Abstract
The remarkable number of real applications under dynamic scenarios is driving a novel ability to generate and gatherinformation.Nowadays,amassiveamountofinforma- tion is generated at a high-speed rate, known as data streams. Moreover, data are collected under evolving environments. Due to memory restrictions, data must be promptly processed and discarded immediately. Therefore, dealing with evolving data streams raises two main questions: (i) how to remember discarded data? and (ii) how to forget outdated data? To main- tain an updated representation of the time-evolving data, this paper proposes fading histograms. Regarding the dynamics of nature, changes in data are detected through a windowing scheme that compares data distributions computed by the fading histograms: the adaptive cumulative windows model (ACWM). The online monitoring of the distance between data distributions is evaluated using a dissimilarity measure based on the asymmetry of the Kullback–Leibler divergence.The experimental results support the ability of fading his- tograms in providing an updated representation of data. Such property works in favor of detecting distribution changes with smaller detection delay time when compared with stan- dard histograms. With respect to the detection of concept changes, the ACWM is compared with 3 known algorithms taken from the literature, using artificial data and using pub- lic data sets, presenting better results. Furthermore, we the proposed method was extended for multidimensional and the experiments performed show the ability of the ACWM for detecting distribution changes in these settings.

Abstract
In this paper, a hexagonal structure of ethanol filled solid core photonic crystal fiber having dispersion shifted properties is presented. The guiding properties, like effective mode area and dispersion, of the ethanol filled photonic crystal fiber are studied by using the full vectorial finite element method. The numerical simulation results show that the selectively filling of the ethanol in the cladding holes of photonic crystal fiber shifts the zero dispersion wavelength from near infrared region (0.98 µm) to mid infrared region (1.55 µm). This kind of photonic crystal fiber structure is very useful for dispersion compensating tool, sensing applications, fiber laser devices and non-linear applications like supercontinuum generation. © 2017 Elsevier GmbH

Abstract
Learning contents creation supported on computer tools has triggered the scientific community for a couple of decades. However, teachers have been facing more and different challenges, namely the emergence of other delivery learning approaches besides the traditional educational settings, the diversification of the student target population, and the recognition of different ways of learning. In education domain, diverse recommender systems have been developed so far for recommending learning activities and more specifically, learning objects. This research work is focused on teaching-learning techniques recommendation to assist teachers by providing them recommendation about which teaching-learning techniques should scaffold teaching-learning activities to be carried out by students. This paper presents a recommender model sustained in diverse elements, namely, a hybrid recommender system, an association rules mechanism to infer possible combinations of teaching-learning techniques, and collaborative work among several actors in education. An evaluation is carried out and the preliminary results are very encouraging, revealing that teachers seem very enthusiastic and motivated to rethink their teaching-learning techniques when designing teaching-learning activities.

Abstract

Abstract
Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.

Abstract
Model-Based learning is a methodology that facilitates students' construction of scientific knowledge, which, sometimes, includes restructuring their mental models. Taking into consideration students' learning process, its aim is to promote a deeper understanding of phenomena's dynamics through the manipulation of models. Our aim was to ascertain whether the use of three different types of models, integrated into an intervention program whose goal was to teach the "seismic effects on soils and buildings", would influence the learning process of graduation students or not. For a better understanding of the results, the data were collected and analyzed through a combination of methods using, simultaneously, quantitative and qualitative method. And results not only confirmed the importance of the use of models, but also led us to the conclusion that despite the potential and limitations of all three models, mixed models are better for restructuring students' mental models and the development of meaningful learning.