The fellowship of research productivity (PQ) granted by the national council for scientific and technological development (CNPq), besides the financial support, renders a significant status among Brazilian researchers of all areas of knowledge. Consequently, both the profile and the criteria for holding a PQ fellowship become of interest to the entire Brazilian scientific community. In this paper, we model the decision criteria as a weighted sum of the scientific production and the supervisory experience of an applicant for PQ fellowship. The scientific production is measured as the number of publications grouped according to the QUALIS system provided by the Brazilian federal agency for the improvement of higher education (CAPES). The Lattes curricula of the successful applicants for the PQ fellowship in the field of mathematics and statistics, along with the curricula of many non-PQ fellows of similar institutions, were used to estimate the criteria adopted in the last call for PQ fellowship in category 2. By allowing a certain tolerance, the model reproduced the decision criteria within acceptable bounds using the previous and the current QUALIS systems over a database composed of 234 curricula. As a consequence, the model may help to decide whether a researcher applicant is worthy to receive a PQ fellowship. Furthermore, it confirmed the recognized value of publications in journals with an elevated rank in the QUALIS system.

Cutting problems consist of cutting a set of objects available in stock in order to produce the desired items in specified quantities and sizes. The cutting process can generate leftovers (which can be reused in the case of new demand) or losses (which are discarded). This paper presents a tree-based heuristic method for minimizing the number of cut bars in the one-dimensional cutting process, satisfying the item demand in an unlimited bar quantity of just one type. The results of simulations are compared with the $RG{R}_{L1}$ algorithm and with the limiting values for this considered type of problem. The results show that the proposed heuristic reduces processing time and the number of bars needed in the cutting process, while it provides a larger leftover (by grouping losses) for the one-dimensional cutting stock problem. The heuristic contributes to reduction in raw materials or manufacturing costs in industrial processes.