Abstract
The diffusion of innovation theory aims to explain how new ideas and practices are disseminated among social system members. A significant number of the existing models is based on the use of parameters which determine the process of innovation adoption, and rely on simple mathematical functions centered in the observation and description of diffusion patterns. These models enable a more explicit diffusion process study, but their use involves the estimation of diffusion coefficients, usually obtained from historical data or chronological series. This raises some application problems in contexts where there is no data or the data is insufficient. This paper proposes the use of evolutionary computation as an alternative approach for the simulation of innovation diffusion within organizations. To overcome some of the problems inherent to existing models an evolutionary algorithm is proposed based on a probabilistic approach. The results of the simulations that were done to validate the algorithm revealed to be very promissing in this context. Simulation experiment results are presented that reveals a very promising approach of the proposed model. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
Here we discuss the teaching of Fourier series concepts in connection with undergraduate Electrical Engineering education. We briefly review some of the basic Fourier series theory, and present an Android application that has been found useful in this context. As expected, the application has an easy-to-use, friendly interface, and can be viewed as a tool to help undergraduate students test and assess the Fourier series expansions on a typical set of signals, whose analytical Fourier series coefficients were found during the theoretical lectures. Additionally, students can also control the total approximation error and the number of terms/harmonics used in the expansion. It seems that our students prefer this Android application to the traditional applet fashioned web-based applications.

Abstract
User interfaces that employ the same display and buttons may look the same but can work very differently depending on exactly how they are implemented. In healthcare, it is critical that interfaces that look the same are the same. Hospitals typically have many types of visually similar infusion pumps, but with different software versions and variation between pump behavior, and this may lead to unexpected adverse events. For example, when entering drug doses into infusion pumps different results may arise when pushing identical sequences of buttons. These differences arise as a result of subtle implementation differences and may lead to large errors users do not notice. Previous work has explored different implementations of a 5-key interface for entering numbers using a new analysis technique, Differential Formal Analysis, where predictions relating to the distribution of errors in terms of the size of the error (out by 10, out by 100 and so on) can be made. The analysis described in the paper extends this work with models of use based on many hours of actual clinical use data. Specifically, we draw on 1,362 days of use of number entry systems, from 19 infusion pumps over a 3 year period in a UK hospital. The paper also suggests some improvements to medical device logs, which will help further evidence-based improvement to medical device safety. © 2013 IEEE.

Abstract
Superefficiency is an important extension of DEA that overcomes some limitations of the traditional models, specifically allowing ranking of efficient units and a unique set of weights for those units. Weights restriction is a well-known technique in the DEA field. When those techniques are applied, weights cluster around its new limits, making its evaluation dependent of its levels. This chapter introduces a new approach to weights adjustment by goal programming techniques, avoiding the imposition of hard restrictions that can even lead to unfeasibility. This method results in models that are more flexible.

Abstract
This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

Abstract
in this article we present a system capable of creating, managing and presenting digital portfolios. Our system innovates by using roles and states during its creation phase. This allows for high quality elements in the portfolio and promotes the students' reflection over them before full integration. The system also complies with the existing standards for e-portfolios. Moreover, it adds an extension to integrate previous created portfolios from different educational levels. In the article we show the need for such extension and describe how the system deals with integration of such diverse portfolios into a single one.