) were sampled. Additional stations without eddy perturbation were analyzed as references. The effect of oceanic mesoscale cyclonic eddies on protistan plankton communities was analyzed by implementing three approaches. (i) V9 18S rRNA gene amplicons were examined to analyze the diversity and structure of the plankton communities and to infer their role in the biological carbon pump. (ii) By assigning functional traits to taxonomically assigned eDNA sequences, functional richness and ecological strategies (ES) were determined. (iii) Grazing experiments were conducted to assess abundance and carbon transfer from prokaryotes to phagotrophic protists.
All three eddies examined in this study differed in their ASV abundance, diversity, and taxonomic composition with the most pronounced differences in the DCM. Dinoflagellates were the most abundant taxa in all three depth layers. Other dominating taxa were radiolarians, Discoba and haptophytes. The trait-approach could only assign ~15% of all ASVs and revealed in general a relatively high functional richness. But no unique ES was determined within a specific eddy. This indicates pronounced functional redundancy, which is recognized to be correlated with ecosystem resilience and robustness by providing a degree of buffering capacity in the face of biodiversity loss. Elevated microbial abundances as well as bacterivory were clearly associated to mesoscale eddy features, albeit with remarkable seasonal fluctuations. Since eddy activity is expected to increase on a global scale in future climate change scenarios, cyclonic eddies could counteract climate change by enhancing carbon sequestration to abyssal depths. The findings demonstrate that cyclonic eddies are unique, heterogeneous, and abundant ecosystems with trapped water masses in which characteristic protistan plankton develop as the eddies age and migrate westward into subtropical oligotrophic offshore waters. Therefore, eddies influence regional protistan plankton diversity qualitatively and quantitatively.
Subject II of my PhD project contributed to the CUSCO field campaign to identify the influence of varying upwelling intensities in combination with distinct light treatments on the whole food web structure and carbon pump in the Humboldt Current System (HCS) off Peru. To accomplish such a task, eight offshore-mesocosms were deployed and two light scenarios (low light, LL; high light, HL) were created by darkening half of the mesocosms. Upwelling was simulated by injecting distinct proportions (0%, 15%, 30% and 45%) of collected deep-water (DW) into each of the moored mesocosms. My aim was to examine the changes in diversity, structure, and trophic role of protistan plankton communities for the induced manipulations by analyzing the V9 18S rRNA gene amplicons and performing short-term grazing experiments.
The upwelling simulations induced a significant increase in alpha diversity under both light conditions. In austral summer, reflected by HL conditions, a generally higher alpha diversity was recorded compared to the austral winter simulation, instigated by LL treatment. Significant alterations of the protistan plankton community structure could likewise be observed. Diatoms were associated to increased levels of DW addition in the mimicked austral winter situation. Under nutrient depletion, chlorophytes exhibited high relative abundances in the simulated austral winter scenario. Dinoflagellates dominated the austral summer condition in all upwelling simulations. Tendencies of reduced unicellular eukaryotes and increased prokaryotic abundances were determined under light impediment. Protistan-mediated mortality of prokaryotes also decreased by ~30% in the mimicked austral winter scenario.
The findings indicate that the microbial loop is a more relevant factor in the structure of the food web in austral summer and is more focused on the utilization of diatoms in austral winter in the HCS off Peru. It was evident that distinct light intensities coupled with multiple upwelling scenarios could lead to alterations in biochemical cycles, trophic interactions, and ecosystem services. Considering the threat of climate change, the predicted relocation of EBUS could limit primary production and lengthen the food web structure with severe socio-economic consequences.
Mixed Isogeometric Methods for Hodge–Laplace Problems induced by Second-Order Hilbert Complexes
(2024)
Partial differential equations (PDEs) play a crucial role in mathematics and physics to describe numerous physical processes. In numerical computations within the scope of PDE problems, the transition from classical to weak solutions is often meaningful. The latter may not precisely satisfy the original PDE, but they fulfill a weak variational formulation, which, in turn, is suitable for the discretization concept of Finite Elements (FE). A central concept in this context is the
well-posed problem. A class of PDE problems for which not only well-posedness statements but also suitable weak formulations are known are the so-called abstract Hodge–Laplace problems. These can be derived from Hilbert complexes and constitute a central aspect of the Finite Element Exterior Calculus (FEEC).
This thesis addresses the discretization of mixed formulations of Hodge-Laplace problems, focusing on two key aspects. Firstly, we utilize Isogeometric Analysis (IGA) as a specific paradigm for discretization, combining geometric representations with Non-Uniform Rational B-Splines (NURBS) and Finite Element discretizations.
Secondly, we primarily concentrate on mixed formulations exhibiting a saddle-point structure and generated from Hilbert complexes with second-order derivative operators. We go beyond the well-known case of the classical de Rham
complex, considering complexes such as the Hessian or elasticity complex. The BGG (Bernstein–Gelfand–Gelfand) method is employed to define and examine these second-order complexes. The main results include proofs of discrete well-posedness and a priori error estimates for two different discretization approaches. One approach demonstrates, through the introduction of a Lagrange multiplier, how the so-called isogeometric discrete differential forms can be reused.
A second method addresses the question of how standard NURBS basis functions, through a modification of the mixed formulation, can also lead to convergent procedures. Numerical tests and examples, conducted using MATLAB and the open-source software GeoPDEs, illustrate the theoretical findings. Our primary application extends to linear elasticity theory, extensively
discussing mixed methods with and without strong symmetry of the stress tensor.
The work demonstrates the potential of IGA in numerical computations, particularly in the challenging scenario of second-order Hilbert complexes. It also provides insights into how IGA and FEEC can be meaningfully combined, even for non-de Rham complexes.
The aim of this thesis is to introduce an equilibrium insurance market model and study its properties and possible applications in risk class management.
First, an insurance market model based on an equilibrium approach is developed. Depending on the premium, the insured will choose the amount of coverage they buy in order to maximize their expected utility. The behavior of the insurer in different market regimes is then compared. While the premiums in markets with perfect competition are calculated in order to make no profit at all, insurers try to maximize their margins in a monopolistic market.
In markets modeled in this way several phenomena become evident. Perhaps the most important one is the so-called push-out effect. When customers with different attributes are insured together, insurance might become so expensive for one type of customers that those agents are better off with buying no insurance at all. The push-out effect was already shown for theoretical examples in the literature. We present a comprehensive analysis of the equilibrium insurance market model and the push-out effect for different insurance products such as life, health and disability insurance contracts using real-life data from different sources. In a concluding chapter we formulate indicators when a push-out can be expected and when not.
Machine learning regression approaches such as neural networks have gained vast popularity in recent years. The exponential growth of computing power has enabled larger and more evolved networks that can perform increasingly complex tasks. In our feasibility study about the use of neural networks in the regression of equilibrium insurance premiums it is shown that this regression is quite robust and the risk of overfitting can almost be excluded -- as long as the regression is performed on at least a few thousand data points.
Grouping customers of different risk types into contracts is important for the stability and the robustness of an insurance market. This motivates the study of the optimal assignment of risk classes into contracts, also known as rating classes. We provide a theoretical framework that makes use of techniques from different mathematical fields such as non-linear optimization, convex analysis, herding theory, game theory and combinatorics. In addition, we are able to show that the market specifications have a large impact on the optimal allocation of risk classes to contracts by the insurer. However, there does not need to be an optimal risk class assignment for each of these specifications.
To address this issue, we present two different approaches, one more theoretical and another that can easily be implemented in practice. An extension of our model to markets with capacity constraints rounds off the topic and extends the applicability of our approach.
River ecosystems are being threatened by rising temperatures, aridity, and salinity due to climate change and increased water abstractions. These threats also put human well-being at risk, as people and rivers are closely connected, particularly in water-scarce regions. We aimed to investigate the relationship between human well-being and biological and physico-chemical river water quality using the arid Draa River basin as a case study. Physico-chemical water measurements, biological monitoring of aquatic macroinvertebrates, and household surveys were used to assess the state of the river water, ecosystem, and human well-being, as well as the associations between them. Salinity levels exceeded maximum permissible values for drinking water in 35 % and irrigation water in 12 % of the sites. Salinity and low flow were associated with low biological quality. Human satisfaction with water quantity and quality, agriculture, the natural environment, and overall life satisfaction were low particularly in the Middle Draa, where 89% of respondents reported emotional distress due to water salinity and scarcity. Drinking and irrigation water quality was generally rated lower in areas characterized by higher levels of water salinity and scarcity. The study found positive associations between the river water quality and biological quality indices, but no significant association between these factors and human satisfaction. These findings suggest that the relationship between human satisfaction and the biological and physicochemical river water quality is complex and that a more comprehensive approach to human well-being is likely needed to establish relationships.
Understanding human crowd behaviour has been an intriguing topic of interdisciplinary research in recent decades. Modelling of crowd dynamics using differential equations is an indispensable approach to unraveling the various complex dynamics involved in such interacting particle systems. Numerical simulation of pedestrian crowd via these mathematical models allows us to study different realistic scenarios beyond the limitations of studies via controlled experiments.
In this thesis, the main objective is to understand and analyse the dynamics in a domain shared by both pedestrians and moving obstacles. We model pedestrian motion by combining the social force concept with the idea of optimal path computation. This leads to a system of ordinary differential equations governing the dynamics of individual pedestrians via the interaction forces (social forces) between them. Additionally, a non-local force term involving the optimal path and desired velocity governs the pedestrian trajectory. The optimal path computation involves solving a time-independent Eikonal equation, which is coupled to the system of ODEs. A hydrodynamic model is developed from this microscopic model via the mean-field limit.
To consider the interaction with moving obstacles in the domain, we model a set of kinematic equations for the obstacle motion. Two kinds of obstacles are considered - "passive", which move in their predefined trajectories and have only a one-way interaction with pedestrians, and "dynamic", which have a feedback interaction with pedestrians and have their trajectories changing dynamically. The coupled model of pedestrians and obstacles is used to discern pedestrian collision avoidance behaviour in different computational scenarios in a long rectangular domain. We observe that pedestrians avoid collisions through route choice strategies that involve changes in speed and path. We extend this model to consider the interaction between pedestrians and vehicular traffic. We appropriately model the interactions of vehicles, following lane traffic, based on the car-following approach. We observe how the deceleration and braking mechanism of vehicles is executed at pedestrian crossings depending on the right of way on the roads.
As a second objective, we study the disease contagion in moving crowds. We consider the influence of the crowd motion in a complex dynamical environment on the course of infection of pedestrians. A hydrodynamic model for multi-group pedestrian flow is derived from the kinetic equations based on a social force model. It is coupled along with an Eikonal equation to a non-local SEIS contagion model for disease spread. Here, apart from the description of local contacts, the influence of contact times has also been modelled. We observe that the nature of the flow and the geometry of the domain lead to changes in density which affect the contact time and, consequently, the rate of spread of infection.
Finally, the social force model is compared to a variable speed based rational behaviour pedestrian model. We derive a hierarchy of the heuristics-based model from microscopic to macroscopic scales and numerically investigate these models in different density scenarios. Various numerical test cases are considered, including uni- and bi-directional flows and scenarios with and without obstacles. We observe that in low-density scenarios, collision avoidance forces arising from the behavioural heuristics give valid results. Whereas in high-density scenarios, repulsive force terms are essential.
The numerical simulations of all the models are carried out using a mesh-free particle method based on least square approximations. The meshfree numerical framework provides an efficient and elegant way to handle complex geometric situations involving boundaries and stationary or moving obstacles.
Lubricated tribological contact processes are important in both nature and in many technical applications. Fluid lubricants play an important role in contact processes, e.g. they reduce friction and cool the contact zone. The fundamentals of lubricated contact processes on the atomistic scale are, however, today not fully understood. A lubricated contact process is defined here as a process, where two solid bodies that are in close proximity and eventually in parts in direct contact, carry out a relative motion, whereat the remaining volume is submersed by a fluid lubricant. Such lubricated contact processes are difficult to examine experimentally. Atomistic simulations are an attractive alternative for investigating the fundamentals of such processes. In this work, molecular dynamics simulations were used for studying different elementary processes of lubricated tribological contacts. A simplified, yet realistic simulation setup was developed in this work for that purpose using classical force fields. In particular, the two solid bodies were fully submersed in the fluid lubricant such that the squeeze-out was realistically modeled. The velocity of the relative motion of the two solid bodies was imposed as a boundary condition. Two types of cases were considered in this work: i) a model system based on synthetic model substances, which enables a direct, but generic, investigation of molecular interaction features on the contact process; and ii) real substance systems, where the force fields describe specific real substances. Using the model system i), also the reproducibility of the findings obtained from the computer experiments was critically assessed. In most cases, also the dry reference case was studied. Both mechanical and thermodynamic properties were studied -- focusing on the influence of lubrication. The following properties were studied: The contact forces, the coefficient of friction, the dislocation behavior in the solid, the chip formation and the formation of the groove, the squeeze-out behavior of the fluid in the contact zone, the local temperature and the energy balance of the system, the adsorption of fluid particles on the solid surfaces, as well as the formation of a tribofilm. Systematic studies were carried out for elucidating the influence of the wetting behavior, the influence of the molecular architecture of the lubricant, and the influence of the lubrication gap height on the contact process. As expected, the presence of a fluid lubricant reduces the temperature in the vicinity of the contact zone. The presence of the lubricant is, moreover, found to have a significant influence on the friction and on the energy balance of the process. The presence of a lubricant reduces the coefficient of friction compared to a dry case in the starting phase of a contact process, while lubricant molecules remain in the contact zone between the two solid bodies. This is a result of an increased normal and slightly decreased tangential force in the starting phase. When the fluid molecules are squeezed out with ongoing contact time and the contact zone is essentially dry, the coefficient of friction is increased by the presence of a fluid compared to a dry case. This is attributed to an imprinting of individual fluid particles into the solid surface, which is energetically unfavorable. By studying the contact process in a wide range of gap height, the entire range of the Stribeck curve is obtained from the molecular simulations. Thereby, the three main lubrication regimes of the Stribeck curve and their transition regions are covered, namely boundary lubrication (significant elastic and plastic deformation of the substrate), mixed lubrication (adsorbed fluid layers dominate the process), and hydrodynamic lubrication (shear flow is set up between the surface and the asperity). The atomistic effects in the different lubrication regimes are elucidated. Notably, the formation of a tribofilm is observed, in which lubricant molecules are immersed into the metal surface. The formation of a tribofilm is found to have important consequences for the contact process. The work done by the relative motion is found to mainly dissipate and thereby heat up the system. Only a minor part of the work causes plastic deformation. Finally, the assumptions, simplifications, and approximations applied in the simulations are critically discussed, which highlights possible future work.
Reactive absorption with amines is the most important technique for the removal of CO2
from gas streams, e.g. from flue gas, natural gas or off-gas from the cement industry.
In this work a rigorous simulation model for the absorption and desorption of CO2 with
an amine-containing solvent is validated using data from pilot plants of various sizes.
This model was then coupled with a detailed simulation of a coal-fired power plant.
The power generation efficiency drop with CO2 capture was determined and process
parameters in the power plant and separation process were optimized. It was shown
that the high energy demand of CO2 separation significantly reduces power generation
efficiencies, which underlines the need for improvements. This can be achieved by better
solvents or by advanced process designs. In this work such improved CO2 separation
processes are described and evaluated by detailed simulation studies.
In order to develop detailed rigorous simulation models for reactive absorption with novel
solvent systems, a precise knowledge of the liquid phase reaction kinetics is necessary.
There are well established techniques for measuring species distributions in equilibirated
aqueous amine solutions by NMR spectrosopy. However, the existing NMR techniques
cannot be used for monitoring fast reactions in these solutions. Therefore, in this work
a novel temperature-controlled micro-reactor NMR probe head was developed which
enables studying reaction kinetics with time constants in the range of seconds.
On this basis, modern solvent systems for CO2 absorption can be characterized and
the scale-up of separation process for future plants can be accompanied using rigorous
process simulation.
Distributed Optimization of Constraint-Coupled Systems via Approximations of the Dual Function
(2024)
This thesis deals with the distributed optimization of constraint-coupled systems. This problem class is often encountered in systems consisting of multiple individual subsystems, which are coupled through shared limited resources. The goal is to optimize each subsystem in a distributed manner while still ensuring that system-wide constraints are satisfied. By introducing dual variables for the system-wide constraints the system-wide problem can be decomposed into individual subproblems. These resulting subproblems can then be coordinated by iteratively adapting the dual variables. This thesis presents two new algorithms that exploit the properties of the dual optimization problem. Both algorithms compute a quadratic surrogate function of the dual function in each iteration, which is optimized to adapt the dual variables. The Quadratically Approximated Dual Ascent (QADA) algorithm computes the surrogate function by solving a regression problem, while the Quasi-Newton Dual Ascent (QNDA) algorithm updates the surrogate function iteratively via a quasi-Newton scheme. Both algorithms employ cutting planes to take the nonsmoothness of the dual function into account. The proposed algorithms are compared to algorithms from the literature on a large number of different benchmark problems, showing superior performance in most cases. In addition to general convex and mixed-integer optimization problems, dual decomposition-based distributed optimization is applied to distributed model predictive control and distributed K-means clustering problems.
Micro milling is a very flexible micro cutting process widely deployed to manufacture miniaturized parts. However, size effects occur when downscaling the cutting processes. They lead to higher mechanical loads on the tools and therefore increased tool wear. Micro milling tools are usually made of cemented carbides due to their mechanical strength and fine grain structure. Technical ceramics as alternative tool materials offer very good mechanical properties as well, with grain sizes well below 1 μ m. In conventional machining, they have proven to be able to reduce tool wear. To transfer these wear improvements to the micro scale, we manufactured all-ceramic micro end mills in previous studies ( ∅ 50 and ∅ 100 μm). Tools made from zirconia (Y-TZP) showed the sharpest cutting edges, and were the best performing in micro milling trials amongst the substrates tested. However, the advantages of the ceramic substrate could not be utilized for the brass and titanium materials tested in those studies. Therefore, in this study the capabilities of all-ceramic micro end mills ( ∅ 50 μ m) in different workpiece materials (1.4404, 1.7225, 3.1325 and PMMA GS) were researched. For the two steels and the aluminum alloy, the ceramic tools did not offer an improvement over the cemented carbide tools used as reference. For the thermoplastic PMMA however, significant improvements could be achieved by utilizing the Y-TZP ceramic tools: Less tool wear, less and more stable cutting forces, and higher surface qualities.
Since their introduction, robots have primarily influenced the industrial world, providing new opportunities and challenges for humans and machinery. With the introduction of lightweight robots and mobile robot platforms, the field of robot applications has been expanded, diversified, and brought closer to society. The increased degree of digitalization and the personalization of goods and products require an enhanced and flexible robot deployment by operating several multi-robot systems along production processes, industrial applications, assembly and packaging lines, transport systems, etc.
Efficient and safe robot operation relies on successful task planning followed by the computation and execution of task-performing motion trajectories. This thesis addresses these issues by developing, implementing, and validating optimization-based methods for task and trajectory planning in robotics, considering certain optimality and performance criteria. The focus is mainly on the time optimality of the presented approaches with respect to both execution and computation time without compromising safe robot use.
Driven by a systematic approach, the basis for the algorithm development is established first by modeling the kinematics and dynamics of the considered robots and identifying required dynamic parameters. In a further step, time-optimal task and trajectory planning algorithms for a single robotic arm are developed. Initially, a hierarchical approach is introduced consisting of two decoupled optimization-based control policies, a binary problem for task planning, and a continuous model predictive trajectory planning problem. The two layers of the hierarchical structure are then merged into a monolithic layer, resulting in a hybrid structure in the form of a mixed-integer optimization problem for inherent task and trajectory planning.
Motivated by a multi-robot deployment, the hierarchical control structure for time-optimal task and trajectory planning is extended for the case of a two-arm robotic system with highly overlapping operational spaces, leading to challenging robot motions with high inter-robot collision potential. To this end, a novel predictive approach for collision avoidance is proposed based on a continuous approximation of the robot geometry, resulting in a nonlinear optimization problem capable of online applications with real-time requirements. Towards a mobile and flexible robot platform, a model predictive path-following controller for an omnidirectional mobile robot is introduced. Here, a time-minimal approach is also applied, which consists of the robot following a given parameterized path as accurately as possible and at maximum speed.
The performance of the proposed algorithms and methods is experimentally analyzed and validated under real conditions on robot demonstrators. Implementation details, including the resulting hardware and software architecture, are presented, followed by a detailed description of the results. Concrete and industry-oriented demonstrators for integrating robotic arms in existing manual processes and the indoor navigation of a mobile robot complete the work.
The impact of cognitive and motivational resources on engangement with automated formative feedback
(2024)
The effectiveness of automated formative feedback highly depends on student feedback engagement that is largely determined by learners’ cognitive and motivational resources. Yet, most studies have only investigated either cognitive resources (e.g., mental effort), or motivational resources (e.g., expectancy-value-cost variables). The purpose of this study is to examine the development (indicated by time) and relationship of 1) cognitive, 2) affective, and 3) behavioral feedback engagement as a function of cognitive and motivational resources in a computer-based learning environment with automated formative feedback. Data was collected from N = 330 German B.Ed. Elementary Education students who worked four consecutive sessions on summarizing texts. Previously invested mental effort (t-1) affected situational expectancy and cost but not situational value. 1) Cognitive feedback engagement was positively associated with previous performance but neither associated with cognitive nor motivational resources. 2) Affective feedback engagement was positively associated with intrinsic value and negatively associated with situational expectancies, invested mental effort and previous performance. 3) Behavioral feedback engagement was positively associated with situational expectancies and invested mental effort. This study contributes to the understanding of student’s cognitive and motivational structures when engaging with automated formative feedback.
Aflatoxins, a group of mycotoxins produced by various mold species within the genus Aspergillus, have been extensively investigated for their potential to contaminate food and feed, rendering them unfit for consumption. Nevertheless, the role of aflatoxins as environmental contaminants in soil, which represents their natural habitat, remains a relatively unexplored area in aflatoxin research. This knowledge gap can be attributed, in part, to the methodological challenges associated with detecting aflatoxins in soil. The main objective of this PhD project was to develop and validate an analytical method that allows monitoring of aflatoxins in soil, and scrutinize the mechanisms and extent of occurrence of aflatoxins in soil, the processes governing their dissipation, and their impact on the soil microbiome and associated soil functions. By utilizing an efficient extraction solvent mixture comprising acetonitrile and water, coupled with an ultrasonication step, recoveries of 78% to 92% were achieved, enabling reliable determination of trace levels in soil ranging from 0.5 to 20 µg kg-1. However, in a field trial conducted in a high-risk model region for aflatoxin contamination in Sub-Saharan Africa, no aflatoxins were detected using this procedure, underscoring the complexities of field monitoring. These challenges encompassed rapid degradation, spatial heterogeneity, and seasonal fluctuations in aflatoxin occurrence. Degradation experiments revealed the importance of microbial and photochemical processes in the dissipation of aflatoxins in soil with half-lives of 20 - 65 days. The rate of dissipation was found to be influenced by soil properties, most notably soil texture and the initial concentration of aflatoxins in the soil. An exposure study provided evidence that aflatoxins do not pose a substantial threat to the soil microbiome, encompassing microbial biomass, activity, and catabolic functionality. This was particularly evident in clayey soils, where the toxicity of aflatoxins diminished significantly due to their strong binding to clay minerals. However, several critical questions remain unanswered, emphasizing the necessity for further research to attain a more comprehensive understanding of the ecological importance of aflatoxins. Future research should prioritize the challenges associated with field monitoring of aflatoxins, elucidate the mechanisms responsible for the dissipation of aflatoxins in soil during microbial and photochemical degradation, and investigate the ecological consequences of aflatoxins in regions heavily affected by aflatoxins, taking into account the interactions between aflatoxins and environmental and anthropogenic stressors. Addressing these questions contributes to a comprehensive understanding of the environmental impact of aflatoxins in soil, ultimately contributing to more effective strategies for aflatoxin management in agriculture.
Modeling and Simulation of Internet of Things Infrastructures for Cyber-Physical Energy Systems
(2024)
This dissertation presents a novel approach to the model-based development and simulation-based validation of Internet of Things (IoT) infrastructures within the context of Cyber-Physical Energy Systems (CPES). CPES represents an evolution in energy management, seamlessly blending physical and cyber components for efficient, secure, and dependable energy distribution. However, the intricate interplay of these components demands innovative modeling and simulation strategies.
The work begins by establishing a robust foundation, exploring essential background elements such as requirements engineering, model-based systems engineering, digitalization approaches, and the intricacies of IoT platforms. It introduces the novel concept of homomorphic encryption, a critical enabler for securing IoT data within CPES.
In the exploration of the state of the art, the dissertation delves into the multifaceted landscape of IoT simulation, emphasizing the significance of versatility, community support, scalability, and synchronization.
The core contribution emerges in the chapter on simulating IoT networks. It introduces a sophisticated framework that encompasses hardware-in-the-loop, software-in-the-loop, and human-in-the-loop simulation. This innovative framework extends the boundaries of conventional simulation, enabling holistic evaluations of IoT systems.
A practical case study on smart energy usage showcases the application of the framework. Detailed SysML models, including requirements, package diagrams, block definition diagrams, internal block diagrams, state machine diagrams, and activity diagrams, are meticulously examined. The performance evaluation encompasses diverse aspects, from hardware and software validation to human interaction.
In conclusion, this dissertation represents a significant leap forward in the integration of IoT infrastructures within CPES. Its contributions extend from a comprehensive understanding of foundational elements to the practical implementation of a holistic simulation framework. This work not only addresses the current challenges but also outlines a path for future research, shaping the landscape of IoT integration within the dynamic realm of CPES. It offers invaluable insights for researchers, engineers, and stakeholders working towards resilient, secure, and energy-efficient infrastructures.
With the expansion of the electromobility and wind energy, the number of frequency inverter-controlled electric motors and generators is increasing. In parallel, the number of the rolling bearing failures caused by inverter-induced parasitic currents also shows an increasing trend. In order to determine the electrical state of the rolling bearing, to develop preventive measures against damages caused by parasitic currents and to support system-level calculations, electrical rolling bearing models have been developed. The models are based on the electrical insulating ability of the lubricant film that develops in the rolling contacts. For the capacitance calculation of the rolling contacts, different correction factors were developed to simplify the complex tribological and electrical interactions of this region. The state-of-the-art correction factors vary widely, and their validity range also differ significantly, which leads to uncertainty in their general application and to the demand for further investigations of this field. In the present work, a combined simulation method is developed that can determine the rolling bearing capacitance of axially loaded rolling bearings. The simulation consists of an electrically extended EHL simulation for calculating the capacitance of the rolling contact, and an electrical FEM simulation for the capacitance calculation of the non-contact regions. With the combination of the resulted capacitance values of the two simulation methods, the total rolling bearing capacitance can be determined with high accuracy and without using correction factors. In addition, due to experimental investigations, the different capacitance sources of the rolling bearing are identified. After the validation of the combined simulation method, it can be applied for the investigation of the different capacitance sources, i.e., to determine their significance compared to the total rolling bearing capacitance. The developed simulation method allows a detailed analysis of the rolling bearing capacitances, taking into account influencing factors that could not be considered before (e.g., oil quantity in the environment of the rolling bearing). As a result, the accurate calculation of the rolling bearing capacitance can improve the prediction of the harmful parasitic currents and help to develop preventive measures against them.
This thesis focuses on the operation of reliability-constrained routes in wireless ad-hoc networks. A complete communication protocol that is capable of guaranteeing a statistical minimum reliability level would have to support several functionalities: first, routes that are capable of supporting the specified Quality of Service requirement have to be discovered. During operation of discovered routes, the current Quality of Service level has to be monitored continuously. Whenever significant deviations are detected and the required level of Quality of Service is endangered, route maintenance has to ensure continuous operation. All four functionalities, route discovery, route operation, route maintenance and collection and distribution of network status information, will be addressed in this thesis.
In the first part of the thesis, we propose a new approach for Quality-of- Service routing in wireless ad-hoc networks called rmin-routing, with the provision of statistical minimum route reliability as main route selection criterion. To achieve specified minimum route reliabilities, we improve the reliability of individual links by well-directed retransmissions, to be applied during the operation of routes. To select among a set of candidate routes, we define and apply route quality criteria concerning network load.
High-quality information about the network status is essential for the discovery and operation of routes and clusters in wireless ad-hoc networks. This requires permanent observation and assessment of nodes, links, and link metrics, and the exchange of gathered status data. In the second part of the thesis, we present cTEx, a configurable topology explorer for wireless ad-hoc networks that efficiently detects and exchanges high-quality network status information during operation.
In the third part, we propose a decentralized algorithm for the discovery and operation of reliability-constrained routes in wireless ad-hoc networks called dRmin-routing. The algorithm uses locally available network status information about network topology and link properties that is collected proactively in order to discover a preliminary route candidate. This is followed by a distributed, reactive search along this preselected route to remove imprecisions of the locally recorded network status before making a final route selection. During route operation, dRmin-routing monitors routes and performs different kinds of route repair actions to maintain route reliability in order to overcome varying link reliabilities.
Knowledge workers face an ever increasing flood of information in their daily work. They live in a “multi-tasking craziness”, involving activities like creating, finding, processing, assessing or organizing information while constantly switching from one context to another, each being associated with different tasks, documents, mails, etc. Hence, their personal information sphere consisting of file, mail and bookmark folders as well as their content, calendar entries, etc. is cluttered with information that has become irrelevant. Finding important information thus gets harder and much of previously gained knowledge is practically lost.
This thesis explores new ways of solving this problem by investigating the potential of self-(re)organizing and especially forgetting-enabled personal knowledge assistants in the given scenario. It utilizes so-called Managed Forgetting, which is an escalating set of measures to overcome the binary keep-or-delete paradigm, ranging from temporal hiding, to condensation, to adaptive reorganization, synchronization, archiving and deletion. Managed Forgetting is combined with two other major ideas: First, it uses the Semantic Desktop as an ecosystem, which brings Semantic Web and thus knowledge graph technologies to a user’s desktop, making it possible to capture and represent major parts of a user’s personal mental model in a machine-understandable way and exploit it in many different applications. Second, the system uses explicated context information – so-called Context Spaces: context is seen as an explicit interaction element users can work with (i.e. a “tangible” object similar to a folder) and in (immersion). The thesis is structured according to the basic interaction cycle with such a system, ranging from evidence collection to information extraction and context elicitation, followed by information value assessment and the actual support measures consisting of self-(re)organization decisions (back-end) and user interface updates (front-end). The system’s data foundation are personal or group knowledge graphs as well as native data. This work makes contributions to all of these aspects, whereas several of them have been investigated and developed in interdisciplinary research with cognitive scientists. On a more general level, searching and trust in such highly autonomous assistants have also been investigated.
In summary, a self-(re)organizing and especially forgetting-enabled support system for information management and knowledge work has been realized. Its different features vary in maturity: the most mature ones are already in practical use (also in industry), while the latest are just well elaborated (position papers) or rough ideas. Different evaluation strategies have been applied ranging from mere data-driven experiments to various user studies. Some of them were rather short-term with controlled laboratory conditions, others less controlled but spanning several months. Different benefits of working with such a system could be quantified, e.g. cognitive offloading effects and reduced task switching/resumption time. Other benefits were gathered qualitatively, e.g. tidiness of the information sphere and its better alignment with the user’s mental model. The presented approach has been shown to hold a lot of potential. In some aspects, however, only first steps have been taken towards tapping it, e.g. several support measures can be further refined and automation further increased.
In recent years, there has been a growing need for accurate 3D scene reconstruction. Recent developments in the automotive industry have led to the increased use of ADAS where 3D reconstruction techniques are used, for example, as part of a collision detection system. For such applications, scene geometry reconstruction is usually performed in the form of depth estimation, where distances to scene objects are obtained.
In general, depth estimation systems can be divided into active and passive. Both systems have their advantages and disadvantages, but passive systems are usually cheaper to produce and easier to assemble and integrate than active systems. Passive systems can be stereo- or multiple-view based. Up to a certain limit, increasing the number of views in multi-view systems usually results in improved depth estimation accuracy.
One potential problem for ensuring the reliability of multi-view systems is the need to accurately estimate the orientation of their optical sensors. One way to ensure sensor placement for multi-view systems is to rigidly fix the sensors at the manufacturing stage. Unlike arbitrary sensor placement, using of a simplified and known sensor placement geometry further simplifies the depth estimation.
We meet with the concept of light field, which parameterizes all visible light passing through all viewpoints by their intersection with angular and spatial planes. When applied to computer vision, this gives us a 2D set of 2D images, where the physical distances between each image are fixed and proportional to each other.
Existing light field depth estimation methods provide good accuracy, which is suitable for industrial applications. However, the main problems of these methods are related to their running time and resource requirements. Most of the algorithms presented in the literature are typically sharpened for accuracy, can only be run on high-performance machines and often require a significant amount of time to process and obtain results.
Real-world applications often have running time requirements. Also, often there is a power-consumption limitation. In this dissertation, we investigate the problem of building a depth estimation system with an light field camera that satisfies the operating time and power consumption constraints without significant loss of estimation accuracy.
First, an algorithm for calibrating light field cameras is proposed, together with an algorithm for automatic calibration refinement, that works on arbitrary captured scenes. An algorithm for classical geometric depth estimation using light field cameras is proposed. Ways to optimize the algorithm for real-time use without significant loss of accuracy are presented. Finally, the ways how the presented depth estimation methods can be extended using modern deep learning paradigms under the two previously mentioned constraints are shown.
Virtual Possibilities: Exploring the Role of Emerging Technologies in Work and Learning Environments
(2024)
The present work aims to investigate whether virtual reality can support learning as well as vocational work environments. To this end, four studies were conducted, with the first set investigating the demands for vocational workers and the impact of input methods on participant performance. These studies laid the foundation needed to create studies incorporating virtual reality research. The second set of studies was concerned with the impact of virtual reality on learning performance as well as the influence of binaural stimuli presentation on task performance. Results of each study are discussed individually and in conjunction with one another. The four studies are further supplemented with further research conducted by the author as well as an analysis of the growing field of virtual reality-based research. The thesis closes by embedding the discussed work into the scientific landscape and tries to give an outlook for virtual reality-based use cases in the future.
The German energy mix, which provides an overview of the sources of electricity available in Germany, is changing as a result of the expansion of renewable energy sources. With this shift towards sustainable energy sources such as wind and solar power, the electricity market situation is also in flux. Whereas in the past there were few uncertainties in electricity generation and only demand was subject to stochastic uncertainties, generation is now subject to stochastic fluctuations as well, especially due to weather dependency. To provide a supportive framework for this different situation, the electricity market has introduced, among other things, the intraday market, products with half-hourly and quarter-hourly time slices, and a modified balancing energy market design. As a result, both electricity price forecasting and optimization issues remain topical.
In this thesis, we first address intraday market modeling and intraday index forecasting. To do so, we move to the level of individual bids in the intraday market and use them to model the limit order books of intraday products. Based on statistics of the modeled limit order books, we present a novel estimator for the intraday indices. Especially for less liquid products, the order book statistics contain relevant information that allows for significantly more accurate predictions in comparison to the benchmark estimator.
Unlike the intraday market, the day ahead market allows smaller companies without their own trading department to participate since it is operated as a market with daily auctions. We optimize the flexibility offer of such a small company in the day ahead market and model the prices with a stochastic multi-factor model already used in the industry. To make this model accessible for stochastic optimization, we discretize it in time and space using scenario trees. Here we present existing algorithms for scenario tree generation as well as our own extensions and adaptations. These are based on the nested distance, which measures the distance between two distributions of stochastic processes. Based on the resulting scenario trees, we apply the stochastic optimization methods of stochastic programming, dynamic programming, and reinforcement learning to illustrate in which context the methods are appropriate.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
Coastal port-industrial areas are becoming increasingly significant due to urban shrinkage, population
decline, and climate change. To address social and economic issues and enhance climate resilience, it
is crucial to anticipate urban shrinkage in both stable and growing coastal areas that are undergoing
economic transformation. Urban planning can better understand the dynamics of planning for urban
shrinkage and climate resilience, as port-industrial areas have a large economic impact on nearby
coastal communities.
This dissertation examines the long-term implications of urban shrinkage in coastal port-industrial
areas in the context of climate change and sea level rise in England. The research problem is that
current urban policy does not adequately address the challenges of urban shrinkage and climate
resilience in these areas. The research questions are: What are the population changes in local areas
in England? What effect does population decline have on changing urbanisation patterns in older
industrial areas? What type of adaptation efforts were made in North East Lincolnshire, England, and
Bremerhaven, Germany, in response to the 2013 tidal surge, and how did this affect urban
shrinkage?
The dissertation applies an integrated concept of Shrinkage-Resilience as a framework for analysis.
The methodology includes a review of existing models and frameworks, as well as case studies of
international and local contexts. The findings suggest that between 2013-2019, 68% of older
industrial areas (including coastal ports) in England are undergoing changing urbanisation patterns
relative to population, land use, and green belt areas, and are key areas for urban policy, such as the
Levelling Up agenda. One of the areas, North East Lincolnshire is discussed and compared to
Bremerhaven. These examples demonstrate the link between Shrinkage-Resilience approaches and
their practical implementation in coastal port-industrial areas affected by urban shrinkage.
This research advances the scientific practice of urban planning and policy-making for shrinking cities
by introducing the approach of Shrinkage-Resilience, which emphasises the importance of
considering long-term social, economic, and environmental impacts in urban shrinkage contexts. This
approach is crucial in the transition to a more sustainable and inclusive society, where the welfare of
present and future generations, the environment, and economic development are taken into
account. The dissertation provides recommendations for urban planning to incorporate policy
changes for shrinking cities and coastal port-industrial areas worldwide, to include disaster risk
reduction and climate change adaptation approaches.
Aquatic habitats are closely linked to the adjacent riparian area. Fluxes of nutrients, energy and matter through emerging aquatic insects are a key component of the aquatic subsidy to terrestrial systems. In fact, adult insects serve as high-quality prey for riparian predators. Stressors impacting the aquatic subsidy can thus translate to consequences for the receiving terrestrial food web, while mechanistic knowledge is extremely limited. Against this background, this thesis aimed at (i) assessing the impact of a model stressor specifically targeting insect emergence, that is the mosquito control agent Bacillus thuringiensis var. israelensis, on quantity, temporal dynamics and (ii) quality of emerging aquatic insects. For this purpose, outdoor floodplain pond mesocosms (n = 6) were employed. Since emergence is, in most cases, no point event but occurs over a longer period emergence was monitored over 3.5 months. The model stressor, i.e., Bti applied three times during spring at 2.88 × 10^9 ITU/ha, shifted the emergence time of aquatic insects, especially of non-biting midges (Diptera: Chironomidae), by ten days with a 26% reduced peak, while the nutrient content was not altered. On this basis, (ii) the propagation of the effects in aquatic subsidy emergence to riparian predators was investigated. Stable isotope analyses were used to assess the diet of a model predator, that is the web-building riparian spider Tetragnatha extensa. Results suggested changes in the composition of the spider’s diet to replace missing Chironomidae by other aquatic and terrestrial prey organisms pointing to further negative consequences. Finally, the thesis aimed at (iii) the understanding of processes underlying an altered emergence of aquatic subsidy mainly consisting of chironomids. Using a laboratory-based test design, populations of Chironomus riparius (n = 6) were assessed for their sensitivity towards Bti under different food qualities (high and low nutritious) before and after a long-term (six months) Bti exposure. Signs of phenotypic adaptation were observed in emergence time and nutrient content over multiple generations, resulting in changes in chironomids’ quantity and quality as food source. Overall, it can be concluded that direct and indirect effects of an aquatic stressor, as well as the adaptive response to it, can alter ecosystems at different levels, including individual, population and community level. Furthermore, this thesis highlights the importance of a temporal perspective when investigating the impact of aquatic stressors beyond ecosystem boundaries. It illustrates potential bottom-up effects on riparian predators through altered emergence of aquatic insects, feeding our understanding of meta-ecosystems and how stressors and their effects are transferred across systems. These insights will support efforts to protect and conserve natural ecosystems.
Metal-organic frameworks (MOFs) have gained increasing attention in the last four decades due to their versatility and unique properties. They are often used as catalysts because they combine advantageous properties of both heterogeneous and homogeneous catalysts. Noble metals are very active in catalysis, but they are expensive, sometimes toxic to the environment and very rare. Therefore, the demand for the substitution of noble metals by commonly available metals such as iron or cobalt is of interest. The complexity and versatility of MOF materials is further enhanced by the use of mixed-linker and mixed-metal approaches or post-synthetic modification reactions.
The aim of this PhD project was to synthesize and characterize MOF-based catalysts which contain Co and Fe and to test the resulting materials in the liquid phase oxidation reaction of alcohols. In the first part of this work, mixed-metal CPO-27(Co,Fe) with three different metal ratios and two different spatial distributions were prepared. The spatial distributions of the metals were either statistically distributed or a core-shell orientation. The resulting catalysts were characterized by powder X-ray diffraction, thermogravimetric analysis and ICP-OES analysis. The results confirmed that the catalysts were highly porous, corresponded to the CPO 27 structure and that the amounts of metals were close to the desired ratios. The materials were then tested in oxidation reactions of benzyl alcohol and 1-phenylethanol. For the parameter optimization, the highly active monometallic CPO-27(Co) catalyst was used and parameters such as temperature and the amount of catalyst, substrate or oxidant (air) were investigated. Bimetallic CPO-27(Co,Fe) catalysts were then tested with the optimized parameters and both conversion and selectivity were compared to the monometallic CPO 27(Co) reference. In general, the rare and expensive cobalt can be partially replaced by cheap but inactive iron without affecting the catalytic activity and in some cases, the distribution of the metals in the MOF lattice have an effect on the catalytic performance.
In the second part of this work, the previously synthesized CPO-27(Co,Fe) catalysts were thermally decomposed in an inert atmosphere to obtain metal species which are encapsulated in a porous carbonaceous matrix via the so called MOF-mediated synthesis. The decomposition was expected to result in unique materials that could not be synthesized by any other route. The resulting materials were characterized by powder X-ray diffraction, N2 physisorption and ICP-OES analysis. The characterization revealed differences between materials prepared from statistically distributed and core-shell-structured CPO-27(Co,Fe). These catalysts were also tested in the oxidation of benzyl alcohol and compared not only within this series but also with the CPO-27 precursors, showing that in some cases the thermally decomposed materials were even more catalytically active than their MOF precursors.
In the last part of this thesis, Co,Fe DUT-5-based catalysts with core-shell structure and statistical distribution, respectively, were synthesized. The first step was to prepare a DUT-5-based framework. For the statistically distributed material, 4,4'-biphenyldicarboxylate, 2,2' bipyridine-5,5'-dicarboxylate and 2-amino-4,4'-biphenyldicarboxylate linkers were mixed with an aluminum salt precursor. The 2,2'-bipyridine-5,5'-dicarboxylate linkers were then used to directly immobilize cobalt ions. The amine-functionalized linkers were post-synthetically modified with salicylaldehyde and the resulting chelating groups were finally used for the immobilization of iron ions. The core-shell backbone consisted of 4,4'-biphenyldicarboxylate and 2,2'-bipyridine-5,5'-dicarboxylate in the core. The first shell contained only unfunctionalized 4,4' biphenyldicarboxylate and the outer shell consisted of a mixture of 2 amino-4,4'-biphenyldicarboxylate and 4,4'-biphenyldicarboxylate linkers. The post-synthetic reactions were then performed analogously to the statistically distributed materials: (1) cobalt immobilization at the bipyridine linkers, (2) insertion of chelating groups at the amine linkers and (3) iron immobilization. All materials were thoroughly characterized after each synthesis step using powder X-ray diffraction, infrared spectroscopy, thermogravimetric analysis, N2 physisorption and ICP-OES analysis. The linker ratios were calculated by 1H NMR of diluted samples. The results confirmed the formation of a porous material with a DUT-5 structure, but the spatial distribution could not be confirmed unambiguously by the methods used. Both materials were tested, together with a monometallic DUT-5 BPyDC(Co) reference, in oxidation reactions cinnamyl alcohol. The results showed significant differences between the statistically distributed and core-shell catalysts, providing evidence for the difference in spatial orientations and the symergistic effects of the two metals.
In summary, novel MOF materials containing Co and Fe were synthesized, characterized and tested in oxidation reactions of primary and secondary alcohols under aerobic conditions. The results confirmed that in some cases a part of rare cobalt could be replaced by cheap and widely available iron without decreasing the catalytic activity and selectivity. In addition, the spatial distribution of the metals can have a direct and massive influence on the catalytic properties and therefore, a thorough characterization is a very important part of the synthesis process.
Gliomas are one of the most common types of primary brain tumors. Among
those, high grade astrocytomas - so-called glioblastoma multiforme - are the
most aggressive type of cancer originating in the brain, leaving patients a median survival time of 15 to 20 months after diagnosis. The invasive behavior
of the tumor leads to considerable difficulties regarding the localization of all
tumor cells, and thus impedes successful therapy. Here, mathematical models
can help to enhance the assessment of the tumor’s extent.
In this thesis, we set up a multiscale model for the evolution of a glioblastoma.
Starting on the microscopic level, we model subcellular binding processes and
velocity dynamics of single cancer cells. From the resulting mesoscopic equation, we derive a macroscopic equation via scaling methods. Combining this
equation with macroscopic descriptions of the tumor environment, a nonlinear
PDE-ODE-system is obtained. We consider several variations of the derived
model, amongst others introducing a new model for therapy by gliadel wafers,
a treatment approach indicated i.a. for recurrent glioblastoma.
We prove global existence of a weak solution to a version of the developed
PDE-ODE-system, containing degenerate diffusion and flux limitation in the
taxis terms of the tumor equation. The nonnegativity and boundedness of all
components of the solution by their biological carrying capacities is shown.
Finally, 2D-simulations are performed, illustrating the influence of different
parts of the model on tumor evolution. The effects of treatment by gliadel
wafers are compared to the therapy outcomes of classical chemotherapy in different settings.
An Efficient Automated Machine Learning Framework for Genomics and Proteomics Sequence Analysis
(2023)
Genomics and Proteomics sequence analyses are the scientific studies of understanding the language of Deoxyribonucleic Acid (DNA), Ribonucleic Acid (RNA) and protein biomolecules with an objective of controlling the production of proteins and understanding their core functionalities. It helps to detect chronic diseases in early stages, root causes of clinical changes, key genetic targets for pharmaceutical development and optimization of therapeutics for various age groups. Most Genomics and Proteomics sequence analysis work is performed using typical wet lab experimental approaches that make use of different genetic diagnostic technologies. However, these approaches are costly, time consuming, skill and labor intensive. Hence, these approaches slow down the process of developing an efficient and economical sequence analysis landscape essential to demystify a variety of cellular processes and functioning of biomolecules in living organisms. To empower manual wet lab experiment driven research, many machine learning based approaches have been developed in recent years. However, these approaches cannot be used in practical environment due to their limited performance. Considering the sensitive and inherently demanding nature of Genomics and Proteomics sequence
analysis which can have very far-reaching as well as serious repercussions on account of misdiagnosis, the main
objective of this research is to develop an efficient automated computational framework for Genomics and Proteomics sequence analysis using the predictive and prescriptive analytical powers of Artificial Intelligence (AI) to significantly improve healthcare operations.
The proposed framework is comprised of 3 main components namely sequence encoding, feature engineering and
discrete or continuous value predictor. The sequence encoding module is equipped with a variety of existing and newly developed sequence encoding algorithms that are capable of generating a rich statistical representation of DNA, RNA and protein raw sequences. The feature engineering module has diverse types of feature selection and dimensionality reduction approaches which can be used to generate the most effective feature space. Furthermore, the discrete and/or continuous value predictor module of the proposed framework contains a wide range of existing machine learning and newly developed deep learning regressors and classifiers. To evaluate the integrity and generalizability of the proposed framework, we have performed a large-scale experimentation over diverse types of Genomics and Proteomics sequence analysis tasks (i.e., DNA, RNA and proteins).
In Genomics analysis, Epigenetic modification detection is one of the key component. It helps clinical researchers and practitioners to distinguish normal cellular activities from malfunctioned ones, which can lead to diverse genetic disorders such as metabolic disorders, cancers, etc. To support this analysis, the proposed framework is used to solve the problem of DNA and Histone modification prediction where it has achieved state-of-the-art performance on 27 publicly available benchmark datasets of 17 different species with best accuracy of 97%. RNA sequence analysis is another vital component of Genomics sequence analysis where the identification of different coding and non-coding RNAs as well as their subcellular localization patterns help to demystify the functions of diverse RNAs, root causes of clinical changes, develop precision medicine and optimize therapeutics. To support this analysis, the proposed framework is utilized for non-coding RNA classification and multi-compartment RNA subcellular localization prediction. Where it achieved state-of-the-art performance on 10 publicly available benchmark datasets of Homo sapiens and Mus Musculus species with best accuracy of 98%.
Proteomics sequence analysis is essential to demystify the virus pathogenesis, host immunity responses, the way
proteins affect or are affected by the cell processes, their structure and core functionalities. To support this analysis, the proposed framework is used for host protein-protein and virus-host protein-protein interaction prediction. It has achieved state-of-the-art performance on 2 publicly available protein protein interaction datasets of Homo Sapiens and Mus Musculus species with best accuracy of 96% and 7 viral host protein protein interaction datasets of multiple hosts and viruses with best accuracy of 94%. Considering the performance and practical significance of proposed framework, we believe proposed framework will help researchers in developing cutting-edge practical applications for diverse Genomic and Proteomic sequence analyses tasks (i.e., DNA, RNA and proteins).
Emission trading systems (ETS) represent a widely used instrument to control greenhouse
gas emissions, while minimizing reduction costs. In an ETS, the desired amount of emissions in
a predefined time period is fixed in advance; corresponding to this amount, tradeable allowances
are handed out or auctioned to companies which underlie the system. Emissions which are not
covered by an allowance are subject to a penalty at the end of the time period.
Emissions depend on non-deterministic parameters such as weather and the state of the
economy. Therefore, it is natural to view emissions as a stochastic quantity. This introduces a
challenge for the companies involved: In planning their abatement actions, they need to avoid
penalty payments without knowing their total amount of emissions. We consider a stochastic control approach to address this problem: In a continuous-time model, we use the rate of
emission abatement as a control in minimizing the costs that arise from penalty payments and
abatement costs. In a simplified variant of this model, the resulting Hamilton-Jacobi-Bellman
(HJB) equation can be solved analytically.
Taking the viewpoint of a regulator of an ETS, our main interest is to determine the resulting
emissions and to evaluate their compliance with the given emission target. Additionally, as an
incentive for investments in low-emission technologies, a high allowance price with low variability
is desirable. Both the resulting emissions and the allowance price are not directly given by the
solution to the stochastic control problem. Instead we need to solve a stochastic differential
equation (SDE), where the abatement rate enters as the drift term. Due to the nature of the
penalty function, the abatement rate is not continuous. This means that classical results on
existence and uniqueness of a solution as well as convergence of numerical methods, such as the
Euler-Maruyama scheme, do not apply. Therefore, we prove similar results under assumptions
suitable for our case. By applying a standard verification theorem, we show that the stochastic
control approach delivers an optimal abatement rate.
We extend the model by considering several consecutive time periods. This enables us to
model the transfer of unused allowances to the subsequent time period. In formulating the
multi-period model, we pursue two different approaches: In the first, we assume the value that
the company anticipates for an unused allowance to be constant throughout one time period.
We proceed similarly to the one-period model and again obtain an analytical solution. In the
second approach, we introduce an additional stochastic process to simulate the evolution of the
anticipated price for an unused allowance.
The model so far assumes that allowances are allocated for free. Therefore, we construct
another model extension to incorporate the auctioning of allowances. Then, additionally the
problem of choosing the optimal demand at the auction needs to be solved. We find that
the auction price equals the allowance price at the beginning of the respective time period.
Furthermore, we show that the resulting emissions as well as the allowance price are unaffected
by the introduction of auctioning in the setting of our model.
To perform numerical simulations, we first solve the characteristic partial differential equation
derived from the HJB equation by applying the method of lines. Then we apply the Euler-
Maruyama scheme to solve the SDE, delivering realizations of the resulting emissions and the
allowance price paths.
Simulation results indicate that, under realistic settings, the probability of non-compliance
with the emission target is quite large. It can be reduced for instance by an increase of the
penalty. In the multi-period model, we observe that by allowing the transfer of allowances to the
subsequent time period, the probability of non-compliance decreases considerably.
Estimation of Motion Vector Fields of Complex Microstructures by Time Series of Volume Images
(2023)
Mechanical tests form one of the pillars in development and assessment of modern materials. In a world that will be forced to handle its resources more carefully in the near future, development of materials that are favorable regarding for example weight or material consumption is inevitable. To guarantee that such materials can also be used in critical infrastructure, such as foamed materials in automotive industry or new types of concrete in civil engineering, mechanical properties like tensile or compressive strength have to be thoroughly described. One method to do so is by so called in situ tests, where the mechanical test is combined with an image acquisition technique such as Computed Tomography.
The resulting time series of volume images comprise the delicate and individual nature of each material. The objective of this thesis is to present and develop methods to unveil this behavior and make the motion accessible by algorithms. The estimation of motion has been tackled by many communities, and two of them have already made big effort to solve the problems we are facing. Digital Volume Correlation (DVC) on the one hand has been developed by material scientists and was applied in many different context in mechanical testing, but almost never produces displacement fields that allocate one vector per voxel. Medical Image Registration (MIR) on the other hand does produce voxel precise estimates, but is limited to very smooth motion estimates.
The unification of both families, DVC and MIR, under one roof, will therefore be illustrated in the first half of this thesis. Using the theory of inverse problems, we lay the mathematical foundations to explain why in our impression none of the families is sufficient to deal with all of the problems that come with motion estimation in in situ tests. We then proceed by presenting a third community in motion estimation, namely Optical flow, which is normally only applied in two dimensions. Nevertheless, within this community algorithms have been developed that meet many of our requirements. Strategies for large displacement exist as well as methods that resolve jumps, and on top the displacement is always calculated on pixel level. This thesis therefore proceeds by extending some of the most successful methods to 3D.
To ensure the competitiveness of our approach, the last part of this thesis deals with a detailed evaluation of proposed extensions. We focus on three types of materials, foam, fibre systems and concrete, and use simulated and real in situ tests to compare the Optical flow based methods to their competitors from DVC and MIR. By using synthetically generated and simulated displacement fields, we also assess the quality of the calculated displacement fields - a novelty in this area. We conclude this thesis by two specialized applications of our algorithm, which show how the voxel-precise displacement fields serve as useful information to engineers in investigating their materials.
In tribology laboratories, the management of material samples and test specimens, the planning and execution of experiments, the evaluation of test data and the longterm storage of results are critical processes. However, despite their criticality, they are carried out manually and typically at a low level of computerization and standardization. Therefore, formats for primary data and aggregated results are wildly different between laboratories, and the interoperability of research data is low. Even within laboratories, low levels of standardization, in combination with ambiguous or non-unique identifiers for data files, test specimens and analysis results greatly reduce data integrity and quality. As a consequence, productivity is low, error rates are high, and the lack or low quality of metadata causes the value of produced data to deteriorate very quickly, which makes the re-use of data, e.g. for data mining and meta studies, practically impossible.
In other fields of science, these are mitigated by the use of Laboratory Information Management Systems (LIMS). However, at the moment, such systems do not exist
in tribological research. The main challenge for the implementation of such a system is that it requires extensive interdisciplinary knowledge from otherwise very
disparate fields: tribology, data and process modelling, quality management, databases and programming. So far, existing solutions are either proprietary, very limited
in their scope or focused on merely storing aggregated results without any support for laboratory operations.
Therefore, this thesis describes fundamentals of information technology, data modelling and programming that are required to build a LIMS for tribology laboratories.
Based on an analysis of a typical workflow of a tribology laboratory, a data model for all relevant entities and processes is designed using object-relational data modelling and object-oriented programming and a relational database is used to provide a reference implementation of such a LIMS. It provides critical functionalities
like a materials database, test specimen management, the planning, execution and evaluation of friction and wear tests, automated procedures for tribometer
parameterization and data transmission, storage and evaluation and for aggregating individual tests into test sets and projects. It improves the quality and long-term usability of data by replacing error-prone human processes by automated variants, e.g. automated collection of metadata and data file transmission, homogenization and storage. The usefulness of the developed LIMS is demonstrated by applying it to Transfer Film Luminance Analysis (TLA), which is a newly developed advanced method for the analysis of the formation and stability of transfer films and their impact on friction and wear, but which produces so much data and requires such a large amount of metadata during evaluation that it can only be performed safely, quickly and reliably by integration into the presented LIMS.
Regulation of sucrose transport between source and sink tissues is critical for plant development and properties. In cells, the dynamic vacuolar sugar homeostasis is maintained by the controlled regulation of the activities of sugar importers and exporters residing in the tonoplast. We show here that the EARLY RESPONSE TO DEHYDRATION6-LIKE4 protein, being the closest homolog to the proton/glucose symporter ERDL6, resides in the vacuolar membrane. We raised both, molecular expression and data deriving from non-aqueous fractionation studies indicating that ERDL4 was involved in glucose and fructose allocation across the tonoplast. Surprisingly, overexpression of ERDL4 increased total sugar levels in leaves, which is due to a concomitantly induced stimulation of TST2 expression, coding for the major vacuolar sugar loader. This conclusion is supported by the notion that tst1-2 knockout lines overexpressing ERDL4 lack increased cellular sugar levels. That ERDL4 activity contributes to the coordination of cellular sugar homeostasis is further indicated by two observations. Firstly, ERDL4 and TST genes exhibit an opposite regulation during a diurnal rhythm, secondly, the ERDL4 gene is markedly expressed during cold acclimation representing a situation in which TST activity needs to be upregulated. Moreover, ERDL4-overexpressing plants show larger size of rosettes and roots, a delayed flowering and increased total seed yield. In summary, we identified a novel factor influencing source to sink transfer of sucrose and by this governing plant organ development.
In this thesis, a new concept to prove Mosco convergence of gradient-type Dirichlet forms within the \(L^2\)-framework of K.~Kuwae and T.~Shioya for varying reference measures is developed.
The goal is, to impose as little additional conditions as possible on the sequence of reference measure \({(\mu_N)}_{N\in \mathbb N}\), apart from weak convergence of measures.
Our approach combines the method of Finite Elements from numerical analysis with the topic of Mosco convergence.
We tackle the problem first on a finite-dimensional substructure of the \(L^2\)-framework, which is induced by finitely many basis functions on the state space \(\mathbb R^d\).
These are shifted and rescaled versions of the archetype tent function \(\chi^{(d)}\).
For \(d=1\) the archetype tent function is given by
\[\chi^{(1)}(x):=\big((-x+1)\land(x+1)\big)\lor 0,\quad x\in\mathbb R.\]
For \(d\geq 2\) we define a natural generalization of \(\chi^{(1)}\) as
\[\chi^{(d)}(x):=\Big(\min_{i,j\in\{1,\dots,d\}}\big(\big\{1+x_i-x_j,1+x_i,1-x_i\big\}\big)\Big)_+,\quad x\in\mathbb R^d.\]
Our strategy to obtain Mosco convergence of
\(\mathcal E^N(u,v)=\int_{\mathbb R^d}\langle\nabla u,\nabla v\rangle_\text{euc}d\mu_N\) towards \(\mathcal E(u,v)=\int_{\mathbb R^d}\langle\nabla u,\nabla v\rangle_\text{euc}d\mu\) for \(N\to\infty\)
involves as a preliminary step to restrict those bilinear forms to arguments \(u,v\) from the vector space spanned by the finite family \(\{\chi^{(d)}(\frac{\,\cdot\,}{r}-\alpha)\) \(|\alpha\in Z\}\) for
a finite index set \(Z\subset\mathbb Z^d\) and a scaling parameter \(r\in(0,\infty)\).
In a diagonal procedure, we consider a zero-sequence of scaling parameters and a sequence of index sets exhausting \(\mathbb Z^d\).
The original problem of Mosco convergence, \(\mathcal E^N\) towards \(\mathcal E\) w.r.t.~arguments \(u,v\) form the respective minimal closed form domains extending the pre-domain \(C_b^1(\mathbb R^d)\), can be solved
by such a diagonal procedure if we ask for some additional conditions on the Radon-Nikodym derivatives \(\rho_N(x)=\frac{d\mu_N(x)}{d x}\), \(N\in\mathbb N\). The essential requirement reads
\[\frac{1}{(2r)^d}\int_{[-r,r]^d}|\rho_N(x)- \rho_N(x+y)|d y \quad \overset{r\to 0}{\longrightarrow} \quad 0 \quad \text{in } L^1(d x),\,
\text{uniformly in } N\in\mathbb N.\]
As an intermediate step towards a setting with an infinite-dimensional state space, we let $E$ be a Suslin space and analyse the Mosco convergence of
\(\mathcal E^N(u,v)=\int_E\int_{\mathbb R^d}\langle\nabla_x u(z,x),\nabla_x v(z,x)\rangle_\text{euc}d\mu_N(z,x)\) with reference measure \(\mu_N\) on \(E\times\mathbb R^d\) for \(N\in\mathbb N\).
The form \(\mathcal E^N\) can be seen as a superposition of gradient-type forms on \(\mathbb R^d\).
Subsequently, we derive an abstract result on Mosco convergence for classical gradient-type Dirichlet forms
\(\mathcal E^N(u,v)=\int_E\langle \nabla u,\nabla v\rangle_Hd\mu_N\) with reference measure \(\mu_N\) on a Suslin space $E$ and a tangential Hilbert space \(H\subseteq E\).
The preceding analysis of superposed gradient-type forms can be used on the component forms \(\mathcal E^{N}_k\), which provide the decomposition
\(\mathcal E^{N}=\sum_k\mathcal E^{N}_k\). The index of the component \(k\) runs over a suitable orthonormal basis of admissible elements in \(H\).
For the asymptotic form \(\mathcal E\) and its component forms \(\mathcal E^k\), we have to assume \(D(\mathcal E)=\bigcap_kD(\mathcal E^k)\) regarding their domains, which is equivalent to the Markov uniqueness of \(\mathcal E\).
The abstract results are tested on an example from statistical mechanics.
Under a scaling limit, tightness of the family of laws for a microscopic dynamical stochastic interface model over \((0,1)^d\) is shown and its asymptotic Dirichlet form identified.
The considered model is based on a sequence of weakly converging Gaussian measures \({(\mu_N)}_{N\in\mathbb N}\) on \(L^2((0,1)^d)\), which are
perturbed by a class of physically relevant non-log-concave densities.
This thesis deals with the simulation of large insurance portfolios. On the one hand, we need to model the contracts' development and the insured collective's structure and dynamics. On the other hand, an important task is the forward projection of the given balance sheet. Questions that are interesting in this context, such as the question of the default probability up to a certain time or the question of whether interest rate promises can be kept in the long term, cannot be answered analytically without strong simplifications. Reasons for this are high dependencies between the insurer's assets and liabilities, interactions between existing and new contracts due to claims on a collective reserve, potential policy features such as a guaranteed interest rate, and individual surrender options of the insured. As a consequence, we need numerical calculations, and especially the volatile financial markets require stochastic simulations. Despite the fact that advances in technology with increasing computing capacities allow for faster computations, a contract-specific simulation of all policies is often an impossible task. This is due to the size and heterogeneity of insurance portfolios, long time horizons, and the number of necessary Monte Carlo simulations. Instead, suitable approximation techniques are required.
In this thesis, we therefore develop compression methods, where the insured collective is grouped into cohorts based on selected contract-related criteria and then only an enormously reduced number of representative contracts needs to be simulated. We also show how to efficiently integrate new contracts into the existing insurance portfolio. Our grouping schemes are flexible, can be applied to any insurance portfolio, and maintain the existing structure of the insured collective. Furthermore, we investigate the efficiency of the compression methods and their quality in approximating the real life insurance portfolio.
For the simulation of the insurance business, we introduce a stochastic asset-liability management (ALM) model. Starting with an initial insurance portfolio, our aim is the forward projection of a given balance sheet structure. We investigate conditions for a long-term stability or stationarity corresponding to the idea of a solid and healthy insurance company. Furthermore, a main result is the proof that our model satisfies the fundamental balance sheet equation at the end of every period, which is in line with the principle of double-entry bookkeeping. We analyze several strategies for investing in the capital market and for financing the due obligations. Motivated by observed weaknesses, we develop new, more sophisticated strategies. In extensive simulation studies, we illustrate the short- and long-term behavior of our ALM model and show impacts of different business forms, the predicted new business, and possible capital market crashes on the profitability and stability of a life insurer.
The fifth-generation (5G) of wireless networks promises to bring new advances, such as a huge increase in mobile data rates, a plunge in communications latency, and an increase in the quality of experience perceived by users that can cope with the ever-increasing demand in Internet traffic. However, the high cost of capital and operational expenditure (CAPEX/OPEX) of the new 5G network and the lack of a killer application hinder its rapid adoption. In this context, Mobile Network Operators (MNOs) have turned their attention to the following idea: opening up their infrastructure so that vertical businesses can leverage the new 5G network to improve their primary businesses and develop new ones. However, deploying multiple isolated vertical applications on top of the same infrastructure poses unique challenges that must be addressed. In this thesis, we provide critical contributions to developing 5G networks to accommodate different vertical applications in an isolated, flexible, and automated manner. This thesis contributions spawn on three main areas: (i) the development of an integrated fronthaul and backhaul network, (ii) the development of a network slicing overbooking algorithm, and (iii) the development of a method to mitigate the noisy neighbors' problem in a vRAN deployment.
Scientific research plays a crucial role in the development of a society. With ever-increasing volumes of scientific publications are now making it extremely challenging to analyze and maintain insights into the scientific communities like collaboration or citation trends and evolution of interests etc. This thesis is an effort towards using scientific publications to provide detailed insights into a scientific community from a range of aspects. The contribution of this thesis is five-fold.
Firstly, this thesis proposes approaches for automatic information extraction from scientific publications. The proposed layout-based approach for this purpose is inspired by how human beings perceive individual references relying only on visual queues. The proposed approach significantly outperforms the existing text-based techniques and is independent of any domain or language.
Secondly, this thesis tackles the problem of identifying meaningful topics from a given publication as the keywords provided in the publication are not always accurate representatives of the publication topic. To rectify this problem, this thesis proposes a state-of-the-art keywords extraction approach that employs a domain ontology along with the detected keywords to perform topic modeling for a given set of publications.
Thirdly, this thesis analyses the disposition of each citation to understand its true essence. For this purpose, we proposes a transformer-based approach for analyzing the impact of each citation appearing in a scientific publication. The impact of a citation can be determined by the inherent sentiment and intent of a citation, which refers to the assessment and motive of an author towards citing a scientific publication.
Furthermore, this thesis quantifies the influence of a research contributor in a scientific community by introducing a new semantic index for researchers that takes both quantitative and qualitative aspects of a citation into account to better represent the prestige of a researcher in a scientific community. Semantic Index is also evaluated for conformity to the guidelines and recommendations of various research funding organizations to assess the impact of a researcher.
In this thesis, all of the aforementioned aspects are packaged together in a single framework called Academic Community Explorer (ACE) 2.0, which automatically extracts and analyzes information from scientific publications and visualizes the insights using several interactive visualizations. These visualizations provide an instant glimpse into the scientific communities from a wide range of aspects with different granularity levels.
Human interferences within the Earth System are accelerating, leading to major impacts and feedback that we are just beginning to understand. Summarized under the term 'global change' these impacts put human and natural systems under ever-increasing stress and impose a threat to human well-being, particularly in the Global South. Global governance bodies have acknowledged that decisive measures have to be taken to mitigate the causes and to adapt to these new conditions. Nevertheless, neither current international nor national pledges and measures reach the effectiveness needed to sustain global human well-being under accelerating global change. On the contrary, competing interests are not only paralyzing the international debate but also playing an increasingly important role in debates over social fragmentation and societal polarization on national and local scales. This interconnectedness of the natural and the social system and its impact on social phenomena such as cooperation and conflicts need to be understood better, to strengthen social resilience to future disturbances, drive societal transformation towards socially desirable futures while at the same time avoiding path dependencies along continuing colonial continuities. As a case example, this thesis provides insights into southwestern Amazonia, where the intertwined challenges of human contribution to global change in all its dimensions, as well as human adaptation and mitigation attempts to the imposed changes become exaggeratedly visible. As such, southwestern Amazonia with its high social, economic, and biological diversity is a good example to study the deep interrelations of humans with nature and the consequences these relations have on social cohesion amid an ecological crisis.
Therefore, this thesis takes a social-ecological perspective on conflicts and social cohesion. Social cohesion is in a wider sense understood as the way "how members of a society, group, or organization relate to each other and work together" (Dany and Dijkzeul 2022, p. 12). In particular in contexts of violence, conflicts, and fragility, little has been investigated on the role of social cohesion to govern public goods and build resilience for (future) environmental crises. At the same time, governments and international decision-makers more and more acknowledge the role of social cohesion _ comprising both relations between social groups and between groups and the state _ to build upon resilience against crises. Facing uncertainty in how natural and social systems react to certain disturbances and shocks, the governance of potential tipping points, is an additional challenge for the governance of social-ecological systems (SES). Therefore, this thesis asks: "How does governance shape pathways towards cooperative or conflictive social-ecological tipping points?" The results of this thesis can be distinguished into theoretical/conceptual results and empirical results. Initial systematic literature research on the nexus of climate change, land use, and conflict revealed, an extensive body of literature on direct effects, for example, drought-related land use conflicts, with diverging opinions on whether global warming increases the risk for conflicts or not. Adding the perspective of indirect implications, we further identified research gaps, and also a lack of policy recognition, concerning the negative externalities on land use and conflict through climate mitigation and adaptation measures. On a conceptual note, taking a social cohesion perspective into the analysis is beneficial to shift the focus from the problem-oriented perspective of vulnerabilities and conflicts to global change and potential resulting conflicts to a solution-oriented perspective of enhancing agency and resilience to strengthen collaboration. The developed Social Cohesion Conceptual Model and the related analytical framework facilitate the incorporation of societal dynamics into the analysis of SES dynamics. In addition, the elaborated Tipping Multiverse Framework took up this idea and enhanced it with a more detailed perspective on the soil ecosystem and the household livelihood system to identify entry points to potential social-ecological tipping cascades. As such, the Tipping Multiverse Framework offered two matrices that can advance the understanding of regional SES by identifying core processes, functioning, and links in each TE and thus provide entry points to identify potential tipping cascades across SES sub-systems. The exemplified application of these two frameworks on southwestern Amazonia shows the analytical potential of both proposed frameworks in advancing the understanding of social-ecological tipping points and potential tipping cascades in a regional SES.
On an empirical note, zooming in on questions of governance by applying a political ecology lens to human security, we find that 'glocal' resource governance often reproduces, amplifies, or creates power imbalances and divisions on and between different scales. Our results show that the winners of resource extraction are mostly found at the national and international scale while local communities receive little benefit and are left vulnerable to externalities. Hence, our study contributes to the existing research by stressing the importance of one underlying question: "governance by whom and for whom?" This question raised the demand to understand the underlying dynamics of resource governance and resulting conflicts. Therefore, we aimed at analyzing how (environmental) institutions influence the major drivers of social-ecological conflicts over land in and around three protected areas, Tambopata (Peru), the Extractive Reserve Chico Mendes (Brazil), and Manuripi (Bolivia). We found that state institutions, in particular, have the following effects on key conflict drivers: Overlapping responsibilities of governance institutions and limited enforcement of regulations protecting and empowering rural and disadvantaged populations, enabling external actors to (illegally) access and control resources in the protected areas. Consequently, the already fragile social contract between the residents of the protected area and its surrounding areas and the central state is further weakened by the expanding influence of criminal organizations that oppose the state's authority. For state institutions to avoid aggravating these conflict drivers but instead better manage them or even contribute to conflict prevention and mitigation, a transformation from reactive to reflexive institutions and the development of new reflexive governance competencies is needed.
This need for reflexive governance becomes particularly visible when sudden disturbances or shocks impact the SES. Our analysis of the impacts of the COVID-19 pandemic on the interconnections of land use change, ecosystem services, human agency, conflict, and cooperation that the pandemic has had a severe influence on the human security of marginalized social groups in southwestern Amazonia. Civil society actions have been an essential strategy in the fight against COVID-19, not just in the health sector but also in the economic, political, social, and cultural realms. However, our research also showed that the pandemic has consolidated and partly renewed criminal structures, while the already weak state has fallen further behind due to additional tasks managing the pandemic and other disasters such as floods.
In conclusion, it can be said that the reflexivity of governance is crucial to foster cooperation and preventing conflicts in the realm of social-ecological systems. By not only reacting to already occurring changes but also reflecting upon potential future changes, governance can shape transformation pathways away from the detrimental and towards life-sustaining pathways. It can do so, by exercising agency across scales to avoid the crossing of detrimental social-ecological tipping points but rather to trigger life-sustaining tipping points that contribute to global social-ecological well-being.
This work is concerned with two often separated disciplines. First, experimental studies in which the effect of cooling rate on martensite transformation and the resulting microstructure in a low-alloy steel is investigated. From this, a possible transformation mechanism is derived. Second, the development of a simulation model which describes the martensitic morphology and its evolution. In this context, a phase field model is presented introducing order parameters to simulate the material state, namely austenite and martensite. The evolution of the order parameters is assumed to follow the time-dependent Ginzburg-Landau equation. A major extension to previous models is the consideration of twelve crystallographic martensite variants corresponding to the Nishiyama-Wassermann orientation relationship. To describe the ordered displacement of atoms during transformation and to account for the martensitic substructure, the well-known phenomenological theory of martensite crystallography is employed. The presented experiments as well as thermodynamic calculations are used as a basis in the identification of model parameters. With the presented model, basic features of the martensitic transformation can be reproduced. These include the martensite start temperature and the hierarchical microstructure consisting of blocks and packets. The sizes of the blocks are in good agreement with the real sizes of the experimental database.
In recent years, deep learning has made substantial improvements in various fields like image understanding, Natural Language Processing (NLP), etc. These huge advancements have led to the release of many commercial applications which aim to help users carry out their daily tasks. Personal digital assistants are one such successful application of NLP, having a diverse userbase from all age groups. NLP tasks like Natural Language Understanding (NLU) and Natural Language Generation (NLG) are core components for building these assistants. However, like any other deep learning model, the growth of NLU & NLG models is directly coupled with tremendous amounts of training examples, which are expensive to collect due to annotator costs. Therefore, this work investigates the methodologies to build NLU and NLG systems in a data-constrained setting.
We evaluate the problem of limited training data in multiple scenarios like limited or no data available when building a new system, availability of a few labeled examples when adding a new feature to an existing system, and changes in the distribution of test data during the lifetime of a deployed system.
Motivated by the standard methods to handle data-constrained settings, we propose novel approaches to generate data and exploit latent representations to overcome performance drops emerging from limited training data.We propose a framework to generate high-quality synthetic data when few training examples are available for a newly added feature for dialogue agents. Our interpretation-to-text model uses existing training data for bootstrapping new features and improves the accuracy of downstream tasks of intent classification and slot labeling. Following, we study a few-shot setting and observe that generation systems face a low semantic coverage problem. Hence, we present an unsupervised NLG algorithm that ensures that all relevant semantic information is present in the generated text.
We also study to see if we really need all training examples for learning a generalized model. We propose a data selection method that selects the most informative training examples to train Visual Question Answering (VQA) models without erosion of accuracy. We leverage the already available inter-annotator agreement and design a diagnostic tool, called (EaSe), that leverages the entropy and semantic similarity of answer patterns.
Finally, we discuss two empirical studies to understand the feature space of VQA models and show how language model pre-training and exploiting multimodal embedding space allows for building data constrained models ensuring minimal or no accuracy losses.
This thesis concerns itself with the long-term behavior of generalized Langevin dynamics with multiplicative noise,
i.e. the solutions to a class of two-component stochastic differential equations in \( \mathbb{R}^{d_1}\times\mathbb{R}^{d_2} \)
subject to outer influence induced by potentials \( \Phi \) and \( \Psi \),
where the stochastic term is only present in the second component, on which it is dependent.
In particular, convergence to an equilibrium defined by an invariant initial distribution \( \mu \) is shown
for weak solutions to the generalized Langevin equation obtained via generalized Dirichlet forms,
and the convergence rate is estimated by applying hypocoercivity methods relying on weak or classical Poincaré inequalities.
As a prerequisite, the space of compactly supported smooth functions is proven to be a domain of essential m-dissipativity
for the associated Kolmogorov backward operator on \(L^2(\mu)\).
In the second part of the thesis, similar Langevin dynamics are considered, however defined on a product of infinite-dimensional separable Hilbert spaces.
The set of finitely based smooth bounded functions is shown to be a domain of essential m-dissipativity for the corresponding Kolmogorov operator \( L \) on \( L^2(\mu) \)
for a Gaussian measure \( \mu \), by applying the previous finite-dimensional result to appropriate restrictions of \( L \).
Under further bounding conditions on the diffusion coefficient relative to the covariance operators of \( \mu \),
hypocoercivity of the generated semigroup is proved, as well as the existence of an associated weakly continuous Markov process
which analytically weakly provides a weak solution to the considered Langevin equation.
The generally unsupervised nature of autoencoder models implies that the main training metric is formulated as the error between input images and their corresponding reconstructions. Different reconstruction loss variations and latent space regularization have been shown to improve model performances depending on the tasks to solve and to induce new desirable properties like disentanglement. Nevertheless, measuring the success in, or enforcing properties by, the input pixel space is a challenging endeavor. In this work, we want to make more efficient use of the available data and provide design choices to be considered in the recording or generation of future datasets to implicitly induce desirable properties during training. To this end, we propose a new sampling technique which matches semantically important parts of the image while randomizing the other parts, leading to salient feature extraction and a neglection of unimportant details. Further, we propose to recursively apply a previously trained autoencoder model, which can then be interpreted as a dynamical system with desirable properties for generalization and uncertainty estimation.
The proposed methods can be combined with any existing reconstruction loss. We give a detailed analysis of the resulting properties on various datasets and show improvements on several computer vision tasks: image and illumination normalization, invariances, synthetic to real generalization, uncertainty estimation and improved classification accuracy by means of simple classifiers in the latent space.
These investigations are adopted in the automotive application of vehicle interior rear seat occupant classification. For the latter, we release a synthetic dataset with several fine-grained extensions such that all the aforementioned topics can be investigated in isolation, or together, in a single application environment. We provide quantitative evidence that machine learning, and in particular deep learning methods cannot readily be used in industrial applications when only a limited amount of variation is available for training. The latter can, however, often be the case because of constraints enforced by the application to be considered and financial limitations.