Overall Model. Figure 2-33 provides a package structure overview of the expected EAST-ADL modelling elements for the braking system architecture, as well as its associated requirements, variability and other non-functional constraints (e.g., timing and dependability), and verification&validation (V&V) cases. The SystemModel (within the 0_TopPackage) contains the entire braking electrical/electronic system architecture, for which specifications at various abstraction levels are applied. Figure 2-34 provides a graphical representation of this multi-level braking electrical/electronic system specification and its related environment model (EnvironmentBBW). Figure 2-33: An overview of packages of an EAST-ADL model in Papyrus. Figure 2-34: The braking electrical/electronic system and its environment in Papyrus. EAST-ADL supports requirements, V&V cases, and the annotations of variability and other non- functional constraints through separate modelling packages shown in Figure 2-35. (Such extension packages are contained in the EAST-ADL ExtensionElements package in Figure 2-35). A requirement model specifies the conditions or capabilities that must be met or possessed by a system or its component. In a model-based approach, requirements are derived, refined, mapped, validated and verified along with the progress of system design. The specifications of variability and other non-functional constraints augment the multi-level system architecture specification with analytical information (e.g. timing, reliability, and safety integrity) for early quality predictions and contract declarations. Normally, an analytical model should have its level of abstraction according to its target artefacts. Figure 2-35: An overview of system model and related EAST-ADL packages for the specifications of requirements, V&V cases, and the annotations of variability and other non-functional constraints in Papyrus.
Overall Model. The best fitting overall model contained elements from each category of covariates, which included ground-based measurements up to remotely sensed data. The residu- als showed no evidence of spatial autocorrelation, indicating that a more sophisticated model taking into account the spatial locations of the sites was not necessary for our analysis. It is noteworthy that the semivariance of the residuals in Accra was greater than the semivariance in Xxxxxxx, which indicated larger variability in the residuals of sites located in Greater Accra compared to Xxxxxxx. The map of the predicted probability of M. ulcerans presence suggests more discordance between model predic- tion and observed outcomes in Greater Accra compared to Xxxxxxx. Such discordant sites provide an opportunity for further investigation at specific spatial locations. My- cobacterium ulcerans negative sites with a high predicted probability of being positive could be re-sampled to verify the negative result, and M. ulcerans positive sites with a low predicted probability of being positive could be re-examined for unobserved covariates that may explain positive results.
