Abstract
[No abstract available]

Abstract
Ca 2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus’ boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca 2+ signal. The analysis package represents a significant improvement to available Ca 2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca 2+ signal extraction workflows.

Abstract
The large-scale deployment of smart home technologies will unlock the flexibility of prosumers, which in turn will be transformed into electricity market services by aggregators. This paper proposes a new network-constrained bidding optimization strategy to coordinate the participation of aggregators of prosumers in the day-ahead energy and secondary reserve markets. This bidding optimization strategy consists of a decentralized approach based on the alternating direction method of multipliers, where aggregators negotiate with the distribution system operator to obtain network-constrained energy and secondary reserve bids. For a case study of 2 aggregators and 1 distribution system operator, the results show that the network-constrained bidding strategy computes cost-effective and network-feasible energy and secondary reserve bids, as opposed to a network-free bidding strategy. In addition, the network-constrained bidding strategy preserves the independent roles of aggregators and the distribution system operator, and the data privacy of all agents.

Abstract
This chapter focuses on new compositions of thermoplastic matrices and reinforcements to process by fused deposition modelling (FDM). The available materials for this additive manufacturing (AM) technique are generally limited to PLA—polylactic acid, ABS—acrylonitrile butadiene styrene and PA—polyamide (NYLON®) with temperature gradients and mechanical behaviours that are not suited for high-performance applications, such as aeronautics and automotive sector. In this work, an intensive research was made in order to evaluate mechanical, thermal and rheological properties considered important for 3D printing of commercial filaments. Results aided in the selection of high-performance reinforced materials for AM. Advanced polymers, such as PEEK—polyether ether ketone and PA66—polyamide 66, were the matrices chosen to produce high service nanocomposite formulations, each with varying amounts of multi-wall carbon nanotubes (MWCNTs). The resulting feedstock materials were characterized using the same techniques as the commercial filaments. Preliminary tests with printed parts of these composites were made in pursuance of their optimal printing parameters to undergo an experimental hybrid system (EHS). © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Unmanned aerial vehicles (UAVs) are used nowadays as a standard tool to derive very high-resolution geospatial data. However, UAV payload limitation imposes the use of not such reliable hardware affecting the georeferencing precision. In the literature it is possible to find numerous studies investigating the parameters influencing UAV-based products quality. Even if new photogrammetry methods could, in theory, avoid the use of ground control points (GCPs), they still play a key role to assure quality products. Nevertheless, usually only the number and distribution of GCPs are taking into account, since both change the geometric accuracy of the final products. In order to improve the understanding of the actual influence of GCPs, in this study we evaluate how can different physical characteristics affect GCPs identification in aerial images. The results demonstrate that GCPs' color, material, size and shape, among others, may influence a precise identification in aerial imagery.

Abstract
As technology evolves, frauds, in all their different forms, become increasingly more complex, as mega-processes such as Luanda Leaks or Panama Papers have recently shown. Fraud detection mechanisms must thus also resort to recent technological developments to ensure that even new forms of complex fraud are detected in time. In this paper we present a system to facilitate fraud detection in Public Procurement in Portugal. It relies on three main components. Data on public procurement and involved entities is acquired from public sources and integrated into a graph-oriented database. A rules-engine enriches these data with information additional information, using legal rules or custom rules defined by the users. Finally, a graph-oriented User Interface is used to support decision-making, allowing users to quickly and efficiently explore and filter information, in a natural and geo-referenced way. The main goal of this system is to increase transparency by facilitating access to relevant information, and in this way contribute to the fairness of the whole public procurement procedure. © 2020, Springer Nature Switzerland AG.