Abstract
This paper introduces the science software of HARMONI. The Instrument Numerical Model simulates the instrument from the optical point of view and provides synthetic exposures simulating detector readouts from data-cubes containing astrophysical scenes. The Data Reduction Software converts raw-data frames into a fully calibrated, scientifically usable data cube. We present the functionalities and the preliminary design of this software, describe some of the methods and algorithms used and highlight the challenges that we will have to face.

Abstract
Although wind power generation has extended a maturity in technology, there are still many concerns regarding the optimal support of regulatory bodies for renewable resources. In this context, the regulatory body should form a market structure or consider market rules and regulations to not only attract investors to renewable power plants, but also provide an efficient market that reflects a safe and clear competition environment. In this paper, an agent-based game-theoretic model is developed to investigate the electricity market behavior under oligopoly circumstances. The proposed model reveals the potential of collusive and strategic behavior of market participants. By employing the proposed model, impacts of different supportive schemes on the behavior of the wind power producer and conventional thermal units are investigated. According to the results obtained, if the regulatory bodies do not consider strategic collusion of market participants, adverse consequences for wind power producers might happen in the long-term horizon. © 2016 IEEE.

Abstract
Smart grid solutions with enabling technologies such as energy management systems (EMSs) and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector. These technologies enable the control of residential consumption, local small-scale generation, and energy storage systems to respond to time-varying prices. However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles (EVs) can introduce even more burden on the operation of these assets, which is an issue that needs special attention. Such extra stress can cause accelerated aging of distribution system assets and significantly affect the reliability of the system. In this paper, the impact of a smart neighborhood load on distribution transformer aging is investigated. The EMS of each household is designed to respond to prices and other signals emitted by the responsive load serving entity within the relevant demand response strategy. An optimization framework based on mixed-integer linear programming is presented in order to define the EMS structure. Then, the equivalent aging of the distribution transformer is examined with a thermal model under different scenarios. The case studies that are presented indicate that the integration of EVs in residential premises may indeed cause accelerated aging of the distribution transformers, while the need to investigate the efficiency of dynamic pricing mechanisms is rendered evident.

Abstract

Abstract
A Subgraph Census (determining the frequency of smaller subgraphs in a network) is an important computational task at the heart of several graph mining algorithms. Here we focus on the g-tries, an efficient state-of-the art data structure. Its algorithm makes extensive use of the graph primitive that checks if a certain edge exists. The original implementation used adjacency matrices in order to make this operation as fast as possible, as is the case with most past approaches. This representation is very expensive in memory usage, limiting the applicability. In this paper we study a number of possible approaches that scale linearly with the number of edges. We make an extensive empirical study of these alternatives in order to find an efficient hybrid approach that combines the best representations. We achieve a performance that is less than 50% slower than the adjacency matrix on average (almost 3 times more efficient than a naive binary search implementation), while being memory efficient and tunable for different memory restrictions.

Abstract
This paper describes a proposal to develop a Tourism Recommendation System based in users enhanced profiles (composed by basic user information, relations between user and a set of stereotypes and user functionality levels). The main focus of this work is to evaluate if user's physical and psychological functionality levels considered in user's profiles creation, will produce significant changes in the recommendation results. This work aims also to contribute with a different way to classify points-of-interest (POI) considering their capacity to receive tourists with certain levels of physical and psychological issues that will be described in this paper sections.