Misidentification of two Brazilian triatomes, Triatoma arthurneivai and Triatoma wygodzinskyi, revealed by geometric morphometrics

Triatoma arthurneivai Lent & Martins and Triatoma wygodzinskyi Lent (Hemiptera: Reduviidae) are two Brazilian species found in the sylvatic environment. Several authors may have misidentified T. arthurneivai and consequently published erroneous information. This work reports the use of geometric morphometric analysis on wings in order to differentiate T. arthurneivai and T. wygodzinskyi, and thus to detect possible misidentifications. Triatomines collected from the field in the states of Minas Gerais and Sao Paulo, and from laboratory colonies, were used. Analyses show a clear differentiation between specimens of T. arthurneivai and T. wygodzinskyi. This indicates that T. arthurneivai populations from Sao Paulo state were misidentified and should be considered as T. wygodzinskyi. This study also suggests that T. arthurneivai is an endemic species from Serra do Cipo, Minas Gerais state.

On the cutting stock problem under stochastic demand

This paper addresses the one-dimensional cutting stock problem when demand is a random variable. The problem is formulated as a two-stage stochastic nonlinear program with recourse. The first stage decision variables are the number of objects to be cut according to a cutting pattern. The second stage decision variables are the number of holding or backordering items due to the decisions made in the first stage. The problem`s objective is to minimize the total expected cost incurred in both stages, due to waste and holding or backordering penalties. A Simplex-based method with column generation is proposed for solving a linear relaxation of the resulting optimization problem. The proposed method is evaluated by using two well-known measures of uncertainty effects in stochastic programming: the value of stochastic solution-VSS-and the expected value of perfect information-EVPI. The optimal two-stage solution is shown to be more effective than the alternative wait-and-see and expected value approaches, even under small variations in the parameters of the problem.