Abstract
In massive data analysis, data usually come in streams. In the last years, several studies have investigated novelty detection in these data streams. Different approaches have been proposed and validated in many application domains. A review of the main aspects of these studies can provide useful information to improve the performance of existing approaches, allow their adaptation to new applications and help to identify new important issues to be addresses in future studies. This article presents and analyses different aspects of novelty detection in data streams, like the offline and online phases, the number of classes considered at each phase, the use of ensemble versus a single classifier, supervised and unsupervised approaches for the learning task, information used for decision model update, forgetting mechanisms for outdated concepts, concept drift treatment, how to distinguish noise and outliers from novelty concepts, classification strategies for data with unknown label, and how to deal with recurring classes. This article also describes several applications of novelty detection in data streams investigated in the literature and discuss important challenges and future research directions.

Abstract
The restructuring of power systems with the introduction of electricity markets and decentralized structures increased the number of participating entities. This is particularly true in generation and retailing which are now provided under competition. Accordingly, it is important to develop models to simulate the behavior of these agents and to optimize their participation in electricity markets. Among them, it is essential to adequately model generation agents namely in countries having a large share of hydro stations. This paper describes an agent-based approach to model the day-ahead electricity market having a particular emphasis on hydro generation. Apart from the characterization of the agents, the paper details the introduction of the Q-Learning algorithm in the model as a way to enhance the performance of generation agents. This paper also presents some preliminary results taking the Portuguese generation system as an example.

Abstract
This paper presents a behavioral model for amorphous indium-gallium-zinc oxide thin-film transistor using artificial neural network (ANN) based equivalent circuit (EC) approach to predict static and dynamic behavior of the device. In addition, TFT parasitic capacitances (C-GS and C-GD) characterization through measurements is also reported. In the proposed model, an EC is derived from the device structure, in terms of electrical lumped elements. Each electrical element in the EC is modeled with an ANN. Then these ANNs are connected together as per the EC and implemented in Verilog-A. The proposed model performance is validated by comparing the circuit simulation results with the measured response of a simple common-source amplifier, which has shown 12.2 dB gain, 50 mu W power consumption and 85 kHz 3-dB frequency with a power supply of 6 V. The same circuit is tested as an inverter and its response is also presented up to 50 kHz, from both simulations and measurements. These results show that the model is capable of capturing both small and large signal behavior of the device to good accuracy, even including the harmonic distortion of the signal (that emphasizes the nonlinear behavior of the parasitic capacitance), making the model suitable for IC design.

Abstract
This paper presents the characterization of fundamental analog and digital circuits with a-IGZO TFTs from measurements performed at normal ambient. The fundamental blocks considered in this work include digital logic gates, a low power single stage high-gain amplifier with capcacitive bootstrapping and a level shifter/buffer. These circuits are important functional blocks in analog/Mixed signal IC design with oxide TFTs. Being fabricated at low temperature (<200 degrees C), they can find potential applications in low-cost large-area flexible systems.

Abstract
Several countries have a significant installed capacity of large-scale reversible hydro power plants. This large-scale storage technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to frequency restoration reserve (FRR), given their ability to control its operating point in pumping mode. This work proposes a strategy to manage the water resource and maximize the power plant revenue by participating in the day ahead market but also providing ancillary services. Moreover, a model to correctly allocate the water resource throughout the year is presented, as well as an evaluation module to calculate the real revenue of the system.

Abstract
In this paper it is presented a usability evaluation of an entertainment platform, well-known, YouTube. The main aim is to study how people with intellectual disabilities interact with the layout and uses their search field option. We intended to observe and analyze performance and satisfaction but also if the interface can give them autonomy on the search option. The results show that participants had a good experience with the interface, however cannot be autonomy with the search option.