University of Bremen

About: University of Bremen is a education organization based out in Bremen, Germany. It is known for research contribution in the topics: Population & Glacial period. The organization has 14563 authors who have published 37279 publications receiving 970381 citations. The organization is also known as: Universität Bremen.

Nonlinear Model Predictive Control

Abstract: Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner These results are complemented by discussions of feasibility and robustness NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine the core of any NMPC controller works An appendix covering NMPC software and accompanying software in MATLAB and C++(downloadable from wwwspringercom/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC

A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25

Abstract: Lung cancer is the most common cause of cancer death worldwide, with over one million cases annually. To identify genetic factors that modify disease risk, we conducted a genome-wide association study by analysing 317,139 single-nucleotide polymorphisms in 1,989 lung cancer cases and 2,625 controls from six central European countries. We identified a locus in chromosome region 15q25 that was strongly associated with lung cancer (P = 9 x 10(-10)). This locus was replicated in five separate lung cancer studies comprising an additional 2,513 lung cancer cases and 4,752 controls (P = 5 x 10(-20) overall), and it was found to account for 14% (attributable risk) of lung cancer cases. Statistically similar risks were observed irrespective of smoking status or propensity to smoke tobacco. The association region contains several genes, including three that encode nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3 and CHRNB4). Such subunits are expressed in neurons and other tissues, in particular alveolar epithelial cells, pulmonary neuroendocrine cells and lung cancer cell lines, and they bind to N'-nitrosonornicotine and potential lung carcinogens. A non-synonymous variant of CHRNA5 that induces an amino acid substitution (D398N) at a highly conserved site in the second intracellular loop of the protein is among the markers with the strongest disease associations. Our results provide compelling evidence of a locus at 15q25 predisposing to lung cancer, and reinforce interest in nicotinic acetylcholine receptors as potential disease candidates and chemopreventative targets.

Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum - Part 1: experiments and large-scale features

Abstract: . A set of coupled ocean-atmosphere simulations using state of the art climate models is now available for the Last Glacial Maximum and the Mid-Holocene through the second phase of the Paleoclimate Modeling Intercomparison Project (PMIP2). This study presents the large-scale features of the simulated climates and compares the new model results to those of the atmospheric models from the first phase of the PMIP, for which sea surface temperature was prescribed or computed using simple slab ocean formulations. We consider the large-scale features of the climate change, pointing out some of the major differences between the different sets of experiments. We show in particular that systematic differences between PMIP1 and PMIP2 simulations are due to the interactive ocean, such as the amplification of the African monsoon at the Mid-Holocene or the change in precipitation in mid-latitudes at the LGM. Also the PMIP2 simulations are in general in better agreement with data than PMIP1 simulations.

The Global Ozone Monitoring Experiment (GOME): Mission Concept and First Scientific Results

Abstract: The Global Ozone Monitoring Experiment (GOME) is a new instrument aboard the European Space Agency’s (ESA) Second European Remote Sensing Satellite (ERS-2), which was launched in April 1995. The main scientific objective of the GOME mission is to determine the global distribution of ozone and several other trace gases, which play an important role in the ozone chemistry of the earth’s stratosphere and troposphere. GOME measures the sunlight scattered from the earth’s atmosphere and/or reflected by the surface in nadir viewing mode in the spectral region 240–790 nm at a moderate spectral resolution of between 0.2 and 0.4 nm. Using the maximum 960-km across-track swath width, the spatial resolution of a GOME ground pixel is 40 × 320 km2 for the majority of the orbit and global coverage is achieved in three days after 43 orbits. Operational data products of GOME as generated by DLR-DFD, the German Data Processing and Archiving Facility (D-PAF) for GOME, comprise absolute radiometrically calibrated e...

Sea ice remote sensing using AMSR‐E 89‐GHz channels

Abstract: [1] Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor Advanced Microwave Scanning Radiometer-EOS (AMSR-E) offers spatial resolutions of approximately 6 × 4 km at 89 GHz, nearly 3 times the resolution of the standard sensor SSM/I at 85 GHz (15 × 13 km). Second, a new algorithm enables estimation of sea ice concentration from the channels near 90 GHz, despite the enhanced atmospheric influence in these channels. This allows full exploitation of their horizontal resolution, which is up to 4 times finer than that of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85-GHz data proposed by Svendsen et al. (1987) with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2, and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79, and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002–2006) between ASI and NASA-Team 2 are below −2 ± 8.8%, and between ASI and Bootstrap are 1.7 ± 10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows better estimation of crucial variables in numerical atmospheric and ocean models, for example, the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.