The healthcare environment is made up of highly complicated interactions between many technologies, activities, and people. Ensuring a solid communication between them is vital to ease the healthcare management. Semantic ontologies are knowledge representation tools that implement abstractions to fully describe a given topic in terms of subjects and relations. This scoping review aims to identify and analyse available ontologies which can depict all the available use-cases that describe the hospital environment in relation to the European project ODIN and its future expansion. The review has been conducted on the Scopus database on January 13th, 2023 using the PRISMA extensions for scoping reviews. Two reviewers screened 3,225 documents emerged from the database search. Further filtering led to a final set of 32 articles to be analysed for the results. A set of 34 ontologies extracted by the identified articles has been analysed and discussed as well. The results of this study will lead to the implementation of a common integrated ontology which could hold information about healthcare entities as well as their semantic relationships, strengthen data exchange and interconnections among people, devices and applications in an expanded scenario which include Internet of Things, robots and Artificial Intelligence.

In this paper the influence of different process parameters on surface roughness responses in plasma jet cutting process was investigated. Experimentations were conducted on shipbuilding aluminium 5083 sheet thickness 8 mm. Experimental work was performed according to Taguchi L27 orthogonal array by varying four parameters such as gas pressure, cutting speed, arc current and cutting height. Due to complexity of manufacturing process and aim to cover wide experimental space few constraints regarding cutting area were defined. Surface roughness parameters Ra and Rz were analysed as cut quality responses. In order to define mathematical model that will be able to describe effects of process parameters on surface roughness artificial intelligence (AI) fuzzy logic (FL) technique was applied. After functional relations between input parameters and surface roughness responses were defined prediction accuracy of developed fuzzy logic model was checked by comparison between experimental and predicted data. Mean absolute percentage error (MAPE) as well as coefficient of determination (R2) were used as validation measures. Finally, optimal process conditions that lead to minimal surface roughness were defined by creating response surface plots.