@phdthesis{Gerner2023,
  author    = {Gerner, Bettina},
  title     = {Improvement of oral antineoplastic therapy by means of pharmacometric approaches \& therapeutic drug monitoring},
  doi       = {10.25972/OPUS-32196},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-321966},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Oral antineoplastic drugs are an important component in the treatment of solid tumour diseases, haematological and immunological malignancies. Oral drug administration is associated with positive features (e.g., non-invasive drug administration, outpatient care with a high level of independence for the patient and reduced costs for the health care system). The systemic exposure after oral intake however is prone to high IIV as it strongly depends on gastrointestinal absorption processes, which are per se characterized by high inter-and intraindividual variability. Disease and patient-specific characteristics (e.g., disease state, concomitant diseases, concomitant medication, patient demographics) may additionally contribute to variability in plasma concentrations between individual patients. In addition, many oral antineoplastic drugs show complex PK, which has not yet been fully investigated and elucidated for all substances. All this may increase the risk of suboptimal plasma exposure (either subtherapeutic or toxic), which may ultimately jeopardise the success of therapy, either through a loss of efficacy or through increased, intolerable adverse drug reactions. TDM can be used to detect suboptimal plasma levels and prevent permanent under- or overexposure. It is essential in the treatment of ACC with mitotane, a substance with unfavourable PK and high IIV. In the current work a HPLC-UV method for the TDM of mitotane using VAMS was developed. A low sample volume (20 µl) of capillary blood was used in the developed method, which facilitates dense sampling e.g., at treatment initiation. However, no reference ranges for measurements from capillary blood are established so far and a simple conversion from capillary concentrations to plasma concentrations was not possible. To date the therapeutic range is established only for plasma concentrations and observed capillary concentrations could not be reliable interpretated.The multi-kinase inhibitor cabozantinib is also used for the treatment of ACC. However, not all PK properties, like the characteristic second peak in the cabozantinib concentration-time profile have been fully understood so far. To gain a mechanistic understanding of the compound, a PBPK model was developed and various theories for modelling the second peak were explored, revealing that EHC of the compound is most plausible. Cabozantinib is mainly metabolized via CYP3A4 and susceptible to DDI with e.g., CYP3A4 inducers. The DDI between cabozantinib and rifampin was investigated with the developed PBPK model and revealed a reduced cabozantinib exposure (AUC) by 77\%. Hence, the combination of cabozantinib with strong CYP inducers should be avoided. If this is not possible, co administration should be monitored using TDM. The model was also used to simulate cabozantinib plasma concentrations at different stages of liver injury. This showed a 64\% and 50\% increase in total exposure for mild and moderate liver injury, respectively.Ruxolitinib is used, among others, for patients with acute and chronic GvHD. These patients often also receive posaconazole for invasive fungal prophylaxis leading to CYP3A4 mediated DDI between both substances. Different dosing recommendations from the FDA and EMA on the use of ruxolitinib in combination with posaconazole complicate clinical use. To simulate the effect of this relevant DDI, two separate PBPK models for ruxolitinib and posaconazole were developed and combined. Predicted ruxolitinib exposure was compared to observed plasma concentrations obtained in GvHD patients. The model simulations showed that the observed ruxolitinib concentrations in these patients were generally higher than the simulated concentrations in healthy individuals, with standard dosing present in both scenarios. According to the developed model, EMA recommended RUX dose reduction seems to be plausible as due to the complexity of the disease and intake of extensive co-medication, RUX plasma concentration can be higher than expected.},
  subject      = {Arzneimittel{\"u}berwachung},
  language  = {en}
}
@phdthesis{Steininger2023,
  author    = {Steininger, Michael},
  title     = {Deep Learning for Geospatial Environmental Regression},
  doi       = {10.25972/OPUS-31312},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313121},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Environmental issues have emerged especially since humans burned fossil fuels, which led to air pollution and climate change that harm the environment. These issues' substantial consequences evoked strong efforts towards assessing the state of our environment. Various environmental machine learning (ML) tasks aid these efforts. These tasks concern environmental data but are common ML tasks otherwise, i.e., datasets are split (training, validatition, test), hyperparameters are optimized on validation data, and test set metrics measure a model's generalizability. This work focuses on the following environmental ML tasks: Regarding air pollution, land use regression (LUR) estimates air pollutant concentrations at locations where no measurements are available based on measured locations and each location's land use (e.g., industry, streets). For LUR, this work uses data from London (modeled) and Zurich (measured). Concerning climate change, a common ML task is model output statistics (MOS), where a climate model's output for a study area is altered to better fit Earth observations and provide more accurate climate data. This work uses the regional climate model (RCM) REMO and Earth observations from the E-OBS dataset for MOS. Another task regarding climate is grain size distribution interpolation where soil properties at locations without measurements are estimated based on the few measured locations. This can provide climate models with soil information, that is important for hydrology. For this task, data from Lower Franconia is used. Such environmental ML tasks commonly have a number of properties: (i) geospatiality, i.e., their data refers to locations relative to the Earth's surface. (ii) The environmental variables to estimate or predict are usually continuous. (iii) Data can be imbalanced due to relatively rare extreme events (e.g., extreme precipitation). (iv) Multiple related potential target variables can be available per location, since measurement devices often contain different sensors. (v) Labels are spatially often only sparsely available since conducting measurements at all locations of interest is usually infeasible. These properties present challenges but also opportunities when designing ML methods for such tasks. In the past, environmental ML tasks have been tackled with conventional ML methods, such as linear regression or random forests (RFs). However, the field of ML has made tremendous leaps beyond these classic models through deep learning (DL). In DL, models use multiple layers of neurons, producing increasingly higher-level feature representations with growing layer depth. DL has made previously infeasible ML tasks feasible, improved the performance for many tasks in comparison to existing ML models significantly, and eliminated the need for manual feature engineering in some domains due to its ability to learn features from raw data. To harness these advantages for environmental domains it is promising to develop novel DL methods for environmental ML tasks. This thesis presents methods for dealing with special challenges and exploiting opportunities inherent to environmental ML tasks in conjunction with DL. To this end, the proposed methods explore the following techniques: (i) Convolutions as in convolutional neural networks (CNNs) to exploit reoccurring spatial patterns in geospatial data. (ii) Posing the problems as regression tasks to estimate the continuous variables. (iii) Density-based weighting to improve estimation performance for rare and extreme events. (iv) Multi-task learning to make use of multiple related target variables. (v) Semi-supervised learning to cope with label sparsity. Using these techniques, this thesis considers four research questions: (i) Can air pollution be estimated without manual feature engineering? This is answered positively by the introduction of the CNN-based LUR model MapLUR as well as the off-the-shelf LUR solution OpenLUR. (ii) Can colocated pollution data improve spatial air pollution models? Multi-task learning for LUR is developed for this, showing potential for improvements with colocated data. (iii) Can DL models improve the quality of climate model outputs? The proposed DL climate MOS architecture ConvMOS demonstrates this. Additionally, semi-supervised training of multilayer perceptrons (MLPs) for grain size distribution interpolation is presented, which can provide improved input data. (iv) Can DL models be taught to better estimate climate extremes? To this end, density-based weighting for imbalanced regression (DenseLoss) is proposed and applied to the DL architecture ConvMOS, improving climate extremes estimation. These methods show how especially DL techniques can be developed for environmental ML tasks with their special characteristics in mind. This allows for better models than previously possible with conventional ML, leading to more accurate assessment and better understanding of the state of our environment.},
  subject      = {Deep learning},
  language  = {en}
}
@phdthesis{Saulin2023,
  author    = {Saulin, Anne Christin},
  title     = {Sustainability of empathy as driver for prosocial behavior and social closeness: insights from computational modelling and functional magnetic resonance imaging},
  doi       = {10.25972/OPUS-30555},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305550},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Empathy, the act of sharing another person's affective state, is a ubiquitous driver for helping others and feeling close to them. These experiences are integral parts of human behavior and society. The studies presented in this dissertation aimed to investigate the sustainability and stability of social closeness and prosocial decision-making driven by empathy and other social motives. In this vein, four studies were conducted in which behavioral and neural indicators of empathy sustainability were identified using model-based functional magnetic resonance imaging (fMRI). Applying reinforcement learning, drift-diffusion modelling (DDM), and fMRI, the first two studies were designed to investigate the formation and sustainability of empathy-related social closeness (study 1) and examined how sustainably empathy led to prosocial behavior (study 2). Using DDM and fMRI, the last two studies investigated how empathy combined with reciprocity, the social norm to return a favor, on the one hand and empathy combined with the motive of outcome maximization on the other hand altered the behavioral and neural social decision process. The results showed that empathy-related social closeness and prosocial decision tendencies persisted even if empathy was rarely reinforced. The sustainability of these empathy effects was related to recalibration of the empathy-related social closeness learning signal (study 1) and the maintenance of a prosocial decision bias (study 2). The findings of study 3 showed that empathy boosted the processing of reciprocity-based social decisions, but not vice versa. Study 4 revealed that empathy-related decisions were modulated by the motive of outcome maximization, depending on individual differences in state empathy. Together, the studies strongly support the concept of empathy as a sustainable driver of social closeness and prosocial behavior.},
  subject      = {Einf{\"u}hlung <Motiv>},
  language  = {en}
}
@phdthesis{Schmitt2022,
  author    = {Schmitt, Norbert},
  title     = {Measurement, Modeling, and Emulation of Power Consumption of Distributed Systems},
  doi       = {10.25972/OPUS-27658},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-276582},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Today's cloud data centers consume an enormous amount of energy, and energy consumption will rise in the future. An estimate from 2012 found that data centers consume about 30 billion watts of power, resulting in about 263TWh of energy usage per year. The energy consumption will rise to 1929TWh until 2030. This projected rise in energy demand is fueled by a growing number of services deployed in the cloud. 50\% of enterprise workloads have been migrated to the cloud in the last decade so far. Additionally, an increasing number of devices are using the cloud to provide functionalities and enable data centers to grow. Estimates say more than 75 billion IoT devices will be in use by 2025. The growing energy demand also increases the amount of CO2 emissions. Assuming a CO2-intensity of 200g CO2 per kWh will get us close to 227 billion tons of CO2. This emission is more than the emissions of all energy-producing power plants in Germany in 2020. However, data centers consume energy because they respond to service requests that are fulfilled through computing resources. Hence, it is not the users and devices that consume the energy in the data center but the software that controls the hardware. While the hardware is physically consuming energy, it is not always responsible for wasting energy. The software itself plays a vital role in reducing the energy consumption and CO2 emissions of data centers. The scenario of our thesis is, therefore, focused on software development. Nevertheless, we must first show developers that software contributes to energy consumption by providing evidence of its influence. The second step is to provide methods to assess an application's power consumption during different phases of the development process and to allow modern DevOps and agile development methods. We, therefore, need to have an automatic selection of system-level energy-consumption models that can accommodate rapid changes in the source code and application-level models allowing developers to locate power-consuming software parts for constant improvements. Afterward, we need emulation to assess the energy efficiency before the actual deployment.},
  subject      = {Leistungsbedarf},
  language  = {en}
}
@phdthesis{Wilde2022,
  author    = {Wilde, Martina},
  title     = {Landslide susceptibility assessment in the Chiconquiaco Mountain Range area, Veracruz (Mexico)},
  doi       = {10.25972/OPUS-27608},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-276085},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {In Mexico, numerous landslides occur each year and Veracruz represents the state with the third highest number of events. Especially the Chiconquiaco Mountain Range, located in the central part of Veracruz, is highly affected by landslides and no detailed information on the spatial distribution of existing landslides or future occurrences is available. This leaves the local population exposed to an unknown threat and unable to react appropriately to this hazard or to consider the potential landslide occurrence in future planning processes. Thus, the overall objective of the present study is to provide a comprehensive assessment of the landslide situation in the Chiconquiaco Mountain Range area. Here, the combination of a site-specific and a regional approach enables to investigate the causes, triggers, and process types as well as to model the landslide susceptibility for the entire study area. For the site-specific approach, the focus lies on characterizing the Capul{\´i}n landslide, which represents one of the largest mass movements in the area. In this context, the task is to develop a multi-methodological concept, which concentrates on cost-effective, flexible and non-invasive methods. This approach shows that the applied methods complement each other very well and their combination allows for a detailed characterization of the landslide. The analyses revealed that the Capul{\´i}n landslide is a complex mass movement type. It comprises rotational movement in the upper parts and translational movement in the lower areas, as well as flow processes at the flank and foot area and therefore, is classified as a compound slide-flow according to Cruden and Varnes (1996). Furthermore, the investigations show that the Capul{\´i}n landslide represents a reactivation of a former process. This is an important new information, especially with regard to the other landslides identified in the study area. Both the road reconstructed after the landslide, which runs through the landslide mass, and the stream causing erosion processes at the foot of the landslide severely affect the stability of the landslide, making it highly susceptible to future reactivation processes. This is particularly important as the landslide is located only few hundred meters from the village El Capul{\´i}n and an extension of the landslide area could cause severe damage. The next step in the landslide assessment consists of integrating the data obtained in the site-specific approach into the regional analysis. Here, the focus lies on transferring the generated data to the entire study area. The developed methodological concept yields applicable results, which is supported by different validation approaches. The susceptibility modeling as well as the landslide inventory reveal that the highest probability of landslides occurrence is related to the areas with moderate slopes covered by slope deposits. These slope deposits comprise material from old mass movements and erosion processes and are highly susceptible to landslides. The results give new insights into the landslide situation in the Chiconquiaco Mountain Range area, since previously landslide occurrence was related to steep slopes of basalt and andesite. The susceptibility map is a contribution to a better assessment of the landslide situation in the study area and simultaneously proves that it is crucial to include specific characteristics of the respective area into the modeling process, otherwise it is possible that the local conditions will not be represented correctly.},
  subject      = {Naturgefahren},
  language  = {en}
}
@phdthesis{Lauton2021,
  author    = {Lauton, Felix},
  title     = {Three Essays on the Procurement of Essential Medicines in Developing Countries},
  doi       = {10.25972/OPUS-22063},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220631},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {The first problem is that of the optimal volume allocation in procurement. The choice of this problem was motivated by a study whose objective was to support decision-making at two procurement organizations for the procurement of Depot Medroxyprogesterone Acetate (DMPA), an injectable contraceptive. At the time of this study, only one supplier that had undergone the costly and lengthy process of WHO pre-qualification was available to these organizations. However, a new entrant supplier was expected to receive WHO qualification within the next year, thus becoming a viable second source for DMPA procurement. When deciding how to allocate the procurement volume between the two suppliers, the buyers had to consider the impact on price as well as risk. Higher allocations to one supplier yield lower prices but expose a buyer to higher supply risks, while an even allocation will result in lower supply risk but also reduce competitive pressure, resulting in higher prices. Our research investigates this single- versus dual-sourcing problem and quantifies in one model the impact of the procurement volume on competition and risk. To support decision-makers, we develop a mathematical framework that accounts for the characteristics of donor-funded global health markets and models the effects of an entrant on purchasing costs and supply risks. Our in-depth analysis provides insights into how the optimal allocation decision is affected by various parameters and explores the trade-off between competition and supply risk. For example, we find that, even if the entrant supplier introduces longer leads times and a higher default risk, the buyer still benefits from dual sourcing. However, these risk-diversification benefits depend heavily on the entrant's in-country registration: If the buyer can ship the entrant's product to only a selected number of countries, the buyer does not benefit from dual sourcing as much as it would if entrant's product could be shipped to all supplied countries. We show that the buyer should be interested in qualifying the entrant's product in countries with high demand first. In the second problem we explore a new tendering mechanism called the postponement tender, which can be useful when buyers in the global health industry want to contract new generics suppliers with uncertain product quality. The mechanism allows a buyer to postpone part of the procurement volume's allocation so the buyer can learn about the unknown quality before allocating the remaining volume to the best supplier in terms of both price and quality. We develop a mathematical model to capture the decision-maker's trade-offs in setting the right split between the initial volume and the postponed volume. Our analysis shows that a buyer can benefit from this mechanism more than it can from a single-sourcing format, as it can decrease the risk of receiving poor quality (in terms of product quality and logistics performance) and even increase competitive pressure between the suppliers, thereby lowering the purchasing costs. By considering market parameters like the buyer's size, the suppliers' value (difference between quality and cost), quality uncertainty, and minimum order volumes, we derive optimal sourcing strategies for various market structures and explore how competition is affected by the buyer's learning about the suppliers' quality through the initial volume. The third problem considers the repeated procurement problem of pharmacies in Kenya that have multi-product inventories. Coordinating orders allows pharmacies to achieve lower procurement prices by using the quantity discounts manufacturers offer and sharing fixed ordering costs, such as logistics costs. However, coordinating and optimizing orders for multiple products is complex and costly. To solve the coordinated procurement problem, also known as the Joint Replenishment Problem (JRP) with quantity discounts, a novel, data-driven inventory policy using sample-average approximation is proposed. The inventory policy is developed based on renewal theory and is evaluated using real-world sales data from Kenyan pharmacies. Multiple benchmarks are used to evaluate the performance of the approach. First, it is compared to the theoretically optimal policy --- that is, a dynamic-programming policy --- in the single-product setting without quantity discounts to show that the proposed policy results in comparable inventory costs. Second, the policy is evaluated for the original multi-product setting with quantity discounts and compared to ex-post optimal costs. The evaluation shows that the policy's performance in the multi-product setting is similar to its performance in the single-product setting (with respect to ex-post optimal costs), suggesting that the proposed policy offers a promising, data-driven solution to these types of multi-product inventory problems.},
  subject      = {Entwicklungsl{\"a}nder},
  language  = {en}
}
@phdthesis{Pirner2018,
  author    = {Pirner, Marlies},
  title     = {Kinetic modelling of gas mixtures},
  edition   = {1. Auflage},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-080-1 (Print)},
  doi       = {10.25972/WUP-978-3-95826-081-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-161077},
  school      = {W{\"u}rzburg University Press},
  pages     = {xi, 222},
  year      = {2018},
  abstract  = {This book deals with the kinetic modelling of gas mixtures. It extends the existing literature in mathematics for one species of gas to the case of gasmixtures. This is more realistic in applications. Thepresentedmodel for gas mixtures is proven to be consistentmeaning it satisfies theconservation laws, it admitsanentropy and an equilibriumstate. Furthermore, we can guarantee the existence, uniqueness and positivity of solutions. Moreover, the model is used for different applications, for example inplasma physics, for fluids with a small deviation from equilibrium and in the case of polyatomic gases.},
  subject      = {Polyatomare Verbindungen},
  language  = {en}
}
@phdthesis{Rygielski2017,
  author    = {Rygielski, Piotr},
  title     = {Flexible Modeling of Data Center Networks for Capacity Management},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146235},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {Nowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as server virtualization and there are multiple competing approaches and technologies. Performance modeling and prediction techniques provide a powerful tool to analyze the performance of modern data centers. However, given the wide variety of network virtualization approaches, no common approach exists for modeling and evaluating the performance of virtualized networks. The performance community has proposed multiple formalisms and models for evaluating the performance of infrastructures based on different network virtualization technologies. The existing performance models can be divided into two main categories: coarse-grained analytical models and highly-detailed simulation models. Analytical performance models are normally defined at a high level of abstraction and thus they abstract many details of the real network and therefore have limited predictive power. On the other hand, simulation models are normally focused on a selected networking technology and take into account many specific performance influencing factors, resulting in detailed models that are tightly bound to a given technology, infrastructure setup, or to a given protocol stack. Existing models are inflexible, that means, they provide a single solution method without providing means for the user to influence the solution accuracy and solution overhead. To allow for flexibility in the performance prediction, the user is required to build multiple different performance models obtaining multiple performance predictions. Each performance prediction may then have different focus, different performance metrics, prediction accuracy, and solving time. The goal of this thesis is to develop a modeling approach that does not require the user to have experience in any of the applied performance modeling formalisms. The approach offers the flexibility in the modeling and analysis by balancing between: (a) generic character and low overhead of coarse-grained analytical models, and (b) the more detailed simulation models with higher prediction accuracy. The contributions of this thesis intersect with technologies and research areas, such as: software engineering, model-driven software development, domain-specific modeling, performance modeling and prediction, networking and data center networks, network virtualization, Software-Defined Networking (SDN), Network Function Virtualization (NFV). The main contributions of this thesis compose the Descartes Network Infrastructure (DNI) approach and include: • Novel modeling abstractions for virtualized network infrastructures. This includes two meta-models that define modeling languages for modeling data center network performance. The DNI and miniDNI meta-models provide means for representing network infrastructures at two different abstraction levels. Regardless of which variant of the DNI meta-model is used, the modeling language provides generic modeling elements allowing to describe the majority of existing and future network technologies, while at the same time abstracting factors that have low influence on the overall performance. I focus on SDN and NFV as examples of modern virtualization technologies. • Network deployment meta-model—an interface between DNI and other meta- models that allows to define mapping between DNI and other descriptive models. The integration with other domain-specific models allows capturing behaviors that are not reflected in the DNI model, for example, software bottlenecks, server virtualization, and middleware overheads. • Flexible model solving with model transformations. The transformations enable solving a DNI model by transforming it into a predictive model. The model transformations vary in size and complexity depending on the amount of data abstracted in the transformation process and provided to the solver. In this thesis, I contribute six transformations that transform DNI models into various predictive models based on the following modeling formalisms: (a) OMNeT++ simulation, (b) Queueing Petri Nets (QPNs), (c) Layered Queueing Networks (LQNs). For each of these formalisms, multiple predictive models are generated (e.g., models with different level of detail): (a) two for OMNeT++, (b) two for QPNs, (c) two for LQNs. Some predictive models can be solved using multiple alternative solvers resulting in up to ten different automated solving methods for a single DNI model. • A model extraction method that supports the modeler in the modeling process by automatically prefilling the DNI model with the network traffic data. The contributed traffic profile abstraction and optimization method provides a trade-off by balancing between the size and the level of detail of the extracted profiles. • A method for selecting feasible solving methods for a DNI model. The method proposes a set of solvers based on trade-off analysis characterizing each transformation with respect to various parameters such as its specific limitations, expected prediction accuracy, expected run-time, required resources in terms of CPU and memory consumption, and scalability. • An evaluation of the approach in the context of two realistic systems. I evaluate the approach with focus on such factors like: prediction of network capacity and interface throughput, applicability, flexibility in trading-off between prediction accuracy and solving time. Despite not focusing on the maximization of the prediction accuracy, I demonstrate that in the majority of cases, the prediction error is low—up to 20\% for uncalibrated models and up to 10\% for calibrated models depending on the solving technique. In summary, this thesis presents the first approach to flexible run-time performance prediction in data center networks, including network based on SDN. It provides ability to flexibly balance between performance prediction accuracy and solving overhead. The approach provides the following key benefits: • It is possible to predict the impact of changes in the data center network on the performance. The changes include: changes in network topology, hardware configuration, traffic load, and applications deployment. • DNI can successfully model and predict the performance of multiple different of network infrastructures including proactive SDN scenarios. • The prediction process is flexible, that is, it provides balance between the granularity of the predictive models and the solving time. The decreased prediction accuracy is usually rewarded with savings of the solving time and consumption of resources required for solving. • The users are enabled to conduct performance analysis using multiple different prediction methods without requiring the expertise and experience in each of the modeling formalisms. The components of the DNI approach can be also applied to scenarios that are not considered in this thesis. The approach is generalizable and applicable for the following examples: (a) networks outside of data centers may be analyzed with DNI as long as the background traffic profile is known; (b) uncalibrated DNI models may serve as a basis for design-time performance analysis; (c) the method for extracting and compacting of traffic profiles may be used for other, non-network workloads as well.},
  subject      = {Modellierung},
  language  = {en}
}
@phdthesis{Hirth2016,
  author    = {Hirth, Matthias Johannes Wilhem},
  title     = {Modeling Crowdsourcing Platforms - A Use-Case Driven Approach},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-14072},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-140726},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Computer systems have replaced human work-force in many parts of everyday life, but there still exists a large number of tasks that cannot be automated, yet. This also includes tasks, which we consider to be rather simple like the categorization of image content or subjective ratings. Traditionally, these tasks have been completed by designated employees or outsourced to specialized companies. However, recently the crowdsourcing paradigm is more and more applied to complete such human-labor intensive tasks. Crowdsourcing aims at leveraging the huge number of Internet users all around the globe, which form a potentially highly available, low-cost, and easy accessible work-force. To enable the distribution of work on a global scale, new web-based services emerged, so called crowdsourcing platforms, that act as mediator between employers posting tasks and workers completing tasks. However, the crowdsourcing approach, especially the large anonymous worker crowd, results in two types of challenges. On the one hand, there are technical challenges like the dimensioning of crowdsourcing platform infrastructure or the interconnection of crowdsourcing platforms and machine clouds to build hybrid services. On the other hand, there are conceptual challenges like identifying reliable workers or migrating traditional off-line work to the crowdsourcing environment. To tackle these challenges, this monograph analyzes and models current crowdsourcing systems to optimize crowdsourcing workflows and the underlying infrastructure. First, a categorization of crowdsourcing tasks and platforms is developed to derive generalizable properties. Based on this categorization and an exemplary analysis of a commercial crowdsourcing platform, models for different aspects of crowdsourcing platforms and crowdsourcing mechanisms are developed. A special focus is put on quality assurance mechanisms for crowdsourcing tasks, where the models are used to assess the suitability and costs of existing approaches for different types of tasks. Further, a novel quality assurance mechanism solely based on user-interactions is proposed and its feasibility is shown. The findings from the analysis of existing platforms, the derived models, and the developed quality assurance mechanisms are finally used to derive best practices for two crowdsourcing use-cases, crowdsourcing-based network measurements and crowdsourcing-based subjective user studies. These two exemplary use-cases cover aspects typical for a large range of crowdsourcing tasks and illustrated the potential benefits, but also resulting challenges when using crowdsourcing. With the ongoing digitalization and globalization of the labor markets, the crowdsourcing paradigm is expected to gain even more importance in the next years. This is already evident in the currently new emerging fields of crowdsourcing, like enterprise crowdsourcing or mobile crowdsourcing. The models developed in the monograph enable platform providers to optimize their current systems and employers to optimize their workflows to increase their commercial success. Moreover, the results help to improve the general understanding of crowdsourcing systems, a key for identifying necessary adaptions and future improvements.},
  subject      = {Open Innovation},
  language  = {en}
}
@phdthesis{Fritsch2013,
  author    = {Fritsch, Sebastian},
  title     = {Spatial and temporal patterns of crop yield and marginal land in the Aral Sea Basin: derivation by combining multi-scale and multi-temporal remote sensing data with alight use efficiency model},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-87939},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 \%. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 \% for cotton and 30.4 \% for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 \% to 23.8 \% for cotton and from -0.4 \% to -19.4 \% for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{ZeeshangebMajeed2014,
  author    = {Zeeshan [geb. Majeed], Saman},
  title     = {Implementation of Bioinformatics Methods for miRNA and Metabolic Modelling},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-102900},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Dynamic interactions and their changes are at the forefront of current research in bioinformatics and systems biology. This thesis focusses on two particular dynamic aspects of cellular adaptation: miRNA and metabolites. miRNAs have an established role in hematopoiesis and megakaryocytopoiesis, and platelet miRNAs have potential as tools for understanding basic mechanisms of platelet function. The thesis highlights the possible role of miRNAs in regulating protein translation in platelet lifespan with relevance to platelet apoptosis and identifying involved pathways and potential key regulatory molecules. Furthermore, corresponding miRNA/target mRNAs in murine platelets are identified. Moreover, key miRNAs involved in aortic aneurysm are predicted by similar techniques. The clinical relevance of miRNAs as biomarkers, targets, resulting later translational therapeutics, and tissue specific restrictors of genes expression in cardiovascular diseases is also discussed. In a second part of thesis we highlight the importance of scientific software solution development in metabolic modelling and how it can be helpful in bioinformatics tool development along with software feature analysis such as performed on metabolic flux analysis applications. We proposed the "Butterfly" approach to implement efficiently scientific software programming. Using this approach, software applications were developed for quantitative Metabolic Flux Analysis and efficient Mass Isotopomer Distribution Analysis (MIDA) in metabolic modelling as well as for data management. "LS-MIDA" allows easy and efficient MIDA analysis and, with a more powerful algorithm and database, the software "Isotopo" allows efficient analysis of metabolic flows, for instance in pathogenic bacteria (Salmonella, Listeria). All three approaches have been published (see Appendices).},
  subject      = {miRNS},
  language  = {en}
}
@phdthesis{Paxian2012,
  author    = {Paxian, Andreas},
  title     = {Future changes in climate means and extremes in the Mediterranean region deduced from a regional climate model},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72155},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {The Mediterranean area reveals a strong vulnerability to future climate change due to a high exposure to projected impacts and a low capacity for adaptation highlighting the need for robust regional or local climate change projections, especially for extreme events strongly affecting the Mediterranean environment. The prevailing study investigates two major topics of the Mediterranean climate variability: the analysis of dynamical downscaling of present-day and future temperature and precipitation means and extremes from global to regional scale and the comprehensive investigation of temperature and rainfall extremes including the estimation of uncertainties and the comparison of different statistical methods for precipitation extremes. For these investigations, several observational datasets of CRU, E-OBS and original stations are used as well as ensemble simulations of the regional climate model REMO driven by the coupled global general circulation model ECHAM5/MPI-OM and applying future greenhouse gas (GHG) emission and land degradation scenarios.},
  subject      = {Mittelmeerraum},
  language  = {en}
}
@phdthesis{Zinner2012,
  author    = {Zinner, Thomas},
  title     = {Performance Modeling of QoE-Aware Multipath Video Transmission in the Future Internet},
  doi       = {10.25972/OPUS-6106},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72324},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Internet applications are becoming more and more flexible to support diverge user demands and network conditions. This is reflected by technical concepts, which provide new adaptation mechanisms to allow fine grained adjustment of the application quality and the corresponding bandwidth requirements. For the case of video streaming, the scalable video codec H.264/SVC allows the flexible adaptation of frame rate, video resolution and image quality with respect to the available network resources. In order to guarantee a good user-perceived quality (Quality of Experience, QoE) it is necessary to adjust and optimize the video quality accurately. But not only have the applications of the current Internet changed. Within network and transport, new technologies evolved during the last years providing a more flexible and efficient usage of data transport and network resources. One of the most promising technologies is Network Virtualization (NV) which is seen as an enabler to overcome the ossification of the Internet stack. It provides means to simultaneously operate multiple logical networks which allow for example application-specific addressing, naming and routing, or their individual resource management. New transport mechanisms like multipath transmission on the network and transport layer aim at an efficient usage of available transport resources. However, the simultaneous transmission of data via heterogeneous transport paths and communication technologies inevitably introduces packet reordering. Additional mechanisms and buffers are required to restore the correct packet order and thus to prevent a disturbance of the data transport. A proper buffer dimensioning as well as the classification of the impact of varying path characteristics like bandwidth and delay require appropriate evaluation methods. Additionally, for a path selection mechanism real time evaluation mechanisms are needed. A better application-network interaction and the corresponding exchange of information enable an efficient adaptation of the application to the network conditions and vice versa. This PhD thesis analyzes a video streaming architecture utilizing multipath transmission and scalable video coding and develops the following optimization possibilities and results: Analysis and dimensioning methods for multipath transmission, quantification of the adaptation possibilities to the current network conditions with respect to the QoE for H.264/SVC, and evaluation and optimization of a future video streaming architecture, which allows a better interaction of application and network.},
  subject      = {Video{\"u}bertragung},
  language  = {en}
}
@phdthesis{Cord2012,
  author    = {Cord, Anna},
  title     = {Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-71021},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called 'biodiversity crisis', which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called 'megadiverse' nations cover less than 10\% of the earth's land surface but support nearly 70\% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico's large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Staehle2011,
  author    = {Staehle, Barbara},
  title     = {Modeling and Optimization Methods for Wireless Sensor and Mesh Networks},
  doi       = {10.25972/OPUS-4967},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-64884},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {Im Internet der Zukunft werden Menschen nicht nur mit Menschen, sondern auch mit „Dingen", und sogar „Dinge" mit „Dingen" kommunizieren. Zus{\"a}tzlich wird das Bed{\"u}rfnis steigen, immer und {\"u}berall Zugang zum Internet zu haben. Folglich gewinnen drahtlose Sensornetze (WSNs) und drahtlose Mesh-Netze (WMNs) an Bedeutung, da sie Daten {\"u}ber die Umwelt ins Internet liefern, beziehungsweise einfache Internet-Zugangsm{\"o}glichkeiten schaffen. In den vier Teilen dieser Arbeit werden unterschiedliche Modellierungs- und Optimierungsmethoden f{\"u}r WSNs und WMNs vorgestellt. Der Energieverbrauch ist die wichtigste Metrik, wenn es darum geht die Kommunikation in einem WSN zu optimieren. Da sich in der Literatur sehr viele unterschiedliche Energiemodelle finden, untersucht der erste Teil der Arbeit welchen Einfluss unterschiedliche Energiemodelle auf die Optimierung von WSNs haben. Aufbauend auf diesen {\"U}berlegungen besch{\"a}ftigt sich der zweite Teil der Arbeit mit drei Problemen, die {\"u}berwunden werden m{\"u}ssen um eine standardisierte energieeffiziente Kommunikations-L{\"o}sung f{\"u}r WSNs basierend auf IEEE 802.15.4 und ZigBee zu realisieren. F{\"u}r WMNs sind beide Probleme von geringem Interesse, die Leistung des Netzes jedoch umso mehr. Der dritte Teil der Arbeit f{\"u}hrt daher Algorithmen f{\"u}r die Berechnung des Max-Min fairen (MMF) Netzwerk-Durchsatzes in WMNs mit mehreren Linkraten und Internet-Gateways ein. Der letzte Teil der Arbeit untersucht die Auswirkungen des LRA-Konzeptes. Dessen grundlegende Idee ist die folgende. Falls f{\"u}r einen Link eine niedrigere Datenrate als theoretisch m{\"o}glich verwendet wird, sinkt zwar der Link-Durchsatz, jedoch ist unter Umst{\"a}nden eine gr{\"o}ßere Anzahl von gleichzeitigen {\"U}bertragungen m{\"o}glich und der Gesamt-Durchsatz des Netzes kann sich erh{\"o}hen. Mithilfe einer analytischen LRA Formulierung und einer systematischen Studie kann gezeigt werden, dass eine netzwerkweite Zuordnung robusterer Datenraten als n{\"o}tig zu einer Erh{\"o}hung des MMF Netzwerk-Durchsatzes f{\"u}hrt. Desweitern kann gezeigt werden, dass sich LRA positiv auf die Leistungsf{\"a}higkeit eines IEEE 802.11 WMNs auswirkt und f{\"u}r die Optimierung des Netzes genutzt werden kann.},
  subject      = {Drahtloses Sensorsystem},
  language  = {en}
}
@phdthesis{Heldens2010,
  author    = {Heldens, Wieke},
  title     = {Use of airborne hyperspectral data and height information to support urban micro climate characterisation},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-48935},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {The urban micro climate has been increasingly recognised as an important aspect for urban planning. Therefore, urban planners need reliable information on the micro climatic characteristics of the urban environment. A suitable spatial scale and large spatial coverage are important requirements for such information. This thesis presents a conceptual framework for the use of airborne hyperspectral data to support urban micro climate characterisation, taking into account the information needs of urban planning. The potential of hyperspectral remote sensing in characterising the micro climate is demonstrated and evaluated by applying HyMap airborne hyperspectral and height data to a case study of the German city of Munich. The developed conceptual framework consists of three parts. The first is concerned with the capabilities of airborne hyperspectral remote sensing to map physical urban characteristics. The high spatial resolution of the sensor allows to separate the relatively small urban objects. The high spectral resolution enables the identification of the large range of surface materials that are used in an urban area at up to sub-pixel level. The surface materials are representative for the urban objects of which the urban landscape is composed. These spatial urban characteristics strongly influence the urban micro climate. The second part of the conceptual framework provides an approach to use the hyperspectral surface information for the characterisation of the urban micro climate. This can be achieved by integrating the remote sensing material map into a micro climate model. Also spatial indicators were found to provide useful information on the micro climate for urban planners. They are commonly used in urban planning to describe building blocks and are related to several micro climatic parameters such as temperature and humidity. The third part of the conceptual framework addresses the combination and presentation of the derived indicators and simulation results under consideration of the planning requirements. Building blocks and urban structural types were found to be an adequate means to group and present the derived information for micro climate related questions to urban planners. The conceptual framework was successfully applied to a case study in Munich. Airborne hyperspectral HyMap data has been used to derive a material map at sub-pixel level by multiple endmember linear spectral unmixing. This technique was developed by the German Research Centre for Geosciences (GFZ) for applications in Dresden and Potsdam. A priori information on building locations was used to support the separation between spectrally similar materials used both on building roofs and non-built surfaces. In addition, surface albedo and leaf area index are derived from the HyMap data. The sub-pixel material map supported by object height data is then used to derive spatial indicators, such as imperviousness or building density. To provide a more detailed micro climate characterisation at building block level, the surface materials, albedo, leaf area index (LAI) and object height are used as input for simulations with the micro climate model ENVI-met. Concluding, this thesis demonstrated the potential of hyperspectral remote sensing to support urban micro climate characterisation. A detailed mapping of surface materials at sub-pixel level could be performed. This provides valuable, detailed information on a large range of spatial characteristics relevant to the assessment of the urban micro climate. The developed conceptual framework has been proven to be applicable to the case study, providing a means to characterise the urban micro climate. The remote sensing products and subsequent micro climatic information are presented at a suitable spatial scale and in understandable maps and graphics. The use of well-known spatial indicators and the framework of urban structural types can simplify the communication with urban planners on the findings on the micro climate. Further research is needed primarily on the sensitivity of the micro climate model towards the remote sensing based input parameters and on the general relation between climate parameters and spatial indicators by comparison with other cities.},
  subject      = {Mikroklima},
  language  = {en}
}
@phdthesis{Mederer2009,
  author    = {Mederer, Joachim},
  title     = {Water Resources and Dynamics of the Troodos Igneous Aquifer-system, Cyprus - Balanced Groundwater Modelling -},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-37306},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2009},
  abstract  = {The study investigates the water resources and aquifer dynamics of the igneous fractured aquifer-system of the Troodos Mountains in Cyprus, using a coupled, finite differences water balance and groundwater modelling approach. The numerical water balance modelling forms the quantitative framework by assessing groundwater recharge and evapotranspiration, which form input parameters for the groundwater flow models. High recharge areas are identified within the heavily fractured Gabbro and Sheeted Dyke formations in the upper Troodos Mountains, while the impervious Pillow Lava promontories - with low precipitation and high evapotranspiration - show unfavourable recharge conditions. Within the water balance studies, evapotranspiration is split into actual evapotranspiration and the so called secondary evapotranspiration, representing the water demand for open waters, moist and irrigated areas. By separating the evapotranspiration of open waters and moist areas from the one of irrigated areas, groundwater abstraction needs are quantified, allowing the simulation of single well abstraction rates in the groundwater flow models. Two sets of balanced groundwater models simulate the aquifer dynamics in the presented study: First, the basic groundwater percolation system is investigated using two-dimensional vertical flow models along geological cross-sections, depicting the entire Troodos Mountains up to a depth of several thousands of metres. The deeply percolating groundwater system starts in the high recharge areas of the upper Troodos, shows quasi stratiform flow in the Gabbro and Sheeted Dyke formations, and rises to the surface in the vicinity of the impervious Pillow Lava promontories. The residence times mostly yield less than 25 years, the ones of the deepest fluxes several hundreds of years. Moreover, inter basin flow and indirect recharge of the Circum Troodos Sedimentary Succession are identified. In a second step, the upper and most productive part of the fractured igneous aquifer-system is investigated in a regional, horizontal groundwater model, including management scenarios and inter catchment flow studies. In a natural scenario without groundwater abstractions, the recovery potential of the aquifer is tested. Predicted future water demand is simulated in an increased abstraction scenario. The results show a high sensitivity to well abstraction rate changes in the Pillow Lava and Basal Group promontories. The changes in groundwater heads range from a few tens of metres up to more than one hundred metres. The sensitivity in the more productive parts of the aquifer-system is lower. Inter-catchment flow studies indicate that - besides the dominant effluent conditions in the Troodos Mountains - single reaches show influent conditions and are sub-flown by groundwater. These fluxes influence the local water balance and generate inter catchment flow. The balanced groundwater models form thus a comprehensive modelling system, supplying future detail models with information concerning boundary conditions and inter-catchment flow, and allowing the simulation of impacts of landuse or climate change scenarios on the dynamics and water resources of the Troodos aquifer-system.},
  subject      = {Zypern},
  language  = {en}
}
@phdthesis{Kuells2000,
  author    = {K{\"u}lls, Christoph},
  title     = {Groundwater of the North-Western Kalahari, Namibia},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-1180680},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2000},
  abstract  = {A quantitative model of groundwater flows contributing to the Goblenz state water scheme at the north-western fringe of the Kalahari was developed within this study. The investigated area corresponds to the Upper Omatako basin and encompasses an outer mountainous rim and sediments of the Kalahari sand desert in the centre. This study revealed the eminent importance of the mountainous rim for the water balance of the Kalahari, both in terms of surface and ground water. A hydrochemical subdivision of groundwater types in the mountain rim around the Kalahari was derived from cluster analysis of hydrochemical groundwater data. The western and south-western secondary aquifers within rocks of the Damara Sequence, the Otavi Mountain karst aquifers of the Tsumeb and Abenab subgroups as well as the Waterberg Etjo sandstone aquifer represent the major hydrochemical groups. Ca/Mg and Sr/Ca ratios allowed to trace the groundwater flow from the Otavi Mountains towards the Kalahari near Goblenz. The Otavi Mountains and the Waterberg were identified as the main recharge areas showing almost no or only little isotopic enrichment by evaporation. Soil water balance modelling confirmed that direct groundwater recharge in hard-rock environments tends to be much higher than in areas covered with thick Kalahari sediments. According to the water balance model average recharge rates in hard-rock exposures with only thin sand cover are between 0.1 and 2.5 \% of mean annual rainfall. Within the Kalahari itself very limited recharge was predicted (< 1 \% of mean annual rainfall). In the Upper Omatako basin the highest recharge probability was found in February in the late rainfall season. The water balance model also indicated that surface runoff is produced sporadically, triggering indirect recharge events. Several sinkholes were discovered in the Otavi Foreland to the north of Goblenz forming short-cuts to the groundwater table and preferential recharge zones. Their relevance for the generation of indirect recharge could be demonstrated by stable isotope variations resulting from observed flood events. Within the Kalahari basin several troughs were identified in the pre-Kalahari surface by GIS-based analyses. A map of saturated thickness of Kalahari sediments revealed that these major troughs are partly saturated with groundwater. The main trough, extending from south-west to north-east, is probably connected to the Goblenz state water scheme and represents a major zone of groundwater confluence, receiving groundwater inflows from several recharge areas in the Upper Omatako basin. As a result of the dominance of mountain front recharge the groundwater of the Kalahari carries an isotopic composition of recharge at higher altitudes. The respective percentages of inflow into the Kalahari from different source areas were determined by a mixing-cell approach. According to the mixing model Goblenz receives most of its inflow (70 to 80 \%) from a shallow Kalahari aquifer in the Otavi Foreland which is connected to the Otavi Mountains. Another 15 to 10 \% of groundwater inflow to the Kalahari at Goblenz derive from the Etjo sandstone aquifer to the south and from inflow of a mixed component. In conclusion, groundwater abstraction at Goblenz will be affected by measures that heavily influence groundwater inflow from the Otavi Mountains, the Waterberg, and the fractured aquifer north of the Waterberg.},
  subject      = {Kalahari},
  language  = {en}
}