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Direct air capture (DAC) combined with subsequent storage (DACCS) is discussed as one promising carbon dioxide removal option. The aim of this paper is to analyse and comparatively classify the resource consumption (land use, renewable energy and water) and costs of possible DAC implementation pathways for Germany. The paths are based on a selected, existing climate neutrality scenario that requires the removal of 20 Mt of carbon dioxide (CO2) per year by DACCS from 2045. The analysis focuses on the so-called “low-temperature” DAC process, which might be more advantageous for Germany than the “high-temperature” one. In four case studies, we examine potential sites in northern, central and southern Germany, thereby using the most suitable renewable energies for electricity and heat generation. We show that the deployment of DAC results in large-scale land use and high energy needs. The land use in the range of 167–353 km2 results mainly from the area required for renewable energy generation. The total electrical energy demand of 14.4 TWh per year, of which 46% is needed to operate heat pumps to supply the heat demand of the DAC process, corresponds to around 1.4% of Germany's envisaged electricity demand in 2045. 20 Mt of water are provided yearly, corresponding to 40% of the city of Cologne‘s water demand (1.1 million inhabitants). The capture of CO2 (DAC) incurs levelised costs of 125–138 EUR per tonne of CO2, whereby the provision of the required energy via photovoltaics in southern Germany represents the lowest value of the four case studies. This does not include the costs associated with balancing its volatility. Taking into account transporting the CO2 via pipeline to the port of Wilhelmshaven, followed by transporting and sequestering the CO2 in geological storage sites in the Norwegian North Sea (DACCS), the levelised costs increase to 161–176 EUR/tCO2. Due to the longer transport distances from southern and central Germany, a northern German site using wind turbines would be the most favourable.
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.
In der wasserbaulichen Forschung werden neben klassischen Messinstrumenten zunehmend kamerabasierte Verfahren genutzt. Diese erlauben neben der Bestimmung von Fließgeschwindigkeiten auch die Detektion der freien Wasseroberfläche oder zeitliche Vermessung von Kolken. Durch die hohen räumlichen und zeitlichen Auflösungen, welche neueste Kamerasensoren liefern, können neue Erkenntnisse in turbulenten, komplexen Strömungen gewonnen werden. Auch in der Praxis können diese Verfahren mit geringem Aufwand wichtige Daten liefern.
Next-generation aircraft designs often incorporate multiple large propellers attached along the wingspan (distributed electric propulsion), leading to highly flexible dynamic systems that can exhibit aeroelastic instabilities. This paper introduces a validated methodology to investigate the aeroelastic instabilities of wing–propeller systems and to understand the dynamic mechanism leading to wing and whirl flutter and transition from one to the other. Factors such as nacelle positions along the wing span and chord and its propulsion system mounting stiffness are considered. Additionally, preliminary design guidelines are proposed for flutter-free wing–propeller systems applicable to novel aircraft designs. The study demonstrates how the critical speed of the wing–propeller systems is influenced by the mounting stiffness and propeller position. Weak mounting stiffnesses result in whirl flutter, while hard mounting stiffnesses lead to wing flutter. For the latter, the position of the propeller along the wing span may change the wing mode shapes and thus the flutter mechanism. Propeller positions closer to the wing tip enhance stability, but pusher configurations are more critical due to the mass distribution behind the elastic axis.
We consider the numerical approximation of second-order semi-linear parabolic stochastic partial differential equations interpreted in the mild sense which we solve on general two-dimensional domains with a C² boundary with homogeneous Dirichlet boundary conditions. The equations are driven by Gaussian additive noise, and several Lipschitz-like conditions are imposed on the nonlinear function. We discretize in space with a spectral Galerkin method and in time using an explicit Euler-like scheme. For irregular shapes, the necessary Dirichlet eigenvalues and eigenfunctions are obtained from a boundary integral equation method. This yields a nonlinear eigenvalue problem, which is discretized using a boundary element collocation method and is solved with the Beyn contour integral algorithm. We present an error analysis as well as numerical results on an exemplary asymmetric shape, and point out limitations of the approach.
The thermal conductivity of components manufactured using Laser Powder Bed Fusion (LPBF), also called Selective Laser Melting (SLM), plays an important role in their processing. Not only does a reduced thermal conductivity cause residual stresses during the process, but it also makes subsequent processes such as the welding of LPBF components more difficult. This article uses 316L stainless steel samples to investigate whether and to what extent the thermal conductivity of specimens can be influenced by different LPBF parameters. To this end, samples are set up using different parameters, orientations, and powder conditions and measured by a heat flow meter using stationary analysis. The heat flow meter set-up used in this study achieves good reproducibility and high measurement accuracy, so that comparative measurements between the various LPBF influencing factors to be tested are possible. In summary, the series of measurements show that the residual porosity of the components has the greatest influence on conductivity. The degradation of the powder due to increased recycling also appears to be detectable. The build-up direction shows no detectable effect in the measurement series.
Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.
Bald eine Dekade ist es her, dass diese annähernd mantraartig wiederholte Phrase Unternehmen zur Umsetzung datenschutzrechtlicher Vorgaben incentivierte. Was ist davon geblieben? Nur wenige in Deutschland verhängte Bußgelder erreichten Millionenhöhe. Hintergrund ist (auch) das deutsche Ordnungswidrigkeitenrecht, welches in einem Spannungsverhältnis zu den Vorgaben der DS-GVO steht. Ein Bußgeldbescheid der Berliner Datenschutzaufsicht gegen die Deutsche Wohnen sollte Auslöser eines langen, fortdauernden Rechtsstreits werden. Auf Vorlage des KG hatte der EuGH in der Rechtssache C-807/21 („Deutsche Wohnen“) erstmals Gelegenheit, sich zur Frage der Bußgeldhaftung zu positionieren.
Seit Ende 2022 prägt das Schlagwort „Künstliche Intelligenz“ (KI) nicht nur den rechtswissenschaftlichen Diskurs. Die allgemeine Verfügbarkeit von generativen KI-Modellen, allen voran die großen Sprachmodelle (Large Language Models, kurz: LLM) wie ChatGPT von OpenAI oder Bing AI von Microsoft, erfreuen sich größter Beliebtheit: LLM sind in der Lage, auf Grundlage statistischer Methoden – eine entsprechende Schnittstelle (Interface) vorausgesetzt – auch technisch wenig versierten Nutzern verständliche Antworten auf ihre Fragen zu liefern. Dabei werden nicht nur umfassend Nutzerdaten verarbeitet, sondern auch auf weitere personenbezogene Daten zugegriffen sowie neue Daten erzeugt. Der Beitrag geht der Frage nach, welche spezifischen datenschutzrechtlichen Herausforderungen sich für Unternehmen beim Einsatz solcher LLM stellen.