Abstract
The economic and social challenges felt in recent years because of the financial crisis impact wave were somehow attenuated by the silent work provided by the third sector institutions. Therefore, the incessant instability of the markets, as well as the population life-expectancy increasing and the implications thereof require new approaches towards pointing strategies to mitigate these problematic situations. For that reason, the development of technological solutions and applications for the private social solidarity institutions is an utmost challenge towards guaranteeing their sustainability and efficiency over time. The adoption of such solutions should be properly conceived to enhance their efficiency of the daily routines and to fulfill and inclusion of all users, while trying to reduce the technological literacy. The development of a technological framework to support the adoption of the practices, selection of technical requirements, and functionalities is seen as a great contribution for setting the roadmap that should be followed. This chapter explores the development of a framework for technological embedding in private social solidarity institutions. © 2018, IGI Global.

Abstract
This paper studies how to optimally capture side information to aid in the reconstruction of high-dimensional signals from low-dimensional random linear and noisy measurements, by assuming that both the signal of interest and the side information signal are drawn from a joint Gaussian mixture model. In particular, we derive sufficient and (occasionally) necessary conditions on the number of linear measurements for the signal reconstruction minimum mean squared error (MMSE) to approach zero in the low-noise regime; moreover, we also derive closed-form linear side information measurement designs for the reconstruction MMSE to approach zero in the low-noise regime. Our designs suggest that a linear projection kernel that optimally captures side information is such that it measures the attributes of side information that are maximally correlated with the signal of interest. A number of experiments both with synthetic and real data confirm that our theoretical results are well aligned with numerical ones. Finally, we offer a case study associated with a panchromatic sharpening (pan sharpening) application in the presence of compressive hyperspectral data that demonstrates that our proposed linear side information measurement designs can lead to reconstruction peak signal-to-noise ratio (PSNR) gains in excess of 2 dB over other approaches in this practical application.

Abstract
This paper addresses the problem of finding the best Brain Emotional Learning (BEL) controller parameters in order to improve the response of a single degree-of-freedom (SDOF) structural system under an earthquake excitation. The control paradigm considered is based on a semi-active system to control the dynamics of a lumped mass-damper-spring model, being carried out by changing the damping force of a magneto-rheological (MR) damper. A typical BEL based controller requires the definition of several parameters which can be proved difficult and non-intuitive to obtain. For this reason, an evolutionary based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization (PSO) method was chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary based algorithm. Moreover, simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

Abstract
Vascular access dysfunction is a serious problem in dialysis units. Some patients have complex dysfunctions that are difficult to resolve. In this article, we report the case a of two patients with radiocephalic arteriovenous fistulae (RC-AVF) who had stenosis/occlusion of the forearm median vein and where we used the basilic vein of the forearm as a solution. We reviewed the use of this surgical solution in RC-AVF. Two male patients on hemodialysis exhibited stenosis/occlusion of the forearm median vein. The forearm basilic vein was isolated and rotated toward the forearm median vein in order to solve RC-AVF problems. One patient had fistula thrombosis 5 months after the procedure, while for the other patient, the fistula continues to work without problems. Literature describes only a few cases using the forearm basilic vein or the brachial vein for fistula recovery. This procedure increased the patency of fistulas. This approach has been proven to be a good solution for solving outflow problems using the superficial or deep veins, increasing fistula patency and avoiding the need to place a central venous catheter and all the related complications.

Abstract
Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a "conditioned" and a real world setup, where the system performed worse compared to the "conditioned" setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.

Abstract
The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.