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Code generation is an effective way to drive the complex system development in model-based systems engineering. Currently, different code generators are developed for different modeling languages to deal with the development of systems with multi-domain. T ...
Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated ...
Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, deve ...
Domain generalization (DG) tackles the problem of learning a model that generalizes to data drawn from a target domain that was unseen during training. A major trend in this area consists of learning a domain-invariant representation by minimizing the disc ...
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifyi ...
This paper proposes a generic and unified model of the power flow (PF) problem for multiterminal hybrid AC/DC networks. The proposed model is an extension of the standard AC-PF. The DC network is treated as an AC one and, in addition to the Slack, PV and P ...
Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...
Localizing the source of an epidemic is a crucial task in many contexts, including the detection of malicious users in social networks and the identification of patient zeros of disease outbreaks. The difficulty of this task lies in the strict limitation ...
What-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a se ...
The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explici ...
