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The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners.
The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies.
This technical report presents results of research projects executed in 2018. Selected projects have presented their results on April 17th and November 14th 2017 at the Future SOC Lab Day events.
Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.
Alles auf Anfang!
(2019)
Im Zuge der Bologna-Reform ist an Hochschulen vieles in Bewegung gekommen. Studium und Lehre sind stärker ins Blickfeld gerückt. Dabei kommt der Studieneingangsphase besondere Bedeutung zu, werden doch hier die Weichen für ein erfolgreiches Studium gestellt. Deshalb ist es verständlich, dass die Hauptanstrengungen der Hochschulen auf den Studieneingang gerichtet sind – ganz nach dem Motto: „Auf den Anfang kommt es an!“. Konsens herrscht dahingehend, dass der Studieneingang neu zu gestalten ist, doch beim „Wie?“ gibt es unterschiedliche Antworten. Zugleich wird immer deutlicher, dass eine wirksame Neugestaltung der Eingangsphase nur mit einer umfassenden Reform des Studiums gelingen kann.
Ziel des vierten Bandes der Potsdamer Beiträge zur Hochschulforschung ist es, eine Zwischenbilanz der Debatte zum Studieneingang zu ziehen. Auf der Basis empirischer Studien werden unterschiedliche Perspektiven auf den Studieneingang eingenommen und Empfehlungen zur Optimierung des Studieneingangs abgeleitet. Die zahlreichen Untersuchungsergebnisse Potsdamer Forschergruppen werden durch weitere nationale sowie internationale Perspektiven ergänzt. Der Band richtet sich an alle, die sich für die Entwicklung an Hochschulen interessieren.