Mars and the Earth are nestling closer than they have for 15 years. The phenomenon – known as a Mars approach – should allow people to see the Red Planet with the naked eye. For Nasa, it is also a perfect opportunity to take advantage of the relatively short distance and send off missions to explore Mars. That is why Mars missions tend to launch roughly every two years – since that is how often the close approaches happen. From the International Space Station, Expedition 42 Flight Engineer Terry W. Virts took this photograph of the Gulf of Mexico and U.S. Gulf Coast at sunset This image of an area on the surface of Mars, approximately 1.5 by 3 kilometers in size, shows frosted gullies on a south-facing slope within a crater.

Nasa is keeping close watch on the Opportunity rover in hope that it will wake up again. The until now unstoppable robot has run into trouble as it moves around the surface of Mars. The red planet is rapidly being encircled by a vast and unprecedented dust storm, meaning that it could be months until there is enough light to charge the rover's batteries and wake it back up again. Officials say they are hopeful the rover will make it through. But there will be no way to know until Opportunity wakes back up again – or doesn't, leaving it dead on the planet's surface.

Nasa's Opportunity rover may be dead on Mars, engineers fear. The little robot has been travelling over the planet for the last 15 years, but has lost contact with its team on Earth. They fear that the rover might never wake back up, and its mission could come to an end. Opportunity ran into problems in recent days when a Martian storm that is covering a quarter of the planet swept over the rover. That blotted out the Sun's light, leaving it unable to charge its batteries. From the International Space Station, Expedition 42 Flight Engineer Terry W. Virts took this photograph of the Gulf of Mexico and U.S. Gulf Coast at sunset This image of an area on the surface of Mars, approximately 1.5 by 3 kilometers in size, shows frosted gullies on a south-facing slope within a crater.

Abstract--In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification. In the world of a globalized economy, maritime surveillance is a vital demand. Besides, the real-time delivery of maritime situation maps is also necessary for a variety of activities: fishing activities control, smuggling detection, EEZ intrusion detection, transshipment detection, maritime pollution monitoring, etc. Over the last decades, the development of terrestrial networks and satellite constellations of Automatic Identification System (AIS) has opened a new era in maritime traffic surveillance. Every day, AIS provides on a global scale hundreds of millions of messages [1], which contain ships' identifiers, their Global Positioning System (GPS) coordinates, their speed, course, etc. The potential of this massive amount of data is clearly of interest if tools and models provide means to efficiently extract, detect and analyze relevant information from these data streams. However, current operational systems, which strongly rely on human experts, can only deal with a limited fraction of AIS data streams. Thus, the development of AIbased systems is a critical challenge.

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide. This study presents an Artificial Intelligence (AI)-based approach to identifying the primary cause of oyster norovirus outbreaks, nowcasting and forecasting the growing risk of oyster norovirus outbreaks in coastal waters. AI models were developed using Artificial Neural Networks (ANNs) and Genetic Programming (GP) methods and time series of epidemiological and environmental data. Input variable selection techniques, including Random Forests (RF) and Forwards Binary Logistic Regression (FBLR), were used to identify the significant model input variables among six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall and various combinations of the variables with different time lags. In terms of nowcasting, a risk-based GP model was developed to nowcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast, showing the true positive and negative rates of 78.53% and 88.82%, respectively.

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.

With the model size I've selected it looks like we could stop with around 15 epochs as there is no further improvement. Model's accuracy is between 0.8 and 0.85.

Spanish treasure fleets that traversed the Atlantic Ocean to the Americas and back were a 16th-century invention as important as free two-day shipping. Organized 70 years after Columbus's first voyage, the fleet was made up of several specialized ships with one primary goal: Exploiting the riches of the New World as efficiently as possible. The San José, the largest galleon and the flagship of one group of Spanish ships that started sailing in the 16th century, was big and -- thanks to 62 bronze cannons engraved with dolphins -- deadly enough to deter or destroy ships, whether pirates or rival nations. On June 8, 1708, during the War of the Spanish Succession, the San José's gunpowder ignited during a battle with British ships, sending 600 sailors to the bottom of the Atlantic Ocean -- along with gold, silver and emeralds from mines in Peru, a total haul valued at some $17 billion in today's dollars. It stands as one of the most expensive maritime losses in history.

A Spanish galleon laden with treasures worth £12.6 billion ($17 billion) that sank to the bottom of the Caribbean 300 years ago was found using an autonomous robot, researchers have revealed. The San Jose, sunk by the Royal Navy, gained a reputation as the'holy grail' of shipwrecks and was carrying one of the most valuable hauls of treasure ever lost at sea. The 62-gun, three-masted galleon, went down on June 8, 1708, with 600 people on board as well as a treasure of gold, silver and emeralds during a battle with British ships in the War of Spanish Succession. The San Jose was located by an underwater autonomous vehicle operated by the Woods Hole Oceanographic Institution (WHOI) back in 2015. The institution said it was keeping its involvement in the discovery quiet out of respect for the Colombian government.

UAVs are tackling everything from disease control to vacuuming up ocean waste to delivering pizza, and more. Drone technology has been used by defense organizations and tech-savvy consumers for quite some time. However, the benefits of this technology extends well beyond just these sectors. With the rising accessibility of drones, many of the most dangerous and high-paying jobs within the commercial sector are ripe for displacement by drone technology. The use cases for safe, cost-effective solutions range from data collection to delivery. And as autonomy and collision-avoidance technologies improve, so too will drones' ability to perform increasingly complex tasks. According to forecasts, the emerging global market for business services using drones is valued at over $127B. As more companies look to capitalize on these commercial opportunities, investment into the drone space continues to grow. A drone or a UAV (unmanned aerial vehicle) typically refers to a pilotless aircraft that operates through a combination of technologies, including computer vision, artificial intelligence, object avoidance tech, and others. But drones can also be ground or sea vehicles that operate autonomously.