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Oil spills are a global environmental challenge with ecological impacts at local and regional scales. In 2019, a major oil spill from an unknown source reached the Brazilian margin and strongly affected tropical marine habitats with high biodiversity. It is therefore important to establish a quick and accessible approach to assist decision-makers for oil spill responses. Based on current Brazilian legislation, only very specific companies (such as organized ports, port facilities, platforms and their auxiliary facilities) are obliged to maintain marine oil spill response structures. This paper aims to develop an approach for mapping seafloor sensitivity to oil spills for continental shelf areas with limited resources and materials. The study was conducted using a pre-existing bathymetric dataset, at mega- and meso-habitat scales. The assessed area extends over 933 km2, between the 10 and 90 m isobaths, comprising tropical marine areas of intense maritime traffic due to the strategic location for important international routes and also due to the presence of ports, terminals and shipyards. From the megahabitat-scale analyses (30 m resolution grid), the areas with greatest topographic complexity were selected for subsequent detailed analysis and the development of a seafloor sensitivity index at a mesohabitat-scale (10 m resolution grid). The results revealed that 12.7% of the area was classified at a high seafloor sensitivity index (SI seafloor), 28.7% was classified at relatively moderate sensitivity, and 58% was classified at low sensitivity. The seafloor sensitivity methodology presented here can be easily applied to other locations to prepare recovery operations, monitoring plans, and for other forms of environmental planning. • First approach to mapping seafloor sensitivity to oil spills in complex Tamandaré Shelf valleys using a sensitivity index. • Study area: 933 km2 impacted by a 2019 oil spill, with 104 km2 exhibiting high morphological complexity. • A moderate to high sensitivity to oil spills was identified in 41.4% of the Tamandaré Shelf valleys area. [ABSTRACT FROM AUTHOR]

Despite the pervasiveness of IoT domotic devices in the home automation landscape, their potential is still quite under-exploited due to the high heterogeneity and the scarce expressivity of the most commonly adopted scenario programming paradigms. The aim of this study is to show that Semantic Web technologies constitute a viable solution to tackle not only the interoperability issues, but also the overall programming complexity of modern IoT home automation scenarios. For this purpose, we developed a knowledge-based home automation system in which scenarios are the result of logical inferences over the IoT sensors data combined with formalised knowledge. In particular, we describe how the SWRL language can be employed to overcome the limitations of the well-known trigger-action paradigm. Through various experiments in three distinct scenarios, we demonstrated the feasibility of the proposed approach and its applicability in a standardised and validated context such as SAREF [ABSTRACT FROM AUTHOR]

To verify the prevalence of alcohol use among health academics of a university center of Várzea Grande, according to socioeconomic and clinical characteristics. Method: Quantitative, descriptive, cross-sectional study conducted with undergraduate health students. They answered a questionnaire of socioeconomic and clinical characterization and Alcohol Use Disorders Identification Test (AUDIT) during October and November 2018. The quantitative variables were analyzed by mean and standard deviation and the qualitative variables by means of absolute and relative frequencies. [ABSTRACT FROM AUTHOR]

Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies that offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to quality, message size (payload), IoT-orientation and standardisation. Here we describe the first attempt to extend SAREF for specific e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO), which plays the role of a reference model. The methodology was enhanced by following a standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development and tests of an IoT Early Warning System prototype that uses ECG data and collision identification to detect accidents with truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed. [ABSTRACT FROM AUTHOR]