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Lubeck, Germany

The Fachhochschule Lübeck is a university in the city of Lübeck in the German state of Schleswig-Holstein. The name of the institution translates as "Lübeck University of Applied science Lübeck" in English, but in conversations and prose it is generally called by its German name or by the acronym of the German name, FHL. Wikipedia.

Lucas P.,Dutch Metrology Institute | Klein S.,FH Luebeck
Biomedizinische Technik | Year: 2015

In various recently published studies, it is argued that there are underestimated risks with infusion technology, i.e., adverse incidents believed to be caused by inadequate administration of the drugs. This is particularly the case for applications involving very low-flow rates, i.e., <1 ml/h and applications involving drug delivery by means of multiple pumps. The risks in infusing are caused by a lack of awareness, incompletely understood properties of the complete drug delivery system and a lack of a proper metrological infrastructure for low-flow rates. Technical challenges such as these were the reason a European research project "Metrology for Drug Delivery" was started in 2011. In this special issue of Biomedical Engineering, the results of that project are discussed. © 2015 by De Gruyter. Source

Faria J.J.,University of Leeds | Krause S.,FH Luebeck | Krause J.,Leibniz Institute of Freshwater Ecology and Inland Fisheries
Behavioral Ecology | Year: 2010

Social information use is common in a wide range of group-living animals, notably in humans. We investigated social information use by pedestrians in a potentially dangerous scenario: at a road crossing. To judge a safe gap in traffic, pedestrians can use social information, such as the crossing behavior of others, and follow others across the road. We tested if pedestrians followed others in this scenario by analyzing pedestrian starting position and crossing order. First, we found that neighbors of a crossing pedestrian tended to cross before other waiting pedestrians and that this tendency was significantly higher in observed pedestrians than in a null model: a simulation in which pedestrians did not follow each other. Also, by fitting the null model, we found that on average a person was 1.5-2.5 times more likely to cross if their neighbor had started to cross. Second, we found that males tended to follow others more than females. Third, we observed that some individuals started to cross and then returned to the roadside. These individuals were more frequently found in groups and tended to start to cross relatively later than other pedestrians. These observations suggest that some of these individuals made incorrect decisions about the timing of their crossing and that this was due to social information use. Finally, we propose that the relatively small benefit of a reduced waiting time came at the cost of an increased risk of injury, making the beneficial value of social information use questionable in this context. © The Author 2010. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. Source

Krause J.,Leibniz Institute of Freshwater Ecology and Inland Fisheries | Krause J.,Humboldt University of Berlin | Krause S.,FH Luebeck | Arlinghaus R.,Leibniz Institute of Freshwater Ecology and Inland Fisheries | And 4 more authors.
Trends in Ecology and Evolution | Year: 2013

The increasing miniaturisation of animal-tracking technology has made it possible to gather exceptionally detailed machine-sensed data on the social dynamics of almost entire populations of individuals, in both terrestrial and aquatic study systems. Here, we review important issues concerning the collection of such data, and their processing and analysis, to identify the most promising approaches in the emerging field of 'reality mining'. Automated technologies can provide data sensing at time intervals small enough to close the gap between social patterns and their underlying processes, providing insights into how social structures arise and change dynamically over different timescales. Especially in conjunction with experimental manipulations, reality mining promises significant advances in basic and applied research on animal social systems. © 2013 Elsevier Ltd. Source

Krause J.,Leibniz Institute of Freshwater Ecology and Inland Fisheries | Krause J.,Humboldt University of Berlin | Ruxton G.D.,University of Glasgow | Krause S.,FH Luebeck
Trends in Ecology and Evolution | Year: 2010

Electronic media have unlocked a hitherto largely untapped potential for swarm intelligence (SI; generally, the realisation that group living can facilitate solving cognitive problems that go beyond the capacity of single animals) in humans with relevance for areas such as company management, prediction of elections, product development and the entertainment industry. SI is a rapidly developing topic that has become a hotbed for both innovative research and wild speculation. Here, we tie together approaches from seemingly disparate areas by means of a general definition of SI to unite SI work on both animal and human groups. Furthermore, we identify criteria that are important for SI to operate and propose areas in which further progress with SI research can be made. © 2009 Elsevier Ltd. All rights reserved. Source

Wilson A.D.M.,Leibniz Institute of Freshwater Ecology and Inland Fisheries | Krause S.,FH Luebeck | Dingemanse N.J.,Ludwig Maximilians University of Munich | Dingemanse N.J.,Max Planck Institute for Ornithology (Seewiesen) | And 2 more authors.
Behavioral Ecology and Sociobiology | Year: 2013

In recent years, animal social interactions have received much attention in terms of personality research (e. g. aggressive or cooperative interactions). However, other components of social behaviour such as those describing the intensity, frequency, directedness and individual repeatability of interactions in animal groups have largely been neglected. Network analysis offers a valuable opportunity to characterize individual consistency of traits in labile social groups and therein provide novel insights to personality research in ways previously not possible using traditional techniques. Should individual network positions be consistently different between individuals under changing conditions, they might reflect expressions of an individual's personality. Here, we discuss a conceptual framework for using network analyses to infer the presence of individual differences and present a statistical test based on randomization techniques for testing the consistency of network positions in individuals. The statistical tools presented are useful because if particular individuals consistently occupy key positions in social networks, then this is also likely to have consequences for their fitness as well as for that of others in the population. These consequences may be particularly significant since individual network position has been shown to be important for the transmission of diseases, socially learnt information and genetic material between individuals and populations. © 2012 Springer-Verlag Berlin Heidelberg. Source

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