This article includes the three phases of a gender-perspective research, whose objective was to identify the factors that promote and inhibit the entry and permanence of female students in the area of engineering at the University of Costa Rica. The study was framed in a mixed qualitative-quantitative design. In the first phase, of qualitative methodology, carried out from 2019 to 2020, individual and group interviews were conducted with students. In the second phase, of quantitative methodology, a survey was applied to female students, to identify the incidence of factors in the decision to enroll and remain in the career. Some of the main findings were the importance of skills in Mathematics, Chemistry and Physics in the choice, the UCR ranking and career statistics, as well as the effect of stereotypes in the potential of women in engineering. Regarding permanence, it is concluded that 47% of the women were affected by the macho environment and 21% by situations of sexual harassment. An interesting result was a high valuation of academic efficacy, self-concept and leadership among female students and how this helps them to continue with their studies. This information allowed the elaboration of a proposal to raise awareness among teachers about the factors that inhibit the choice and permanence of female students in in the field of Engineering to be put into practice in the Faculty.

Wind energy is a great alternative for green and emerging countries, such as Costa Rica, in their search for clean energy sources. With the aim of reducing the costs of the control system in wind farms, this work proposes a nonlinear model for a 5 MW wind turbine, which is compared with an existing model of a commercial turbine. The results obtained show that the model reproduces the dynamics of the system with sufficient accuracy. Furthermore, simulations demonstrate the feasibility of implementing a predictive controller to manipulate the pitch angle and thereby regulate the generator speed, ensuring compliance with power variation standards. In this sense, the controller proves to be an optimal and promising solution for closed-loop control in the face of stochastic disturbances in wind speed.

This research seeks to contribute to the management of international reserves of central banks due to the importance of an adequate estimation of interest rates of U.S. Treasury bonds and the respective yield curve, where in recent years the strong influence exerted by global crises such as the COVID-19 pandemic and geopolitical conflicts on the evolution of these rates has been observed. The study is based on the case of the Central Bank of Costa Rica, for which three mathematical prediction models were proposed using a vector autoregressive model and two variants of the Nelson-Siegel dynamic model, based on the yield curve and using financial indexes and macroeconomic variables of the United States, such as consumer price indexes, inflation expectations, manufacturing capacity, and the federal funds rate, among others. The parameters of the models were adjusted with optimization techniques and linear estimators such as the Kalman Filter, validating the results with those previously published in the state of the art and with an updated database from 2000 to 2022. A comparison of the accuracy of the yield curve forecast was carried out, managing to approximate with very low error rates the interest rates with different maturities. The incorporation of the developed models has the potential to become an important support and reference to maintain and generate higher returns.