With plans for brand new megacities, allowing women to drive and foreign-run cinemas, Saudi Arabia's Crown Prince Mohammed bin Salman is on a charm offensive trying to promote his country as an international investment destination. The strategy aims at luring foreign money to help the world's biggest oil exporter create a new economy away from oil dependency in order to prevent future instability. On Wednesday, the International Monetary Fund (IMF) said Riyadh's break-even oil price for 2018 is likely to be around $88 a barrel. North Sea Brent is currently trading down around $74 a barrel. And although the oil price is up considerably from 2014, the director of the IMF's Middle East department Jihad Azour said the focus in Saudi needs to remain on economic and social reforms.

In the wake of the 2010 Deepwater Horizon disaster in the Gulf of Mexico, oceanographer Kaitlin Frasier of the University of California, San Diego, set out to assess the damage that the massive oil spill caused. "We needed to know what happened to marine mammals," she says. Specifically, Frasier was concerned with the spill's impact on dolphin populations. Trying to track the animals from the surface is expensive and time consuming, so Frasier used a different approach: deploying hydrophones to the seabed to passively record every sound in the ocean. By separating out dolphin vocalizations from the general thrum of ocean noise, Frasier hoped to detect trends in the animals' population density.

An autonomous robot will be deployed to an offshore oil and gas platform in the North Sea later this year, in a first for the sector. The £4m project's backers said the move was designed to take humans out of dangerous and dull jobs, and reinvent oil and gas as an industry of the future. Under the pilot scheme, the robot will initially be deployed at the French oil firm Total's gas plant on Shetland before being sent to join the 120 workers on the company's Alwyn platform, 440km north-east of Aberdeen. The machine, made by Austrian firm Taurob and supported on the software side by German university TU Darmstadt, will be used for visual inspections and detecting gas leaks. Rebecca Allison, asset integrity solution centre manager at the publicly-funded Oil and Gas Technology Centre, insisted autonomous robots would not be used to cut the wage burden of offshore workers who are paid a premium for working in tough, remote conditions.

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data.

Gervais' beaked whales are easily one of the most elusive mammals to swim through our oceans. Most of the information we have about them comes from studies of corpses that have washed ashore, and the first live whale was only spotted about 20 years ago. On February 27, photographer and videographer Patrick Dykstra captured what may be the first drone or aerial footage of Gervais' beaked whales. He was filming about three miles off the west coast of Dominica in the Caribbean Sea. Dykstra and his Picture Adventure Expeditions team accidentally came across the rare beaked whales when they were filming sperm whales for an upcoming production.

Consider a number of wind turbines scattered throughout the North Sea. Each turbine is equipped with a Query processing over data streams is a key aspect of Big sensor, which continuously records temperature levels of key Data applications. For instance, algorithmic trading relies on devices within the turbine and sends those readings to a data real-time analysis of stock tickers and financial news items centre monitoring the functioning of the turbines. Temperature (Nuti et al. 2011); oil and gas companies continuously monitor levels are streamed by sensors using a ternary predicate and analyse data coming from their wellsites in order Temp, whose arguments identify the device, the temperature to detect equipment malfunction and predict maintenance level, and the time of the reading. A monitoring task in the needs (Cosad et al. 2009); network providers perform realtime data centre is to track the activation of cooling measures in analysis of network flow data to identify traffic anomalies each turbine, record temperature-induced malfunctions and and DoS attacks (Münz and Carle 2007).

The North Atlantic Ocean punches far above its weight when it comes to scrubbing carbon dioxide. While it accounts for less than 1.5 percent of the total surface area of the world's oceans, it captures about 20 percent of the CO2 sequestered by the seas. Cold ocean waters help trap planet-warming carbon dioxide lingering in the atmosphere. Then, algae soak up that carbon dioxide during photosynthesis, just like grasses and trees do on land. From the information collected thus far, the scientists have concluded that the spring bloom is preceded by a "winter simmer," when algae tend to lay low.

A'crazy' artificial island in the North Sea that could supply renewable energy to 80 million people in Europe is set to open in 2027. Plans for the 2.3 square mile (6 square km) landmass suggest it will be surrounded by fields of offshore wind turbines and come with its own airstrip and harbour. The'North Sea Wind Power Hub', which will be home to a small team of permanent staff, will send electricity via long-distance cables to Britain and the Netherlands, and later to Denmark, Germany, Norway and Belgium. An artificial island (artist's impression pictured) with an airstrip and harbour is set to be built in the North Sea to help power Europe. Dogger Bank, 78 miles (125 km) off the East Yorkshire coast, has been identified as a potential shallow and windy building site for the £1.3 billion ($1.75 billion) project.

Every day, it seems that there is another innovation regarding how artificial intelligence can be used to do things humans couldn't do on their own. Several weeks ago, it was reported that scientists can now use AI to listen to conversations that dolphins are having with each other; an algorithm assists a research team go through millions of echolocation clicks made by these marine mammals found in the Gulf of Mexico. Although this may not seem important, learning about how new innovations with AI sensor technology can sift through ecological data may help to see how climate change and oil spills impact dolphins in this region while being a catalyst to other possible breakthroughs with artificial intelligence. Machine Learning is a component of computer science that allows computers the ability to understand without being programmed explicitly. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data.

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