Architectural modelling for robotics: RoboArch and the CorteX example.

The need for robotic systems to be verified grows as robots are increasingly used in complex applications with safety implications. Model-driven engineering and domain-specific languages (DSLs) have proven useful in the development of complex systems. RoboChart is a DSL for modelling robot software controllers using state machines and a simple component model. It is distinctive in that it has a formal semantics and support for automated verification. Our work enriches RoboChart with support for modelling architectures and architectural patterns used in the robotics domain. Support is in the shape of an additional DSL, RoboArch, whose primitive concepts encapsulate the notion of a layered architecture and architectural patterns for use in the design of the layers that are only informally described in the literature. A RoboArch model can be used to generate automatically a sketch of a RoboChart model, and the rules for automatic generation define a semantics for RoboArch. Additional patterns can be formalised by extending RoboArch. In this paper, we present RoboArch, and give a perspective of how it can be used in conjunction with CorteX, a software framework developed for the nuclear industry.

Methane and Carbon Dioxide Emissions From Reservoirs: Controls and Upscaling.

Estimating carbon dioxide (CO2) and methane (CH4) emission rates from reservoirs is important for regional and national greenhouse gas inventories. A lack of methodologically consistent data sets for many parts of the world, including agriculturally intensive areas of the United States, poses a major challenge to the development of models for predicting emission rates. In this study, we used a systematic approach to measure CO2 and CH4 diffusive and ebullitive emission rates from 32 reservoirs distributed across an agricultural to forested land use gradient in the United States. We found that all reservoirs were a source of CH4 to the atmosphere, with ebullition being the dominant emission pathway in 75% of the systems. Ebullition was a negligible emission pathway for CO2, and 65% of sampled reservoirs were a net CO2 sink. Boosted regression trees (BRTs), a type of machine learning algorithm, identified reservoir morphology and watershed agricultural land use as important predictors of emission rates. We used the BRT to predict CH4 emission rates for reservoirs in the U.S. state of Ohio and estimate they are the fourth largest anthropogenic CH4 source in the state. Our work demonstrates that CH4 emission rates for reservoirs in our study region can be predicted from information in readily available national geodatabases. Expanded sampling campaigns could generate the data needed to train models for upscaling in other U.S. regions or nationally.