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Die Transformation des deutschen Energiesystems aus der Perspektive der Bevölkerung
No full textZiel des Beitrags ist es, auf Basis der Einstellungen der Bevölkerung den aktuell durch die Bundesregierung verfolgten Energietransformationspfad zu bewerten und mögliche Anpassungen aufzuzeigen. Die Einstellungen der Bevölkerung werden dazu mit Hilfe einer zweistufigen deutschlandweiten repräsentativen Telefonbefragung erfasst. In der ersten Befragung von Oktober bis November 2013 werden zunächst die verschiedenen Akzeptanzfaktoren hinsichtlich ihrer Bedeutung bewertet. Darauf basierend werden in der zweiten Befragung von Februar bis März 2014 ausgewählte Akzeptanzfaktoren hinsichtlich ihrer Ausgestaltung untersucht und Zahlungsbereitschaften für einen Transformationspfad bestimmt. Die Ergebnisse der ersten Befragung zeigen, dass umweltbezogene Faktoren, wie die Reduzierung von global und lokal wirkenden Emissionen, durch die Bevölkerung als überdurchschnittlich wichtig eingestuft werden. Hingegen wird volkswirtschaftlichen Faktoren, wie Beschäftigung oder niedrige Energiekosten für die Wirtschaft, eine eher geringe Bedeutung zugemessen. In der zweiten Befragung konnte eine deutliche Präferenz für eine nachhaltige Transformation des deutschen Energiesystems, d. h. eine starke Reduktion von Emissionen und ein starker Ausbau von Erneuerbaren Energien, ermittelt werden. Neben dem Kernenergieausstieg wird zudem von einer deutlichen Mehrheit ein Ausstieg aus der Braunkohle-Verstromung gefordert. Die zusätzliche Zahlungsbereitschaft beim Strombezug zeigt zudem die Bereitschaft zur Unterstützung eines solchen Transformationspfades. Insgesamt zeigen die Ergebnisse, dass die Präferenzen der deutschen Bevölkerung mit dem Transformationspfad der Bundesregierung im Wesentlichen übereinstimmen. Inwiefern die betrachteten Einstellungen der Bevölkerung, wie die ablehnende Haltung zur weiteren Nutzung von Braunkohle, über die Zeit stabil sind, sollte in Zukunft noch genauer untersucht werden – insbesondere im Zusammenhang mit den Herausforderungen steigender Stromkosten und der Gewährleistung der Versorgungssicherheit.This study aims to evaluate the energy transition pathway of the German government based on the public opinion and points out possible adjustments. The assessment of the public opinion is based on a two-step nationwide representative telephone survey. In the first survey, conducted between October and November 2013, several acceptance factors are evaluated in terms of importance. Based on the first survey, the second survey, conducted between February and March 2014, analyses selected acceptance factors in more detail and determine the willingness-to-pay for a transition pathway. As the findings of the first survey show, environmental factors, like the reduction of globally and locally effective emissions, are rated as considerably important by the German population. Whereas economic factors, like the impact on employment or lower energy cost for the economy, are emphasized with minor importance. The second survey determines a preference for a more sustainable transition of the German energy system, this is, a stronger reduction of emissions as well as a stronger development of renewable energies. Besides the phase-out of nuclear power, a considerable majority also demand the phase-out of lignite power. The support for a more sustainable transition pathway is also pointed out by a higher willingness to pay. Nonetheless, the overall results indicate that the public opinion is basically in line with the transition pathway of the German government. Further investigation is needed to determine whether a negative attitude with respect to further usage of lignite power stabilizes in the future. This is especially an issue if challenges of higher energy prices as well as of security of supply occur
Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale
No full textPrevious studies investigating multi-sensor fusion for the collection of soil information have shown variable improvements, and the underlying prediction mechanisms are not sufficiently understood for spectrally-active and -inactive properties. Our objective was to study prediction mechanisms and benefits of model fusion by measuring mid-infrared (MIR) and X-ray fluorescence (XRF) spectra, texture, total and labile organic carbon (OC) and nitrogen (N) content, pH, and cation exchange capacity (CEC) for n = 117 soils from an arable field in Germany. Partial least squares regression models underwent a three-fold training/testing procedure using MIR spectra or elemental concentrations derived from XRF spectra. Additionally, two sequential hybrid and two high-level fusion approaches were tested. For the studied field, MIR was superior for organic properties (ratio of prediction to interquartile distance of validation (RPIQV) for total OC = 7.7 and N = 5.0)), while XRF was superior for inorganic properties (RPIQV for clay = 3.4, silt = 3.0, and sand = 1.8). Even the optimal fusion approach brought little to no accuracy improvement for these properties. The high XRF accuracy for clay and silt is explained by the large number of elements with variable importance in the projection scores >1 (Fe ≈ Ni > Si ≈ Al ≈ Mg > Mn ≈ K ≈ Pb (clay only) ≈ Cr) with strong spearman correlations (±0.57 < rs < ±0.90) with clay and silt. For spectrally-inactive properties relying on indirect prediction mechanisms, the relative improvements from the optimal fusion approach compared to the best single spectrometer were marginal for pH (3.2% increase in RPIQV versus MIR alone) but more pronounced for labile OC (9.3% versus MIR) and CEC (12% versus XRF). Dominance of a suboptimal spectrometer in a fusion approach worsened performance compared to the best single spectrometer. Granger-Ramanathan averaging, which weights predictions according to accuracy in training, is therefore recommended as a robust approach to capturing the potential benefits of multiple sensors
Monoammonium Trimetaphosphimate (NH4)H2(PO2NH)3
No full textTrimetaphosphimates show a rich structural variability in both cation coordination and anion arrangement. They are precursors for crystalline, as well as amorphous oxonitridophosphates. The ammonium trimetaphosphimate (NH4)H2(PO2NH)3 is formed during the decomposition of the corresponding acid in solution. The monoclinic crystal structure of the monoammonium salt was elucidated by single crystal X-ray diffraction using synchrotron radiation. The trimetaphosphimate monoanions exhibit a twist conformation and form crankshaft-like stacks along [100], which have so far only been observed in (NH4)3(PO2NH)3·H2O and Ag3(PO2NH)3. (NH4)H2(PO2NH)3 decomposes at 170 °C, forming a poorly crystalline phase. Therefore, it is a model system and possible precursor for the synthesis of oxonitridophosphates
Triage in Times of COVID-19: A Moral Dilemma
No full textWe present evidence from choice experiments on hypothetical triage decisions in a pandemic. Respondents
have to decide who out of two patients gets ventilation. Patients are described in terms of attributes such
as short-term survival chance, long-term life expectancy, and their current ventilation status. Attributes
are derived from the ethical discourse among experts regarding triage guidelines during the COVID-19
pandemic and differ in the extent to which they are salient from a utilitarian or deontological perspective.
Empirically, we find that although nonexperts agree with experts in prioritizing utilitarian attributes in
triage decisions, nonexperts also consider the adherence to the norm of wearing face masks as particularly
relevant. Furthermore, our study supports Greene and colleagues’ dual-process model of moral judgment;
we find that utilitarian attributes are more decisive for respondents with a greater inclination toward
utilitarianism and for respondents with a greater tendency toward reflection
Symmetry-Reduction for Reduced Order Modelling of Fluid Flows
No full textLearning low-dimensional representations of dynamical systems is a central focus in modern research. However, the chaotic nature and high dimensionality of many complex systems present significant challenges.
These systems often possess symmetries, which can be exploited to simplify the underlying structure of their attractors. This thesis introduces several methods for symmetry reduction - a process that removes the influence of symmetries in high-dimensional systems - facilitating the discovery of low-dimensional dynamics.
The first approach leverages Siamese neural networks with embedded autoencoders to remove discrete symmetries from data. Continuous symmetries are then addressed using Spatial Transformer Networks. The second approach develops sym-metry-invariant polynomials for discrete cyclic symmetry groups of arbitrary order, enabling symmetry reduction in a broad range of systems. For continuous symmetries, we use first Fourier mode slices. In a third approach, we introduce a continuous symmetry reduction technique based on the method of slices, where PCA modes serve as templates to align the data.
We apply the first two approaches to three illustrative examples inspired by fluid flows in the atmosphere, i.e. the Lorenz system, periodic cylinder wake simulations, as well as periodic and chaotic Kolmogorov flow. For these systems, low-dimensional representations of the attractors are learned using PCA and autoencoders. The second approach also extends to modeling the dynamics of these low-dimensional representations using neural ODEs. The final approach is applied to real-world atmospheric dynamics, specifically the geopotential height anomalies in the Northern Hemisphere. Due to the system's extreme complexity and it not being in equilibrium, dimensionality reduction beyond PCA is not employed here.
Across all methods, we assess modeling errors and compare predictions using several metrics, consistently finding that symmetry reduction improves the accuracy and reduces the data requirements for low-dimensional models.:Abstract ............................................................... i
Acknowledgements .................................................. ii
I Introduction .................................................... 1
II Methodology ................................................... 7
II.1 Dimensionality Reduction .................................... 7
II.1.1 Choosing an Embedding Dimension ...................... 7
II.1.2 PCA .......................................................... 8
II.1.3 Multilayer Perceptrons ................................... 9
II.1.4 Autoencoders ............................................. 11
II.1.5 Sequential PCA and Autoencoders ..................... 11
II.2 Symmetry Reduction ......................................... 12
II.2.1 Symmetries and Dimensionality Reduction ............ 15
II.2.2 Continuous Symmetry Reduction ....................... 17
II.2.2.1 Method of Slices ...................................... 18
II.2.2.2 First Fourier Mode Slice ............................. 20
II.2.2.3 PCA Based Slices ..................................... 21
II.2.2.4 Spatial Transformer Networks ...................... 21
II.2.3 Discrete Symmetry Reduction ......................... 24
II.2.3.1 Fundamental Domains ............................. 24
II.2.3.2 Siamese Networks for Fundamental Domains ...... 25
II.2.3.3 Invariant Polynomials for C₂ ...................... 26
II.2.3.4 Invariant Polynomials for Cₙ ...................... 27
II.2.3.5 Trajectory Reconstruction .......................... 32
II.3 Predictive Modelling ..................................... 33
III Results .......................................................... 36
III.1 Lorenz Attractor ............................................ 36
III.1.1 Physical System ....................................... 36
III.1.2 Symmetry Reduction using Fundamental Domains and Siamese Networks ....................................................... 38
III.1.3 Symmetry Reduction using Invariant Polynomials ... 41
III.1.4 Conclusion ................................................ 43
III.2 Cylinder Flow ................................................. 44
III.2.1 Physical System ....................................... 44
III.2.2 Symmetry Reduction using Siamese Networks ....... 47
III.2.3 Symmetry Reduction using Invariant Polynomials ... 50
III.2.4 Conclusion ................................................ 56
III.3 Kolmogorov-Flow ........................................... 57
III.3.1 Physical System ....................................... 57
III.3.2 Symmetry Reduction using Siamese Networks and Spatial Transformer Networks ............................................. 62
III.3.2.1 Periodic Dynamics ................................... 62
III.3.2.2 Chaotic Dynamics ................................... 66
III.3.3 Symmetry Reduction using Invariant Polynomials ... 70
III.3.3.1 Periodic Dynamics ................................... 76
III.3.3.2 Chaotic Dynamics ................................... 79
III.3.4 Conclusion ................................................ 90
III.4 Geopotential Height in the Northern Hemisphere ... 91
III.4.1 Physical System ....................................... 91
III.4.2 Symmetry Reduction using PCA Bases Slices ........ 94
III.4.3 Conclusion ................................................ 98
IV Final Conclusion & Outlook ................................ 10
Prolyl endopeptidase is involved in the degradation of neural cell adhesion molecules in vitro
No full textMembrane-associated glycoprotein neural cell adhesion molecule
(NCAM) and its polysialylated form (PSA-NCAM) play an important
role in brain plasticity by regulating cell–cell interactions. Here, we
demonstrate that the cytosolic serine protease prolyl endopeptidase
(PREP) is able to regulate NCAM and PSA-NCAM. Using a SH-SY5Y
neuroblastoma cell line with stable overexpression of PREP, we found
a remarkable loss of PSA-NCAM, reduced levels of NCAM180 and
NCAM140 protein species, and a significant increase in the NCAM
immunoreactive band migrating at an apparent molecular weight of
120 kDa in PREP-overexpressing cells. Moreover, increased levels of
NCAM fragments were found in the concentrated medium derived
from PREP-overexpressing cells. PREP overexpression selectively
induced an activation of matrix metalloproteinase-9 (MMP-9), which
could be involved in the observed degradation of NCAM, as MMP-9
neutralization reduced the levels of NCAM fragments in cell culture
medium.We propose that increased PREP levels promote epidermal
growth factor receptor (EGFR) signaling, which in turn activates
MMP-9. In conclusion, our findings provide evidence for newly discovered
roles for PREP in mechanisms regulating cellular
plasticity through NCAM and PSA-NCAM
