This study, firstly, provides an up-to-date global review of the potential, technologies, prototypes, installed capacities, and projects related to ocean renewable energy including wave, tidal, and thermal, and salinity gradient sources. Secondly, as a case study, we present a preliminary assessment of the wave, ocean current, and thermal gradient sources along the Brazilian coastline. The global status of the technological maturity of the projects, their different stages of development, and the current global installed capacity for different sources indicate the most promising technologies considering the trend of global interest. In Brazil, despite the extensive coastline and the fact that almost 82% of the Brazilian electricity matrix is renewable, ocean renewable energy resources are still unexplored. The results, using oceanographic fields produced by numerical models, show the significant potential of ocean thermal and wave energy sources in the northern and southern regions of the Brazilian coast, which could contribute as complementary supply sources in the national electricity matrix.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

Coastal environments are highly dynamic and sensitive to interference and variations caused by the numerous natural and anthropogenic agents. The northern coast of Northeastern Brazil has undergone intense erosion in recent years. However, the construction of the Pecém Port modified the beach and shoreline features differently from the adjacent areas. This article describes using remote-sensing images integrated with a Geographic Information System to evaluate the sedimentary balance and morphological changes observed in the sandy beach area that was affected by the construction of Pecém Port. Two methods were applied to the RapidEye images to quantify the short-time changes that occurred on the coast. The Digital Shoreline Analysis System method showed that the beach width increased west to the port, which was calculated by the Change Polygon Approach determined by intersecting and uniting polygons to estimate the difference between the areas over time. The short-time analysis results showed changing coastal morphology, demonstrating that the anthropic interventions in the region are transforming significantly the natural elements that make up the region landscape. Between 2011 and 2014, the investigated beach stretch of approximately 3 km suffered an accretion process of more than 102,000 m² over 3 years. The high spatial resolution of satellite images, digital processing imaging techniques and geostatistical methods were effective in this study, allowing understanding the recent changes in the area.

Least-squares migration (LSM) is an effective technique for mitigating blurring effects and migration artifacts generated by limited data frequency bandwidth, incomplete coverage of geometry, source signature, and unbalanced amplitudes caused by complex wavefield propagation in the subsurface. Migration deconvolution (MD) is an image-domain approach for LSM that approximates the Hessian operator using a set of precomputed point spread functions. We have developed a new workflow by integrating the MD and domain decomposition (DD) methods. DD techniques aim to solve large and complex linear systems by splitting problems into smaller parts, facilitating parallel computing, and providing a higher convergence in iterative algorithms. We suggest that instead of solving the problem in a unique domain, as conventionally performed, splitting the problem into subdomains that overlap and solve each of them independently. We accelerate the convergence rate of the conjugate-gradient solver by applying the DD methods to retrieve better reflectivity, which is mainly visible in regions with low amplitudes. Moreover, using the pseudo-Hessian operator, the convergence of the algorithm is accelerated, suggesting that the inverse problem becomes better conditioned. Experiments using the synthetic Pluto model demonstrate that our algorithm dramatically reduces the required number of iterations while providing a considerable enhancement in image resolution and better continuity of poorly illuminated events.

Sea-surface petroleum pollution is observed as “oil slicks” (i.e., “oil spills” or “oil seeps”) and can be confused with “look-alike slicks” (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate “ocean-slick signatures” (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction).