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Klagenfurt am Worthersee, Austria

The University of Klagenfurt is a federal Austrian University and the largest research and higher education institution in the Austrian province of Carinthia. It has its main campus in Klagenfurt, with additional facilities in Austria's two largest cities Vienna and Graz. Efforts to found the University began in 1964, and succeeded in 1970, during the term of Governor Sima. Today, the University holds faculties of Humanities & Social science, Management & Economics, Technology, and Interdisciplinary Studies. The School of Interdisciplinary Studies also runs departments in Vienna and Graz; the Dean of IFF, Verena Winiwarter, was elected Austrian "Scientist of the Year" 2013. In addition to the departments and units of the four faculties, the University also hosts a number of central facilities such as the Robert Musil Institute , the University Cultural Center , the build! Gründerzentrum , the University Sports Center , and the Klagenfurt University Library.The incumbent President of the University is Oliver Vitouch, a cognitive psychologist and former faculty member of the University of Vienna and the Max Planck Institute for Human Development in Berlin. Norbert Frei chairs the Academic Senate, Robert Rebhahn the Board of Trustees.Klagenfurt is the southernmost university in the German-speaking countries and supports bi- and multilingualism, especially in the context of the Slovenian minority in Carinthia . Wikipedia.

The aim of this paper is to show the economic potential of demand response (DR) on household level at Central European market conditions. Thereby, required economic benefits for consumers' participation, the realistic load shifting potential at household level and the estimation of essential intelligent infrastructure costs are discussed. The core of this paper builds a case-study applying spot market-oriented load shifting from the supplier's point of view by using Austrian electricity market data, household load profiles as well as a heat pump and e-car charging load profile. It is demonstrated which cost savings for suppliers can be derived from such load shifting procedure at household level. Furthermore, upper cost limits for intelligent infrastructure in order to break-even are derived. Results suggest to take a critical look at European discussions on DR implementation on household level, showing that at Central European market conditions the potential for DR at household level is restricted to significant loads and hence, the applied load shifting strategy is only beneficial with application to heat pumps. In contrast, the frequently discussed shifting of conventional household devices' loads (such as washing machines) economically does not add up. © 2013 Elsevier Ltd.

Bouchachia A.,Klagenfurt University
Neurocomputing | Year: 2011

Self-adaptation is an inherent part of any natural and intelligent system. Specifically, it is about the ability of a system to reconcile its requirements or goal of existence with the environment it is interacting with, by adopting an optimal behavior. Self-adaptation becomes crucial when the environment changes dynamically over time. In this paper, we investigate self-adaptation of classification systems at three levels: (1) natural adaptation of the base learners to change in the environment, (2) contributive adaptation when combining the base learners in an ensemble, and (3) structural adaptation of the combination as a form of dynamic ensemble. The present study focuses on neural network classification systems to handle a special facet of self-adaptation, that is, incremental learning (IL). With IL, the system self-adjusts to accommodate new and possibly non-stationary data samples arriving over time. The paper discusses various IL algorithms and shows how the three adaptation levels are inherent in the system's architecture proposed and how this architecture is efficient in dealing with dynamic change in the presence of various types of data drift when applying these IL algorithms. © 2011 Elsevier B.V.

The recent policy debates about orientating research, technology and innovation policy towards societal challenges, rather than economic growth objectives only, call for new lines of argumentation to systematically legitimize policy interventions. While the multi-level perspective on long-term transitions has attracted quite some interest over the past years as a framework for dealing with long-term processes of transformative change, but the innovation systems approach is still the dominant perspective for devising innovation policy. Innovation systems approaches stress the importance of improving innovation capabilities of firms and the institutional settings to support them, but they are less suited for dealing with the strategic challenges of transforming systems of innovation, production and consumption, and thus with long-term challenges such as climate change or resource depletion. It is therefore suggested to consider insights from transition studies more prominently in a policy framework that is based on the innovation systems approach and the associated notion of 'failures'. We propose a comprehensive framework that allows legitimizing and devising policies for transformative change that draws on a combination of market failures, structural system failures and transformational system failures. © 2012 Elsevier B.V. All rights reserved.

Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: KBBE.2013.3.5-03 | Award Amount: 3.77M | Year: 2014

G-TwYST will execute rat feeding trials with GM maize NK 603 based on OECD Test Guidelines and according to EFSA considerations. In the case of maize NK603 two 90-day and a combined 2-year chronic toxicity/carcinogenicity study will be performed. By combining the results of the G-TwYST project with those of the GRACE project (90-day and 1-year study with maize MON810) it will be possible for the first time to describe the potential medium term and long term toxic effects of the two above-mentioned events. Partners will strictly comply with international standards and norms concerning feeding trials and closely collaborate with EFSA. Feeding stuff used in the trials will be produced according to the principles of good agricultural practice. The project will analyse and report the results of the feeding trials and develop recommendations on the scientific justification and added value of long-term feeding trials for GMO risk assessment. The project will ensure scientific excellence, independence and transparency of both the research process and the results. Transparency and accessibility of project plans and results is a key characteristic of the project and will be ensured by establishing a project website and by using an open access database set up by GRACE as information hubs. Results will be published as open access journal papers. Dedicated engagement, communication, and dissemination activities will target scientists, policy makers and a broad range of stakeholders. Participatory steps will be included in the planning as well as in the interpretation/conclusion phase. Moreover, the views of risk assessment and regulatory bodies as well as wider societal issues will also be taken into consideration. The results of the project will enable risk managers drawing conclusions with regard to framework of the currently applicable GM food/feed risk assessment requirements and procedures in the EU.

Hungerlander P.,Klagenfurt University
International Journal of Production Research | Year: 2014

In this paper we discuss two particular layout problems, namely the Single-Row Equidistant Facility Layout Problem (SREFLP) and the Single-Row Facility Layout Problem (SRFLP). Our aim is to consolidate the two respective branches in the layout literature. We show that the SREFLP is not only a special case of the Quadratic Assignment Problem but also a special case of the SRFLP. This new connection is relevant as the strongest exact methods for the SRFLP outperform the best approaches specialised to the SREFLP. We describe and compare the exact approaches for the SRFLP, the SREFLP and Linear Arrangement that is again a special case of the SREFLP. In a computational study we showcase that the strongest exact approach for the SRFLP clearly outperforms the strongest exact approach tailored to the SREFLP on medium and large benchmark instances from the literature. © 2013 Taylor & Francis.

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