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In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided.
In this paper we consider the stochastic primitive equation for geophysical flows subject to transport noise and turbulent pressure. Admitting very rough noise terms, the global existence and uniqueness of solutions to this stochastic partial differential equation are proven using stochastic maximal
-regularity, the theory of critical spaces for stochastic evolution equations, and global a priori bounds. Compared to other results in this direction, we do not need any smallness assumption on the transport noise which acts directly on the velocity field and we also allow rougher noise terms. The adaptation to Stratonovich type noise and, more generally, to variable viscosity and/or conductivity are discussed as well.
Municipal wastewater is an interesting source of phosphorus and several processes for the recovery of phosphorus from this source have been described. These processes yield magnesium ammonium phosphate (MAP), a valuable fertilizer. In these processes, pH shifts and the addition of chemicals are used to influence the species distribution in the solution such as to finally obtain the desired product and to prevent the co-precipitation of salts of heavy metal ions. Elucidating these species distributions experimentally is a challenging and cumbersome task. Therefore, in the present work, a thermodynamic model was developed that can be used for predicting the species distributions in the various steps of the recovery process. The model combines the extended Debye-Hückel equation for the prediction of activity coefficients with dissociation constants and solubility product data from the literature and contains no parameters that need to be adjusted to process data. The model was successfully tested by comparison to experimental data for the Stuttgart process from the literature and used for analyzing the different process steps. Furthermore, it was demonstrated how the model can be used for optimizing the process.
Aflatoxins, a group of mycotoxins produced by various mold species within the genus Aspergillus, have been extensively investigated for their potential to contaminate food and feed, rendering them unfit for consumption. Nevertheless, the role of aflatoxins as environmental contaminants in soil, which represents their natural habitat, remains a relatively unexplored area in aflatoxin research. This knowledge gap can be attributed, in part, to the methodological challenges associated with detecting aflatoxins in soil. The main objective of this PhD project was to develop and validate an analytical method that allows monitoring of aflatoxins in soil, and scrutinize the mechanisms and extent of occurrence of aflatoxins in soil, the processes governing their dissipation, and their impact on the soil microbiome and associated soil functions. By utilizing an efficient extraction solvent mixture comprising acetonitrile and water, coupled with an ultrasonication step, recoveries of 78% to 92% were achieved, enabling reliable determination of trace levels in soil ranging from 0.5 to 20 µg kg-1. However, in a field trial conducted in a high-risk model region for aflatoxin contamination in Sub-Saharan Africa, no aflatoxins were detected using this procedure, underscoring the complexities of field monitoring. These challenges encompassed rapid degradation, spatial heterogeneity, and seasonal fluctuations in aflatoxin occurrence. Degradation experiments revealed the importance of microbial and photochemical processes in the dissipation of aflatoxins in soil with half-lives of 20 - 65 days. The rate of dissipation was found to be influenced by soil properties, most notably soil texture and the initial concentration of aflatoxins in the soil. An exposure study provided evidence that aflatoxins do not pose a substantial threat to the soil microbiome, encompassing microbial biomass, activity, and catabolic functionality. This was particularly evident in clayey soils, where the toxicity of aflatoxins diminished significantly due to their strong binding to clay minerals. However, several critical questions remain unanswered, emphasizing the necessity for further research to attain a more comprehensive understanding of the ecological importance of aflatoxins. Future research should prioritize the challenges associated with field monitoring of aflatoxins, elucidate the mechanisms responsible for the dissipation of aflatoxins in soil during microbial and photochemical degradation, and investigate the ecological consequences of aflatoxins in regions heavily affected by aflatoxins, taking into account the interactions between aflatoxins and environmental and anthropogenic stressors. Addressing these questions contributes to a comprehensive understanding of the environmental impact of aflatoxins in soil, ultimately contributing to more effective strategies for aflatoxin management in agriculture.
In recent decades, academia has addressed a wide range of research topics in the field of ethical decision-making. Besides a great amount of research on ethical consumption, also the domain of ethical investments increasingly moves in the focus of scholars. While in this area most research focuses on whether socially or environmentally sustainable businesses outperform traditional investments financially or investigates the character traits as well as other socio-demographic factors of ethical investors, the impact of sustainable corporate conduct on the investment intentions of private investors still requires further research. Hence, we conducted two studies to shed more light on this highly relevant topic. After discussing the current state of research, in our first empirical study, we explore whether besides the traditional triad of risk, return, and liquidity, also sustainability exerts a significant impact on the willingness to invest. As hypothesized, we find that sustainability shows a clear and decisive impact in addition to the traditional factors. In a consecutive study, we investigate deeper into the sustainability-willingness to invest link. Here, our results show that improved sustainability might not pay off in terms of investment attractiveness, however and conversely, it certainly harms to conduct business in a non-sustainable manner, which cannot even be compensated by an increased return.
As a consequence of the real estate market crash after 2008, large investors invested a significant amount of wealth into single-family houses to construct a portfolio of rental dwellings, whose income is securitized in the capital. In some local housing markets, these investors own remarkable numbers of single-family houses. Furthermore, their trading activities have resulted in a new investment strategy, which exacerbates property wealth concentration and polarization. This new investment strategy and its portfolio optimization inspire curiosity about its influence on housing markets. This paper first aims to find an optimal portfolio strategy by employing an expected utility optimization from the terminal wealth, which adopts a stochastic model that includes a variety of economic states to estimate house prices. Second, it aims to analyze the effect of large investors on the housing market. The results show the investment strategies of large investors depend on the balance among economic state, maintenance cost, rental income, interest rate and investment willingness of large investors to housing and their effect depends on the state of the economy.
Dataflow process networks (DPNs) are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. While the general model of computation (MoC) of DPNs does not impose further restrictions, many different subclasses of DPNs representing different dataflow MoCs have been considered over time. These classes mainly differ in the kinds of behaviors of the processes. A DPN may be heterogeneous in that different processes in the network belong to different classes of DPNs. A heterogeneous DPN can therefore be effectively used to model and to implement different components of a system with different kinds of processes and, therefore, different dataflow MoCs. This paper presents a model-based design based on different dataflow MoCs including their heterogeneous combinations. In particular, it covers the automatic software synthesis of systems from DPN models. The main objective is to validate, evaluate and compare the artifacts exhibited by different dataflow MoCs at the implementation level of systems under the supervision of a common design tool. Moreover, this work also offers an efficient synthesis method that targets and exploits heterogeneity in DPNs by generating implementations based on the kinds of behaviors of the processes. The proposed synthesis method provides a tool chain including different specialized code generators for specific dataflow MoCs, and a runtime system that finally maps models using a combination of different dataflow MoCs on cross-vendor target hardware.
Quantum Annealing (QA) is a metaheuristic for solving optimization problems in a time-efficient manner. Therefore, quantum mechanical effects are used to compute and evaluate many possible solutions of an optimization problem simultaneously. Recent studies have shown the potential of QA for solving such complex assignment problems within milliseconds. This also applies for the field of job shop scheduling, where the existing approaches however focus on small problem sizes. To assess the full potential of QA in this area for industry-scale problem formulations, it is necessary to consider larger problem instances and to evaluate the potentials of computing these job shop scheduling problems while finding a near-optimal solution in a time-efficient manner. Consequently, this paper presents a QA-based job shop scheduling. In particular, flexible job shop scheduling problems in various sizes are computed with QA, demonstrating the efficiency of the approach regarding scalability, solutions quality, and computing time. For the evaluation of the proposed approach, the solutions are compared in a scientific benchmark with state-of-the-art algorithms for solving flexible job shop scheduling problems. The results indicate that QA has the potential for solving flexible job shop scheduling problems in a time efficient manner. Even large problem instances can be computed within seconds, which offers the possibility for application in industry.
Es werden Ergebnisse aus einer Kontaktsimulation vorgestellt, welche die Oberflächenveränderung eines Axiallagers infolge von unerwünschtem elektrischem Stromdurchgang bei Mischreibung zeigen. Das hierzu entwickelte Modell berücksichtigt neben den Oberflächenrauheiten auch das nichtlineare Materialverhalten des Wälzlagerwerkstoffes. Im Gegensatz zu bekannten Modellierungsmethoden für ähnliche Problemstellungen, wird hier ein neuartiger Ansatz auf Basis einer gekoppelten Euler- Lagrange- Finite Element Simulation entwickelt. Das Modell liefert, mit experimentell geschädigten Oberflächen als Eingangsgröße, Erkenntnisse zum Traganteilsverhalten und weiterer mechanischer Kenngrößen infolge kombinierter mechanischer und elektrischer Belastungen.