Auswertung von elektromyographischen Signalen zur Steuerung von Exoskeletten

Zusammenfassung  Diese Arbeit stellt ein Modell und ein System zur Steuerung von Exoskeletten mit Hilfe von elektrischen Signalen vor, die an den Muskeln des Benutzers gemessen werden. Anhand dieser Signale wird der eigene Drehmomentbeitrag des Benutzer zur gewünschten Bewegung abgesch?tzt, und ein einstellbarer Faktor bestimmt das vom Exoskelett hinzugefügte Drehmoment in Bezug auf den Beitrag des Benutzers. Die Signale werden durch ein komplexes biomechanisches Modell ausgewertet. CR subject classification  J.3 ; J.7 ; I.6.5     

Bandpass sorting of heterogeneous cells using a single surface acoustic wave transducer pair

Separation and sorting of biological entities (viruses, bacteria, and cells) is a critical step in any microfluidic lab-on-a-chip device. Acoustofluidics platforms have demonstrated their ability to use physical characteristics of cells to perform label-free separation. Bandpass-type sorting methods of medium-sized entities from a mixture have been presented using acoustic techniques; however, they require multiple transducers, lack support for various target populations, can be sensitive to flow variations, or have not been verified for continuous flow sorting of biological cells. To our knowledge, this paper presents the first acoustic bandpass method that overcomes all these limitations and presents an inherently reconfigurable technique with a single transducer pair for stable continuous flow sorting of blood cells. The sorting method is first demonstrated for polystyrene particles of sizes 6, 10, and 14.5 μm in diameter with measured purity and efficiency coefficients above 75 ± 6% and 85 ± 9%, respectively. The sorting strategy was further validated in the separation of red blood cells from white blood cells and 1 μm polystyrene particles with 78 ± 8% efficiency and 74 ± 6% purity, respectively, at a flow rate of at least 1 μl/min, enabling to process finger prick blood samples within minutes.     

Why extraversion is not enough: the mediating role of initial peer network centrality linking personality to long-term academic performance

Academic performance (i.e., grade point average) determines career entry factors as well as career success and is thus crucial for students’ future careers. Besides individual factors such as personality traits, individuals’ social embeddedness has been shown to enhance performance. Regarding academic performance, relationships to fellow students (peers), which bundle into one’s developmental network, are a valuable source of psychosocial and career support because occupying central positions within a social network provides the benefit of being able to access career-enhancing resources. Integrating individual and social-contextual factors for the purpose of examining academic performance is therefore plausible. Research results indicate that personality, especially extraversion, might predict performance through network centrality. In this study, we examined this assumption by focusing on extraversion and centrality in the peer developmental network of recently acquainted German psychology bachelor students (N?=?47, 15% male). In a longitudinal design, we analyzed the impact of extraversion and centrality on students’ academic performance at the end of their studies. Results revealed that centrality (i.e., popularity) mediates the relationship of extraversion with academic performance, indicating that extraverted students (regardless of their agreeableness) are more popular among their peers, which, in turn, enhances their academic performance. That is, the likelihood of getting superior final grades depends on whether students manage to attract peers at the very beginning of their university life, which is easier for extraverts. These findings emphasize the importance of the social embeddedness of people, highlight its long-term effects on performance, and yield several implications for research and practice.     

Toppling hierarchies? Media and information literacies,ethnicity, and performative media practices

This article suggests how we should study media and information literacies (MIL) and do so at a time, when young people nurture these literacies through multiple media practices and across spaces of learning. Our basic argument is this: in order to gain a robust knowledge base for the development of MIL we need to study literacy practices beyond print literacy and numeracy, and we need to study these practices beyond formal spaces of learning. The argument is unfolded with particular focus on ethnic minority youth since this group routinely figures as under-achieving in studies of school literacy, such as Programme for International Student Assessment. Based on a brief overview of literacy studies in view of digitization and a critical examination of recent studies of youthful media practices and ethnicity, the argument is illustrated through an empirical analysis that draws on results from a nationally representative survey of media uses among Danes aged 13–23 years. The analysis demonstrates that ethnic minority youth offer the most serious challenge to existing literacy hierarchies found in formal education. We discuss the implications of these results for educational policy-making and for future research on MIL, advocating inclusive approaches in terms of media for learning and spaces of learning.     

Efficient reverse k-nearest neighbor estimation

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.     

Selective search, sectoral patterns, and the impact on product innovation performance

The shift toward more open and interconnected innovation activities has been a major topic in recent academic and practitioner discussions. Firms must connect their in-house R&D activities with external partners, such as leading customers or universities, to increase the effectiveness of their innovation activities. Hence, management needs to define where to search for valuable knowledge in its environment. In this paper we argue that knowledge search has to reflect the heterogeneity of various knowledge sources with regard to the knowledge they can provide and how these sources can be activated. We hypothesize that search strategies driven by science, suppliers and the product market will contribute differently to innovation success with new-to-market versus imitated products. Moreover, we explore the effect of these types of knowledge search within different sectoral patterns of innovation. Our empirical analysis rests on a sample of almost 5000 firms from five Western European countries. The results support our hypotheses and highlight the potentials and shortcomings of different types of knowledge search.