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(2019)
Searching optimal continuous-thrust trajectories is usually a difficult and time-consuming task. The solution quality of traditional optimal-control methods depends strongly on an adequate initial guess because the solution is typically close to the initial guess, which may be far from the (unknown) global optimum. Evolutionary neurocontrol attacks continuous-thrust optimization problems from the perspective of artificial intelligence and machine learning, combining artificial neural networks and evolutionary algorithms. This chapter describes the method and shows some example results for single- and multi-phase continuous-thrust trajectory optimization problems to assess its performance. Evolutionary neurocontrol can explore the trajectory search space more exhaustively than a human expert can do with traditional optimal-control methods. Especially for difficult problems, it usually finds solutions that are closer to the global optimum. Another fundamental advantage is that continuous-thrust trajectories can be optimized without an initial guess and without expert supervision.
Die urbane Mobilität ist im Wandel und insbesondere neue innovative Geschäftsmodelle werden einen wesentlichen Teil zur Lösung von künftigen Mobilitätsbedürfnissen beitragen. Die sogenannte „Shared Mobility“ gilt aktuell neben der Elektrifizierung des Antriebes und autonomem Fahrzeugtechnologien als einer der wichtigsten Trendthemen in der Automobilindustrie. Neue Mobilitätsdienstleistungen verlangen dabei verstärkt auch neue Fahrzeugkonzepte.
Wind loads have great impact on many engineering structures. Wind storms often cause irreparable damage to the buildings which are exposed to it. Along with the earthquakes, wind represents one of the most common environmental load on structures and is relevant for limit state design. Modern wind codes indicate calculation procedures allowing engineers to deal with structural systems, which are susceptible to conduct wind-excited oscillations. In the codes approximate formulas for wind buffeting are specified which relate the dynamic problem to rather abstract parameter functions. The complete theory behind is not visible in order to simplify the applicability of the procedures. This chapter derives the underlying basic relations of the spectral method for wind buffeting and explains the main important applications of it in order to elucidate part of the theoretical background of computations after the new codes. The stochasticity of the wind processes is addressed, and the analysis of analytical as well as measurement based power spectra is outlined. Short MATLAB codes are added to the Appendix 3 which carry out the computation of a single sided auto-spectrum from a statistically stationary, discrete stochastic process. Two examples are presented.
The use of transgenic animal models has transformed our knowledge of complex biochemical pathways in vivo. It has allowed disease processes to be modelled and used in the development of new disease prevention and treatment strategies. They can also be used to define cell- and tissue-specific pathways of gene regulation. A further major application is in the area of preclinical development where such models can be used to define pathways of chemical toxicity, and the pathways that regulate drug disposition. One major application of this approach is the humanisation of mice for the proteins that control drug metabolism and disposition. Such models can have numerous applications in the development of drugs and in their more sophisticated use in the clinic.
Genetically humanized mice for proteins involved in drug metabolism and toxicity and mice engrafted with human hepatocytes are emerging as promising in vivo models for improved prediction of the pharmacokinetic, drug–drug interaction, and safety characteristics of compounds in humans. This is an overview on the genetically humanized and chimeric liver-humanized mouse models, which are illustrated with examples of their utility in drug metabolism and toxicity studies. The models are compared to give guidance for selection of the most appropriate model by highlighting advantages and disadvantages to be carefully considered when used for studies in drug discovery and development.
In the past decade, many IS researchers focused on researching the phenomenon of Big Data. At the same time, the relevance of data protection gets more attention than ever before. In particular, since the enactment of the European General Data Protection Regulation in May 2018 Information Systems research should provide answers for protecting personal data. The article at hand presents a structuring framework for Big Data research outcome and the consideration of data protection. IS Researchers might use the framework in order to structure Big Data literature and to identify research gaps that should be addressed in the future.