Abstract
Software development is a knowledge-intensive activity. Software products usually start as a simple idea, or a vision, and then progress towards a final deliverable product. Along this evolution, there is a lot of knowledge that is captured, organized, and shared, leading to new knowledge, both as a whole and within specific development activities. The concept of "Ba" provides a foundation to advance individual and collective knowledge, which describes knowledge creation as a spiral involving tacit and explicit knowledge: the Socialization, Externalization, Combination, Internalization model (a.k.a. SECI model). By applying this foundation to software development, we found issues that may hinder the effective knowledge management cycle. In this paper, we present a vision and a set of requirements for tools to overcome such issues and therefore better support the whole process of software knowledge evolution.

Abstract
Environmental problems related to the conventional generators have motivated governmental policies all over the world in order to incorporate alternative power sources to reduce greenhouse gas (GHG) emissions and fossil-fuel consumption. On the one hand, wind power generation can increase GHG emissions of the others conventional generators connected to the system. On the other hand, wind energy is characterized by its variability that imposes challenges in the operation of the power system. In order to integrate GHG emissions estimation and the uncertainty related to the wind power generation, a probabilistic point of view is presented in this paper to estimate the probability distribution function (PDF) of the GHG emissions of a typical insular power system. The PDF of power production of each conventional generator is calculated, and then the expected value of the total GHG emissions is estimated.

Abstract

Abstract
This book presents a careful selection of the contributions presented at the Mathematical Methods in Engineering (MME10) International Symposium, held at the Polytechnic Institute of Coimbra- Engineering Institute of Coimbra (IPC/ISEC), Portugal, October 21-24, 2010. The volume discusses recent developments about theoretical and applied mathematics toward the solution of engineering problems, thus covering a wide range of topics, such as: Automatic Control, Autonomous Systems, Computer Science, Dynamical Systems and Control, Electronics, Finance and Economics, Fluid Mechanics and Heat Transfer, Fractional Mathematics, Fractional Transforms and Their Applications, Fuzzy Sets and Systems, Image and Signal Analysis, Image Processing, Mechanics, Mechatronics, Motor Control and Human Movement Analysis, Nonlinear Dynamics, Partial Differential Equations, Robotics, Acoustics, Vibration and Control, and Wavelets. © Springer Science+Business Media Dordrecht 2014.

Abstract
This paper analyzes the scientific literature produced recently about new product development (NPD) in order to identify and classify the major fields of knowledge, possible connections, as well as the nature of articles and analytics tools used. A review of the literature was implemented on 461 articles focused on NPD, published in three leading journals between 2000 and 2010. It was possible to create 13 main categories, in which innovation, research and development, NPD products and processes and NPD teams stand out. Results conclude that NPD is multifaceted, includes multiple concepts and interrelated categories. Moreover, future studies need to complement qualitative with quantitative research and to analyze how small and large firms differ.

Abstract
Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in the numerous screening mammograms. A more recent trend includes the development of pre-CAD systems aiming at identifying normal mammograms instead of detecting suspicious ones. Normal breasts are screened-out from the process, leaving radiologists more time to focus on more difficult cases. In this work, a new approach for the identification of normal breasts is presented. Considering that even breasts with malignant findings are mostly constituted by normal tissue, the breast area is divided into blocks which are then compared pairwise. If all blocks are very similar, the breast is labelled as normal, and as suspicious otherwise. Features characterizing the pairwise block similarity and characterizing the intra-block pixel distribution are used to design a predictive method based on machine learning techniques. The proposed solution was applied on a real world screening setting composed by nearly 18000 mammograms. Results are similar to the more complex state of the art approaches by correctly identifying more than 20% of the normal mammograms. These results suggest the usefulness of the relative comparison instead of the absolute classification. When properly used, simple statistics can suffice to distinguish the clearly normal breasts.