Abstract
This paper analyzes the impact on market prices of the policies that have been adopted in Brazil to foster electricity from renewable energy sources (RES-E), namely wind power. In recent years the Brazilian Government implemented a series of policies that enabled a strong growth of RES-E. Recently more than 14 GW of wind and solar power were contracted. However, as most of the assets are concentrated in specific regions, these policies will induce price differences among areas of the country. In this scope, this paper describes a System Dynamics based model of the Brazilian generation system to evaluate the impact on prices from the deployment of these new sources. The paper describes simulations using realistic data for the Brazilian power system and the results suggest that the difference of prices in the country tend to increase since the Northeast region of the country concentrates most of the wind parks.

Abstract
This paper proposes an ensemble method for time series forecasting tasks. Combining different forecasting models is a common approach to tackle these problems. State-of-the-art methods track the loss of the available models and adapt their weights accordingly. Metalearning strategies such as stacking are also used in these tasks. We propose a metalearning approach for adaptively combining forecasting models that specializes them across the time series. Our assumption is that different forecasting models have different areas of expertise and a varying relative performance. Moreover, many time series show recurring structures due to factors such as seasonality. Therefore, the ability of a method to deal with changes in relative performance of models as well as recurrent changes in the data distribution can be very useful in dynamic environments. Our approach is based on an ensemble of heterogeneous forecasters, arbitrated by a metalearning model. This strategy is designed to cope with the different dynamics of time series and quickly adapt the ensemble to regime changes. We validate our proposal using time series from several real world domains. Empirical results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters.

Abstract
Selecting objects outside user's arm-reach in Virtual Reality still poses significant challenges. Techniques proposed to overcome such limitations often follow arm-extension metaphors or favor the use of selection volumes combined with ray-casting. Nonetheless, these approaches work for room sized and sparse environments, and they do not scale to larger scenarios with many objects. We introduce PRECIOUS, a novel mid-air technique for selecting out-of-reach objects. It employs an iterative progressive refinement, using cone-casting to select multiple objects and moving users closer to them in each step, allowing accurate selections. A user evaluation showed that PRECIOUS compares favorably against existing approaches, being the most versatile.

Abstract
Optimising the design of water distribution networks (WDNs) is a well-known problem that has been studied by numerous researchers. This work proposes a heuristic based on simulated annealing and improved by using concepts from the cross-entropy method. The proposed optimization approach is presented and used in two case studies of different complexity. The results show not only a fall in the computational effort of the new approach relative to simulated annealing but also include a comparison with other heuristic results from the literature, used to solve the same problems.

Abstract
Currently, many libraries, either academic or public, possess information systems to support their operations. Although libraries are becoming more aware of the potential of data analytics in supporting library management decisions, there is still a long way to go to take plenty advantage of the information collected. This paper proposes a prescriptive analytics solution to enhance the service provided by libraries, by optimizing libraries layout. The quantitative method introduced aims to identify layout configurations that minimize the time spent by clients in picking books from the library. A new multi-floor layout optimization algorithm is developed, based on the pairwise exchange method heuristic. A real data sample of approximately 66.000 loans, taken from the information system of a European Engineering School's library, was analyzed and processed. The method proposed was used to improve the library's current departments configuration, achieving an improvement of 13.2% in terms of walking distance to collect the books. The results corroborate the effectiveness of the method proposed and its potential in supporting library management decisions.

Abstract
Recent advances in sequencing allow the study of all identified human genes (22,000 protein encoding genes), which have differential expression between tissues. However, current knowledge on gene interactions lags behind, especially when one of the elements encodes a mitochondrial protein (1500). Mitochondrial proteins are encoded either by mitochondrial DNA (mtDNA; 13 proteins) or by nuclear DNA (nDNA; the remaining), which implies a coordinated communication between the two genomes. Since mitochondria coordinate several life-critical cellular activities, namely energy production and cell death, deregulation of this communication is implicated in many complex diseases such as neurodegenerative diseases, cancer and diabetes. Thus, this work aimed to identify high co-expression groups between mitochondrial genes-all genes, and associated protein networks in several human tissues (Genotype-Tissue Expression database). We developed a pipeline and a web tree viewer that is available at GitHub (https://github.com/Pereira-lab/CoExpression). Biologically, we confirmed the existence of highly correlated pairs of mitochondrial-all protein encoding genes, which act in pathways of functional importance such as energy production and metabolite synthesis, especially in brain tissues. The strongest correlation between mtDNA genes are with genes encoded by this genome, showing that correlation among genes encoded by the same genome is more efficient.