Abstract
This paper presents a newedge-swap heuristic for generating spanning trees with a minimum number of branch vertices, i.e. vertices of degree greater than two. This problem was introduced in Gargano et al. (Lect Notes Comput Sci 2380:355-365, 2002) and has been called the minimum branch vertices problem by Cerulli et al. (Comput Optim Appl 42:353-370, 2009). The heuristic starts with a random spanning tree and iteratively reduces the number of branch vertices by swapping tree edges with edges not currently in the tree. It can be easily implemented as a multi-start heuristic. We report on extensive computational experiments comparing single-start and multi-start variants on our heuristic with other heuristics previously proposed in the literature.

Abstract
Sights of sovereign debt crisis spread among financial players started in late 2009 as a result of the rising private and government debt levels worldwide. In 2010 news developments concerning Spain and Italy lead European nations to implement several financial support measures such as the European Financial Stability Facility. In an established crisis context, it was searched for evidence of nonlinearities, structural breaks and cointegration between interest rates and stock market prices in order to evaluate the impact effect analysis of the European markets contamination by sovereign debt. Four European markets under stress were examined using the United States of America as benchmark. It was found evidence in the crisis regime especially for Portugal, obtaining the greatest nonlinear threshold adjustment between interest rates and stock market returns. Moreover, significant structural breaks were found at the end of 2010 and the null hypothesis of no cointegration was consistently rejected.

Abstract
In order to behave autonomously, it is desirable for robots to have the ability to use human supervision and learn from different input sources (perception, gestures, verbal and textual descriptions etc). In many machine learning tasks, the supervision is directed specifically towards machines and hence is straight forward clearly annotated examples. But this is not always very practical and recently it was found that the most preferred interface to robots is natural language. Also the supervision might only be available in a rather indirect form, which may be vague and incomplete. This is frequently the case when humans teach other humans since they may assume a particular context and existing world knowledge. We explore this idea here in the setting of conceptualizing objects and scene layouts. Initially the robot undergoes training from a human in recognizing some objects in the world and armed with this acquired knowledge it sets out in the world to explore and learn more higher level concepts like static scene layouts and environment activities. Here it has to exploit its learned knowledge and ground language into perception to use inputs from different sources that might have overlapping as well as novel information. When exploring, we assume that the robot is given visual input, without explicit type labels for objects, and also that it has access to more or less generic linguistic descriptions of scene layout. Thus our task here is to learn the spatial structure of a scene layout and simultaneously visual object models it was not trained on. In this paper, we present a cognitive architecture and learning framework for robot learning through natural human supervision and using multiple input sources by grounding language in perception. Copyright

Abstract
Available wireless sensor networks targeting the domain of healthcare enables the development of new applications and services in the context of E-Health. Such networks play an important role in several scenarios of patient monitoring, particularly those where data collection is vital for diagnosis and/or research purposes. However, despite emerging solutions, wearable sensors still depend on users' acceptance. One proposed solution to improve wearability relies on the use of smaller sensing nodes, requiring more energy-efficient networks, due to smaller room available for energy sources. In such context, smaller wireless sensor network nodes are required to work long time periods without human intervention and, at the same time, to provide appropriate levels of reliability and quality of service. Satisfaction of these two goals depends on several key factors, such as the routing protocol, the network topology, and energy efficiency. This paper offers a solution to increase the network lifetime based on a new Energy-Aware Objective Function used to design a Routing Protocol for Low-Power and Lossy Networks. The proposed Objective Function uses the Expected Transmission Count Metric and the Remaining Energy on each sensor node to compute the best paths to route data packets across the network. When compared with state of the art solutions, the proposed method increases the network lifetime by 21% and reduces the peaks of energy consumption by 12%. In this way, wireless sensor network nodes wearability can be improved, making them smaller and lighter, while maintaining the required performance.

Abstract
Power system regulators and operators are creating conditions for encouraging the participation of the demand-side into reserve markets. The electric vehicle (EV), when aggregated by a market agent, holds sufficient flexibility for offering reserve bids. Nevertheless, due to the stochastic nature of the drivers' behavior and market variables, forecasting and optimization algorithms are necessary for supporting an EV aggregator participating in the electricity market. This paper describes a new day-ahead optimization model between energy and secondary reserve bids and an operational management algorithm that coordinates EV charging in order to minimize differences between contracted and realized values. The use of forecasts for EV and market prices is included, as well as a market settlement scheme that includes a penalty term for reserve shortage. The optimization framework is evaluated in a test case constructed with synthetic time series for EV and market data from the Iberian electricity market.

Abstract
Industrial processes use energy to transform raw materials and intermediate goods into final products. Many efforts have been done on the minimization of energy costs in industrial plants. Apart from working on "how" an industrial process is implemented, it is possible to reduce the energy costs by focusing on "when" it is performed. Although, some manufacturing plants (e.g. refining or petrochemical plants) can be inflexible with respect to time due to interdependencies in processes that must be respected for performance and safety reasons, there are other industrial segments, such as alumina plants or discrete manufacturing, with more degrees of flexibility. These manufacturing plants can consider a more flexible scheduling of the most energy-intensive processes in response to dynamic prices and overall condition of the electricity market. In this scenario, requests for energy can be encoded by means of a formal structure called flex-offers, then aggregated (joining several flex-offers into a bigger one) and sent to the market, scheduled, disaggregated and transformed into consumption plans, and eventually, into production schedules for given industrial plant. In this paper, we describe the flex-offer concept and how it can be applied to industrial and home automation scenarios. The architecture proposed in this paper aims to be adaptable to multiples scenarios (industrial, home and building automation, etc.), thus providing the foundations for different concept implementations using multiple technologies or supporting various kinds of devices.