Austrian Institute of Technology

About: Austrian Institute of Technology is a facility organization based out in Vienna, Austria. It is known for research contribution in the topics: Smart grid & Computer science. The organization has 2083 authors who have published 5221 publications receiving 109343 citations. The organization is also known as: Austrian Research Centers.

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

Abstract: Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.

The Photodetector Array Camera and Spectrometer (PACS) on the Herschel Space Observatory

Abstract: The Photodetector Array Camera and Spectrometer (PACS) is one of the three science instruments on ESA's far infrared and submil- limetre observatory. It employs two Ge:Ga photoconductor arrays (stressed and unstressed) with 16 × 25 pixels, each, and two filled silicon bolometer arrays with 16 × 32 and 32 × 64 pixels, respectively, to perform integral-field spectroscopy and imaging photom- etry in the 60−210 μm wavelength regime. In photometry mode, it simultaneously images two bands, 60−85 μ mo r 85−125 μ ma nd 125−210 μm, over a field of view of ∼1.75 � × 3.5 � , with close to Nyquist beam sampling in each band. In spectroscopy mode, it images afi eld of 47 �� × 47 �� , resolved into 5 × 5 pixels, with an instantaneous spectral coverage of ∼ 1500 km s −1 and a spectral resolution of ∼175 km s −1 . We summarise the design of the instrument, describe observing modes, calibration, and data analysis methods, and present our current assessment of the in-orbit performance of the instrument based on the performance verification tests. PACS is fully operational, and the achieved performance is close to or better than the pre-launch predictions.

Plant growth-promoting bacteria in the rhizo- and endosphere of plants: Their role, colonization, mechanisms involved and prospects for utilization

Abstract: In both managed and natural ecosystems, beneficial plant-associated bacteria play a key role in supporting and/or increasing plant health and growth. Plant growth-promoting bacteria (PGPB) can be applied in agricultural production or for the phytoremediation of pollutants. However, because of their capacity to confer plant beneficial effects, efficient colonization of the plant environment is of utmost importance. The majority of plant-associated bacteria derives from the soil environment. They may migrate to the rhizosphere and subsequently the rhizoplane of their hosts before they are able to show beneficial effects. Some rhizoplane colonizing bacteria can also penetrate plant roots, and some strains may move to aerial plant parts, with a decreasing bacterial density in comparison to rhizosphere or root colonizing populations. A better understanding on colonization processes has been obtained mostly by microscopic visualisation as well as by analysing the characteristics of mutants carrying disfunctional genes potentially involved in colonization. In this review we describe the individual steps of plant colonization and survey the known mechanisms responsible for rhizosphere and endophytic competence. The understanding of colonization processes is important to better predict how bacteria interact with plants and whether they are likely to establish themselves in the plant environment after field application as biofertilisers or biocontrol agents.

The Hidden World within Plants: Ecological and Evolutionary Considerations for Defining Functioning of Microbial Endophytes

Abstract: All plants are inhabited internally by diverse microbial communities comprising bacterial, archaeal, fungal, and protistic taxa. These microorganisms showing endophytic lifestyles play crucial roles in plant development, growth, fitness, and diversification. The increasing awareness of and information on endophytes provide insight into the complexity of the plant microbiome. The nature of plant-endophyte interactions ranges from mutualism to pathogenicity. This depends on a set of abiotic and biotic factors, including the genotypes of plants and microbes, environmental conditions, and the dynamic network of interactions within the plant biome. In this review, we address the concept of endophytism, considering the latest insights into evolution, plant ecosystem functioning, and multipartite interactions.

Learning a variational network for reconstruction of accelerated MRI data.

Abstract: PURPOSE To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. THEORY AND METHODS Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. RESULTS The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. CONCLUSION Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.