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International audience; In the beginning of 2022, the world is still fighting the crisis caused by the COVID-19 outbreak. The scientific community is still dedicating significant efforts to identify which are the better strategies to mitigate the pandemic and establish how and when to apply them. Modeling and simulation are a common method to replicate and foresee the behavior of the epidemic curve, but traditional analytical models are not capable to explain and reproduce the real evolution of the number of infections and deaths as they only concentrate in the epidemiological aspects of the virus. The COVID-19 crisis has an impact in all fundamental levels of society, and this is the reason why its modeling requires a global perspective and a holistic approach. Though the engineering scope is not common in the study of public health crises, this paper concludes that some engineering tools such as systems analysis and control theory may be the answer to build a high-fidelity model to support the decision-making facing the emergency.

Over the past two decades, the promoters of Model Based Systems Engineering (MBSE) have encouraged systems engineers to transition from document-centric approaches to model-based ones. Literacy of systems engineers in reading, sharing and elaborating models has therefore become an issue. Whatever the modeling language, elaboration of models is a highly complex intellectual process and SysML is no exception. Feedback from industry practitioners and MBSE lecturers suggests that developers of SysML models often stumble on the same problem: thinking about the system before modeling it in SysML. The authors of this paper propose to ease that elaboration process by using mind maps. With their graphic form and rather flexible way of organizing ideas, mind maps turn out to be a good candidate to help thinking about the system. Unlike approaches that directly switch from mind maps to SysML diagrams dedicated to one specific system, this paper introduces an intermediate step: mind maps first enable elaboration of diagrams patterns. The latter may in turn be instantiated onto one or several systems. Without loss of generality, the proposed approach is step-wise illustrated on real-time systems monitored by software controllers. Patterns are proposed to cover need expression, requirement capture, use case driven analysis and design.

A prominent pathway for the aeronautical industry to meet contemporary challenges is to explore overall vehicular efficiency gains enabled by various functional and structural distributions and/or synergies between onboard systems. To that end, this paper combines analytical first-principle-based methods and principles of systems engineering and focuses on the Environmental Control System (ECS). The objective is to develop means for simple blank-sheet design of complete system architectures, which would help unlock potentially obscured parts of the system design space. Basic thermodynamics is employed, complemented with “Function- Behaviour-Structure-Experience” systems engineering framework. The method presented in the paper enables users to initialise the design from a primitive abstract system architecture described by elementary physical processes, and then carry out a sequence of decisions and design material systems architecture, i.e. concepts that respond to the system requirements. The preliminary results present development of architectures representative both of traditional pneumatic and innovative electrical ECS concepts. Energy consumption figures of merit (thermodynamic efficiency, exergy destruction rate) are used as guidelines during the design i.e. for a given flight condition, the designer can assess the influence of each choice on the overall system energy consumption. Trade-offs between architectural design choices and figures of merit are thus rendered transparent in preliminary architecture design. In this paper the figures of merit are based on thermodynamic energy efficiency; in perspectives the method can include other constraints such as e.g. weight, volume, cost, or other, with long-term objective of enabling a comprehensive multi-disciplinary multi-system aeroplane architecture design scheme.

International audience; COVID-19 recently grew as a major problem worldwide and is considered the pandemic of the third century. Among the issues in dealing with this pandemic is the urgency, the bad communication, the decentralization of decision and the mismanagement of this crisis. In order to address these issues, the authors propose the use of Internet of Things and Artificial Intelligence. The Internet of Things proposes several solutions to the aforementioned problems, while using the Genetic Algorithm in AI to calibrate the proposed generalized SEIR mathematical model. This model, when calibrated to the existent recorded and confirmed data in Lebanon, will help predict the number of infected individuals. Thus, it will better prepare for the fight with the virus.

Innovation is a vital element in the dynamic world we live in, aiming at add-ing value to developing new systems. Obsolescence on the other hand com-prehends the deterioration of the system or component capacity to operate when it is no longer suitable to fulfil the required function, even if it still operates and can be manufactured and supported. As innovative and obsolete products can be seen as opposites, the proactive management of obsoles-cence encompasses the principles of innovation. Obsolescence has so far been mainly addressed from a reactive point of view. Realizing the impact of the conceptual design phase on a system’s life-cycle cost, this paper presents how a model-based system engineering approach, proactively assessing obso-lescence risks, leverages with innovation considerations. In doing so, the pa-per builds on technology readiness levels (TRL) to identify critical compo-nents and innovation considerations. With clearly identified risks, designers can knowingly decide whether to improve a design, by making it robust and resilient, or to accept the risks and develop an obsolescence management plan. The paper is illustrated with an example from the automotive industry.

COVID-19 recently grew as a major problem worldwide and is considered the pandemic of the third century. Among the issues in dealing with this pandemic is the urgency, the bad communication, the decentralization of decision and the mismanagement of this crisis. In order to address these issues, the authors propose the use of Internet of Things and Artificial Intelligence. The Internet of Things proposes several solutions to the aforementioned problems, while using the Genetic Algorithm in AI to calibrate the proposed generalized SEIR mathematical model. This model, when calibrated to the existent recorded and confirmed data in Lebanon, will help predict the number of infected individuals. Thus, it will better prepare for the fight with the virus.

Aircraft systems and associated technologies have been con- stantly evolving over the years with an increasing number of components and actors interacting together in a global chain. Given this ever-growing complexity, the use of simulation in the design and validation phases of emerging systems is essential and allows to significantly accelerate some prototyping phases, especially in cross-domain concepts. This article de- scribes analysis and experiments done at ISAE-SUPAERO for running large scale and hybrid real-time simulations for aircraft using the HLA standard (High Level Architecture).

Case-Based Reasoning (CBR) allows emulating the human inference of solutions to problems profiting from previous experience. The integration of CBR with ontologies, structured organization of semantic knowledge, has been in the attention for some time, aiming to create powerful knowledge-intensive systems capable of proposing appropriate solutions to problems. This entails having collected an appropriate number of previous cases as well as having established a suitable ontology for the application domain. This paper focuses on the integration of CBR and ontologies to support the case representation, case base storage, and semantic similarity estimation. Different alternatives for such integration are explored and the approach has been tested in the creation of a Decision-Support System for the design of predictive maintenance systems. This work opens the window to significantly improve the capabilities of a CBR system by using the knowledge materialization and the reasoning features of a specific domain ontology.