Abstract
The pursuit of unhealthy behaviors, such as smoking or binge drinking, not only carries various downside risks, but also provides pleasure. A parsimonious model, used in the literature to explain the decision to pursue an unhealthy activity, represents that decision as a tradeoff between risks and benefits. We build on this literature by surveying a rural population in South Africa to elicit the perceived riskiness and the perceived pleasure for various risky activities and to examine how these perceptions relate to the pursuit of four specific unhealthy behaviors: frequent smoking, problem drinking, seatbelt nonuse, and risky sex. We show that perceived pleasure is a significant predictor for three of the behaviors and that perceived riskiness is a significant predictor for two of them. We also show that the correlation between the riskiness rating and behavior is significantly different from the correlation between the pleasure rating and behavior for three of the four behaviors. Finally, we show that the effect of pleasure is significantly greater than the effect of riskiness in determining drinking and risky sex, while the effects of pleasure and riskiness are not different from each other in determining smoking and seatbelt nonuse. We discuss how our findings can be used to inform the design of health promotion strategies.

Abstract
Manufacturing companies are currently forced to reconsider their production processes by adopting more flexible, robust, and adaptive systems, aiming to improve their competitiveness. Multiagent systems (MASs) technology is suitable to address this challenge by providing an alternative way to design these complex systems based on the decentralization of the control functions over distributed entities. This paper describes the installation of a MAS solution in an industrial factory plant producing laundry washing machines. The installed solution focuses on the integration of quality and process control, and contributes to the maximization of the factory profitability facing changing conditions by applying self-adaptation procedures at the local and global levels. The preliminary results show improvements in the production efficiency and product quality, as well as a reduction of the scrap costs.

Abstract
Generalized additive models are well-known as a powerful and palatable predictive modelling technique. Scorecards, the discretized version of generalized additive models, are a long-established method in the industry, due to its balance between simplicity and performance. Scorecards are easy to apply and easy to understand. Moreover, in spite of their simplicity, scorecards can model nonlinear relationships between the inputs and the value to be predicted. In the scientific community, scorecards have been largely overlooked in favor of more recent models such as neural networks or support vector machines. In this paper, we address scorecard development, introducing a new formulation more suitable to support regularization. We tackle both the binary and the ordinal data classification problems. In both settings, the proposed methodology shows advantages when evaluated using real datasets.

Abstract
In the past two decades, grid computing have fostered advances in several scientific domains by making resources available to a wide community and bridging scientific gaps. Grid infrastructures have been harnessing computational resources all around the world allowing all kinds of parallelisms to be explored. Other approaches to parallel and distributed computing still exist like the use of dedicated high-performance (HPC) infrastructures, and the use of clouds for computing and storage, but grid computing continues to be the predominant technology used for scientific computing in Europe, through the European Grid Infrastructure (EGI) and the European Middleware Initiative (EMI). Currently, there is a trend towards the use of cloud technologies for computing and storage. In Europe, this trend is being followed by taking advantage of all the experiences gained from building grid infrastructures and the technologies developed around them (resource management orchestration, unified job description languages, security, user interfaces, programming models, and scheduling policies, among others). As a result, the European Grid Infrastructure Federated Cloud is being built on top of the grid infrastructure already available. After almost two decades of the development of grid software and components and the emergence of competing technologies, now is the time to discuss current trends and to assess future prospects. When organizing this book, the authors considered contributions that would review the current grid computing scenario as well as contributions that would summarize the main tools and technologies used so far. The chapters in this book provide reviews for the following topics: a) performance prediction for parallel and distributed computing systems, b) resource sharing on computational grids, c) economic models for resource management, and d) programming frameworks. The chapters address grid issues such as a) the challenges of designing efficient job schedulers for production grids, b) scalability analysis of bag-of-tasks applications, c) the energy efficiency of resource reservation-based scheduling, and d) the development of parallel applications using the grid environment. Additionally, the following tools are presented: a) a programming framework based on the concept of a pluggable grid service that avoids explicit calls to grid services in scientific code and b) a desktop grid framework that runs on top of a cloud and can be deployed on the fly. The authors were each invited to contribute a chapter to this book, which were carefully revised and selected based on their originality and the value of their contribution to the overall discussion on grid computing and its future prospects.

Abstract
Considering the new paradigm in the industry, where clients request small amounts of a growing range of products, the times of crisis and the fierce competition for businesses survival, it is crucial for Organizations to be able to reduce the waste of raw materials. In the manufacturing industry, the waste of materials could be minimized with the introduction of decision support systems in the process. In this paper we propose an information system that enables the optimization of purchases and consumption of materials, promoting waste reduction and boosting profitability.

Abstract
The adoption of social media networks by organizations has been increasing, mainly by using more social networks but also by constantly increasing on the number of messages and received comments posted on these channels. Interestingly, this process apparently has not been accompanied by a carefully planned and strategically design process to provide the essential alignment with organizational goals. This study is framed in the tertiary sector, the Higher Education Sector (HES), which despite its peculiarities, is no exception to the above limitations, and is facing an increased competitive environment. In this paper we present a sector benchmarking process, and the respective analysis, to provide insights on the sector's tendency, as well as a threefold classification of the sector's social media strategies being pursued. The analysis builds upon a regulatory communication framework and respective editorial model. We describe the results of our automatic text-mining and categorization information system, specifically developed to address and analyze the seven categories of HES' social media messages. Our results show that social media strategies have been focusing essentially on mediatization and building/maintaining the organizational image/reputation as well as on advertising educational services, but completely neglecting the dialogical dimension intrinsically linked to social media environments. (C) 2015 The Authors. Published by Elsevier B.V.