Distribution and Valence of the Cations in Spinel Systems with Iron and Vanadium, III X-ray Investigation and Mössbauer Spectra of the Spinel System ZnFeVO4-Fe3O4

The spinel system Zni_zFe2:[Fei+2;Vi_I]04 has been prepared by heating mixtures of ZnFeV(>4 and Fe3Ü4 at 1000 °C. The lattice constants, the oxygen parameters and the Mössbauer parameters show that a transition from a nearly normal to an inverse distribu-tion of Fe(II) and Fe(III) exists

Middleware für Ubiquitäre Systeme: Ein Modellgetriebener Ansatz

Dieser Dissertation liegt die Hypothese zugrunde, dass modell-getriebene Softwareentwicklung (MDSD) den Widerspruch zwischen "top-down"- und "bottom-up"- Entwicklung durch einen "middle-out" Ansatz auflöst, welcher zwischen Technologie und Abstraktion vermittelt. MDSD wird als Mittel verwendet, um Middleware für Ubiquitäre Systeme auf dem einen Turm von Modellen zu bauen, ohne den Bezug zur konkreten Technologie zu verlieren

Spatial Interpolation of Air Quality Data with Multidimensional Gaussian Processes

The central question of this paper is whether interpolation techniques applied to a distributed sensor network can indeed provide more information than using the constant background of an urban reference station to measure air pollution. We compare different interpolation techniques based on temporal-spatial machine learning in terms of their applicability for correctly predicting personal exposure. Using a dataset of stationary low-cost sensors, we estimate exposure on a route through the city and compare it to mobile measurements. The results show that while different machine learning-based interpolation methods yield quite different results, validation of machine learning-based approaches is still challenging

Smart Data Innovation Challenges: Abschlussbericht zum Projekt SDI- C Förderkennzeichen: 01IS19030A-G

Das Projekt „Smart Data Innovation Challenges“ wurde vom 01.08.2019 bis zum 31.12.2022 vom Bundesministerium für Bildung und Forschung (BMBF) gefördert (Förderkennkennzeichen 01IS19030A-G). Das Projekt wurde in Kontext des Smart Data Innovation Labs (SDIL, www.sdil.de ) durchgeführt

Towards Extracting Causal Graph Structures from Trade Data and Smart Financial Portfolio Risk Management

Risk managers of asset management companies monitor portfolio risk metrics such as the Value at Risk in order to analyze and to communicate the risks timely to portfolio managers, and to ensure regulatory compliance. They must investigate the possible causes if a portfolio risk significantly increases or breaches a regulatory limit. However, monitoring can quickly become overwhelming, time and labor-intensive as each risk manager has to deal with over a hundred portfolios, numerous daily market data, and hundreds of risk factors of the supervised portfolios and of their securities. Particularly, understanding the interrelations between incidents in different portfolios beyond high level indicators is important. However, analyzing these interrelations manually is one of the most difficult tasks. In this paper, we describe and demonstrate how automatically generating causal graphs can address the capacity problem of practitioners in risk management, who are facing more and more capital markets based risk data daily on the portfolio level alone. Based on a proof of concept implementation, we compare a pairwise causal-inference-based approach with a clustering-based construction approach. We discuss the advantages and disadvantages of both approaches, both computationally and based on the resulting structure. Based on our initial findings, we outline further challenges and research topics