Archaeologists recently discovered a Roman shipwreck in the eastern Mediterranean. The ship and its cargo are both in good condition, despite being 2,000 years old. The wreck, named the Fiskardo after the nearby Roman Empire port of the same name, is the largest shipwreck found in the region to date. The Fiskardo is filled with amphorae -- large terracotta pots that were used in the Roman Empire for transporting goods such as wine, grain, and olive oil. CNN reported, "The survey was carried out by the Oceanus network of the University of Patras, using artificial intelligence image-processing techniques."

Climate-driven change in the Gulf of Maine is raising new threats that "red tides" will become more frequent and prolonged. But at the same time, powerful new data collection techniques and artificial intelligence are providing more precise ways to predict where and when toxic algae will bloom. One of those new machine learning prediction models has been developed by a former intern at Bigelow Labs in East Boothbay. In a busy shed on a Portland wharf, workers for Bangs Island Mussels sort and clean shellfish hauled from Casco Bay that morning. Wholesaler George Parr has come to pay a visit.

A mathematical study claims to have proven the long-held belief that the Battle of Britain could have easily been won by the Germans if not for tactical ineptitude. University of York researchers have created a computer model that uses a statistical technique called'weighted bootstrapping' to re-imagine the 1940 battle under different circumstances. It identifies two enormous blunders by notorious Nazi commander Hermann Goering - a trained fighter pilot - who led the assault that crippled the Nazi effort and helped Britain win. The researchers say it provides statistical backing to many historians' belief that if Germany had launched an attack immediately after Winston Churchill's famous'Battle of Britain' speech on June 18, rather than three weeks later on July 10, and targeted airfields rather than cities and populated areas, the Nazis would probably have been victorious. This would have crippled the British response by decimating the number of fighter pilots and destroying vital radar systems used to track German planes, paving the way for a naval and land invasion.

A model and expansion plan have been developed to optimally determine microgrid designs as they evolve to dynamically react to changing conditions and to exploit energy storage capabilities. In the wake of the highly electrified future ahead of us, the role of energy storage is crucial wherever distributed generation is abundant, such as microgrid settings. Given the variety of storage options that are recently becoming more economical, determining which type of storage technology to invest in, along with the appropriate timing and capacity becomes a critical research question. In problems where the investment timing is of high priority, like this one, developing analytical and systematic frameworks for rigorously considering these issues is indispensable. From a business perspective, these strategic frameworks will aim to optimize the process of investment planning, by leveraging novel approaches and by capturing all the problem details that traditional approaches are unable to. Reinforcement learning algorithms have recently proven to be successful in problems where sequential decision-making is inherent. In the operations planning area, these algorithms are already used but mostly in short-term problems with well-defined constraints and low levels of uncertainty modeling. On the contrary, in this work, we expand and tailor these techniques to long-term investment planning by utilizing model-free approaches, like the Q-learning algorithm, combined with simulation-based models. We find that specific types of energy storage units, including the vanadium-redox battery, can be expected to be at the core of the future microgrid applications, and therefore, require further attention. Another key finding is that the optimal storage capacity threshold for a system depends heavily on the price movements of the available storage units in the market.

Aviation Maintenance Administrationman 3rd Class Shea Wright, assigned to the Skinny Dragons of Patrol Squadron (VP) 4, recovers a squadron P-8A Poseidon maritime patrol and reconnaissance aircraft following an anti-submarine warfare mission over the Atlantic Ocean, Nov. 30, 2019. The increasingly global reach of Chinese nuclear-armed ballistic missile submarines, armed with JL-2 weapons reportedly able to hit parts of the U.S., continues to inspire an ongoing Navy effort to accelerate production of attack submarines, prepare long-dwell drones for deployment to the Pacific and continue acquisition of torpedo-armed sub-hunting planes such as the P-8/A Poseidon. The Navy has been moving quickly to increase its fleet of Poseidon's on an accelerated timetable; in the Navy's 2020 budget, the service was authorized for a near term increase in Poseidon production by three, moving funding for the year up for nine Poseidons, as cited in a report from USNI news. Last year, the Navy awarded Boeing a $2.4 billion deal to produce 19 more P-8A Poseidon surveillance and attack planes. The Poseidon increase appears to align with the service's overall Pacific theater strategy, which makes a point to sustain peaceful, yet vital surveillance and Freedom of Navigation missions in the region.

Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Every company is going to experience a cyberattack; what's hard to know is how to prepare and how to respond. Protecting an industrial process is a lot more complicated than downloading the latest anti-virus software, and most executives do not know where to begin. More than half of electric utility executives surveyed by the Ponemon Institute, which studies cybersecurity, said they expect a cyberattack on a significant piece of infrastructure in the next 12 months. Only 42 percent said their defenses were high. They listed their problems as lack of skilled workers, fragmented control systems and slow detection of system breaches. Only 31 percent said they were prepared to respond to an attack.

We conduct a theoretical and numerical study of the aliased spectral densities and inverse operators of Mat\'ern covariance functions on regular grids. We apply our results to provide clarity on the properties of a popular approximation based on stochastic partial differential equations; we find that it can approximate the aliased spectral density and the covariance operator well as the grid spacing goes to zero, but it does not provide increasingly accurate approximations to the inverse operator as the grid spacing goes to zero. If a sparse approximation to the inverse is desired, we suggest instead to select a KL-divergence-minimizing sparse approximation and demonstrate in simulations that these sparse approximations deliver accurate Mat\'ern parameter estimates, while the SPDE approximation over-estimates spatial dependence.

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

With the help of artificial intelligence, BP says it needs 40% fewer workers to keep its natural gas ... [ ] flowing in Wyoming. A visitor to one of BP's natural gas fields in Wyoming a few years ago might have noticed an odd sight: smartphones in plastic bags tied to pumps with zip ties. This was an early test of a multistate initiative by the oil giant to link a network of Wi-Fi sensors to an artificial intelligence system--one that now operates the Wamsutter field in Wyoming with far less human oversight than before. Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry's 150,000 U.S. workers. Giant energy companies like Shell and BP are investing billions to bring artificial intelligence to new refineries, oilfields and deepwater drilling platforms.