Kinetic conversion of CO to CH4 in the Solar System

Some of the most interesting chemistry in the Solar System involves changes in the oxidation state of the simple carbon species. The chemical pathways for the conversion of CH4 to CO and CO2 are for the most part known. The reverse process, the reduction of CO to CH4, is, however, poorly understood. This is surprising in view of the importance of the reduction process in the chemistry of the Solar System. Recently we investigated the chemical kinetics of a hitherto unsuspected reaction. It is argued that the formation of the methoxy radical (CH3O) from H+H2CO may play an essential role in the reduction of CO to CH4. The rate coefficient for this reaction has been estimated using the approximate theory of J. Troe and transition state theory. We will discuss the implications of this reaction for the chemistry of CO on Jupiter, in the solar nebula, for interpreting the laboratory experiments of A. Bar-Nun and A. Shaviv and A. Bar-Nun and S. Chang, and for organic synthesis in the prebiotic terrestrial atmosphere. The possible relation of CO reduction in the solar nebula and polyoxymethylene observed in comet Halley will be discussed

Probabilistic models to describe the dynamics of migrating microbial communities

In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered the conduit and those that have become entrained into the flow due to seeding from biofilms. Examples include fluids through conduits such as drinking water pipe networks, endotracheal tubes, catheters and ventilation systems. Here we present two probabilistic models to describe changes in the composition of bulk fluid microbial communities as they are transported through a conduit whilst exposed to biofilm communities. The first (discrete) model simulates absolute numbers of individual cells, whereas the other (continuous) model simulates the relative abundance of taxa in the bulk fluid. The discrete model is founded on a birth-death process whereby the community changes one individual at a time and the numbers of cells in the system can vary. The continuous model is a stochastic differential equation derived from the discrete model and can also accommodate changes in the carrying capacity of the bulk fluid. These models provide a novel Lagrangian framework to investigate and predict the dynamics of migrating microbial communities. In this paper we compare the two models, discuss their merits, possible applications and present simulation results in the context of drinking water distribution systems. Our results provide novel insight into the effects of stochastic dynamics on the composition of non-stationary microbial communities that are exposed to biofilms and provides a new avenue for modelling microbial dynamics in systems where fluids are being transported

Recording from two neurons: second order stimulus reconstruction from spike trains and population coding

We study the reconstruction of visual stimuli from spike trains, recording simultaneously from the two H1 neurons located in the lobula plate of the fly Chrysomya megacephala. The fly views two types of stimuli, corresponding to rotational and translational displacements. If the reconstructed stimulus is to be represented by a Volterra series and correlations between spikes are to be taken into account, first order expansions are insufficient and we have to go to second order, at least. In this case higher order correlation functions have to be manipulated, whose size may become prohibitively large. We therefore develop a Gaussian-like representation for fourth order correlation functions, which works exceedingly well in the case of the fly. The reconstructions using this Gaussian-like representation are very similar to the reconstructions using the experimental correlation functions. The overall contribution to rotational stimulus reconstruction of the second order kernels - measured by a chi-squared averaged over the whole experiment - is only about 8% of the first order contribution. Yet if we introduce an instant-dependent chi-square to measure the contribution of second order kernels at special events, we observe an up to 100% improvement. As may be expected, for translational stimuli the reconstructions are rather poor. The Gaussian-like representation could be a valuable aid in population coding with large number of neurons

Bank strategic asset allocation under a unified risk measure

Most available bank asset allocation models use several risk measures as constraints; as a consequence, the comparison of the risk between different asset allocation strategies is often difficult, since each strategy is subject to several risks. With this research, we create a simulation–optimization methodology that measures interest rate, credit and liquidity risks in a unified manner. The associated risk events, such as interest rate increases, liquidity outflows or spikes in defaults are generated using the same simulation engine, giving as output a single risk measure (the probability of failure, used by ratings agencies) that aggregates those risks under the same simulation engine. Finally, we use our methodology to determine Pareto fronts for the optimal balance sheet allocations and minimum-risk strategies. As a result, several findings emerge, such as: 1) Risk is dependent on the income stream; 2) Allocation to book value assets is preferable; 3) Under low rate environments, a full allocation to cash is very risky and is not the minimum risk strategy; 4) Banks can make investments in stocks in environments of high prospective returns and low leverage.info:eu-repo/semantics/acceptedVersio