Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
8 Ergebnisse
Fabian Schäfer,
Manuel Walther,
Prof. Dr. Dominik Grimm,
Alexander Hübner
Assigning inpatients to hospital beds impacts patient satisfaction and the workload of nurses and doctors. The assignment is subject to unknown inpatient arrivals, in particular for emergency patients. Hospitals, therefore, need to deal with uncertainty on actual bed requirements and potential shortage situations as bed capacities are limited. This paper develops a model and solution approach for solving the patient bed-assignment problem that is based on a machine learning (ML) approach to forecasting emergency patients. First, it contributes by improving the anticipation of emergency patients using ML approaches, incorporating weather data, time and dates, important local and regional events, as well as current and historical occupancy levels. Drawing on real-life data from a large case hospital, we were able to improve forecasting accuracy for emergency inpatient arrivals. We achieved up to 17% better root mean square error (RMSE) when using ML methods compared to a baseline approach relying on averages for historical arrival rates. We further show that the ML methods outperform time series forecasts. Second, we develop a new hyper-heuristic for solving real-life problem instances based on the pilot method and a specialized greedy look-ahead (GLA) heuristic. When applying the hyper-heuristic in test sets we were able to increase the objective function by up to 5.3% in comparison to the benchmark approach in [40]. A benchmark with a Genetic Algorithm shows also the superiority of the hyper-heuristic. Third, the combination of ML for emergency patient admission forecasting with advanced optimization through the hyper-heuristic allowed us to obtain an improvement of up to 3.3% on a real-life problem.
Laubwälder des Spessart. Ökosystembetrachtungen (2023) Vortrag an der VHS Aschaffenburg im Auftrag des Landesbundes für Vogelschutz am 12.10.2023 .
Leonie Hahn,
Dr. Markus Schmidt,
Prof. Dr. Andreas Rothe,
Prof. Dr. habil. Carsten Lorz,
Prof. Dr. Christian Zang
Bewässerung von Forstkulturen: Früherkennung des Bewässerungsbedarfs und praxistaugliche Bewässerunsgmethoden (2023) Vortrag auf der Jahrestagung der Arbeitsgemeinschaft Forstliche Standorts- und Vegetationskunde (AFSV e.V., Sektion im DVFFA) am 11.10.2023 in Lehesten/Frankenwald .
Protein thermostability is important in many areas of biotechnology, including enzyme engineering and protein-hybrid optoelectronics. Ever-growing protein databases and information on stability at different temperatures allow the training of machine learning models to predict whether proteins are thermophilic. In silico predictions could reduce costs and accelerate the development process by guiding researchers to more promising candidates. Existing models for predicting protein thermophilicity rely mainly on features derived from physicochemical properties. Recently, modern protein language models that directly use sequence information have demonstrated superior performance in several tasks. In this study, we evaluate the usefulness of protein language model embeddings for thermophilicity prediction with ProLaTherm, a Protein Language model-based Thermophilicity predictor. ProLaTherm significantly outperforms all feature-, sequence- and literature-based comparison partners on multiple evaluation metrics. In terms of the Matthew’s correlation coefficient, ProLaTherm outperforms the second-best competitor by 18.1% in a nested cross-validation setup. Using proteins from species not overlapping with species from the training data, ProLaTherm outperforms all competitors by at least 9.7%. On these data, it misclassified only one nonthermophilic protein as thermophilic. Furthermore, it correctly identified 97.4% of all thermophilic proteins in our test set with an optimal growth temperature above 70°C.
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Prof. Dr. Volker Zahner
Mikrokosmos Schwarzspechthöhle (2023) Vortrag beim Landesbund für Vogel und Naturschutz in Friedberg am 05.10.2023 .
Text Medienbeitrag,
Simon Haslauer,
Prof. Dr. Josef Kainz,
Prof. Dr. Stefan Wittkopf
Umweltschützern gelten Kachelöfen und Pelletheizungen als klimaschädlich, Waldbauern halten das Heizen mit Holz dagegen für unverzichtbar. Die Energieagentur Ebersberg-München bemüht sich um eine Schlichtung der Diskussion.
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Gerhard Radlmayr
Referent für Wissenstransfer und Forschungskommunikation
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