Gaussian processes for choosing laser parameters for driven, dissipative Rydberg aggregates

To facilitate quantum simulation of open quantum systems at finite temperatures, an important ingredient is to achieve thermalization on a given time-scale. We consider a Rydberg aggregate (an arrangement of Rydberg atoms that interact via long-range interactions) embedded in a laser-driven atomic environment. For the smallest aggregate (two atoms), suitable laser parameters can be found by brute force scanning of the four tunable laser parameters. For more atoms, however, such parameter scans are too computationally costly. Here we apply Gaussian processes to predict the thermalization performance as a function of the laser parameters for two-atom and four-atom aggregates. These predictions perform remarkably well using just 1000 simulations, demonstrating the utility of Gaussian processes in an atomic physics setting. Using this approach, we find and present effective laser parameters for generating thermalization, the robustness of these parameters to variation, as well as different thermalization dynamics

Investigations of Fluid-Structure-Coupling and Turbulence Model Effects on the DLR Results of the Fifth AIAA CFD Drag Prediction Workshop

The accurate calculation of aerodynamic forces and moments is of significant importance during the design phase of an aircraft. Reynolds-averaged Navier-Stokes (RANS) based Computational Fluid Dynamics (CFD) has been strongly developed over the last two decades regarding robustness, efficiency, and capabilities for aerodynamically complex configurations. Incremental aerodynamic coefficients of different designs can be calculated with an acceptable reliability at the cruise design point of transonic aircraft for non-separated flows. But regarding absolute values as well as increments at off-design significant challenges still exist to compute aerodynamic data and the underlying flow physics with the accuracy required. In addition to drag, pitching moments are difficult to predict because small deviations of the pressure distributions, e.g. due to neglecting wing bending and twisting caused by the aerodynamic loads can result in large discrepancies compared to experimental data. Flow separations that start to develop at off-design conditions, e.g. in corner-flows, at trailing edges, or shock induced, can have a strong impact on the predictions of aerodynamic coefficients too. Based on these challenges faced by the CFD community a working group of the AIAA Applied Aerodynamics Technical Committee initiated in 2001 the CFD Drag Prediction Workshop (DPW) series resulting in five international workshops. The results of the participants and the committee are summarized in more than 120 papers. The latest, fifth workshop took place in June 2012 in conjunction with the 30th AIAA Applied Aerodynamics Conference. The results in this paper will evaluate the influence of static aeroelastic wing deformations onto pressure distributions and overall aerodynamic coefficients based on the NASA finite element structural model and the common grids

An efficient method to calculate excitation energy transfer in light harvesting systems. Application to the FMO complex

A master equation, derived from the non-Markovian quantum state diffusion (NMQSD), is used to calculate excitation energy transfer in the photosynthetic Fenna-Matthews-Olson (FMO) pigment-protein complex at various temperatures. This approach allows us to treat spectral densities that contain explicitly the coupling to internal vibrational modes of the chromophores. Moreover, the method is very efficient, with the result that the transfer dynamics can be calculated within about one minute on a standard PC, making systematic investigations w.r.t. parameter variations tractable. After demonstrating that our approach is able to reproduce the results of the numerically exact hierarchical equations of motion (HEOM) approach, we show how the inclusion of vibrational modes influences the transfer

Perspectives on weak interactions in complex materials at different length scales

Nanocomposite materials consist of nanometer-sized quantum objects such as atoms, molecules, voids or nanoparticles embedded in a host material. These quantum objects can be exploited as a super-structure, which can be designed to create material properties targeted for specific applications. For electromagnetism, such targeted properties include field enhancements around the bandgap of a semiconductor used for solar cells, directional decay in topological insulators, high kinetic inductance in superconducting circuits, and many more. Despite very different application areas, all of these properties are united by the common aim of exploiting collective interaction effects between quantum objects. The literature on the topic spreads over very many different disciplines and scientific communities. In this review, we present a cross-disciplinary overview of different approaches for the creation, analysis and theoretical description of nanocomposites with applications related to electromagnetic properties

Entdeckung eines Pseudoausbruches mit Carbapenem-resistenten Acinetobacter baumannii

Am Nationalen Referenzzentrum für gramnegative Krankenhauserreger wurde im November und Dezember 2020 das gehäufte Auftreten von Isolaten des Acinetobacter baumannii-Komplex mit Carbapenemresistenz beobachtet. Der Beitrag beschreibt die in Kooperation mit dem Robert Koch-Institut durchgeführte Ausbruchsuntersuchung und die Aufklärung als Pseudoausbruch.Peer Reviewe

Perspectives on weak interactions in complex materials at different length scales

Nanocomposite materials consist of nanometer-sized quantum objects such as atoms, molecules, voids or nanoparticles embedded in a host material. These quantum objects can be exploited as a super-structure, which can be designed to create material properties targeted for specific applications. For electromagnetism, such targeted properties include field enhancements around the bandgap of a semiconductor used for solar cells, directional decay in topological insulators, high kinetic inductance in superconducting circuits, and many more. Despite very different application areas, all of these properties are united by the common aim of exploiting collective interaction effects between quantum objects. The literature on the topic spreads over very many different disciplines and scientific communities. In this review, we present a cross-disciplinary overview of different approaches for the creation, analysis and theoretical description of nanocomposites with applications related to electromagnetic properties

Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses