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Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.

Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.

SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.

To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

Even with the Saudi Arabian Government's discretionary measures to mitigate the spread of the coronavirus disease 2019 (COVID-19), the economic sectors were not spared from the damage. Thus, the paper aims to use a computable general equilibrium (CGE) model to evaluate the impact of COVID-19 on the Kingdom of Saudi Arabia's (KSA) economy, with a special focus on small and medium enterprises (SMEs) and production. These influence the level of poverty.

The paper adopted the social accounting matrix (SAM) for Saudi Arabia built in 2021 by Imtithal Althumairi from Saudi Arabia's 2017 SAM. The model represents a snapshot of the economy and different flows that exist within the tasks and institutions. Two simulations (mild and severe) were conducted because of the focus on the distributional outcomes.

Decrease in job creation and economic growth were significant evidence from the study's findings. Findings show that more families hit below the poverty line because the negative impacts of the pandemic have shifted the income allocation curve. Findings show that the weakest of the poor are mitigated by government social grants during the pandemic.

The paper is restricted to the relevant literature relating to the impact of COVID-19 on Saudi Arabia's economy and evaluated using the SAM model. Moreover, the COVID-19 is still an ongoing scenario; thus, the model should be updated as data utilised for the operationalisation are made available.

The information from the suggested model can be suitable to measure the degree of the harm, and thus, the likely extent of the desirable policy feedback. Also, the model can be updated, as data are made available and formulated policies based on the updated data implemented by the policymakers.

Apart from the recovery planning of SMEs during the pandemic, the paper intends to stir up Saudi Arabia's policymakers through the macro-micro model to recovery planning and resilience of the economy with emphasis on mitigating unemployment.