Abstract
In recent years the agri-food sector has suffered a series of changes that lead to a driving need for an inclusion of ICT and Information Systems in their production and business processes. One of the agri-food sector entities that has introduced a complex set of technologies and IS in their processes was the mushrooms farms, mainly due to the urging need to control all the variables inherent to the mushroom but also to the surrounding environment. With this paper we aim to present a proposal for an amplified sensing system, composed by a set of wireless sensing nodes with the ability to control environmental indicators, which could act as a complement to a process control system. The Sousacamp Group was used, as case study, to not only identify the environmental indicators that needed to be sensed but also to test an initial version of the proposed system.

Abstract
In this paper the orchestration of wearable sensors with human computation is explored to provide map metadata for blind navigation. Technological navigation aids for blind must provide accurate information about the environment and select the best path to reach a chosen destination. Urban barriers represent dangers for the blind users. The dynamism of smart cities promotes a constant change of these dangers and therefore a potentially “dangerous territory” for these users. Previous work demonstrated that redundant solutions in smart environments complemented by human computation could provide a reliable and trustful data source for a new generation of blind navigation systems. We propose and discuss a modular architecture, which interacts with environmental sensors to gather information and process the acquired data with advanced algorithms empowered by human computation. The gathered metadata should enable the creation of “happy maps” that are delivered to blind users through a previously developed navigation system.

Abstract
This paper reports the research that was developed to predict biding curves submitted by generation players to the Market Operator of the Iberian Electricity Market. In this scope, we used a data set based on publicly available information from the website of the Market Operator to develop a two-step ANN prediction model. The first step involves the prediction of the amount of energy bidden at zero price and the second ANN predicts the parameters of the equation of the line that better approximates the remaining bid curve. The tests were done using information of a large generation player but this approach can be replicated to other players so that the individual predicted curves can be composed in order to obtain the aggregated selling curve for each hour of the next day.

Abstract
A virtual enterprise management platform is proposed with the objective to simplify the formation, management, adaptation and monitoring of the dynamic manufacturing process in VE. Within this platform an integration black box is highlighted to monitor the process data. This black box is used as a smart object to collect updated data or information from the business process or equipment within the VE. The raw data for predictive maintenance is collected by the black box from programmable logic controllers via gateway interface. A dashboard user interface is also designed and developed within the scope of this research that acts as a visualization tool to display the process monitored data. The theme of this research is designed and developed within the scope of the European Commission NMP priority of the Seventh RTD Framework Programme for the ADVENTURE (ADaptive Virtual ENterprise ManufacTURing Environment) project. © Springer International Publishing Switzerland 2015.

Abstract
Violent crime is a well known social problem affecting both the quality of life and the economical development of a society. Its prediction is therefore an important asset for law enforcement agencies, since due to budget constraints, the optimization of resources is of extreme importance. In this work, we tackle both aspects: prediction and optimization. We propose to predict violent crime using regression and optimize the distribution of police officers through an Integer Linear Programming formulation, taking into account the previous predictions. Although some of the optimization data are synthetic, we propose it as a possible approach for the problem. Experiments showed that Random Forest performs better among the other evaluated learners, after applying the SmoteR algorithm to cope with the rare extreme values. The most severe violent crime rates were predicted for southern states, in accordance with state reports. Accordingly, these were the states with more police officers assigned during optimization.

Abstract
Background Physicians' ability to make cost-effective decisions has been shown to be affected by their knowledge of health care costs. This study assessed whether Portuguese family physicians are aware of the costs of the most frequently prescribed diagnostic and laboratory tests. Methods A cross-sectional study was conducted in a representative sample of Portuguese family physicians, using computer-assisted telephone interviews for data collection. A Likert scale was used to assess physician's level of agreement with four statements about health care costs. Family physicians were also asked to estimate the costs of diagnostic and laboratory tests. Each physician's cost estimate was compared with the true cost and the absolute error was calculated. Results One-quarter (24%; 95% confidence interval: 23%-25%) of all cost estimates were accurate to within 25% of the true cost, with 55% (95% IC: 53-56) overestimating and 21% (95% IC: 20-22) underestimating the true actual cost. The majority (76%) of family physicians thought they did not have or were uncertain as to whether they had adequate knowledge of diagnostic and laboratory test costs, and only 7% reported receiving adequate education. The majority of the family physicians (82%) said that they had adequate access to information about the diagnostic and laboratory test costs. Thirty-three percent thought that costs did not influence their decision to order tests, while 27% were uncertain. Conclusions Portuguese family physicians have limited awareness of diagnostic and laboratory test costs, and our results demonstrate a need for improved education in this area. Further research should focus on identifying whether interventions in cost knowledge actually change ordering behavior, in identifying optimal methods to disseminate cost information, and on improving the cost-effectiveness of care.