Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Rice husk ash as both pozzolanic admixture and internal curing agent in ultra-high performance concrete

Abstract: The present study investigated the effects of mesoporous amorphous rice husk ash (RHA) on compressive strength, portlandite content, autogenous shrinkage and internal relative humidity (RH) of ultra-high performance concretes (UHPCs) with and without ground granulated blast-furnace slag (GGBS) under different treatments. The results were compared with those of UHPCs containing silica fume (SF). Because of the mesoporous structure, RHA can absorb an amount of aqueous phase to decrease the free water content and to supply thereafter water for further hydrations of cementitious materials. Hence, compressive strength of RHA-blended samples is enhanced. The highly water absorbing RHA delays and slows down the decrease in the internal RH (self-desiccation) of UHPCs, and hence strongly mitigates autogenous shrinkage of UHPCs compared to SF. The combination of GGBS and RHA or SF improves the properties of UHPC. These results suggest that RHA acts as both highly pozzolanic admixture and internal curing agent in UHPC.

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Abstract: In this study, ensemble models using the Bates–Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models. Originally, this study is aimed to investigate the proposed models for forecasting of chlorophyll a concentration. However, the modeling procedure was repeated for water salinity forecasting to evaluate the generality of the approach. The ensemble models are employed for forecasting purposes in Hilo Bay, Hawaii. Moreover, the efficacy of the forecasting models for up to three days in advance is investigated. To predict chlorophyll a and salinity with different lead, the previous daily time series up to three lags are decomposed via different wavelet functions to be applied as input parameters of the models. Further, outputs of the different wavelet-ANN models are combined using the least square boosting ensemble and Bates–Granger techniques to achieve more accurate and more reliable forecasts. To examine the efficiency and reliability of the proposed models for different lead times, uncertainty analysis is conducted for the best single wavelet-ANN and ensemble models as well. The results indicate that accurate forecasts of water temperature and salinity up to three days ahead can be achieved using the ensemble models. Increasing the time horizon, the reliability and accuracy of the models decrease. Ensemble models are found to be superior to the best single models for both forecasting variables and for all the three lead times. The results of this study are promising with respect to multi-step forecasting of water quality parameters such as chlorophyll a and salinity, important indicators of ecosystem status in coastal and ocean regions.

Intrinsic plagiarism detection

Abstract: Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Though these approaches perform well in identifying copied or even modified passages, they assume a closed world: a reference collection must be given against which a plagiarized document can be compared. This raises the question whether plagiarized passages within a document can be detected automatically if no reference is given, e. g. if the plagiarized passages stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism detection. The paper is devoted to this problem class; it shows that it is possible to identify potentially plagiarized passages by analyzing a single document with respect to variations in writing style. Our contributions are fourfold: (i) a taxonomy of plagiarism delicts along with detection methods, (ii) new features for the quantification of style aspects, (iii) a publicly available plagiarism corpus for benchmark comparisons, and (iv) promising results in non-trivial plagiarism detection settings: in our experiments we achieved recall values of 85% with a precision of 75% and better.