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The AlpArray seismic network
(2018)
The AlpArray programme is a multinational, European consortium to advance our understanding of orogenesis and its relationship to mantle dynamics, plate reorganizations, surface processes and seismic hazard in the Alps-Apennines-Carpathians-Dinarides orogenic system. The AlpArray Seismic Network has been deployed with contributions from 36 institutions from 11 countries to map physical properties of the lithosphere and asthenosphere in 3D and thus to obtain new, high-resolution geophysical images of structures from the surface down to the base of the mantle transition zone. With over 600 broadband stations operated for 2 years, this seismic experiment is one of the largest simultaneously operated seismological networks in the academic domain, employing hexagonal coverage with station spacing at less than 52 km. This dense and regularly spaced experiment is made possible by the coordinated coeval deployment of temporary stations from numerous national pools, including ocean-bottom seismometers, which were funded by different national agencies. They combine with permanent networks, which also required the cooperation of many different operators. Together these stations ultimately fill coverage gaps. Following a short overview of previous large-scale seismological experiments in the Alpine region, we here present the goals, construction, deployment, characteristics and data management of the AlpArray Seismic Network, which will provide data that is expected to be unprecedented in quality to image the complex Alpine mountains at depth.
Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.
Background
In many species males face a higher predation risk than females because males display elaborate traits that evolved under sexual selection, which may attract not only females but also predators. Females are, therefore, predicted to avoid such conspicuous males under predation risk. The present study was designed to investigate predator-induced changes of female mating preferences in Atlantic mollies (Poecilia mexicana). Males of this species show a pronounced polymorphism in body size and coloration, and females prefer large, colorful males in the absence of predators.
Results
In dichotomous choice tests predator-naïve (lab-reared) females altered their initial preference for larger males in the presence of the cichlid Cichlasoma salvini, a natural predator of P. mexicana, and preferred small males instead. This effect was considerably weaker when females were confronted visually with the non-piscivorous cichlid Vieja bifasciata or the introduced non-piscivorous Nile tilapia (Oreochromis niloticus). In contrast, predator experienced (wild-caught) females did not respond to the same extent to the presence of a predator, most likely due to a learned ability to evaluate their predators' motivation to prey.
Conclusions
Our study highlights that (a) predatory fish can have a profound influence on the expression of mating preferences of their prey (thus potentially affecting the strength of sexual selection), and females may alter their mate choice behavior strategically to reduce their own exposure to predators. (b) Prey species can evolve visual predator recognition mechanisms and alter their mate choice only when a natural predator is present. (c) Finally, experiential effects can play an important role, and prey species may learn to evaluate the motivational state of their predators.
Background: In many species males face a higher predation risk than females because males display elaborate traits that evolved under sexual selection, which may attract not only females but also predators. Females are, therefore, predicted to avoid such conspicuous males under predation risk. The present study was designed to investigate predator-induced changes of female mating preferences in Atlantic mollies (Poecilia mexicana). Males of this species show a pronounced polymorphism in body size and coloration, and females prefer large, colorful males in the absence of predators.
Results: In dichotomous choice tests predator-naive (lab-reared) females altered their initial preference for larger males in the presence of the cichlid Cichlasoma salvini, a natural predator of P. mexicana, and preferred small males instead. This effect was considerably weaker when females were confronted visually with the non-piscivorous cichlid Vieja bifasciata or the introduced non-piscivorous Nile tilapia (Oreochromis niloticus). In contrast, predator experienced (wild-caught) females did not respond to the same extent to the presence of a predator, most likely due to a learned ability to evaluate their predators' motivation to prey.
Conclusions: Our study highlights that (a) predatory fish can have a profound influence on the expression of mating preferences of their prey (thus potentially affecting the strength of sexual selection), and females may alter their mate choice behavior strategically to reduce their own exposure to predators. (b) Prey species can evolve visual predator recognition mechanisms and alter their mate choice only when a natural predator is present. (c) Finally, experiential effects can play an important role, and prey species may learn to evaluate the motivational state of their predators.
Extra-tropical circulation systems impede poleward moisture advection by the Indian Summer Monsoon. In this context, the Himalayan range is believed to insulate the south Asian circulation from extra-tropical influences and to delineate the northern extent of the Indian Summer Monsoon in central Asia. Paleoclimatic evidence, however, suggests increased moisture availability in the Early Holocene north of the Himalayan range which is attributed to an intensification of the Indian Summer Monsoon. Nevertheless, mechanisms leading to a surpassing of the Himalayan range and the northern maximum extent of summer monsoonal influence remain unknown. Here we show that the Kunlun barrier on the northern Tibetan Plateau [similar to 36 degrees N] delimits Indian Summer Monsoon precipitation during the Holocene. The presence of the barrier relocates the insulation effect 1,000 km further north, allowing a continental low intensity branch of the Indian Summer Monsoon which is persistent throughout the Holocene. Precipitation intensities at its northern extent seem to be driven by differentiated solar heating of the Northern Hemisphere indicating dependency on energy-gradients rather than absolute radiation intensities. The identified spatial constraints of monsoonal precipitation will facilitate the prediction of future monsoonal precipitation patterns in Central Asia under varying climatic conditions.
Thema des Workshops waren alle Fragen, die sich der Vermittlung von Informatikgegenständen im Hochschulbereich widmen. Dazu gehören u.a.: - fachdidaktische Konzepte der Vermittlung einzelner Informatikgegenstände - methodische Lösungen, wie spezielle Lehr- und Lernformen, Durchführungskonzepte - Studienkonzepte und Curricula, insbesondere im Zusammenhang mit Bachelor- und Masterstudiengängen - E-Learning-Ansätze, wenn sie ein erkennbares didaktisches Konzept verfolgen empirische Ergebnisse und Vergleichsstudien. Die Fachtagung widmete sich ausgewählten Fragestellungen dieses Themenkomplexes, die durch Vorträge ausgewiesener Experten, durch eingereichte Beiträge und durch eine Präsentation intensiv behandelt wurden.
The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen-evergreen transition zone in Central Yakutia and the tundra-taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, ). The dataset includes structure-from-motion (SfM) point clouds and red-green-blue (RGB) and red-green-near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, ). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch ( and ) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, ). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. Larix gmeliniiLarix cajanderiDataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, ). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. <br /> The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra-taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.