Anpassung des Metamodells PerPot an den ausdauerorientierten Laufsport zur Trainings- und Wettkampfoptimierung

Schon seit einigen Jahrzehnten wird die Sportwissenschaft durch computergestützte Methoden in ihrer Arbeit unterstützt. Mit der stetigen Weiterentwicklung der Technik kann seit einigen Jahren auch zunehmend die Sportpraxis von deren Einsatz profitieren. Mathematische und informatische Modelle sowie Algorithmen werden zur Leistungsoptimierung sowohl im Mannschafts- als auch im Individualsport genutzt. In der vorliegenden Arbeit wird das von Prof. Perl im Jahr 2000 entwickelte Metamodell PerPot an den ausdauerorientierten Laufsport angepasst. Die Änderungen betreffen sowohl die interne Modellstruktur als auch die Art der Ermittlung der Modellparameter. Damit das Modell in der Sportpraxis eingesetzt werden kann, wurde ein Kalibrierungs-Test entwickelt, mit dem die spezifischen Modellparameter an den jeweiligen Sportler individuell angepasst werden. Mit dem angepassten Modell ist es möglich, aus gegebenen Geschwindigkeitsprofilen die korrespondierenden Herzfrequenzverläufe abzubilden. Mit dem auf den Athleten eingestellten Modell können anschliessend Simulationen von Läufen durch die Eingabe von Geschwindigkeitsprofilen durchgeführt werden. Die Simulationen können in der Praxis zur Optimierung des Trainings und der Wettkämpfe verwendet werden. Das Training kann durch die Ermittlung einer simulativ bestimmten individuellen anaeroben Schwellenherzfrequenz optimal gesteuert werden. Die statistische Auswertung der PerPot-Schwelle zeigt signifikante Übereinstimmungen mit den in der Sportpraxis üblichen invasiv bestimmten Laktatschwellen. Die Wettkämpfe können durch die Ermittlung eines optimalen Geschwindigkeitsprofils durch verschiedene simulationsbasierte Optimierungsverfahren unterstützt werden. Bei der neuesten Methode erhält der Athlet sogar im Laufe des Wettkampfs aktuelle Prognosen, die auf den Geschwindigkeits- und Herzfrequenzdaten basieren, die während des Wettkampfs gemessen werden. Die mit PerPot optimierten Wettkampfzielzeiten für die Athleten zeigen eine hohe Prognosegüte im Vergleich zu den tatsächlich erreichten Zielzeiten.

Prediction of new crystal structures under extreme conditions

One of the most important challenges in chemistry and material science is the connection between the contents of a compound and its chemical and physical properties. In solids, these are greatly influenced by the crystal structure.rnrnThe prediction of hitherto unknown crystal structures with regard to external conditions like pressure and temperature is therefore one of the most important goals to achieve in theoretical chemistry. The stable structure of a compound is the global minimum of the potential energy surface, which is the high dimensional representation of the enthalpy of the investigated system with respect to its structural parameters. The fact that the complexity of the problem grows exponentially with the system size is the reason why it can only be solved via heuristic strategies.rnrnImprovements to the artificial bee colony method, where the local exploration of the potential energy surface is done by a high number of independent walkers, are developed and implemented. This results in an improved communication scheme between these walkers. This directs the search towards the most promising areas of the potential energy surface.rnrnThe minima hopping method uses short molecular dynamics simulations at elevated temperatures to direct the structure search from one local minimum of the potential energy surface to the next. A modification, where the local information around each minimum is extracted and used in an optimization of the search direction, is developed and implemented. Our method uses this local information to increase the probability of finding new, lower local minima. This leads to an enhanced performance in the global optimization algorithm.rnrnHydrogen is a highly relevant system, due to the possibility of finding a metallic phase and even superconductor with a high critical temperature. An application of a structure prediction method on SiH12 finds stable crystal structures in this material. Additionally, it becomes metallic at relatively low pressures.

Packing a trunk : a physically based approach with full motional freedom

Geometric packing problems may be formulated mathematically as constrained optimization problems. But finding a good solution is a challenging task. The more complicated the geometry of the container or the objects to be packed, the more complex the non-penetration constraints become. In this work we propose the use of a physics engine that simulates a system of colliding rigid bodies. It is a tool to resolve interpenetration conflicts and to optimize configurations locally. We develop an efficient and easy-to-implement physics engine that is specialized for collision detection and contact handling. In succession of the development of this engine a number of novel algorithms for distance calculation and intersection volume were designed and imple- mented, which are presented in this work. They are highly specialized to pro- vide fast responses for cuboids and triangles as input geometry whereas the concepts they are based on can easily be extended to other convex shapes. Especially noteworthy in this context is our ε-distance algorithm - a novel application that is not only very robust and fast but also compact in its im- plementation. Several state-of-the-art third party implementations are being presented and we show that our implementations beat them in runtime and robustness. The packing algorithm that lies on top of the physics engine is a Monte Carlo based approach implemented for packing cuboids into a container described by a triangle soup. We give an implementation for the SAE J1100 variant of the trunk packing problem. We compare this implementation to several established approaches and we show that it gives better results in faster time than these existing implementations.

Optimization of RNA-based transgene expression by targeting Protein Kinase R

The aim of this thesis was to establish a method for repeated transfection of in vitro transcribed RNA (IVT-RNA) leading to a sustained protein expression lasting for days or even weeks. Once transfected cells recognize IVT-RNA as "non-self" and initiate defense pathways leading to an upregulated interferon (IFN) response and stalled translation. In this work Protein Kinase R (PKR) was identified as the main effector molecule mediating this cellular response. We assessed four strategies to inhibit PKR and the IFN response: A small molecule PKR inhibitor enhanced protein expression and hampered the induction of IFN-transcripts, but had to be excluded due to cytotoxicity. A siRNA mediated PKR knockdown and the overexpression of a kinase inactive PKR mutant elevated the protein expression, but the down-regulation of the IFN response was insufficient. The co-transfer of the viral inhibitors of PKR and the IFN response was most successful. The use of E3, K3 and B18R co-transfection enabled repeated IVT-RNA-based transfection of human fibroblasts. Thus, the developed protocol allows a continuous IVT-RNA encoded protein expression of proteins, which could be the basis for the generation of induced pluripotent stem cells (iPS) for several therapeutic applications in regenerative medicine or drug research.