Abstract
This paper aims to point up the critical topics that need to be addressed towards a fully implementation of lean management as well as the adoption of learning mechanisms and information and communication systems in order to attain organisational differentiation through performance enhancement and integration of management practices that favour business sustainability. Firstly, we will try to explain the complexity of a construction project and how lean principles can be adapted to the construction industry, as well as some practical examples of the adoption of new technologies. Then, it will be explained how knowledge management and learning mechanisms can be critical in attaining a competitive advantage in this sector. Finally, a conceptual model will be presented based on the literature studies and the main results obtained from the focus group conducted with project managers from the construction area.

Abstract
In a recent article [1], David Parnas questions the traditional use of formal methods in software development, which he considers an underdeveloped body of knowledge and therefore of little hope for the software industry. He confronts the reader with the following statement, at some stage: "We must learn to use mathematics in software development, but we need to question, and be prepared to discard, most of the methods that we have been discussing and promoting for all these years." At the core of Parnas objections we find the contrast between the current ad-hoc (re)invention of mathematical concepts which are cumbersome and a burden to use and elegant (and therefore useful) concepts which are neglected, often for cultural or (lack of) background reasons. © 2012 Springer-Verlag.

Abstract
In our study, remote environmental sensing is presented using a standard optical time domain reflectometer (OTDR). The measurement of environmental parameters using optical sensors is an expanding area of research with growing importance. Fiber optic sensors are an interesting solution for that due to their high sensitivity, small size, and capability for on-site, real-time, remote, and distributed sensing capabilities. Our multiplexing sensing scheme approach uses transmissive filters (long period gratings - LPGs) interrogated by the OTDR return pulses. The loss induced at the resonance wavelengths varies with changes in the environment refractive index, temperature or other physical parameters. Experimental results show that the insertion of an erbium amplifier improves the measurement resolution in certain situations. Further analysis show that a remote multiplexed sensing scheme allows us to perform simple and low cost real time measurement of refractive index and temperature over long distances.

Abstract
A classical approach to program derivation is to progressively extend a simple specification and then incrementally refine it to an implementation. We claim this approach is hard or impractical when reverse engineering legacy software architectures. We present a case study that shows optimizations and pushouts-in addition to refinements and extensions-are essential for practical stepwise development of complex software architectures. Copyright 2012 ACM.

Abstract
Support Vector Machines (SVMs) have become a well succeed algorithm due to the good performance it achieves on different learning problems. However, to perform well the SVM formulation requires adjustments on its model. Avoiding the trial and error procedure, the automatic SVM parameter selection is a way to deal with this. The automatic parameter selection is commonly considered an optimization problem whose goal is to find suitable configuration of parameters which attends some learning problem. In the current work, we propose a study of the combination of Meta-learning (ML) with Particle Swarm Optimization (PSO) algorithms to optimize the SVM model, seeking for combinations of parameters which maximize the success rate of SVM. ML is used to recommend SVM parameters, to a given input problem, based on well-succeeded parameters adopted in previous similar problems. In this combination, initial solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of candidate search points, in the search process, to find an adequate solution, would be less expensive. In our work, we implemented five benchmarks PSO approaches applied to select two SVM parameters for classification. The experiments consist in comparing the performance of the search algorithms using a traditional random initialization and using ML suggestions as initial population. This research analysed the influence of meta-learning on convergence of the optimization algorithms, verifying that the combination of PSO techniques with ML obtained solutions with higher quality on a set of 40 classification problems.

Abstract
An electrical dynamic interrogation technique previously reported by the authors for long-period grating sensors is now progressed by relying its operation exclusively on the modulation of a DFB Laser. The analysis of the detected first and second harmonic generated by the electrical modulation of the DFB Laser allows generating an optical signal proportional to the LPG spectral shift and resilient to optical power fluctuations along the system. This concept permits attenuating the effect of the 1/f noise of the photodetection, amplification and processing electronics on the sensing head resolution. This technique is employed in a multiplexing sensing scheme that measures refractive index variations.