Robust detection of exotic infectious diseases in animal herds: A comparative study of three decision methodologies under severe uncertainty

When animals are transported and pass through customs, some of them may have dangerous infectious diseases. Typically, due to the cost of testing, not all animals are tested: a reasonable selection must be made. How to test effectively whilst avoiding costly disease outbreaks? First, we extend a model proposed in the literature for the detection of invasive species to suit our purpose. Secondly, we explore and compare three decision methodologies on the problem at hand, namely, Bayesian statistics, info-gap theory and imprecise probability theory, all of which are designed to handle severe uncertainty. We show that, under rather general conditions, every info-gap solution is maximal with respect to a suitably chosen imprecise probability model, and that therefore, perhaps surprisingly, the set of maximal options can be inferred at least partly---and sometimes entirely---from an info-gap analysis

Two-state imprecise Markov chains for statistical modelling of two-state non-Markovian processes.

This paper proposes a method for fitting a two-state imprecise Markov chain to time series data from a twostate non-Markovian process. Such non-Markovian processes are common in practical applications. We focus on how to fit modelling parameters based on data from a process where time to transition is not exponentially distributed, thereby violating the Markov assumption. We do so by first fitting a many-state (i.e. having more than two states) Markov chain to the data, through its associated phase-type distribution. Then, we lump the process to a two-state imprecise Markov chain. In practical applications, a two-state imprecise Markov chain might be more convenient than a many-state Markov chain, as we have closed analytic expressions for typical quantities of interest (including the lower and upper expectation of any function of the state at any point in time). A numerical example demonstrates how the entire inference process (fitting and prediction) can be done using Markov chain Monte Carlo, for a given set of prior distributions on the parameters. In particular, we numerically identify the set of posterior densities and posterior lower and upper expectations on all model parameters and predictive quantities. We compare our inferences under a range of sample sizes and model assumptions. Keywords: imprecise Markov chain, estimation, reliability, Markov assumption, MCM

The GHG emissions and economic performance of the Colombian palm oil sector; current status and long-term perspectives

Increasing oil palm plantations, both for obtaining crude palm oil (CPO) and for the production of biobased products, have generated growing concern about the impact of greenhouse gas (GHG) emissions on the environment. Colombia has the potential to produce sustainable biobased products from oil palm. Nevertheless, national GHG emissions have not yet been reported by this sector. Achieving the collection of the total primary data from the oil palm sector, in Colombia, entails a tremendous challenge. Notwithstanding, for this study, the data collection of 70% of the production of fresh fruit bunches (FFB) was achieved. Therefore, current situation of CPO production in Colombia is analyzed, including 1) GHG emissions calculation, 2) net energy ratio (NER), and 3) economic performance. Moreover, the analysis includes two future scenarios, where the CPO production chain is optimized to reduce GHG emissions. Future scenario A produces biodiesel (BD), biogas, cogeneration, and compost; while future scenario B produces BD, biogas, cogeneration, and pellets. The methodology, for all the scenarios, includes life-cycle assessment and economic analysis evaluation. The results show a significant potential for improving the current palm oil production, including a 55% reduction in GHG emissions. The impact of land-use change must be mitigated to reduce GHG emissions. Therefore, a sustainable oil palm expansion should be in areas with low carbon stock or areas suitable/available to the crop (e.g., cropland, pastureland). Avoiding the deforestation of natural forests is required. Besides, crop yield should be increased to minimize the land use, using biomass to produce biobased products, and capture biogas to reduce methane emissions. In the biodiesel production life-cycle, the NER analysis shows the fossil energy consumed is lower than the renewable energy produced. Regarding the economic performance, it shows that in an optimized production chain, the capital expenditure and operational expenditure will decrease by approximately 20%

Effects of hyaluronate, strontium and prolonged incubation on different sperm parameters.

The aim of this study was to evaluate the effect of hyaluronate and strontium with short and prolonged incubation periods on different sperm parameters. For sperm intended for in vitro fertilization good progressive motility, a high percentage of normal form, a suitable linear velocity and a low acrosome-reacted proportion of sperm are required. The semen washing procedure in the self-migration method with medium containing hyaluronate Ca2+ after 3 h of incubation complies better with these criteria