International Climate Agreements, Cost Reductions and Convergence of Partisan Politics

In recent years, differences between traditional and green parties have been leveled with respect to climate protection. We show that this partial convergence in party platforms can be explained by international climate agreements, effectively reducing greenhouse gas emissions. We set up a voting model in which political parties differ in their preferences for climate protection and in which (national) climate protection causes both resource costs and distortions in the international allocation of production. International agreements, which reduce greenhouse gas emissions, decrease effective abatement costs. This affects traditional parties in a different way than green parties, since a lower preference for climate protection implies a higher price (cost) elasticity of demand. Thus, climate agreements can lead to more political consensus within countries, even if politicians are partisans. We also point out that increasing flexibility and efficiency in abatement mechanisms is preferable to forming a climate coalition that focuses directly on emission reduction commitments.climate protection, political economy, green parties, platform convergence

International climate agreements, cost reductions and convergence of Partisan politics

In recent years, differences between traditional and green parties have been leveled with respect to climate protection. We show that this partial convergence in party platforms can be explained by international climate agreements, effectively reducing greenhouse gas emissions. We set up a voting model in which political parties differ in their preferences for climate protection and in which (national) climate protection causes both resource costs and distortions in the international allocation of production. International agreements, which reduce greenhouse gas emissions, decrease effective abatement costs. This affects traditional parties in a different way than green parties, since a lower preference for climate protection implies a higher price (cost) elasticity of demand. Thus, climate agreements can lead to more political consensus within countries, even if politicians are partisans. We also point out that increasing flexibility and efficiency in abatement mechanisms is preferable to forming a climate coalition that focuses directly on emission reduction commitments

A Multi-Chamber System for Analyzing the Outgassing, Deposition, and Associated Optical Degradation Properties of Materials in a Vacuum

We report on the Camera Materials Test Chamber, a multi-vessel apparatus which analyzes the outgassing consequences of candidate materials for use in the vacuum cryostat of a new telescope camera. The system measures the outgassing products and rates of samples of materials at different temperatures, and collects films of outgassing products to measure the effects on light transmission in six optical bands. The design of the apparatus minimizes potential measurement errors introduced by background contamination.Comment: 9 pages, 10 figures, published in RSI (minor edits made to match journal accepted version

Cluster Structure in Cosmological Simulations I: Correlation to Observables, Mass Estimates, and Evolution

We use Enzo, a hybrid Eulerian AMR/N-body code including non-gravitational heating and cooling, to explore the morphology of the X-ray gas in clusters of galaxies and its evolution in current generation cosmological simulations. We employ and compare two observationally motivated structure measures: power ratios and centroid shift. Overall, the structure of our simulated clusters compares remarkably well to low-redshift observations, although some differences remain that may point to incomplete gas physics. We find no dependence on cluster structure in the mass-observable scaling relations, T_X-M and Y_X-M, when using the true cluster masses. However, estimates of the total mass based on the assumption of hydrostatic equilibrium, as assumed in observational studies, are systematically low. We show that the hydrostatic mass bias strongly correlates with cluster structure and, more weakly, with cluster mass. When the hydrostatic masses are used, the mass-observable scaling relations and gas mass fractions depend significantly on cluster morphology, and the true relations are not recovered even if the most relaxed clusters are used. We show that cluster structure, via the power ratios, can be used to effectively correct the hydrostatic mass estimates and mass-scaling relations, suggesting that we can calibrate for this systematic effect in cosmological studies. Similar to observational studies, we find that cluster structure, particularly centroid shift, evolves with redshift. This evolution is mild but will lead to additional errors at high redshift. Projection along the line of sight leads to significant uncertainty in the structure of individual clusters: less than 50% of clusters which appear relaxed in projection based on our structure measures are truly relaxed.Comment: 57 pages, 18 figures, accepted to ApJ, updated definition of T_X and M_gas but results unchanged, for version with full resolution figures, see http://www.ociw.edu/~tesla/sims.ps.g

Continuous Modeling and Optimization Approaches for Manufacturing Systems

This thesis is concerned with two macroscopic models that are based on hyper- bolic partial differential equations (PDE) with discontinuous flux functions. The first model describes the material flow of an entire production line with finite buffers. We consider different solutions of the model, present the novel DFG- method (Discontinuous Flux Godunov), and compare the results with other established numerical methods. Additionally, we investigate a restricted optimization problem with respect to partial differential equations with discontinuous flux functions and consider two different solution approaches that are based on the adjoint method and the mixed integer problem (MIP). Further, we extend the model and its optimization problem to network structures. The second model describe the material flow on conveyor belts with obstacle interactions. We introduce a novel two dimensional model with a discontinuous and a non-local flux function. We consider a finite volume method and the discon- tinuous Galerkin method for solving this model. Finally, we validate the model with real data and present a numerical study with respect to the introduced solution methods

Deep neural networks as surrogate models for time-efficient manufacturing process optimisation

Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). Results reveal an improved predictive accuracy as more process-relevant information from the supplied simulations can be extracted. Application of the DNN in an SBO-framework for blank holder optimisation shows improved convergence compared to classical evolutionary algorithms. Thus, DNNs are a promising option for future surrogates in SBO

Near surface roughness estimation: A parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients () on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only the outflow of the test field and rain intensity are known. For this purpose, a framework was developed to parameterize shallow water depth (< 1 cm) -dependent roughness coefficients. To test the robustness of the framework, three different formulations of depth-dependent roughness and a constant Manning coefficient are used by comparing the measured discharge under different rainfall intensities with simulations in a 2D-hydraulic model. We identified a strong dependency of Manning’s on the degree of vegetation cover and -type as well as an influence of consecutive rainfall events. This finally leads to a more robust parameterization of near surface roughness for hydrodynamic modelling, which is particularly important for the simulation of heavy rainfall events

Near surface roughness estimation: a parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two-dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only the outflow of the test field and rain intensity are known. For this purpose, a framework was developed to parameterize shallow water depth ( cm) -dependent roughness coefficients. To test the robustness of the framework, three different formulations of depth-dependent roughness and a constant Manning coefficient are used by comparing the measured discharge under different rainfall intensities with simulations in a 2D-hydraulic model. We identified a strong dependency of Manning’s on the degree of vegetation cover and -type as well as an influence of consecutive rainfall events. This finally leads to a more robust parameterization of near surface roughness for hydrodynamic modelling, which is particularly important for the simulation of heavy rainfall events

The Extremely Luminous Quasar Survey in the Pan-STARRS 1 Footprint (PS-ELQS)

We present the results of the Extremely Luminous Quasar Survey in the $3\pi$ survey of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS; PS1). This effort applies the successful quasar selection strategy of the Extremely Luminous Survey in the Sloan Digital Sky Survey footprint ($\sim12,000\,\rm{deg}^2$) to a much larger area ($\sim\rm{21486}\,\rm{deg}^2$). This spectroscopic survey targets the most luminous quasars ($M_{1450}\le-26.5$; $m_{i}\le18.5$) at intermediate redshifts ($z\ge2.8$). Candidates are selected based on a near-infrared JKW2 color cut using WISE AllWISE and 2MASS photometry to mainly reject stellar contaminants. Photometric redshifts ($z_{\rm{reg}}$) and star-quasar classifications for each candidate are calculated from near-infrared and optical photometry using the supervised machine learning technique random forests. We select 806 quasar candidates at $z_{\rm{reg}}\ge2.8$ from a parent sample of 74318 sources. After exclusion of known sources and rejection of candidates with unreliable photometry, we have taken optical identification spectra for 290 of our 334 good PS-ELQS candidates. We report the discovery of 190 new $z\ge2.8$ quasars and an additional 28 quasars at lower redshifts. A total of 44 good PS-ELQS candidates remain unobserved. Including all known quasars at $z\ge2.8$, our quasar selection method has a selection efficiency of at least $77\%$. At lower declinations $-30\le\rm{Decl.}\le0$ we approximately triple the known population of extremely luminous quasars. We provide the PS-ELQS quasar catalog with a total of 592 luminous quasars ($m_{i}\le18.5$, $z\ge2.8$). This unique sample will not only be able to provide constraints on the volume density and quasar clustering of extremely luminous quasars, but also offers valuable targets for studies of the intergalactic medium.Comment: 34 pages, 10 figures, accepted to ApJ

Proximity-induced spin ordering at the interface between a ferromagnetic metal and a magnetic semiconductor

14 páginas, 5 figuras.-- PACS number8s): 73.40.Sx, 75.70.-iWe carry on a theoretical investigation of the conditions for the appearance and/or modification of spin ordering in a dilute magnetic semiconductor that is in contact with a ferromagnetic metal. We show that the magnetic proximity effect has a rather complex physical nature in this system. Allowing for the hybridization between the ferromagnetic metal and semiconductor electron states, we calculate the spin polarization and spin susceptibility of carriers in the semiconductor layer near the contact. The peculiar mechanism of indirect exchange coupling that occurs between local spins dissolved in the semiconductor host when a dilute magnetic semiconductor is in contact with a ferromagnetic metal is analyzed. The structure of the proximity-induced ordering of local moments in a dilute magnetic semiconductor is qualitatively described within a mean-field approach. On the basis of our results, we interpret the experimental data on Fe/(Ga,Mn)As and Py/(Ga,Mn)As layered structures.The work was partially supported by the University of the Basque Country [Proyecto GV-UPV/EHU under Grant No. IT-366-07), Spanish Ministerio de Ciencia y Tecnología (Grant No. FIS2007-66711-C02-01)] , and by RFBR (Grant No. 10-02-00118). S.C. also acknowledges financial support by PRIN 2007 under Project No. 2007FW3MJX003. V.V.T. acknowledges financial support by Ikerbasque (Basque Foundation for Science).Peer reviewe