In order to circumvent the problem of state-space explosion of large-scale Markovian models, the stochastic process algebra PEPA has been given a fluid semantics based on ordinary differential equations, treating all entities as continuous. However, low numbers of instances and/or relatively slow dynamics may make such approximation too coarse for some parts of the system. To deal with such situations, we propose an hybrid semantics lying between these two extremes, treating parts of the system as discrete and stochastic and others as continuous and deterministic. The underlying mathematical object for the quantitative evaluation is a stochastic hybrid automaton. A case study of a client/server system with breakdowns and repairs is used to discuss the accuracy and the cost of this hybrid analysis.
We provide Stochastic Concurrent Constraint Programming (sCCP), a stochastic process algebra based on CCP, with a semantics in terms of hybrid automata. We associate with each sCCP program both a stochastic and a non-deterministic hybrid automaton. Then, we compare such automata with the standard stochastic semantics (given by a Continuous Time Markov Chain) and the one based on ordinary differential equations, obtained by a fluid-flow approximation technique. We discuss in detail two case studies: Repressilator and the Circadian Clock, with particular regard to the robustness exhibited by the different semantic models and to the effect of discreteness in dynamical evolution of such systems.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.