The protection of critical engineering infrastructures is vital to today's society, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This control is based on the measurement of physical quantities that characterize the structural behavior, such as displacements, strains and stresses. The analysis of monitoring data and its evaluation against physical and mathematical models is the strongest tool to assess the safety of the structural behavior. Commonly, dam specialists use multiple linear regression models to analyze the dam response, which is a wellknown approach among dam engineers since the 1950s decade. Nowadays, the data acquisition paradigm is changing from a manual process, where measurements were taken with low frequency (e.g., on a weekly basis), to a fully automated process that allows much higher frequencies. This new paradigm escalates the potential of data analytics on top of monitoring data, but, on the other hand, increases data quality issues related to anomalies in the acquisition process. This chapter presents the full data lifecycle in the safety control of large-scale civil engineering infrastructures (focused on dams), from the data acquisition process, data processing and storage, data quality and outlier detection, and data analysis. A strong focus is made on the use of machine learning techniques for data analysis, where the common multiple linear regression analysis is compared with deep learning strategies, namely recurrent neural networks. Demonstration scenarios are presented based on data obtained from monitoring systems of concrete dams under operation in Portugal.

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Macrophages have unique surface receptors that might recognize preferentially several moieties present on the surface of infecting organisms, including in the bacterial cell wall. Benefiting from a similar composition regarding the referred moieties, polysaccharides might be good candidates to compose the matrix of drug carriers aimed at macrophage targeting, as they can use the same recognition pathways of the infecting organisms. Carrageenan (CRG), a polysaccharide extracted from red edible seaweed, is an interesting possibility for the approach of directly targeting alveolar macrophages, as its composition is reported to be recognized by several macrophage lectin receptors. Inhalable starch/CRG microparticles were successfully produced, effectively associating isoniazid (96%) and rifabutin (74%) simultaneously.Furthermore, the produced microparticles presented adequate aerodynamic properties for pulmonary delivery with potential to reach the respiratory zone, with a mass median aerodynamic diameter (MMAD) between 3.3-3.9 µm. It was further demonstrated that the antitubercular activity of the drugs remained unchanged after encapsulation. The formulation evidenced no cytotoxic effects on lung epithelial cells (A549), although mild toxicity was observed on macrophage-differentiated THP-1 cells for the drug loaded formulation. Starch/CRG microparticles also exhibited a propensity to be captured by macrophages in a dose-dependent manner, as well as an ability to activate the target cells. This work provides indications on the potential of the starch/CRG carriers to interact with macrophages, thus providing a platform for drug delivery in the context of macrophage intracellular diseases. Additionally, if tuberculosis is focused, these microparticles can be used as inhalable drug carriers.