Abstract
The economic sector of contact centers is growing by more than 8% a year. It is a multidisciplinary area in which information systems are decisive to organizations' success. Contact Centers' Information Systems deal with real time requisites and critical business information. A theory-building research shows a framework with 12 key design factors to consider, which managers might use to develop projects and researchers may adopt for further investigation in the area of Contact Center design. This work intends to provide a valuable link between the research community and practitioners in industry.

Abstract
The energy storage system (ESS), as well as its efficient management, represents a key factor for the success of electric vehicles. Due to well-known technological constraints in ESSs, there has been a growing interest in combining various types of energy sources with complementary features. Among the many possible combinations, our interest here lies in the hybridization of batteries and supercapacitors (SCs) with an active parallel arrangement, i.e., the sources are connected to the direct current (dc) bus through two bidirectional dc-dc converters (step-up). Based on this ESS topology, a robust dc-link controller is employed to regulate the dc-bus voltage and track the SCs current in spite of uncertainties in the system. For this purpose, we start by showing that the converters' uncertainty, e. g., the powertrain load, can be modeled as a convex polytope. The dc-link controller is then posed as a robust linear-quadratic regulator problem and, by exploring the convex polytope, converted in a linear matrix inequalities framework, which can efficiently be solved by numerical means. Finally, the operation envelope of the controller is extended by scheduling the gains according to the energy sources' voltages, which is an important feature to cope with the voltage variations in the SCs. To analyze the performance of the control architecture, a reduced-scale prototype was built. The experimental results show that, compared with the nonrobust and non-gain-scheduled controllers, the proposed dc-link controller offers better transient response and robustness to disturbances. Furthermore, the global performance of the controller is evaluated during certain driving cycles.

Abstract
Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift. This explicit change detection mechanism provides useful information about the dynamics of the process generating data, faster adaption to changes and generates compact rule sets. The experimental evaluation shows this method is able to learn fast and compact rule sets from evolving streams in comparison to alternative methods. © 2012 Springer-Verlag.

Abstract
Interest in the characterization and evolution of science has sparked a vast amount of literature. The manner in which this field of knowledge is depicted is highly diversified, giving rise to different styles of reporting the history of science. Not with standing the relevance of the subject for a broader understanding of how knowledge is produced, certified and appropriated, a simple and informative framework is missing. Proposing a simple categorization of the methods for assessing the evolution of science, our study highlights the changing process of scientific traditions, portraying the evolution of science as shifting towards hybrid and quantitative methodologies, namely those based on bibliometric techniques, as they arguably rely less on the judgments and perceptions of researchers, and have a higher degree of certainty. © EuroJournals Publishing, Inc. 2012.

Abstract
A major challenge when accessing protected multimedia content in heterogeneous usage environments is the ability to provide acceptable levels of quality of experience to all involving users. Additionally, different levels of protection should be possible to be addressed when manipulating the content towards the quality of experience maximization. This paper describes the use of a context-aware and Digital Rights Management (DRM)-enabled content adaptation platform towards meeting these challenges. The platform was conceived to deliver advanced content adaptation within different application scenarios, among which Virtual Collaboration (VC) was central. Descriptions of use cases implemented by the platform in heterogeneous VC environments are provided. Conducted experiments have highlighted the benefits to users when compared to an operation without the platform. Results of different adaptations suitable to sensed context conditions are also provided and analyzed. A brief description of the platform functionality is included together with pointers to additional information.

Abstract