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Abstract: We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain. [Copyright &y& Elsevier]

Student dropout in Engineering Education is an important problem which has been studied from different perspectives, as well as using different techniques. This manuscript describes the methodology used in order to address this question in the context of learning analytics. Bayesian networks (BNs) have been used as they provide adequate methods for the representation, interpretation and contextualisation of data. The proposed approach is illustrated through a case study about Computer Science (CS) dropout at the University of Castilla-La Mancha (Spain), which is close to 40%. To that end, several BNs were obtained from a database which contained 383 records representing both academic and social data of the students enrolled in the CS degree during four courses. Then, these probabilistic models were interpreted and evaluated. The results obtained revealed that the best model that fits the data is provided by the K2 algorithm although the great heterogeneity of the data studied did not permit the adjustment of the dropout profile of the student too accurately. Nonetheless, the methodology described here can be taken as a reference for future works. [ABSTRACT FROM AUTHOR]