Abstract
Computer programming is a complex domain both to teach and learn. This incited endeavors to find methods that could mitigate at least some of the existing barriers. In the last years, automatic assessment has been playing an important role in reducing the burden of teachers in the assessment of students' attempts to solve programming exercises and fostering the autonomy of students by allowing them to practice in any place and at any time with timely feedback. Even more recent development is the use of gamification in computer programming education in order to raise the enjoyment and engagement of students. Despite its rising spread, until now, there is not a programming exercise specification format addressing the needs of gamification, such as the definition of challenges, the underlying storyline, including the links to other exercises, or the rewards for solving challenges in form of points, badges or virtual items. Such a data format would allow the exchange of ready-to-use programming exercises along with the gamification-related data among different educational institutions and courses, providing instructors a possibility to make use of gamification in their courses without having to invest their own time in defining gamification rules themselves. In this paper, we analyze a set of concepts related to programming gamification developed in our previous work to identify the requirements for the specification of a gamified exercise format. (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
The potentialities of using mobile devices such as smartphones for teaching/learning purposes are huge. However, in some teaching areas its use is still residual. The use of mobile applications in the context of teaching PLC programming techniques is addressed in this work. The MIT App-Inventor II is deployed to develop mobile applications for learning purposes. An android based application entitled Time-Counts is proposed here, developed to support the teaching/learning process of timers. Preliminary results regarding its use by students are presented. © 2019, Springer International Publishing AG, part of Springer Nature.

Abstract
This study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved.

Abstract
Due to influences by power system restructuring, fuel price uncertainties, future demand forecasting, and utilities and transmission lines availability, demand response (DR) programs for consumers have gained more attention. One important DR scheme is the emergency demand response program (EDRP). This paper focuses on simultaneous implementation of security-constraint unit commitment (SCUC) and EDRP by using an economic model. Moreover, a stochastic optimization method is employed for realistic modelling. Since the combined implementation of SCUC and EDRP results in a complex nonlinear optimization problem, a linearization method to ensure computational efficiency is used. The proposed model is formulated as two-stage Stochastic Mixed-Integer Programming ( SMIP) model implemented using GAMS. The implemented model is tested on three case studies using the IEEE 24-bus system. Results are analyzed with a focus on the impact of demand elasticity and electricity prices.

Abstract
In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams. © Springer Nature Switzerland AG 2019.

Abstract