This paper discusses a switching method which can be used to combine two sequential universal source coding algorithms. The switching method treats these two algorithms as black-boxes and can only use their estimates of the probability distributions for the consecutive symbols of the source sequence. Three weighting algorithms based on this switching method are presented. Empirical results show that all three weighting algorithms give a performance better than the performance of the source coding algorithms they combine.

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Phlebotomus papatasi (Scopoli) (Diptera: Psychodidae) is the most important vector of Leishmania major, and previous experiments revealed that Leishmania development in the sand fly midgut is significantly affected by temperature. Therefore, we maintained blood-fed P. papatasi females at 23 or 28 degrees C to understand the effect of temperature on bloodmeal digestion and developmental times of this sand fly. At the lower temperature, the metabolic processes were slower and developmental times were longer: defecation, oviposition, and egg hatch started later and took longer to complete. Also, the mortality of blood-fed females was significantly lower. The defecation of bloodmeal remains was delayed for 12-36 h at 23 degrees C compared with the group maintained at 28 degrees C. Such delay would provide more time for Leishmania to establish the midgut infection and could partially explain the increased susceptibility of P. papatasi to Leishmania major at 23 degrees C. In both experimental groups, blood-fed females laid similar numbers of eggs (mean 60 and 70, maximum 104 and 115 per female). Egg numbers were positively correlated with the amount of hematin excreted in feces of ovipositing females. In parallel experiments, autogeny was recorded in 8% of females. The autogenous egg batches were smaller (mean, 12; range, 1-39), but they all produced viable larvae.

A sequence of goals scored during sport match is modelled as a realization of two dependent random point processes. It is assumed that the scoring intensity of each team has several components depending on time or on factors describing the teams and other conditions of the match. This dependence is modelled with the aid of a semi-parametric multiplicative regression model of intensity. A method of model evaluation is presented and demonstrated on a real data set. Prediction obtained from the model via the Monte Carlo simulation is compared with real results.

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