Klimaservice für die Klimafolgen- und Anpassungsforschung in der Metropolregion Hamburg

Vorstellung von Projektergebnissen aus KLIMZUG-NORD bezüglich jährliche und saisonale Temperatur- und Niederschlagsänderungen zur Mitte und Ende des 21. Jahrhunderts, sowie Ergebnisse aus dem Projekt Hamburg 2K. In Hamburg 2K wird analysiert, was eine Begrenzung auf eine Temperaturänderung von 2K für Hamburg bedeutet. Ausgewertet wurden Temperatur- und Niederschlagsänderungen sowie ausgewählte Indices

Benthic ecosystem functioning under climate change: modelling the bioturbation potential for benthic key species in the southern North Sea

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Auswirkungen des globalen Klimawandels auf Extremwasserstände in der Nordsee

Vorhersage und ProjektionEffekte des Klimawandels in der Atmosphäre und im Ozean können das Risiko von lokalen Sturmfluten oder Hochwasserereignissen in Flüssen und Ästuaren potentiell erhöhen. Basierend auf Modellergebnissen von hoch-aufgelösten Klimaprojektionen für das 21. Jahrhundert sollen sowohl Antriebsmechanismen und dominierende Variabilitätsmoden von extrem hohen Pegelständen an der kontinentalen Nordseeküste als auch mögliche zukünftige Änderungen in der Dynamik damit verbundener Wetterverhältnisse identifiziert werden. Der Schwerpunkt liegt dabei in der Untersuchung des Wechselspiels von extern und intern generierten Sturmfluten, Gezeitenströmungen sowie hydro-meteorologischen Ereignissen, welche erstmalig mittels eines regional gekoppelten (Atmosphäre-Ozean) Klimamodells konsistent simuliert werden können. Die besondere Eignung des verwendeten Modellsystems wurde in Lang et al. (2019) demonstriert: Wiederkehrpegelstände in der Deutschen Bucht, simuliert für das vergangene Jahrtausend, zeigen gute Übereinstimmung mit Beobachtungswerten der letzten 100 Jahre. Statistische Unsicherheiten bezüglich angewendeter Extremwertstatistiken werden durch das große Ensemble mit 30 Realisationen reduziert. Über eine große Bandbreite räumlicher und zeitlicher Skalen werden anthropogen induzierte Klimaänderungssignale und die natürliche Variabilität des dynamischen Systems analysiert. Die Ergebnisse sollen auch dazu verwendet werden, Hypothesen über zukünftige klimabedingte Änderungen zu testen, welche durch Extrapolation aus Beobachtungsdaten abgeleitet wurden. Unsere noch laufenden Simulationen sind in der Klimaforschung bisher einzigartig aufgrund der Kombination von hoher räumlicher und zeitlicher Auflösung (bis zu 5 km Ozean, 25 km Atmosphäre, 1-stündlicher Output), der interaktiven Kopplung zwischen Atmosphäre, Ozean und Land, der Ensemble-Größe und der transienten Simulation des Zeitraumes 1950-2100 (2006-2100 auf Basis RCP 8.5). Die Modellergebnisse werden auch in Zusammenarbeit mit anderen Wissenschaftlern des BMBF-finanzierten Projektes ClimXtreme erstellt und analysiert. Mit unserem Beitrag wollen wir dieses Projekt, unsere Vorgehensweise und erste Ergebnisse vorstellen

Social competence improves the performance of biomimetic robots leading live fish

Collective motion is commonly modeled with static interaction rules between agents. Substantial empirical evidence indicates, however, that animals may adapt their interaction rules depending on a variety of factors and social contexts. Here, we hypothesized that leadership performance is linked to the leader's responsiveness to the follower's actions and we predicted that a leader is followed longer if it adapts to the follower's avoidance movements. We tested this prediction with live guppies that interacted with a biomimetic robotic fish programmed to act as a 'socially competent' leader. Fish that were avoiding the robot were approached more carefully in future approaches. In two separate experiments we then asked how the leadership performance of the socially competent robot leader differed to that of a robot leader that either approached all fish in the same, non-responsive, way or one that did change its approach behavior randomly, irrespective of the fish's actions. We found that (1) behavioral variability itself appears attractive and that socially competent robots are better leaders which (2) require fewer approach attempts to (3) elicit longer average following behavior than non-competent agents. This work provides evidence that social responsiveness to avoidance reactions plays a role in the social dynamics of guppies. We showcase how social responsiveness can be modeled and tested directly embedded in a living animal model using adaptive, interactive robots

Unstructured Finite-Volume Arbitrary Lagrangian / Eulerian Interface Tracking computational framework for incompressible two-phase flows with surfactants (presentation)

Presentation at the APS 76th Annual Meeting of the Division of Fluid Dynamics (DFD23)We present an open-source computational framework that implements the unstructured Finite-Volume Arbitrary Lagrangian / Eulerian (ALE) Interface Tracking method for incompressible two-phase flows with surfactants. The framework implements the Interface Tracking ALE method for incompressible two-phase flows using a segregated solution algorithm for solving coupled Navier-Stokes equations with interfacial jump conditions. The Finite Area method discretizes transport equations on curved and evolving fluid interfaces. The open-source implementation as an OpenFOAM module also contains the Sub-Grid-Scale (SGS) model for handling extremely narrow boundary layers of passively transported scalars with very small diffusivity. The SGS model significantly reduces resolution requirements for species transport across the fluid interface. The surface and bulk transport of surfactants and the SGS model are verified using (semi-)analytical verification cases. We also discuss complex setups, e.g., of a rising bubble at high Peclet-numbers under the influence of soluble surfactants

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery

Study of ordered hadron chains with the ATLAS detector

La lista completa de autores que integran el documento puede consultarse en el archivo

A search for resonances decaying into a Higgs boson and a new particle X in the XH→qqbb final state with the ATLAS detector

A search for heavy resonances decaying into a Higgs boson ($H$) and a new particle ($X$) is reported, utilizing 36.1 fb$^{-1}$ of proton-proton collision data at $\sqrt{s} =$ 13 TeV collected during 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider. The particle $X$ is assumed to decay to a pair of light quarks, and the fully hadronic final state $XH \rightarrow q\bar q'b\bar b$ is analysed. The search considers the regime of high $XH$ resonance masses, where the $X$ and $H$ bosons are both highly Lorentz-boosted and are each reconstructed using a single jet with large radius parameter. A two-dimensional phase space of $XH$ mass versus $X$ mass is scanned for evidence of a signal, over a range of $XH$ resonance mass values between 1 TeV and 4 TeV, and for $X$ particles with masses from 50 GeV to 1000 GeV. All search results are consistent with the expectations for the background due to Standard Model processes, and 95% CL upper limits are set, as a function of $XH$ and $X$ masses, on the production cross-section of the $XH\rightarrow q\bar q'b\bar b$ resonance