Abstract
The common challenges faced by companies/industries are often related to the 'iron triangle' categories, namely: deadlines, budget, scope and quality. The Project Management knowledge area proposes a systematic approach to overcome these difficulties, with the purpose of acquiring new knowledge and skills, as well as by implementing methodologies, tools, processes and techniques. The textile and clothing industry, or fashion industry, is not immune to the same type of challenges. The products of the fashion chain are collections, which are developed according to market specifications, trends and market profiles. These specifications evolve very rapidly and, therefore, have their main objective in the development of new and/or innovative products. To deliver a new collection, the whole process of elaborating these products is executed based on a per project approach. Each collection is unique, having a well-defined beginning and end. The development of a fashion collection should consider the processes that, at the industrial level, will originate the final product, thus requiring a methodology that integrates the development of the project from the initial phase - conception of the product idea - until the final stage - start of production / launch to the market. By trying to identify project management approaches in the Portuguese textile and clothing industry, this study allowed us to conclude that, despite of these organizations considering as being developing or involved in projects, the approaches followed are not formal, structured and systematic. Furthermore, there is still no complete application of any of the known project management methodologies.

Abstract
The introduction of metallic nanostructures in optical fibers has revolutionized the field of plasmonic sensors since they produce sharper and fine-tuned resonances resulting in higher sensitivities and resolutions. This article evaluates the performance of three different plasmonic optical fiber sensors based on D-type and suspended core fibers with metallic nanowires. It addresses how their different materials, geometry of the components, and their relative position can influence the coupling between the localized plasmonic modes and the guided optical mode. It also evaluates how that affects the spatial distributions of optical power of the different modes and consequently their overlap and coupling, which ultimately impacts the sensor performance. In this work, we use numerical simulations based on finite element methods to validate the importance of tailoring the features of the guided optical mode to promote an enhanced coupling with the localized modes. The results in terms of sensitivity and resolution demonstrate the advantages of using suspended core fibers with metallic nanowires.

Abstract
In this article we present beatings, a web application for the exploration of tuning and temperaments which pays particular attention to auditory phenomena resulting from the interaction of the spectral components of a sound, and in particular to the pitch fusion and the amplitude modulations occurring between the spectral peaks a critical bandwidth apart. By providing a simple, yet effective, visualization of the temporal evolution of this auditory phenomena we aim to foster new research in the pursuit of perceptually grounded principles explaining Western tonal harmonic syntax, as well as provide a tool for musical practice and education, areas where the old art of musical tunings and temperaments, with the notable exception of early music studies, appears to have long been neglected in favour of the practical advantages of equal temperament. Copyright: © 2016 Rui Penha et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
This article discusses a method that performs gesture recognition, with the objective of extracting characteristics of the segmented hand, from dynamic images captured from a webcam and identifying signal patterns. With this method it is possible to manipulate simulated multirobots that perform specific movements. The method consists of the Continuously Adaptive Mean-SHIFT algorithm, followed by the Threshold segmentation algorithm and Deep Learning through Boltzmann restricted machines. As a result, an accuracy of 82.2%. © CLAWAR Association.

Abstract

Abstract
This paper presents a new solution algorithm to solve the resource-constrained project scheduling problem with activity splitting and setup times. The option of splitting activities, known as activity preemption, has been studied in literature from various angles, and an overview of the main contributions will be given. The solution algorithm makes use of a meta-heuristic search for the resource-constrained project scheduling problem (RCPSP) using network transformations to split activities in subparts. More precisely, the project network is split up such that all possible preemptive parts are incorporated into an extended network as so-called activity segments, and setup times are incorporated between the different activity segments. Due to the inherent complexity to solve the problem for such huge project networks, a solution approach is proposed that selects the appropriate activity segments and ignores the remaining segments using a boolean satisfiability problem solver, and afterwards schedules these projects to near-optimality with the renewable resource constraints. The algorithm has been tested using a large computational experiment with five types of setup times. Moreover, an extension to the problem with overlaps between preemptive parts of activities has been proposed and it is shown that our algorithm can easily cope with this extension without changing it. Computational experiments show that activity preemption sometimes leads to makespan reductions without requiring a lot of splits in the activities. Moreover, is shown that the degree of these makespan reductions depends on the network and resource indicators of the project instance.