Analytische Chemie
Filtern
Dokumenttyp
- Zeitschriftenartikel (1)
- Vortrag (1)
Sprache
- Englisch (2)
Schlagworte
- Review (2) (entfernen)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (1)
HEAs represent a relatively new class of materials. The the alloy concept is fundamentally different from the most conventional materials and alloys that are used today. Recently, the focus of HEA designs is more application-based. For that purpose, the elements of interest are carefully selected and multiple phases as well as micro-structures are deliberately adjusted. Currently, only limited attention has been paid to weldability of HEA. This encompasses possible effects on metallurgy and its influence on the desired properties. It remains open if welding causes e.g. considerable number of intermetallic phases or segregations and their effect on weld joint properties. For that reason, the scope of this study is to summarize already available studies on welding of HEAs with respect to the HEA-type, the applied welding process and its influence on the weld joint properties.
The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms.