Abstract
In this work we address the Single-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. This problem is NP-Hard, therefore we propose a hybrid heuristic to solve it. Our goal is not only to apply an ant colony optimization (ACO) algorithm to such a problem, but also to provide an insight on the behaviour of the parameters in the performance of the algorithm. The performance of the ACO algorithm is improved with the hybridization of a local search (LS) procedure. The core ACO procedure is used to mainly deal with the exploration of the search space, while the LS is incorporated to further cope with the exploitation of the best solutions found. The method we have developed has proven to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics. Our algorithm was able to improve upon some of their results in terms of solution quality, proving that the HACO algorithm is a very good alternative approach to solve these problems. In addition, our algorithm is substantially faster at achieving these improved solutions. Furthermore, the magnitude of the reduction of the computational requirements grows with problem size.

Abstract
A Fabry-Perot microcavity tip temperature sensor based on a special design double-cladding optical fiber is proposed. The produced fiber has pure silica core and outer cladding and a silica ring doped with phosphorous. The whole ring region is removed by chemical etching post-processing. Consequently, light will be guided in the core region. In a first step, the double-cladding optical fiber is spliced to single mode fiber. Afterwards, the tip is etched in a solution of 48% hydrofluoric acid. The inner cladding will be etched faster, and the core becomes suspended and surrounded by air. The Fabry-Perot microcavity tip sensor is subjected to temperature, and a linear sensitivity of 14.6 pm/degrees C is obtained.

Abstract
As the size and cost of embedded devices continue to decrease, it becomes economically feasible to densely deploy networks with very large quantities of such nodes, and thus enabling the implementation of networks with increasingly larger number of nodes becomes a relevant problem. In this paper we describe a novel algorithm to obtain the number of live nodes with a very low time-complexity. In particular, we develop a mechanism to estimate the number of nodes or the number of proposed values (COUNT), with a time complexity that increases sublinearly with the number of nodes. The approach we propose is based on the wise exploitation of dominance-based protocols and offers excellent scalability properties for emerging applications in dense Cyber Physical Systems.

Abstract

Abstract
Manufacturing small and medium-sized enterprises (SMEs) are drivers for economic and social development. Their importance and competitive challenges are increasing interest and attention on private and public sectors due to hyper-competition environment. In order to establish an environment that, not only, addresses hyper-competition but also sustainability, a Meta-Organization is proposed to support advanced manufacturing business models, namely, Virtual and Ubiquitous Enterprises. This Meta-Organization is organizational platform supported by information and communication technologies (ICT), such as Web 3.0. The paper introduces a framework to approach Ubiquitous Virtual Enterprises using Meta-Organization and Manufacturing Web 3.0, adopted by manufacturing SMEs. This paper is organized in six chapters. The chapter 1 presents the environment context that manufacturing SMEs faces and introduces the framework used in the paper. In chapter 2, a review addressing Meta-Organization in context of Ubiquitous Virtual Enterprises is presented. Chapter 3 covers the topic of Meta-Organizations members, specifically manufacturing SMEs. In chapter 4, to meet the technological platform Manufacturing Web 3.0 some Web 3.0 technologies are presented. Chapter 5 presents shortly the overall framework. Finally, chapter 6 draws conclusions. (C) 2013 The Authors. Published by Elsevier B.V.

Abstract
This paper proposes the impact assessment of the jobs order in the optimal time of operations in a Flexible Job Shop Scheduling Problem. In this work a real assembly cell was studied: the AIP-PRIMECA cell at the Universite de Valenciennes et du Hainaut-Cambresis, in France, which is considered as a Flexible Job Shop problem. The problem consists in finding the machines operations schedule, taking into account the precedence constraints. The main objective is to minimize the batch makespan, i.e. the finish time of the last operation completed in the schedule. Shortly, the present study consists in evaluating if the jobs order affects the optimal time of the operations schedule. The genetic algorithm was used to solve the optimization problem. As a conclusion, it's assessed that the jobs order influence the optimal time.