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Institution

Vienna University of Technology

EducationVienna, Austria
About: Vienna University of Technology is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Laser & Cloud computing. The organization has 16723 authors who have published 49341 publications receiving 1302168 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors examined the predictive performance of various flood regionalisation methods for the ungauged catchment case, based on a jack-knifing comparison of locally estimated and regionalised flood quantiles for 575 Austrian catchments, 122 of which have a record length of 40 years or more.

245 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider irreversible efficiency improvements as a major reason for the moderate growth in energy demand after the plummeting of the oil price in 1985 and test different econometric models to take into account efficiency indicators.

245 citations

Journal ArticleDOI
TL;DR: This tutorial review shows that both types of cluster-based materials are limiting cases with many options for varying both the cluster units as well as the connecting organic entities.
Abstract: Clusters as building blocks have been used for two types of inorganic–organic hybrid materials. The first are hybrid polymers, with polymer-like properties and structures, where the cluster units crosslink the polymer chains. They are prepared by co-polymerization of organic monomers with functional ligands attached to the clusters. The second type is crystalline metal–organic framework structures which are obtained by coordination chemistry approaches, i.e. by coordinating multifunctional organic ligands to cluster units. This tutorial review shows that both types of cluster-based materials are limiting cases with many options for varying both the cluster units as well as the connecting organic entities.

244 citations

Journal ArticleDOI
TL;DR: The need to actively promote and pursue the use of a "large catchment sample" approach to modeling the rainfall–runoff process, thereby balancing depth with breadth is discussed.
Abstract: A holy grail of hydrology is to understand catchment processes well enough that models can provide detailed simulations across a variety of hydrologic settings at multiple spatiotemporal scales, and under changing environmental conditions. Clearly, this cannot be achieved only through intensive place-based investigation at a small number of heavily instrumented catchments, or by empirical methods that do not fully exploit our understanding of hydrology. In this opinion paper, we discuss the need to actively promote and pursue the use of a "large catchment sample" approach to modeling the rainfall–runoff process, thereby balancing depth with breadth. We examine the history of such investigations, discuss the benefits (improved process understanding resulting in robustness of prediction at ungauged locations and under change), examine some practical challenges to implementation and, finally, provide perspectives on issues that need to be taken into account as we move forward. Ultimately, our objective is to provoke further discussion and participation, and to promote a potentially important theme for the upcoming Scientific Decade of the International Association of Hydrological Sciences entitled Panta Rhei.

244 citations

Journal ArticleDOI
TL;DR: In this article, the analysis of the adsorption behavior of bisphenol-A (CAS 85-05-7), 17 beta-estradiole, 17 alpha-ethinylstradiole and 17 Alpha-Este-Radiole to activated and to inactivated sludge from wastewater treatment plants (WWTPs) was investigated.

243 citations


Authors

Showing all 16934 results

NameH-indexPapersCitations
Krzysztof Matyjaszewski1691431128585
Wolfgang Wagner1562342123391
Marco Zanetti1451439104610
Sridhara Dasu1401675103185
Duncan Carlsmith1381660103642
Ulrich Heintz136168899829
Matthew Herndon133173297466
Frank Würthwein133158494613
Alain Hervé132127987763
Manfred Jeitler132127889645
David Taylor131246993220
Roberto Covarelli131151689981
Patricia McBride129123081787
David Smith1292184100917
Lindsey Gray129117081317
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023171
2022379
20212,527
20202,811
20192,846
20182,650