Abstract
In interactive systems, the ability to select virtual objects is essential. In immersive virtual environments, object selection is usually done at arm's length in mid-air by directly intersecting the desired object with the user's hand. However, selecting objects outside user's arm-reach still poses significant challenges, which direct approaches fail to address. Techniques proposed to overcome such limitations often follow an arm-extension metaphor or favor selection volumes combined with ray-casting. Nonetheless, while these approaches work for room sized environments, they hardly scale up to larger scenarios with many objects. In this paper, we introduce a new taxonomy to classify existing selection techniques. In its wake, we propose PRECIOUS, a novel mid-air technique for selecting out-of-reach objects, featuring iterative refinement in Virtual Reality, an hitherto untried approach in this context. While comparable techniques have been developed for non-stereo and non-immersive environments, these are not suitable to Immersive Virtual Reality. Our technique is the first to employ an iterative progressive refinement in such settings. It uses cone-casting to select multiple objects and moves the user closer to them in each refinement step, to allow accurate selection of the desired target. A user evaluation showed that PRECIOUS compares favorably against state-of-the-art approaches. Indeed, our results indicate that PRECIOUS is a versatile approach to out-of-reach target acquisition, combining accurate selection with consistent task completion times across different scenarios.

Abstract
We consider a Unit Commitment Problem (UCP) addressing not only the economic objective of minimizing the total production costs-as is done in the standard UCP-but also addressing environmental concerns. Our approach utilizes a multi-objective formulation and includes in the objective function a criterion to minimize the emission of pollutants. Environmental concerns are having a significant impact on the operation of power systems related to the emissions from fossil-fuelled power plants. However, the standard UCP, which minimizes just the total production costs, is inadequate to address environmental concerns. We propose to address the UCP with environmental concerns as a multi-objective problem and use a metaheuristic approach combined with a non-dominated sorting procedure to solve it. The metaheuristic developed is a variant of an evolutionary algorithm, known as Biased Random Key Genetic Algorithm. Computational experiments have been carried out on benchmark problems with up to 100 generation units for a 24 h scheduling horizon. The performance of the method, as well as the quality, diversity and the distribution characteristics of the solutions obtained are analysed. It is shown that the method proposed compares favourably against alternative approaches in most cases analysed.

Abstract
In this paper, different complexity levels are analyzed for modeling photovoltaic cells. The electrical circuit approach is the technique mostly used to describe photovoltaic cell behavior. Single and double diode models are two typical representations for this purpose. The parameters number necessary to represent photovoltaic cells in single and double diode models are studied in this paper and its impact on characterizing I-P and P-V curves is explored to discuss parameters role in providing more accuracy. The modeling approach allows to adequately simulate photovoltaic array systems taking into account the compromise between the accuracy and simplicity. The mathematical models are implemented in Matlab/Simulink by using the Newton-Raphson method.

Abstract
In this paper, a mixed-integer linear programing (MILP) model for the stochastic clearing of electricity markets with probabilistic participants is proposed. It is assumed that the sources of uncertainty in the market comes both from generation and demand side. The wind generating unit and electric vehicle aggregator are the supposed sources of uncertainty in the system. For the compensation of probable deviation of stochastic participants, flexible generation and demand will offer for the reserve activation. The two-stage model takes into account the day-ahead cost as well as the expected balancing costs due to probabilistic behavior of uncertain participants. A scenario-based approach is used to model the probabilistic participants. The proposed model stochastically clears the market and the results discuss the lower costs obtained by incorporating various resources of uncertainty and flexibility in the market.

Abstract
The paradigm of health care delivery is slowly aligning with the needs and habits of modern patients. Mobile computing may be a solution to respond to the growing trend and need for health care sharing and collaboration, enabling the redesign of processes giving rise to new models of health care delivery. Seeking to determine the situation in Portugal regarding mobile computing initiatives in this domain (mobile health) and their status of implementation, and following the methodology of Levac et al., we conducted a survey and an exploratory study whose results were mirrored in a matrix developed for this purpose. The mapping of the study aims to summarize the acquired knowledge in an accessible and summarized format so that decision-makers, practitioners and consumers can make effective use of the findings.

Abstract