Over the last two decades, Vibrio vulnificus infections have emerged as an increasingly serious public health threat along the German Baltic coast. To manage related risks, near real-time (NRT) modelling of V. vulnificus quantities has often been proposed. Such models require spatially explicit input data, for example, from remote sensing or numerical model products. We tested if data from a hydrodynamic, a meteorological, and a biogeochemical model are suitable as input for an NRT model system by coupling it with field samples and assessing the models’ ability to capture known ecological parameters of V. vulnificus. We also identify the most important predictors for V. vulnificus in the Baltic Sea by leveraging the St. Nicolas House Analysis. Using a 27-year time series of sea surface temperature, we have investigated trends of V. vulnificus season length, which pinpoint hotspots mainly in the east of our study region. Our results underline the importance of water temperature and salinity on V. vulnificus abundance but also highlight the potential of air temperature, oxygen, and precipitation to serve as predictors in a statistical model, albeit their relationship with V. vulnificus may not be causal. The evaluated models cannot be used in an NRT model system due to data availability constraints, but promising alternatives are presented. The results provide a valuable basis for a future NRT model for V. vulnificus in the Baltic Sea.
A sound basis for an interdisciplinary dialogue is highly important for cross-domain fusion (CDF) dealing with knowledge transfer between working groups situated in different research disciplines. In this paper, we present a literature-based concept map as one example to start an interdisciplinary dialogue between disciplines for the showcase of the concept “coast” and illustrate how such a concept map can be used to explicate various computer science challenges and provide inputs for CDF. We use the strengths of a concept map to display gathered knowledge and perspectives, and hence view the different disciplines have on “coast” and further highlight inter- and intra-categorical connections between several disciplines. With this example, we also want to point out the importance for the understanding of data originating from different disciplines and to raise awareness about the various methods and models that provide data and information for CDF approaches.
This article is a contribution to the Informatik Spektrum special issue „Cross-Domain Fusion“ – Heft 2. Terminologies are paramount to establish robust communication within interdisciplinary working groups inside and outside academia. To find the “common language” is hence essential and sometimes a long way to go. Within the idea of Cross Domain Fusion, we want to tackle this issue from the very beginning. Therefore, we set up a database based on the open source MediaWiki content management system. In this dictionary, a dedicated consortium from different disciplines evaluates terminologies used in Cross Domain Fusion and provides them within the Dialogue:Wiki. The aim is to provide accessible insight into commonalities and differences between different domain-specific terminologies to foster cross domain exchange.
The combination of machine learning techniques with process analytics like process mining might even significantly elevate novel insights into time series data collections. To efficiently analyze time series by process mining and to convey confidence into the analysis result, requires bridging challenges. The purpose of this article is to discuss these challenges and to present initial solutions.
<p>Mt Etna, Europe&#8217;s largest active volcano, is located directly on the Sicilian coastline of the Ionian Sea. In addition to frequent Strombolian eruptions, Etna&#8217;s south-eastern flank is currently sliding seawards at a rate of several centimetres per year. Over the past decade, scientists from multiple countries have intensely studied the submerged sector of the volcano and its continental margin, with their results showing that the well-known onshore flank instability proceeds far into the sea and can be measured by marine geodetic networks. Nevertheless, the relationship between volcanic activity and deformation of the continental margin is still unclear, and various scenarios &#8211; from small-scale disintegration over geological time periods to abrupt catastrophic failure &#8211; have been suggested.</p><p>During RV Meteor&#8217;s expedition M178 (Nov &#8211; Dec 2021), we revisited the continental margin offshore Mt Etna and conducted dedicated repeated shallow- and deep-water multibeam surveys. In addition, several gravity cores were recovered from the prominent amphitheatre structure, intra-slope basins, and the proposed southern boundary of Mt Etna&#8217;s moving flank. We use the baseline bathymetric data, acquired during RV Meteor&#8217;s cruise M86/2 in 2011/2012, to investigate and image changes within the geomorphological and geological setting offshore Etna by comparing them with the new multibeam data. The repeated bathymetry shows minor changes compared to the baseline study, but favours the suggestion of sediment re-deposition in the proximal to distal sectors of the continental margin. Our preliminary results from the sediment record provide evidence for syn- and post sedimentary deformation, with clear indications of compressional and extensional periods at the crest of the prominent amphitheatre structure. Furthermore, sediment cores show that the southern boundary ridge, north of the Catania Canyon, hosts several heavily reworked and disintegrated sediment patches, which indicates active deformation within the intra-slope micro-basins at the crest of the ridge.</p><p>The results of this project will increase our understanding of how landslides nucleate in extremely active settings such as offshore Mt Etna. Furthermore, the findings will be used to better assess the hazard potential of the sliding flank of the giant volcano and will feed into numerical modelling of the various scenarios that have been postulated for Mt Etna.</p>
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