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To assess the effect of intercropping on malting quality a field trial with spring barley (Hordeum vulgare) and legume (pea) as well as non-legume (camelina and linseed) intercrops in two additive seeding ratios as well as sole cops was established in 2017 at the organic experimental station of University of Applied Sciences Osnabrück in North-Western Germany. Two tested malting barley cultivars (cv. Marthe and cv. Odilia) showed different performance, but all variants achieved brewing quality. Results after two years indicate that linseed and camelina were able to limit protein content. For best land-use efficiency of malting barley production intercropping with linseed showed best results. Mixed intercropping can help to promote internal efficiency loops and is therefore a promising sustainable intensification strategy for more resilient future crop production under changing climate conditions.
Durch die Verbreitung von VR und den möglichen Potenzialen für die Landschaftsarchitektur, gewinnt die Auseinandersetzung mit der Verwendung als Präsentationsmedium an Relevanz. Aufgrund der geringen Anzahl an Fallstudien zu dieser Thematik, war es das Ziel dieser Arbeit, eine vollständige und praxisnahe VR-Erfahrung anhand eines Beispielprojekts zu erstellen. Hierfür wurde das Wohnbauprojekt „Teilerhöfe“ in Hannover ausgewählt.
Im Rahmen der Arbeit konnte ein Arbeitsablauf für die Erstellung einer VR-Erfahrung aufgestellt und erfolgreich durchgeführt werden. Das Ergebnis bildet eine auf der Unreal Engine basierende virtuelle Echtzeit-Umgebung, die durch ein VR-Headset erkundet werden kann. Trotz einiger notwendiger Kompromisse konnten alle Inhalte des Entwurfs angemessen dargestellt werden. Auf dieser Basis werden mögliche Potenziale und Grenzen für den Einsatz in der Landschaftsarchitektur diskutiert. Hieraus resultiert die Erkenntnis, das VREs einen erheblichen Mehrwert bieten können, aber mit Bedacht eingesetzt werden sollten.
In this paper, we evaluate the application of Bayesian Optimization (BO) to discrete event simulation (DES) models. In a first step, we create a simple model, for which we know the optimal set of parameter values in advance. We implement the model in SimPy, a framework for DES written in Python. We then interpret the simulation model as a black box function subject to optimization. We show that it is possible to find the optimal set of parameter values using the open source library GPyOpt. To enhance our evaluation, we create a second and more complex model. To better handle the complexity of the model, and to add a visual component, we build the second model in Simio, a commercial off-the-shelf simulation modeling tool. To apply BO to a model in Simio, we use the Simio API to write an extension for optimization plug-ins. This extension encapsulates the logic of the BO algorithm, which we deployed as a web service in the cloud.
The fact that simulation models are black box functions with regard to their behavior and the influence of their input parameters makes them an apparent candidate for Bayesian Optimization (BO). Simulation models are multivariable and stochastic, and their behavior is to a large extent unpredictable. In particular, we do not know for sure which input parameters to adjust to maximize (or minimize) the model’s outcome. In addition, the complex models can take a substantial amount of time to run.
Bayesian Optimization is a sequential and self-learning algorithm to optimize black box functions similar to as we find them in simulation models: they contain a set of parameters for which we want to identify the optimal set, they are expensive to evaluate, and they exhibit stochastic noise. BO has proven to efficiently optimize black box functions from varius disciplines. Among those, and most notably, it is successfully applied in machine learning algorithms to optimize hyperparameters.