The combined action of waves, surges and tides can cause flooding, erosion and dune and structure overtopping in many coastal regions. Addressing emergency and risk management in these areas require a combination of targeted campaigns and real-time data that measure all phenomena at stake and can be used to develop comprehensive monitoring platforms. These monitoring platforms can support the development of prediction tools that address all hazards in an integrated way. Herein, we present a methodology focused on multi-hazard coastal alert and risk, and its implementation in a tailored WebGIS platform. The MOSAIC platform offers a one-stop-shop capacity to access in-situ and remote sensing data, and hydrodynamic and morphodynamic predictions, supported by numerical models: SCHISM and XBeach. Information is structured on a local observatory scale, with regional forcings available for the correct interpretation of local hazards effects. This implementation can be further applied and extended to other coastal zones. The MOSAIC platform also provides access to a detailed database of past hazardous events, organized along several risk indicators, for the western coast of Portugal. The combination of features in the platform provides a unique repository of hazard information to support end-users for both emergency and long term risk planning actions.
Coastline Watch aims to assess the best practices to continuously monitor changes caused by natural processes (such as waves, tides and currents) and strengthen by human intervention or global climate changes. The current solution processes coastal information from Satellite open data for monitoring and makes insitu measurements using Unmanned Aerial Vehicles (UAV) for impact analysis. It uses a collaborative infrastructure to make processing requests, analyze and share the resulting outcomes with a project team. The first experimental processing service implements a shoreline monitoring chain to detect changes, to establish trends and indicators and to identify potential risks and critical areas. The service uses Landsat program data and it is prepared to support other open or commercial satellites, such as the Sentinel program. After identifying critical areas, the solution uses a methodology to determine impact of changes by making aerial data acquisitions and then estimating surface volumes changes. A cloud infrastructure, based on Amazon AWS technology, provides a distributed environment solution composed by a web portal, processing resources and an OGC database. The portal allows the users to generate new coastal products based on automated scripts, to share the results with the team and to view/download the final products. The service is being demonstrated on the coastal areas of Figueira da Foz and Óbidos lagoon over specific timeframes, being iteratively fine-tuned with users/researchers feedback. The current infrastructure is still under consolidation, with the final goal to provide automated processing tools and a methodology that could be collaboratively and continuously updated by researchers and professionals to generate data from new areas and update existing ones.
OBJECTIVE: To evaluate the efficacy of hypnosis for management of claustrophobia in patients submitted to magnetic resonance imaging. MATERIALS AND METHODS: Twenty claustrophobic patients referred for magnetic resonance imaging under sedation were submitted to hypnosis using the Braid technique. The patients susceptible to hypnosis were submitted to magnetic resonance imaging under hypnotic trance without using sedative drugs. RESULTS: Out of the sample, 18 (90%) patients were susceptible to the technique. Of the 16 hypnotizable subjects who were submitted to magnetic resonance imaging, 15 (93.8%) could complete the examination under hypnotic trance, with no sign of claustrophobia and without need of sedative drugs. CONCLUSION: Hypnosis is an alternative to anesthetic sedation for claustrophobic patients who must undergo magnetic resonance imaging. Keywords: Hypnosis; Claustrophobia; Magnetic resonance imaging.OBJETIVO: Testar a eficácia da hipnose para o controle de claustrofobia em pacientes submetidos a exames de ressonância magnética. MATERIAIS E MÉTODOS: Vinte pacientes claustrofóbicos, com indicação de sedação para ressonância magnética, foram submetidos a hipnose pela técnica de Braid. Os pacientes suscetíveis à hipnose foram encaminhados para realização do exame em estado de transe hipnótico, sem uso de medicamentos para sedação. RESULTADOS: Da amostra estudada, 18 casos (90%) foram suscetíveis à técnica. Dos 16 pacientes sensíveis à hipnose que compareceram para a ressonância magnética, 15 (93,8%) realizaram o exame em transe hipnótico, sem ocorrência de crise de claustrofobia e sem necessitar de medicamentos para sedação. CONCLUSÃO: Hipnose é uma alternativa para a sedação medicamentosa em pacientes claustrofóbicos que necessitam realizar ressonância magnética. Unitermos: Hipnose; Claustrofobia; Imagem por ressonância magnética. AbstractResumo
The expected huge amount of connected cars and applications with varying Quality of Service (QoS) demands still depend on agile/flexible networking infrastructure to deal with dynamic service requests to the control plane, which may become a bottleneck for 5G and Beyond Software-Defined Network (SDN) based Internet of Vehicles (IoV). At the heart of this issue is the need for an architecture and optimization mechanisms that benefit from cutting edge technologies while granting latency bounds in order to control and manage the dynamic nature of IoV. To this end, this article proposes an autonomic software-defined vehicular architecture grounded on the synergy of Multi-access Edge Computing (MEC) and Network Functions Virtualization (NFV) along with a heuristic approach and an exact model based on linear programming to efficiently optimize the dynamic resource allocation of SDN controllers, ensuring load balancing between controllers and employing reserve resources for tolerance in case of demand variation. The analyses carried out in this article consider: (a) to avoid waste of limited MEC resources, (b) to devise load balancing among controllers, (c) management complexity, and (d) to support scalability in dense IoV scenarios. The results show that the heuristic efficiently manages the environment even in highly dynamic and dense scenarios.
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