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Mit Cabin Essence, einem Songtitel der Beach Boys, fügt die Künstlerin weiteres Material hinzu, das sich sowohl mit dem Mythos des amerikanischen Westens beschäftigt, aber auch selbst zum Anlass für Spekulationen wurde. Und wo einst nach Gold geschürft wurde, ging es zu einem späteren Zeitpunkt, in Silicon Valley, um „Data Mining\". Kalifornien und das Verschwinden des Außen, hg. v. Diedrich Diederichsen und Anselm Franke.

Background The angiosperm family Bromeliaceae comprises over 3.500 species characterized by exceptionally high morphological and ecological diversity, but a very low genetic variation. In many genera, plants are vegetatively very similar which makes determination of non flowering bromeliads difficult. This is particularly problematic with living collections where plants are often cultivated over decades without flowering. DNA barcoding is therefore a very promising approach to provide reliable and convenient assistance in species determination. However, the observed low genetic variation of canonical barcoding markers in bromeliads causes problems. Result In this study the low-copy nuclear gene Agt1 is identified as a novel DNA barcoding marker suitable for molecular identification of closely related bromeliad species. Combining a comparatively slowly evolving exon sequence with an adjacent, genetically highly variable intron, correctly matching MegaBLAST based species identification rate was found to be approximately double the highest rate yet reported for bromeliads using other barcode markers. Conclusion In the present work, we characterize Agt1 as a novel plant DNA barcoding marker to be used for barcoding of bromeliads, a plant group with low genetic variation. Moreover, we provide a comprehensive marker sequence dataset for further use in the bromeliad research community.

An amendment to this paper has been published and can be accessed via the original article.

The share of wind power for electricity supply is increasing worldwide. This highly variable resource requires the improved prediction of power output for network stability. The interaction between wind farm wakes and the atmospheric boundary layer (ABL) introduces uncertainties in power production that warrant detailed investigation. The flow downwind of wind farms is characterized by a reduction in wind speed and an increase in turbulence, which both vary with atmospheric conditions. During the American WAKE experimeNt (AWAKEN), the Technische Universität Braunschweig conducted measurement flights with a research aircraft upwind and downwind of onshore wind farms in the southern Great Plains in Oklahoma in the USA. This study utilizes data from 20 flights conducted at approximately hub height in September 2023 to investigate the wind field variability downwind of the wind farms and vertical profiles to observe atmospheric stratification. The flights were aligned perpendicular to the main wind direction downwind of the King Plains and Armadillo Flats wind farms. Additionally, lidar data from both upwind and downwind ground-based measurement sites and sonic anemometer data were used for comprehensive analysis. Results indicate that under stable ABL conditions, the wake persists at greater downwind distances with a higher velocity deficit in the wake relative to the undisturbed flow compared to unstable stratification. In homogeneous terrain under stable conditions, wake recovery to 95 % occurs between a distance of 4.5 and 9 km downwind of the wind farm. In the semi-complex terrain characterized by shallow hills, slopes, and valleys, the wake exhibits a higher velocity deficit compared to homogeneous terrain, while in some cases the wake was amplified by the terrain resulting in higher velocity deficit 10 km downwind of the wind farm compared to the measurements closer to the wind farm. The turbulent kinetic energy (TKE) and “TKE difference” was found to be a valuable measure in understanding wakes in a semi-complex terrain, showing a clear wake recovery and formation depending on the stratification of the ABL.

A profound knowledge of pollutant emissions and transport processes is essential to better assess the impact on local air quality, which ultimately affects human health. This is of special importance in the proximity of airports, as flight activities are a major source of ultrafine aerosol particles (UFP) that are associated with adverse health effects. A quantification of the aerosol population in the horizontal and in particular in the vertical distribution has not been sufficiently characterized so far, but is of crucial relevance, as the atmospheric boundary layer (ABL) is strongly interacting with aerosols. For this purpose, the fixed-wing research drone called ALADINA (Application of Light-weight Aircraft for Detecting in-situ Aerosol) was operated at a distance of approximately 4 km downwind of the German airport Berlin Brandenburg (BER) on October 11–19, 2021. During the investigation period, 140 vertical profiles of different meteorological parameters and aerosol particle sizes were obtained on six measurement days between the surface and up to a maximum altitude of 750 m above ground level (a.g.l.). The investigations indicate several features: The stability of the ABL is a key characteristic for the vertical distribution of aerosol population with highest concentrations close to ground. Inversion layers further enhance horizontal transport so that airport pollutants can be moved to a further distance away. The airborne observations of total particle number concentration (TNC) coincide with ground-based data from fix-point sites. They show a high variability depending on the distance to the plume as well as upwind position and highest concentrations of TNC related to rush hours of airport operations.

Artificial intelligence (AI) is already driving scientific breakthroughs in a variety of research fields, ranging from the life sciences to mathematics. This raises a critical question: can AI be applied both responsibly and effectively to address complex and interconnected sustainability challenges? This report is the result of a collaboration between the Stockholm resilience Centre (Stockholm University), the Potsdam Institute for Climate Impact Research (PIK), and Google DeepMind. Our work explores the potential and limitations of using AI as a research method to help tackle eight broad sustainability challenges. The results build on iterated expert dialogues and assessments, a systematic AI-supported literature overview including over 8,500 academic publications, and expert deep-dives into eight specific issue areas. The report also includes recommendations to sustainability scientists, research funders, the private sector, and philanthropies.