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Housing in metabolic cages can induce a pronounced stress response. Metabolic cage systems imply housing mice on metal wire mesh for the collection of urine and feces in addition to monitoring food and water intake. Moreover, mice are single-housed, and no nesting, bedding, or enrichment material is provided, which is often argued to have a not negligible impact on animal welfare due to cold stress. We therefore attempted to reduce stress during metabolic cage housing for mice by comparing an innovative metabolic cage (IMC) with a commercially available metabolic cage from Tecniplast GmbH (TMC) and a control cage. Substantial refinement measures were incorporated into the IMC cage design. In the frame of a multifactorial approach for severity assessment, parameters such as body weight, body composition, food intake, cage and body surface temperature (thermal imaging), mRNA expression of uncoupling protein 1 (Ucp1) in brown adipose tissue (BAT), fur score, and fecal corticosterone metabolites (CMs) were included. Female and male C57BL/6J mice were single-housed for 24 h in either conventional Macrolon cages (control), IMC, or TMC for two sessions. Body weight decreased less in the IMC (females—1st restraint: 6.94%; 2nd restraint: 6.89%; males—1st restraint: 8.08%; 2nd restraint: 5.82%) compared to the TMC (females—1st restraint: 13.2%; 2nd restraint: 15.0%; males—1st restraint: 13.1%; 2nd restraint: 14.9%) and the IMC possessed a higher cage temperature (females—1st restraint: 23.7°C; 2nd restraint: 23.5 °C; males—1st restraint: 23.3 °C; 2nd restraint: 23.5 °C) compared with the TMC (females—1st restraint: 22.4 °C; 2nd restraint: 22.5 °C; males—1st restraint: 22.6 °C; 2nd restraint: 22.4 °C). The concentration of fecal corticosterone metabolites in the TMC (females—1st restraint: 1376 ng/g dry weight (DW); 2nd restraint: 2098 ng/g DW; males—1st restraint: 1030 ng/g DW; 2nd restraint: 1163 ng/g DW) was higher compared to control cage housing (females—1st restraint:
640 ng/g DW; 2nd restraint: 941 ng/g DW; males—1st restraint: 504 ng/g DW; 2nd restraint: 537 ng/g DW). Our results show the stress potential induced by metabolic cage restraint that is markedly influenced by the lower housing temperature. The IMC represents a first attempt to target cold stress reduction during metabolic cage application thereby producing more animal welfare friendly data.
Sulfur is essential for the functionality of some important biomolecules in humans. Biomolecules like the Iron-sulfur clusters, tRNAs, Molybdenum cofactor, and some vitamins. The trafficking of sulfur involves proteins collectively called sulfurtransferase. Among these are TUM1, MOCS3, and NFS1.
This research investigated the role of TUM1 for molybdenum cofactor biosynthesis and cytosolic tRNA thiolation in humans. The rhodanese-like protein MOCS3 and the L-cysteine desulfurase (NFS1) have been previously demonstrated to interact with TUM1. These interactions suggested a dual function of TUM1 in sulfur transfer for Moco biosynthesis and cytosolic tRNA thiolation. TUM1 deficiency has been implicated to be responsible for a rare inheritable disorder known as mercaptolactate cysteine disulfiduria (MCDU), which is associated with a mental disorder. This mental disorder is similar to the symptoms of sulfite oxidase deficiency which is characterised by neurological disorders. Therefore, the role of TUM1 as a sulfurtransferase in humans was investigated, in CRISPR/Cas9 generated TUM1 knockout HEK 293T cell lines.
For the first time, TUM1 was implicated in Moco biosynthesis in humans by quantifying the intermediate product cPMP and Moco using HPLC. Comparing the TUM1 knockout cell lines to the wild-type, accumulation and reduction of cPMP and Moco were observed respectively. The effect of TUM1 knockout on the activity of a Moco-dependent enzyme, Sulfite oxidase, was also investigated. Sulfite oxidase is essential for the detoxification of sulfite to sulfate. Sulfite oxidase activity and protein abundance were reduced due to less availability of Moco. This shows that TUM1 is essential for efficient sulfur transfer for Moco biosynthesis. Reduction in cystathionin -lyase in TUM1 knockout cells was quantified, a possible coping mechanism of the cell against sulfite production through cysteine catabolism.
Secondly, the involvement of TUM1 in tRNA thio-modification at the wobble Uridine-34 was reported by quantifying the amount of mcm5s2U and mcm5U via HPLC. The reduction and accumulation of mcm5s2U and mcm5U in TUM1 knockout cells were observed in the nucleoside analysis. Herein, exogenous treatment with NaHS, a hydrogen sulfide donor, rescued the Moco biosynthesis, cytosolic tRNA thiolation, and cell proliferation deficits in TUM1 knockout cells.
Further, TUM1 was shown to impact mitochondria bioenergetics through the measurement of the oxygen consumption rate and extracellular acidification rate (ECAR) via the seahorse cell Mito stress analyzer. Reduction in total ATP production was also measured. This reveals how important TUM1 is for H2S biosynthesis in the mitochondria of HEK 293T.
Finally, the inhibition of NFS1 in HEK 293T and purified NFS1 protein by 2-methylene 3-quinuclidinone was demonstrated via spectrophotometric and radioactivity quantification. Inhibition of NFS1 by MQ further affected the iron-sulfur cluster-dependent enzyme aconitase activity.
Due to anthropogenic greenhouse gas emissions, Earth’s average surface temperature is steadily increasing. As a consequence, many weather extremes are likely to become more frequent and intense. This poses a threat to natural and human systems, with local impacts capable of destroying exposed assets and infrastructure, and disrupting economic and societal activity. Yet, these effects are not locally confined to the directly affected regions, as they can trigger indirect economic repercussions through loss propagation along supply chains. As a result, local extremes yield a potentially global economic response. To build economic resilience and design effective adaptation measures that mitigate adverse socio-economic impacts of ongoing climate change, it is crucial to gain a comprehensive understanding of indirect impacts and the underlying economic mechanisms.
Presenting six articles in this thesis, I contribute towards this understanding. To this end, I expand on local impacts under current and future climate, the resulting global economic response, as well as the methods and tools to analyze this response.
Starting with a traditional assessment of weather extremes under climate change, the first article investigates extreme snowfall in the Northern Hemisphere until the end of the century. Analyzing an ensemble of global climate model projections reveals an increase of the most extreme snowfall, while mean snowfall decreases.
Assessing repercussions beyond local impacts, I employ numerical simulations to compute indirect economic effects from weather extremes with the numerical agent-based shock propagation model Acclimate. This model is used in conjunction with the recently emerged storyline framework, which involves analyzing the impacts of a particular reference extreme event and comparing them to impacts in plausible counterfactual scenarios under various climate or socio-economic conditions. Using this approach, I introduce three primary storylines that shed light on the complex mechanisms underlying economic loss propagation.
In the second and third articles of this thesis, I analyze storylines for the historical Hurricanes Sandy (2012) and Harvey (2017) in the USA. For this, I first estimate local economic output losses and then simulate the resulting global economic response with Acclimate. The storyline for Hurricane Sandy thereby focuses on global consumption price anomalies and the resulting changes in consumption. I find that the local economic disruption leads to a global wave-like economic price ripple, with upstream effects propagating in the supplier direction and downstream effects in the buyer direction. Initially, an upstream demand reduction causes consumption price decreases, followed by a downstream supply shortage and increasing prices, before the anomalies decay in a normalization phase. A dominant upstream or downstream effect leads to net consumption gains or losses of a region, respectively. Moreover, I demonstrate that a longer direct economic shock intensifies the downstream effect for many regions, leading to an overall consumption loss.
The third article of my thesis builds upon the developed loss estimation method by incorporating projections to future global warming levels. I use these projections to explore how the global production response to Hurricane Harvey would change under further increased global warming. The results show that, while the USA is able to nationally offset direct losses in the reference configuration, other countries have to compensate for increasing shares of counterfactual future losses. This compensation is mainly achieved by large exporting countries, but gradually shifts towards smaller regions. These findings not only highlight the economy’s ability to flexibly mitigate disaster losses to a certain extent, but also reveal the vulnerability and economic disadvantage of regions that are exposed to extreme weather events.
The storyline in the fourth article of my thesis investigates the interaction between global economic stress and the propagation of losses from weather extremes. I examine indirect impacts of weather extremes — tropical cyclones, heat stress, and river floods — worldwide under two different economic conditions: an unstressed economy and a globally stressed economy, as seen during the Covid-19 pandemic. I demonstrate that the adverse effects of weather extremes on global consumption are strongly amplified when the economy is under stress. Specifically, consumption losses in the USA and China double and triple, respectively, due to the global economy’s decreased capacity for disaster loss compensation. An aggravated scarcity intensifies the price response, causing consumption losses to increase.
Advancing on the methods and tools used here, the final two articles in my thesis extend the agent-based model Acclimate and formalize the storyline approach. With the model extension described in the fifth article, regional consumers make rational choices on the goods bought such that their utility is maximized under a constrained budget. In an out-of-equilibrium economy, these rational consumers are shown to temporarily increase consumption of certain goods in spite of rising prices.
The sixth article of my thesis proposes a formalization of the storyline framework, drawing on multiple studies including storylines presented in this thesis. The proposed guideline defines eight central elements that can be used to construct a storyline.
Overall, this thesis contributes towards a better understanding of economic repercussions of weather extremes. It achieves this by providing assessments of local direct impacts, highlighting mechanisms and impacts of loss propagation, and advancing on methods and tools used.
Magmatic-hydrothermal systems form a variety of ore deposits at different proximities to upper-crustal hydrous magma chambers, ranging from greisenization in the roof zone of the intrusion, porphyry mineralization at intermediate depths to epithermal vein deposits near the surface. The physical transport processes and chemical precipitation mechanisms vary between deposit types and are often still debated.
The majority of magmatic-hydrothermal ore deposits are located along the Pacific Ring of Fire, whose eastern part is characterized by the Mesozoic to Cenozoic orogenic belts of the western North and South Americas, namely the American Cordillera. Major magmatic-hydrothermal ore deposits along the American Cordillera include (i) porphyry Cu(-Mo-Au) deposits (along the western cordilleras of Mexico, the western U.S., Canada, Chile, Peru, and Argentina); (ii) Climax- (and sub−) type Mo deposits (Colorado Mineral Belt and northern New Mexico); and (iii) porphyry and IS-type epithermal Sn(-W-Ag) deposits of the Central Andean Tin Belt (Bolivia, Peru and northern Argentina).
The individual studies presented in this thesis primarily focus on the formation of different styles of mineralization located at different proximities to the intrusion in magmatic-hydrothermal systems along the American Cordillera. This includes (i) two individual geochemical studies on the Sweet Home Mine in the Colorado Mineral Belt (potential endmember of peripheral Climax-type mineralization); (ii) one numerical modeling study setup in a generic porphyry Cu-environment; and (iii) a numerical modeling study on the Central Andean Tin Belt-type Pirquitas Mine in NW Argentina.
Microthermometric data of fluid inclusions trapped in greisen quartz and fluorite from the Sweet Home Mine (Detroit City Portal) suggest that the early-stage mineralization precipitated from low- to medium-salinity (1.5-11.5 wt.% equiv. NaCl), CO2-bearing fluids at temperatures between 360 and 415°C and at depths of at least 3.5 km. Stable isotope and noble gas isotope data indicate that greisen formation and base metal mineralization at the Sweet Home Mine was related to fluids of different origins. Early magmatic fluids were the principal source for mantle-derived volatiles (CO2, H2S/SO2, noble gases), which subsequently mixed with significant amounts of heated meteoric water. Mixing of magmatic fluids with meteoric water is constrained by δ2Hw-δ18Ow relationships of fluid inclusions. The deep hydrothermal mineralization at the Sweet Home Mine shows features similar to deep hydrothermal vein mineralization at Climax-type Mo deposits or on their periphery. This suggests that fluid migration and the deposition of ore and gangue minerals in the Sweet Home Mine was triggered by a deep-seated magmatic intrusion.
The second study on the Sweet Home Mine presents Re-Os molybdenite ages of 65.86±0.30 Ma from a Mo-mineralized major normal fault, namely the Contact Structure, and multimineral Rb-Sr isochron ages of 26.26±0.38 Ma and 25.3±3.0 Ma from gangue minerals in greisen assemblages. The age data imply that mineralization at the Sweet Home Mine formed in two separate events: Late Cretaceous (Laramide-related) and Oligocene (Rio Grande Rift-related). Thus, the age of Mo mineralization at the Sweet Home Mine clearly predates that of the Oligocene Climax-type deposits elsewhere in the Colorado Mineral Belt. The Re-Os and Rb-Sr ages also constrain the age of the latest deformation along the Contact Structure to between 62.77±0.50 Ma and 26.26±0.38 Ma, which was employed and/or crosscut by Late Cretaceous and Oligocene fluids. Along the Contact Structure Late Cretaceous molybdenite is spatially associated with Oligocene minerals in the same vein system, a feature that precludes molybdenite recrystallization or reprecipitation by Oligocene ore fluids.
Ore precipitation in porphyry copper systems is generally characterized by metal zoning (Cu-Mo to Zn-Pb-Ag), which is suggested to be variably related to solubility decreases during fluid cooling, fluid-rock interactions, partitioning during fluid phase separation and mixing with external fluids. The numerical modeling study setup in a generic porphyry Cu-environment presents new advances of a numerical process model by considering published constraints on the temperature- and salinity-dependent solubility of Cu, Pb and Zn in the ore fluid. This study investigates the roles of vapor-brine separation, halite saturation, initial metal contents, fluid mixing, and remobilization as first-order controls of the physical hydrology on ore formation. The results show that the magmatic vapor and brine phases ascend with different residence times but as miscible fluid mixtures, with salinity increases generating metal-undersaturated bulk fluids. The release rates of magmatic fluids affect the location of the thermohaline fronts, leading to contrasting mechanisms for ore precipitation: higher rates result in halite saturation without significant metal zoning, lower rates produce zoned ore shells due to mixing with meteoric water. Varying metal contents can affect the order of the final metal precipitation sequence. Redissolution of precipitated metals results in zoned ore shell patterns in more peripheral locations and also decouples halite saturation from ore precipitation.
The epithermal Pirquitas Sn-Ag-Pb-Zn mine in NW Argentina is hosted in a domain of metamorphosed sediments without geological evidence for volcanic activity within a distance of about 10 km from the deposit. However, recent geochemical studies of ore-stage fluid inclusions indicate a significant contribution of magmatic volatiles. This study tested different formation models by applying an existing numerical process model for porphyry-epithermal systems with a magmatic intrusion located either at a distance of about 10 km underneath the nearest active volcano or hidden underneath the deposit. The results show that the migration of the ore fluid over a 10-km distance results in metal precipitation by cooling before the deposit site is reached. In contrast, simulations with a hidden magmatic intrusion beneath the Pirquitas deposit are in line with field observations, which include mineralized hydrothermal breccias in the deposit area.
Hybrid nanomaterials offer the combination of individual properties of different types of nanoparticles. Some strategies for the development of new nanostructures in larger scale rely on the self-assembly of nanoparticles as a bottom-up approach. The use of templates provides ordered assemblies in defined patterns. In a typical soft-template, nanoparticles and other surface-active agents are incorporated into non-miscible liquids. The resulting self-organized dispersions will mediate nanoparticle interactions to control the subsequent self-assembly. Especially interactions between nanoparticles of very different dispersibility and functionality can be directed at a liquid-liquid interface.
In this project, water-in-oil microemulsions were formulated from quasi-ternary mixtures with Aerosol-OT as surfactant. Oleyl-capped superparamagnetic iron oxide and/or silver nanoparticles were incorporated in the continuous organic phase, while polyethyleneimine-stabilized gold nanoparticles were confined in the dispersed water droplets. Each type of nanoparticle can modulate the surfactant film and the inter-droplet interactions in diverse ways, and their combination causes synergistic effects. Interfacial assemblies of nanoparticles resulted after phase-separation. On one hand, from a biphasic Winsor type II system at low surfactant concentration, drop-casting of the upper phase afforded thin films of ordered nanoparticles in filament-like networks. Detailed characterization proved that this templated assembly over a surface is based on the controlled clustering of nanoparticles and the elongation of the microemulsion droplets. This process offers versatility to use different nanoparticle compositions by keeping the surface functionalization, in different solvents and over different surfaces. On the other hand, a magnetic heterocoagulate was formed at higher surfactant concentration, whose phase-transfer from oleic acid to water was possible with another auxiliary surfactant in ethanol-water mixture. When the original components were initially mixed under heating, defined oil-in-water, magnetic-responsive nanostructures were obtained, consisting on water-dispersible nanoparticle domains embedded by a matrix-shell of oil-dispersible nanoparticles.
Herein, two different approaches were demonstrated to form diverse hybrid nanostructures from reverse microemulsions as self-organized dispersions of the same components. This shows that microemulsions are versatile soft-templates not only for the synthesis of nanoparticles, but also for their self-assembly, which suggest new approaches towards the production of new sophisticated nanomaterials in larger scale.
Volcanoes are one of the Earth’s most dynamic zones and responsible for many changes in our planet. Volcano seismology aims to provide an understanding of the physical processes in volcanic systems and anticipate the style and timing of eruptions by analyzing the seismic records. Volcanic tremor signals are usually observed in the seismic records before or during volcanic eruptions. Their analysis contributes to evaluate the evolving volcanic activity and potentially predict eruptions. Years of continuous seismic monitoring now provide useful information for operational eruption forecasting. The continuously growing amount of seismic recordings, however, poses a challenge for analysis, information extraction, and interpretation, to support timely decision making during volcanic crises. Furthermore, the complexity of eruption processes and precursory activities makes the analysis challenging.
A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving our understanding of the underlying physical processes. Some similar issues (data reduction, source separation, extraction, and classification) are addressed in the context of music information retrieval (MIR). The signal characteristics of acoustic and seismic recordings comprise a number of similarities. This thesis is going beyond classical signal analysis techniques usually employed in seismology by exploiting similarities of seismic and acoustic signals and building the information retrieval strategy on the expertise developed in the field of MIR.
First, inspired by the idea of harmonic–percussive separation (HPS) in musical signal processing, I have developed a method to extract harmonic volcanic tremor signals and to detect transient events from seismic recordings. This provides a clean tremor signal suitable for tremor investigation along with a characteristic function suitable for earthquake detection. Second, using HPS algorithms, I have developed a noise reduction technique for seismic signals. This method is especially useful for denoising ocean bottom seismometers, which are highly contaminated by noise. The advantage of this method compared to other denoising techniques is that it doesn’t introduce distortion to the broadband earthquake waveforms, which makes it reliable for different applications in passive seismological analysis. Third, to address the challenge of extracting information from high-dimensional data and investigating the complex eruptive phases, I have developed an advanced machine learning model that results in a comprehensive signal processing scheme for volcanic tremors. Using this method seismic signatures of major eruptive phases can be automatically detected. This helps to provide a chronology of the volcanic system. Also, this model is capable to detect weak precursory volcanic tremors prior to the eruption, which could be used as an indicator of imminent eruptive activity. The extracted patterns of seismicity and their temporal variations finally provide an explanation for the transition mechanism between eruptive phases.
Predator-forager interactions are a major factor in evolutionary adaptation of many species, as predators need to gain energy by consuming prey species, and foragers needs to avoid the worst fate of mortality while still consuming resources for energetic gains. In this evolutionary arms race, the foragers have constantly evolved anti-predator behaviours (e.g. foraging activity changes). To describe all these complex changes, researchers developed the framework of the landscape of fear, that is, the spatio-temporal variation of perceived predation risk. This concept simplifies all the involved ecological processes into one framework, by integrating animal biology and distribution with habitat characteristics. Researchers can then evaluate the perception of predation risk in prey species, what are the behavioural responses of the prey and, therefore, understand the cascading effects of landscapes of fear at the resource levels (tri-trophic effects). Although tri-trophic effects are well studied at the predator-prey interaction level, little is known on how the forager-resource interactions are part of the overall cascading effects of landscapes of fear, despite the changes of forager feeding behaviour - that occur with perceived predation risk - affecting directly the level of the resources.
This thesis aimed to evaluate the cascading effects of the landscape of fear on biodiversity of resources, and how the feeding behaviour and movement of foragers shaped the final resource species composition (potential coexistence mechanisms). We studied the changes caused by landscapes of fear on wild and captive rodent communities and evaluated: the cascading effects of different landscapes of fear on a tri-trophic system (I), the effects of fear on a forager’s movement patterns and dietary preferences (II) and cascading effects of different types of predation risk (terrestrial versus avian, III).
In Chapter I, we applied a novel measure to evaluate the cascading effects of fear at the level of resources, by quantifying the diversity of resources left after the foragers gave-up on foraging (diversity at the giving-up density). We tested the measure at different spatial levels (local and regional) and observed that with decreased perceived predation risk, the density and biodiversity of resources also decreased. Foragers left a very dissimilar community of resources based on perceived risk and resources functional traits, and therefore acted as an equalising mechanism.
In Chapter II, we wanted to understand further the decision-making processes of rodents in different landscapes of fear, namely, in which resource species rodents decided to forage on (based on three functional traits: size, nutrients and shape) and how they moved depending on perceived predation risk. In safe landscapes, individuals increased their feeding activity and movements and despite the increased costs, they visited more often patches that were further away from their central-place. Despite a preference for the bigger resources regardless of risk, when perceived predation risk was low, individuals changed their preference to fat-rich resources.
In Chapter III, we evaluated the cascading effects of two different types of predation risk in rodents: terrestrial (raccoon) versus avian predation risk. Raccoon presence or absence did not alter the rodents feeding behaviour in different landscapes of fear. Rodent’s showed risk avoidance behaviours towards avian predators (spatial risk avoidance), but not towards raccoons (lack of temporal risk avoidance).
By analysing the effects of fear in tri-trophic systems, we were able to deepen the knowledge of how non-consumptive effects of predators affect the behaviour of foragers, and quantitatively measure the cascading effects at the level of resources with a novel measure. Foragers are at the core of the ecological processes and responses to the landscape of fear, acting as variable coexistence agents for resource species depending on perceived predation risk. This newly found measures and knowledge can be applied to more trophic chains, and inform researchers on biodiversity patterns originating from landscapes of fear.
Continental rifts are key geodynamic regions where the complex interplay of magmatism and faulting activity can be studied to understand the driving forces of extension and the formation of new divergent plate boundaries. Well-preserved rift morphology can provide a wealth of information on the growth, interaction, and linkage of normal-fault systems through time. If rift basins are preserved over longer geologic time periods, sedimentary archives generated during extensional processes may mirror tectonic and climatic influences on erosional and sedimentary processes that have varied over time. Rift basins are furthermore strategic areas for hydrocarbon and geothermal energy exploration, and they play a central role in species dispersal and evolution as well as providing or inhibiting hydrologic connectivity along basins at emerging plate boundaries.
The Cenozoic East African rift system (EARS) is one of the most important continental extension zones, reflecting a range of evolutionary stages from an early rift stage with isolated basins in Malawi to an advanced stage of continental extension in southern Afar. Consequently, the EARS is an ideal natural laboratory that lends itself to the study of different stages in the breakup of a continent. The volcanically and seismically active eastern branch of the EARS is characterized by multiple, laterally offset tectonic and magmatic segments where adjacent extensional basins facilitate crustal extension either across a broad deformation zone or via major transfer faulting. The Broadly Rifted Zone (BRZ) in southern Ethiopia is an integral part of the eastern branch of the EARS; in this region, rift segments of the southern Ethiopian Rift (sMER) and northern Kenyan Rift (nKR) propagate in opposite directions in a region with one of the earliest manifestations of volcanism and extensional tectonism in East Africa. The basin margins of the Chew-Bahir Basin and the Gofa Province, characterized by a semi-arid climate and largely uniform lithology, provide ideal conditions for studying the tectonic and geomorphologic features of this complex kinematic transfer zone, but more importantly, this area is suitable for characterizing and quantifying the overlap between the propagating structures of the sMER and nKR and the resulting deformation patterns of the BRZ transfer zones.
In this study, I have combined data from thermochronology, thermal modeling, morphometry, paleomagnetic analysis, geochronology, and geomorphological field observations with information from published studies to reconstruct the spatiotemporal relationship between volcanism and fault activity in the BRZ and quantify the deformation patterns of the overlapping rift segments. I present the following results: (1) new thermochronological data from the en-échelon basin margins and footwall blocks of the rift flanks and morphometric results verified in the field to link different phases of magmatism and faulting during extension and infer geomorphological landscape features related to the current tectonic interaction between the nKR and the sMER; (2) temporally constrained paleomagnetic data from the BRZ overlap zone between the Ethiopian and Kenyan rifts to quantitatively determine block rotation between the two segments. Combining the collected data, time-temperature histories of thermal modeling results from representative samples show well-defined deformation phases between 25–20 Ma, 15–9Ma, and ~5 Ma to the present. Each deformation phase is characterized by the onset of rapid cooling (>2°C/Ma) of the crust associated with uplift or exhumation of the rift shoulder. After an initial, spatially very diffuse phase of extension, the rift has gradually evolved into a system of connected structures formed in an increasingly focused rift zone during the last 5 Ma. Regarding the morphometric analysis of the rift structures, it can be shown that normalized slope indices of the river courses, spatial arrangement of knickpoints in the river longitudinal profiles of the footwall blocks, local relief values, and the average maximum values of the slope of the river profiles indicate a gradual increase in the extension rate from north (Sawula basin: mature) to south (Chew Bahir: young). The complexity of the structural evolution of the BRZ overlap zone between nKR and sMER is further emphasized by the documentation of crustal blocks around a vertical axis. A comparison of the mean directions obtained for the Eo-Oligocene (Ds=352.6°, Is=-17.0°, N=18, α95=5.5°) and Miocene (Ds=2.9°, Is=0.9°, N=9, α95=12.4°) volcanics relative to the pole for stable South Africa and with respect to the corresponding ages of the analyzed units record a significant counterclockwise rotation of ~11.1°± 6.4° and insignificant CCW rotation of ~3.2° ± 11.5°, respectively.
Modern datasets often exhibit diverse, feature-rich, unstructured data, and they are massive in size. This is the case of social networks, human genome, and e-commerce databases. As Artificial Intelligence (AI) systems are increasingly used to detect pattern in data and predict future outcome, there are growing concerns on their ability to process large amounts of data. Motivated by these concerns, we study the problem of designing AI systems that are scalable to very large and heterogeneous data-sets.
Many AI systems require to solve combinatorial optimization problems in their course of action. These optimization problems are typically NP-hard, and they may exhibit additional side constraints. However, the underlying objective functions often exhibit additional properties. These properties can be exploited to design suitable optimization algorithms. One of these properties is the well-studied notion of submodularity, which captures diminishing returns. Submodularity is often found in real-world applications. Furthermore, many relevant applications exhibit generalizations of this property.
In this thesis, we propose new scalable optimization algorithms for combinatorial problems with diminishing returns. Specifically, we focus on three problems, the Maximum Entropy Sampling problem, Video Summarization, and Feature Selection. For each problem, we propose new algorithms that work at scale. These algorithms are based on a variety of techniques, such as forward step-wise selection and adaptive sampling. Our proposed algorithms yield strong approximation guarantees, and the perform well experimentally.
We first study the Maximum Entropy Sampling problem. This problem consists of selecting a subset of random variables from a larger set, that maximize the entropy. By using diminishing return properties, we develop a simple forward step-wise selection optimization algorithm for this problem. Then, we study the problem of selecting a subset of frames, that represent a given video. Again, this problem corresponds to a submodular maximization problem. We provide a new adaptive sampling algorithm for this problem, suitable to handle the complex side constraints imposed by the application. We conclude by studying Feature Selection. In this case, the underlying objective functions generalize the notion of submodularity. We provide a new adaptive sequencing algorithm for this problem, based on the Orthogonal Matching Pursuit paradigm.
Overall, we study practically relevant combinatorial problems, and we propose new algorithms to solve them. We demonstrate that these algorithms are suitable to handle massive datasets. However, our analysis is not problem-specific, and our results can be applied to other domains, if diminishing return properties hold. We hope that the flexibility of our framework inspires further research into scalability in AI.
Solar photocatalysis is the one of leading concepts of research in the current paradigm of sustainable chemical industry. For actual practical implementation of sunlight-driven catalytic processes in organic synthesis, a cheap, efficient, versatile and robust heterogeneous catalyst is necessary. Carbon nitrides are a class of organic semiconductors who are known to fulfill these requirements.
First, current state of solar photocatalysis in economy, industry and lab research is overviewed, outlining EU project funding, prospective synthetic and reforming bulk processes, small scale solar organic chemistry, and existing reactor designs and prototypes, concluding feasibility of the approach.
Then, the photocatalytic aerobic cleavage of oximes to corresponding aldehydes and ketones by anionic poly(heptazine imide) carbon nitride is discussed. The reaction provides a feasible method of deprotection and formation of carbonyl compounds from nitrosation products and serves as a convenient model to study chromoselectivity and photophysics of energy transfer in heterogeneous photocatalysis.
Afterwards, the ability of mesoporous graphitic carbon nitride to conduct proton-coupled electron transfer was utilized for the direct oxygenation of 1,3-oxazolidin-2-ones to corresponding 1,3-oxazlidine-2,4-diones. This reaction provides an easier access to a key scaffold of diverse types of drugs and agrochemicals.
Finally, a series of novel carbon nitrides based on poly(triazine imide) and poly(heptazine imide) structure was synthesized from cyanamide and potassium rhodizonate. These catalysts demonstrated a good performance in a set of photocatalytic benchmark reactions, including aerobic oxidation, dual nickel photoredox catalysis, hydrogen peroxide evolution and chromoselective transformation of organosulfur precursors.
Concluding, the scope of carbon nitride utilization for net-oxidative and net-neutral photocatalytic processes was expanded, and a new tunable platform for catalyst synthesis was discovered.