Abstract
Project management is a critical success factor for any kind of project. When projects involve complex multi-organisational structures, requiring demanding collaborative processes, project management is a complex system in itself. Although this topic has been studied for decades, the ever-changing morphology of projects induced by the increasingly intricate financing schemes, call for a frequent and updated understanding of how the projects initiate, run, and close. Large-scale integrated collaborative projects are a recent example of complex collaborative projects that were not studied yet but can provide important insights for the project and innovation management fields. These projects are carried out typically by a large consortium including research organisations, sectorial technological centres, technology providers and end-user companies, having a significant impact on the technological innovation produced for a specific sector. This chapter reports a multiple case study of four large-scale integrated projects, in Portugal, following an inductive research design. The results showed that collaboration creation and collaboration management are crucial processes for such projects, with challenges intensified by the differences in goals and expectations of researchers and practitioners. The chapter will hopefully contribute to the future development of new models, tools and techniques that will improve the efficiency and effectiveness of this type of projects, by providing a systematisation of the challenges faced and how they were overcome during the cases analysed. © 2021, Springer Nature Switzerland AG.

Abstract
Providing efficient support mechanisms for renewable energy promotion has drawn much attention from researchers in the recent years. The connection of a new renewable power plant to the transmission system has impacts on different electricity market indices since the other strategic generation units change their behaviour in the new multi-agent environment. In this paper, as the main contribution to the previous literature, a combination of multi-criteria decision-making approach and multi-agent modelling technique is developed to obtain the maximum possible profits for an intended renewable generation plan and also direct the investment to be located in a way to improve electricity market indices besides supporting renewable energy promotion. Fuzzy Q-learning electricity market modelling approach in combination with the technique for order preference by similarity (TOPSIS) is used as a new decision support system for promotion of renewable energy for the first time in the literature. The proposed interactive multi-criteria decision-making framework between the independent system operator (ISO) and the renewable power plant planner provides a win win situation that improve market indices while help the renewable power plant planning. The effectiveness of the proposed method is examined on the IEEE 30-bus test system and the results are discussed.

Abstract
In this work, we review the most important achievements of an INESC TEC long-period-grating-based fiber optic Michelson and Mach-Zehnder configuration modal interferometer with coherence addressing and heterodyne interrogation as a sensing structure for measuring environmental refractive index and temperature. The theory for Long Period Grating (LPG) interferometers and coherence addressing and heterodyne interrogation is presented. To increase the sensitivity to external refractive index and temperature, several LPG interferometers parameters are studied, including order of cladding mode, a reduction of the fiber diameter, different type of fiber, cavity length and the antisymmetric nature of cladding modes.

Abstract
Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object to be manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a ? -greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNN models (RexNext, MobileNet, MNASNet and DenseNet). Results show that the best performance in our application was reached by MobileNet, with an average of 84 % accuracy after training in simulated environment.

Abstract
The planning of the transmission network is an issue that, over the years, has received much attention, particularly due to the impact that this infrastructure has on the safe and reliable functioning of electrical systems. The search for solutions addressing climate change has led to several changes in the functioning of electrical systems, particularly concerning the increasing integration of renewable electricity production. However, in recent years, changes in the load side of the electrical system have also emerged. In particular, electric mobility has been developing, and a high penetration of electric vehicles (EVs) is expected in near future. This consumption is supplied by the distribution system but will impact the transmission network. Naturally, the amount of energy used by EVs is subject to uncertainties, which makes the problem complex. Those uncertainties cannot be easily modeled using statistical distributions because of the reduced history of available information. The transmission system operator (TSO) needs an efficient tool to analyze the adequacy of the transmission network to supply the distribution networks with high penetration of EVs. In this paper, a methodology based on symmetric/constrained fuzzy power flow is proposed to find the optimal investment policy at the transmission level while satisfying the technical constraints. The concept of dual variables provided by Lagrange multipliers, the natural result of the nonlinear optimization problem, is used to obtain the most promising reinforcement options considering the actual structure of the transmission network. The proposed model is tested on an IEEE 14-bus system.

Abstract
Biogenic amines (BAs) are well-known biomolecules, mostly for their toxic and carcinogenic effects. Commonly, they are used as an indicator of quality preservation in food and beverages since their presence in higher concentrations is associated with poor quality. With respect to BA's metabolic pathways, time plays a crucial factor in their formation. They are mainly formed by microbial decarboxylation of amino acids, which is closely related to food deterioration, therefore, making them unfit for human consumption. Pathogenic microorganisms grow in food without any noticeable change in odor, appearance, or taste, thus, they can reach toxic concentrations. The present review provides an overview of the most recent literature on BAs with special emphasis on food matrixes, including a description of the typical BA assay formats, along with its general structure, according to the biorecognition elements used (enzymes, nucleic acids, whole cells, and antibodies). The extensive and significant amount of research that has been done to the investigation of biorecognition elements, transducers, and their integration in biosensors, over the years has been reviewed.