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Many features constitute an adaptive system such as user model, interface specification, business functionalities and service implementation. Context awareness is an important facet of service oriented application creation dealing with the gathering, use and representation of context information. Generally, user interfaces and services communities work separately on the adaptation process and do not converge. The aim of this work is to manage the system’s functionalities and the user interface that delivers data to the relevant consumer early since the design phase until the code generation.

The authors discuss how a unified method based on a model-driven architecture for adaptive user interface and pervasive service creation eases the work of designers and developers, limits incompatibility issues and supports dynamic generation of systems adapted to different contexts of use.

The proposed approach is able to support a semi-automatic ubiquitous application generation with service, behavior, presentation and content adaptation.

In this paper, the authors tackle context-awareness at two levels: system functionalities and user interface generation. The authors also tackle adaptation at the specification and implementation levels.

In view of the intensive spread of Coronavirus disease 2019 (COVID-19) and in order to reduce the rate of spread of this disease; the objective of this article is to propose an approach to detect in real time suspect person of Coronavirus disease 2019 (COVID-19).

The ubiquitous computing offers a new opportunity to reshape the form of conventional solutions for personalized services according to the contextual situations of each environment. The health system is seen as a key part of ubiquitous computing, which means that health services are available anytime, anywhere to monitor patients based on their context. This paper aims to design and validate a contextual model for ubiquitous health systems designed to detect in real time suspect person of COVID-19, to reduce the propagation of this infectious disease and to take the necessary instructions.

This paper presents the performance results of the COVID-19 detection approach. Thus, the reduction of the COVID-19 propagation rate thanks to the real-time intervention of the system.

Following the COVID-19 pandemic spread, the authors tried to find a solution to detect the disease in real time. In this paper, a real-time COVID-19 detection system based on the ontological description supported by Semantic Web Rule Language (SWRL) rules was developed. The proposed ontology contains all relevant concepts related to COVID-19, including personal information, location, symptoms, risk factors, laboratory test results and treatment planning. The SWRL rules are constructed from medical recommendations.