Abstract. Given a parametric Markov model, we consider the problem of computing the rational function expressing the probability of reaching a given set of states. To attack this principal problem, Daws has suggested to first convert the Markov chain into a finite automaton, from which a regular expression is computed. Afterwards, this expression is evaluated to a closed form function representing the reachability probability. This paper investigates how this idea can be turned into an effective procedure. It turns out that the bottleneck lies in the growth of the regular expression relative to the number of states (n Θ(log n) ). We therefore proceed differently, by tightly intertwining the regular expression computation with its evaluation. This allows us to arrive at an effective method that avoids this blow up in most practical cases. We give a detailed account of the approach, also extending to parametric models with rewards and with non-determinism. Experimental evidence is provided, illustrating that our implementation provides meaningful insights on non-trivial models.

The Pearl River Delta region (PRD) of China has long suffered from severe ground-level ozone pollution. Knowledge of the sources of volatile organic compounds (VOCs) is essential for ozone chemistry. In this work, a speciated VOC emission inventory was established on the basis of updated emissions and local VOC source profiles. The top 10 species, in terms of ozone formation potentials (OFPs), consisted of isoprene, mp-xylene, toluene, ethylene, propene, o-xylene, 1,2,4-trimethylbenzene, 2-methyl-2-butene, 1-butene, and alpha-pinene. These species contributed only 35.9% to VOCs emissions but accounted for 64.1% of the OFP in the region. The spatial patterns of the VOC source inventory agreed well with city-based source apportionment results, especially for vehicle emissions and industry plus VOC product-related emissions. Mapping of the OFPs and measured ozone concentrations indicated that the formation of higher ozone in the south and southeast of the PRD region differed from that in the Conghua area, a remote area in the north of the PRD. We recommend that the priorities for the control of VOC sources include motorcycles, gasoline vehicles, and solvent use because of their larger OFP contributions.

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Abstract. Given a parametric Markov model, we consider the problem of computing the formula expressing the probability of reaching a given set of states. To attack this principal problem, Daws has suggested to first convert the Markov chain into a finite automaton, from which a regular expression is computed. Afterwards, this expression is evaluated to a closed form expression representing the reachability probability. This paper investigates how this idea can be turned into an effective procedure. It turns out that the bottleneck lies in an exponential growth of the regular expression relative to the number of states. We therefore proceed differently, by tightly intertwining the regular expression computation with its evaluation. This allows us to arrive at an effective method that avoids the exponential blow up in most practical cases. We give a detailed account of the approach, also extending to parametric models with rewards and with non-determinism. Experimental evidence is provided, illustrating that our implementation provides meaningful insights on non-trivial models.

Abstract. Abstraction techniques based on simulation relations have become an important and effective proof technique to avoid the infamous state space explosion problem. In the context of Markov chains, strong and weak simulation relations have been proposed [17,6], together with corresponding decision algorithms [3,5], but it is as yet unclear whether they can be used as effectively as their non-stochastic counterparts. This paper presents drastically improved algorithms to decide whether one (discrete-or continuous-time) Markov chain strongly or weakly simulates another. The key innovation is the use of parametric maximum flow techniques to amortize computations.

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Activated carbon (AC)-supported molybdenum catalysts, either with or without a potassium promoter, were prepared by the incipient wetness impregnation method. The materials were characterized using differential thermal analysis (DTA) and temperature programmed reduction (TPR), and were used for mixed alcohol synthesis from syngas (CO+H2). DTA results showed that a new phase, related to the interaction between Mo species and the AC support, is formed during the calcination of the Mo/AC catalyst, and the introduction of a K promoter has noticeable effect on the interaction. TPR results indicated that the Mo is more difficult to reduce after being placed onto the AC support, and the addition of a K promoter greatly promotes the formation of Mo species reducible at relatively low temperatures, while it retards the generation of Mo species that are reducible only at higher temperatures. These differences in the reduction behavior of the catalysts are atributed to the interaction between the active components (Mo and K) and the support. Potassium-doping significantly promotes the formation of alcohols at the expense of CO conversion, especially to hydrocarbons. It is postulated that Mo species with intermediate valence values (averaged around +3.5) are more likely to be the active phase(s) for alcohol synthesis from CO hydrogenation, while those with lower Mo valences are probably responsible for the production of hydrocarbons.

Abstract. We discuss conceptional and foundational aspects of Markov automata [22]. We place this model in the context of continuous-and discrete-time Markov chains, probabilistic automata and interactive Markov chains, and provide insight into the parallel execution of such models. We further give a detailled account of the concept of relations on distributions, and discuss how this can generalise known notions of weak simulation and bisimulation, such as to fuse sequences of internal transitions.