Abstract
The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were mixed in different percentages and transformed into mini-burgers. These were stored at 3 degrees C, then examined at 0, 72, 160, and 240 h for deteriorative microorganisms. Mini-burgers was submitted to Soxhlet extraction, following which lipid extracts were analyzed by FTIR. The principal component analysis (PCA) described the studied adulteration using four principal components with an explained variance of 95.60%. PCA showed that the absorbance in the spectral region from 721, 1097, 1370, 1464, 1655, 2805, to 2935, 3009 cm 1 may be attributed to biochemical fingerprints related to differences between SS and OM. The partial least squares regression (PLS-R) predicted the presence/absence of adulteration in fish samples of an external set with high accuracy. The proposed methods have the advantage of allowing quick measurements, despite the storage time of the adulterated fish. FTIR combined with chemometrics showed that a methodology to identify the adulteration of SS with OM can be established, even when stored for different periods of time.

Abstract
Technological evolutions lead to changes in production processes; the Fourth Industrial Revolution has been called Industry 4.0, as it integrates Cyber-Physical Systems and the Internet of Things into supply chains. Large complex networks are the core structure of Industry 4.0: any node in a network can demand a task, which can be answered by one node or a set of them, collaboratively, when they are connected. In this paper, the aim is to verify how (i) network's connectivity (average degree) and (ii) the number of levels covered in nodes search impacts the total of production tasks completely performed in the network. To achieve the goal of this paper, two hypotheses were formulated and tested in a computer simulation environment developed based on a modeling and simulation study. Results showed that the higher the network's average degree is (their nodes are more connected), the greater are the number of tasks performed; in addition, generally, the greater are the levels defined in the search for nodes, the more tasks are completely executed. This paper's main limitations are related to the simulation process, which led to a simplification of production process. The results found can be applied in several Industry 4.0 networks, such as additive manufacturing and collaborative networks, and this paper is original due to the use of simulation to test this kind of hypotheses in an Industry 4.0 production network.

Abstract
This paper proposes a logistic planner with supplementary 3D spatial representations to enhance and interact with traditional logistic systems on the context of mobile manipulators performing internal logistics operations. By defining a hierarchical structure, the logistic world model, as the central entity synchronized between multiple system components, the reliability and accuracy of the logistic system is strengthened. The proposed approach aims at implementing a robust and intuitive solution for the set-up of mobile manipulator based logistic systems. The logistic planner includes a web based interface for fast setup of the warehouse layout based on robot sensing, as well as the definition of missions for the fleet of robotic systems.

Abstract
A new fabrication method of polymeric optical fiber tweezers with a multi-mode tip is presented. Preliminary results show higher robustness, improved ability for 2D trapping and differentiation of particles based on back-scattering analysis. © OSA 2018 © 2018 The Author(s)

Abstract
A Dynamic Manufacturing Network (DMN) is the manufacturing industry application of the Virtual Enterprise (VE) business model based on real time information sharing and process integration. DMNs are normally formed and supported by a collaborative platform previously designed and built by a preexisting strategic partnership. The collaborative platform forms and tracks each DMN through all phases of its life cycle which leads to the accumulation and storage of large historical datasets on partner and customer characteristics and actions. This data holds the key to customer and manufacturer behavioral patterns and performances that can further be used in the decision making processes. In this study, we have focused on tackling this widely neglected research opportunity, by integrating manufacturer, order and customer data and characteristics into DMN formation and planning. The developed big data analytics approach consists of TOPSIS, fuzzy inference system and multi objective optimization techniques. Initially, by integrating the TOPSIS multi criteria decision making technique with a fuzzy inference system (FIS) we have computed indices for Manufacturer reliability and Order priority. Then we developed a multi-objective mixed integer linear programming (MILP) model to generate efficient solutions minimizing cost and assigning more reliable manufacturers to orders with higher priority.

Abstract
The BSc in Systems Engineering, taught at the School of Engineering of the Porto Polytechnic (ISEP–PPorto), includes the development of team projects at the end of each semester. These projects are intended to be multidisciplinary, to allow students getting a systems perspective of the problems, and as a way to integrate knowledge from different scientific areas. To integrate the knowledge in electronics, microcontrollers and programming, common in several distinct systems nowadays, there is a course named Systems Labs I (LSIS1). This paper discusses the experience of teaching this course using a robotics based approach. The analysis is based on a survey conducted to the students enrolled in the course in the last three academic years, allowing to conclude that the overall students experience with the attendance of LSIS1 course is good. © 2018 by World Scientific Publishing Co. Pte. Ltd.