Abstract
The evolution of technology in the last decade predicts a future where technology is disseminated in the environment in order to merge with the environment itself, being omnipresent. This world paradigm has been associated with the Internet of Things concept or, according to several authors, with the Internet of Everything concept. The potential massive dispersion of technology, which, with the progress of nanotechnology, will be increasingly miniaturized, combined with high-performance and universal communication networks, will revolutionize the concept of monitoring, resulting in profound changes in various sectors of activity and knowledge. In this context, this article presents a cloud-based Internet of Things architecture in order to respond to the growing needs of monitoring by the Civil Protection Public Organizations, who are responsible for monitoring several parameters and produce multiple information and reports in the context of the security of populations and territories. Finally, it is presented a prototype implemented according to the proposed architecture capable of monitoring monoliths dispersed in the territory, as a way of acting preventively, avoiding merely reactive actions after disaster situations. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The economic sector of tourism has gained significant weight in the economy of many countries, highlighting the weight of this sector in Portugal. However, the inconsistency and seasonality of demand causes companies linked to the sector to encounter difficulties regarding the planning and management of resources allocated to the activity. It is often the case that there are periods of economic loss caused by a small volume of demand that is insufficient to support the costs of the activity. In this context, this article proposes a system that, based on intelligent data analysis, allows a hotel chain to segment customers and enhance exclusive offers to minimize fluctuation and demand gaps in hotel units installed in thermal instances.

Abstract
The economic sector of tourism has gained significant weight in the economy of many countries, highlighting the weight of this sector in Portugal. However, the inconsistency and seasonality of demand causes companies linked to the sector to encounter difficulties regarding the planning and management of resources allocated to the activity. It is often the case that there are periods of economic loss caused by a small volume of demand that is insufficient to support the costs of the activity. In this context, this article proposes a system that, based on intelligent data analysis, allows a hotel chain to segment customers and enhance exclusive offers to minimize fluctuation and demand gaps in hotel units installed in thermal instances. © 2021 AISTI.

Abstract
Tourism is an information-intensive sector and today's tourist is hungry for information about everything that surrounds him and is increasingly demanding about the mechanisms that are made available for access and interaction with information. This new reality requires rethinking many of the existing solutions. In this context, the Internet of Things (IoT) is revolutionizing the way we think, design and implement Information and Communication Technologies (ICT) solutions for the tourism sector, opening up unprecedented opportunities in terms of how we can provide information and services. This new reality is enabling reengineering the interaction-process between tourists and its surrounding space. For heritage spaces, typically visited by countless tourists, there is an opportunity to rethink the entire process of supporting the interpretation and fruition of heritage, carried out by tourists. In order to understand how this reengineering can be carried out, a review of the state of the art is carried out with regard to how the IoT has been applied in the context of tourism. Then, the methodology that governed the creation of a conceptual model based on IoT is clearly defined, capable of transforming the way physical spaces of tourist interest can be interpreted and how their fruition can be improved. Particular importance is given to the contextualization of the experience, since the information provided must be adjusted to the visitor, according to their profile, which may necessarily reflect different types of interest or prior knowledge about the space. Finally, this article presents a conceptual model where its components are described and where it is discussed how the model can transform the experience of visiting touristic spaces and how tourists can access information and services that entities promoters of these spaces wish to make available. In the dissertation carried out, important aspects of the model and the gains it may generate for the revitalization and promotion of heritage are discussed.

Abstract
To identify the most frequently developed topics in the area of Big Data and Digital Marketing, a quantitative analysis was developed in December 2020. This analysis was focused on 750 publications and later on 67 publications on Big Data and Digital Marketing from the Scopus database. A bibliometric analysis was developed using the VOSviewer software and the technique of term co-occurrence and author co-authorship. Clusters were found for each of the analyzed situations.

Abstract
In this work, X-ray fluorescence (XRF) and Laser-induced breakdown spectroscopy (LIBS) analyses were applied to samples of quartz, montebrasite, and turquoise hydrothermal veins in the Argemela Tin Mine (Central Portugal). Montebrasite (LiAl(PO4)(OH,F)) is potentially the main ore mineral; with its alteration, lithium (Li) can disseminate into other minerals. A hand sample was cut and analyzed by XRF and LIBS for several elements of interest including Cu, P, Al, Si, and Li. Although XRF cannot measure Li, results from its analysis are effective for distinguishing turquoise from montebrasite. LIBS analysis complemented this study, making it possible to conclude that turquoise does not contain any significant Li in its structure. The difference in spot size between the techniques (5 mm vs. 300 mu m for XRF and LIBS, respectively) resulted in a poorer performance by XRF in accurately identifying mixed minerals. A thin section was petrographically characterized and mapped using LIBS. The mapping results demonstrate the possibility of the successful identification of minerals and their alterations on a thin section. The results of XRF analysis and LIBS mapping in petrographic sections demonstrate the efficacy of these methods as tools for element and mineral identification, which can be important in exploration and mining phases, complementing more traditional techniques.