Purpose: The relations telework-work interference with the family (WIF), telework-family interference with work (FIW), and telework-performance have been widely studied; however, results of different investigations are contradictory. This may be related to third variables that moderate the effect of relations. The purpose of this paper is to analyze the moderating effect of worker responsibilities outside of the work environment on telework-FIW and telework-WIF relations, as well as the moderating effect of control by the supervisor on teleworkers in the telework-performance relation.Design/methodology/approach: A total of 92 teleworkers were interviewed, and 72 non-teleworkers who work in four public institutions. Non-teleworkers work in the same departments as teleworkers, and carry out similar functions. In addition, 33 supervisors were interviewed who evaluated performance of both groups. Hierarchical lineal regression analysis models were used to evaluate the influence of telework on the dependent variables.Findings: The results obtained reveal that where there are low-responsibility levels, teleworkers present a lower FIW than non-teleworkers; however, with high levels of responsibility, teleworkers show higher FIW. Additionally, supervisors’ control of teleworkers was found to have a negative effect on their pro-activity and adaptability to tasks.Originality/value: The findings provide new empirical evidence about the effect of moderating variables in the relation between telework-work-family conflict and telework-performance. Besides the results provide practical and useful implications to organizations that implement telework programs.

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO₂ emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.

Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance.

El análisis del fenómeno de la deserción estudiantil en contextos universitarios resulta complejo por la interacción de múltiples factores sociales, familiares, individuales e institucionales. El propósito del estudio fue describir algunas variables sociodemográficas y motivacionales asociadas a la deserción de estudiantes de primer ingreso de programas de grado en las cohortes del 2011, 2012 y 2013, con estudiantes activos y desertores de una institución de educación superior estatal de Costa Rica. Para tal fin, se construyó un instrumento de preguntas cerradas y abiertas, el cual se validó con el juicio de expertos y un grupo focal. Posteriormente, con la técnica de la encuesta se aplicó el instrumento por vía WEB y telefónica a una muestra no probabilística de 519 estudiantes. Entre los resultados de la prueba chi-cuadrada se encontró que la condición de abandono se asoció con: no matricular la carrera que más deseaba, el poco interés por estudiar en la carrera en la cual fue admitido y el deseo de estudiar en otra institución. El principal hallazgo del estudio es que las variables motivacionales son las que más inciden en el abandono. A partir de estos resultados se recomienda una apropiada inducción vocacional, para reducir las probabilidades de deserción, así como generar una estrategia para mantener la motivación durante los dos primeros años de ingreso a la universidad.

The phenomenon of dropout, by its complexity and educational and social impact, has been extensively studied to understand the specific causes. In this line of research, the purpose of this study was to analyze explanatory and predictive models of student dropout from university studies at the Instituto Tecnológico de Costa Rica (TEC), based on many variables recorded in the institutional system indicators. The first stage of the analysis considered multinomial regression models to identify the influence of these variables on the dropout. In the second analysis, six machine learning algorithms were evaluated in order to find a model that would predict student dropout. Data analysis showed that the probability of dropping out is related to sociodemographic variables, study program, academic history, scholarship and other benefits, and performance after first semester. In addition, the best predictor of dropout algorithm was the “random forest”, a probability of 0.83 to predict the dropout correctly and to capture 34% of the actual student dropout. These results are the first step toward building a more robust predictive model of dropout, which will contribute to preventive decision making in this university.

The prediction of leaf wetness duration (LWD) is an issue of interest for disease prevention in coffee plantations, forests, and other crops. This study analyzed different LWD prediction approaches using machine learning and meteorological and temporal variables as the models’ input. The information was collected through meteorological stations placed in coffee plantations in six different regions of Costa Rica, and the leaf wetness duration was measured by sensors installed in the same regions. The best prediction models had a mean absolute error of around 60 min per day. Our results demonstrate that for LWD modeling, it is not convenient to aggregate records at a daily level. The model performance was better when the records were collected at intervals of 15 min instead of 30 min.