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The access of students with sensory deficits to quality educational services persists as one of the challenges of higher education worldwide. This work aims to determine the level of access and technical support that three Ecuadorian universities offer to students with hearing impairment for the development of reading skills. The mixed approach of scientific research is used for data collection. The instrument used is the Inclusive Education Access Questionnaire created by Real (2011). The techniques used were: contextualized observation, in-depth interview, and survey. The results show that in the universities studied, limitations persist in providing technical support to students with sensory disabilities, reducing the development of reading skills in the group studied. The research concluded that to improve the reading skills of students with hearing impairment, the participating universities must install pedagogical/technical supports, design intervention routes, and strengthen the knowledge and motivation of the teaching staff to implement curricular adaptations and specific tutorials.

This paper builds toward the convergence of future and digital literacies as catalysts for transformative learning experiences. Based on a systematic analysis of qualitative data obtained from an entrepreneurship digital program with women from marginalized communities in Northeast Mexico (Monterrey), we examine the transformative potential of education. This is done with the use of a participatory action research paradigm and a decolonial perspective with the use of participant observation, semi-structured interviews and ethnographic tools. This study systematizes the experiences of a group of women who transformed their living conditions with the technological intervention and meaningful learning experiences, highlighting their life stories and success cases. This research evidences that future and digital literacies are key components to developing transformative learning experiences that have an impact on a community's social and economic improvement to think about different and possible lifestyles. Results also show the personal and learning profile of each participant and its relationship with the development of a specific scenario on the intersection of the skills in the areas of future and digital literacies. Discussions are built toward the possibilities of improving people’s present conditions when enhancing their future and digital skills. Thus, a component of social transformation and educational innovation and technology is present throughout the text.

This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.

The integration of renewable energies, specifically solar energy, in electric distribution systems is increasingly common. For an optimal operation, it is very important to forecast the final net demand of the power distribution network, considering the variability of solar energy combined with the variability of the electric energy consumption habits of population. This paper presents the methodology followed to forecast the net demand in a power distribution substation. Two approaches are considered, the net demand direct prediction, and the indirect prediction with the forecasts of PV power generation and load demand. Artificial Neural Network (ANN) based models and autoregressive models with exogenous variables (ARX) are used to predict the net demand, directly and indirectly, for the 24 hours of the day-ahead. The methodology is applied to a medium voltage distribution substation and the direct and indirect forecasts are compared.