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We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home.
Perovskite solar cells represent one of the recent success stories in photovoltaics. The device efficiency has been steadily increasing over the past years, but further work is needed to enhance the performance, for example, through the reduction of defects to prevent carrier recombination. SCAPS-1D simulations were performed to assess efficiency limits and identify approaches to decrease the impact of defects, through the selection of an optimal hole-transport material and a hole-collecting electrode. Particular attention was given to evaluation of the influence of bulk defects within light-absorbing CH3NH3SnI3 layers. In addition, the study demonstrates the influence of interface defects at the TiO2/CH3NH3SnI3 (IL1) and CH3NH3SnI3/HTL (IL2) interfaces across the similar range of defect densities. Finally, the optimal device architecture TiO2/CH3NH3SnI3/Cu2O is proposed for the given absorber layer using the readily available Cu2O hole-transporting material with PCE = 27.95%, FF = 84.05%, V-OC = 1.02 V and J(SC) = 32.60 mA/cm(2), providing optimal performance and enhanced resistance to defects.
Point processes are a common methodology to model sets of events. From earthquakes to social media posts, from the arrival times of neuronal spikes to the timing of crimes, from stock prices to disease spreading -- these phenomena can be reduced to the occurrences of events concentrated in points. Often, these events happen one after the other defining a time--series.
Models of point processes can be used to deepen our understanding of such events and for classification and prediction. Such models include an underlying random process that generates the events. This work uses Bayesian methodology to infer the underlying generative process from observed data. Our contribution is twofold -- we develop new models and new inference methods for these processes.
We propose a model that extends the family of point processes where the occurrence of an event depends on the previous events. This family is known as Hawkes processes. Whereas in most existing models of such processes, past events are assumed to have only an excitatory effect on future events, we focus on the newly developed nonlinear Hawkes process, where past events could have excitatory and inhibitory effects. After defining the model, we present its inference method and apply it to data from different fields, among others, to neuronal activity.
The second model described in the thesis concerns a specific instance of point processes --- the decision process underlying human gaze control. This process results in a series of fixated locations in an image. We developed a new model to describe this process, motivated by the known Exploration--Exploitation dilemma. Alongside the model, we present a Bayesian inference algorithm to infer the model parameters.
Remaining in the realm of human scene viewing, we identify the lack of best practices for Bayesian inference in this field. We survey four popular algorithms and compare their performances for parameter inference in two scan path models.
The novel models and inference algorithms presented in this dissertation enrich the understanding of point process data and allow us to uncover meaningful insights.
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.
The lithosphere is often assumed to reside in a thermal steady-state when quantitatively describing the temperature distribution in continental interiors and sedimentary basins, but also at active plate boundaries. Here, we investigate the applicability limit of this assumption at slowly deforming continental rifts. To this aim, we assess the tectonic thermal imprint in numerical experiments that cover a range of realistic rift configurations. For each model scenario, the deviation from thermal equilibrium is evaluated. This is done by comparing the transient temperature field of every model to a corresponding steady-state model with an identical structural configuration. We find that the validity of the thermal steady-state assumption strongly depends on rift type, divergence velocity, sampling location, and depth within the rift. Maximum differences between transient and steady-state models occur in narrow rifts, at the rift sides, and if the extension rate exceeds 0.5-2 mm/a. Wide rifts, however, reside close to thermal steady-state even for high extension velocities. The transient imprint of rifting appears to be overall negligible for shallow isotherms with a temperature less than 100 degrees C. Contrarily, a steady-state treatment of deep crustal isotherms leads to an underestimation of crustal temperatures, especially for narrow rift settings. Thus, not only relatively fast rifts like the Gulf of Corinth, Red Sea, and Main Ethiopian Rift, but even slow rifts like the Kenya Rift, Rhine Graben, and Rio Grande Rift must be expected to feature a pronounced transient component in the temperature field and to therefore violate the thermal steady-state assumption for deeper crustal isotherms.
Expanding modeling notations
(2022)
Creativity is a common aspect of business processes and thus needs a proper representation through process modeling notations. However, creative processes constitute highly flexible process elements, as new and unforeseeable outcome is developed. This presents a challenge for modeling languages. Current methods representing creative-intensive work are rather less able to capture creative specifics which are relevant to successfully run and manage these processes. We outline the concept of creative-intensive processes and present an example from a game design process in order to derive critical process aspects relevant for its modeling. Six aspects are detected, with first and foremost: process flexibility, as well as temporal uncertainty, experience, types of creative problems, phases of the creative process and individual criteria. By first analyzing what aspects of creative work modeling notations already cover, we further discuss which modeling extensions need to be developed to better represent creativity within business processes. We argue that a proper representation of creative work would not just improve the management of those processes, but can further enable process actors to more efficiently run these creative processes and adjust them to better fit to the creative needs.
Floodplains have been degraded in Central Europe for centuries, resulting in less dynamic and less diverse ecosystems than in the past. They provide essential ecosystem services like nutrient retention to improve overall water quality and thus fulfill naturally what EU legislation demands, but this service is impaired by reduced connectivity patterns. Along the second-longest river in Europe, the Danube, restoration measures have been carried out and are planned for the near future in the Austrian Danube Floodplain National Park in accordance with navigation purposes. We investigated nutrient retention capacity in seven currently differently connected side arms and the effects of proposed restoration measures using two complementary modeling approaches. We modeled nutrient retention capacity in two scenarios considering different hydrological conditions, as well as the consequences of planned restoration measures for side arm connectivity. With existing monitoring data on hydrology, nitrate, and total phosphorus concentrations for three side arms, we applied a statistical model and compared these results to a semi-empirical retention model. The latter was originally developed for larger scales, based on transferable causalities of retention processes and set up for this floodplain with publicly available data. Both model outcomes are in a comparable range for NO3-N (77-198 kg ha(-1)yr(-1)) and TP (1.4-5.7 kg ha(-1)yr(-1)) retention and agree in calculating higher retention in floodplains, where reconnection allows more frequent inundation events. However, the differences in the model results are significant for specific aspects especially during high flows, where the semi-empirical model complements the statistical model. On the other hand, the statistical model complements the semi-empirical model when taking into account nutrient retention at times of no connection between the remaining water bodies left in the floodplain. Overall, both models show clearly that nutrient retention in the Danube floodplains can be enhanced by restoring lateral hydrological reconnection and, for all planned measures, a positive effect on the overall water quality of the Danube River is expected. Still, a frequently hydrologically connected stretch of national park is insufficient to improve the water quality of the whole Upper Danube, and more functional floodplains are required.
Groningen is the largest onshore gas field under production in Europe. The pressure depletion of the gas field started in 1963. In 1991, the first induced micro-earthquakes have been located at reservoir level with increasing rates in the following decades. Most of these events are of magnitude less than 2.0 and cannot be felt. However, maximum observed magnitudes continuously increased over the years until the largest, significant event with ML=3.6 was recorded in 2014, which finally led to the decision to reduce the production. This causal sequence displays the crucial role of understanding and modeling the relation between production and induced seismicity for economic planing and hazard assessment. Here we test whether the induced seismicity related to gas exploration can be modeled by the statistical response of fault networks with rate-and-state-dependent frictional behavior. We use the long and complete local seismic catalog and additionally detailed information on production-induced changes at the reservoir level to test different seismicity models. Both the changes of the fluid pressure and of the reservoir compaction are tested as input to approximate the Coulomb stress changes. We find that the rate-and-state model with a constant tectonic background seismicity rate can reproduce the observed long delay of the seismicity onset. In contrast, so-called Coulomb failure models with instantaneous earthquake nucleation need to assume that all faults are initially far from a critical state of stress to explain the delay. Our rate-and-state model based on the fluid pore pressure fits the spatiotemporal pattern of the seismicity best, where the fit further improves by taking the fault density and orientation into account. Despite its simplicity with only three free parameters, the rate-and-state model can reproduce the main statistical features of the observed activity.
For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies.
The impressive number of stream gauges in Chile, combined with a suite of past and recent large earthquakes, makes Chile a unique natural laboratory to study several streams that recorded responses to multiple seismic events. We document changes in discharge in eight streams in Chile following two or more large earthquakes. In all cases, discharge increases. Changes in discharge occur for peak ground velocities greater than about 7-11cm/s. Above that threshold, the magnitude of both the increase in discharge and the total excess water do not increase with increasing peak ground velocities. While these observations are consistent with previous work in California, they conflict with lab experiments that show that the magnitude of permeability changes increases with increasing amplitude of ground motion. Instead, our study suggests that streamflow responses are binary. Plain Language Summary Earthquakes deform and shake the surface and the ground below. These changes may affect groundwater flows by increasing the permeability along newly formed cracks and/or clearing clogged pores. As a result, groundwater flow may substantially increase after earthquakes and remain elevated for several months. Here we document streamflow anomalies following multiple high magnitude earthquakes in multiple streams in one of the most earthquake prone regions worldwide, Chile. We take advantage of the dense monitoring network in Chile that recorded streamflow since the 1940s. We show that once a critical ground motion is exceeded, streamflow responses to earthquakes can be expected.