The government in Fukushima, Japan released drone footage Thursday showing the progression made in the area's rebuilding process six years after an earthquake, tsunami and nuclear meltdown devastated the region. The videos showed a multitude of areas in the prefecture, including Iwaki City, about 30 miles south of the Fukushima plant, and Futaba, a town 11 miles north of the plant. The videos also showed reconstruction on roads and coastlines, areas severely damaged by the earthquake and tsunami. The government has been working for six years to revive the area. Earlier in May, a bill was enacted to accelerate reconstruction by using state funding to aid the decontamination process in certain districts, according to the Japan Times.

SEE ALSO: Drones are smuggling so much contraband into prisons that the UK created a'squad' Turgeon had used a drone, which he says he later returned, to record video of Native American-led protests against the construction of the Dakota Access Pipeline, which would run through Native American land. For this, Turgeon was arrested and charged with felony and misdemeanor counts of reckless endangerment as well as a misdemeanor count of physical obstruction of a government function, according to Motherboard. At about 33 minutes into the video below, you can see what is alleged to be Turgeon's drone flying nowhere close to a North Dakota Highway Patrol plane that is also in frame. The misdemeanor reckless endangerment charge came from allegedly flying a drone above protesters, "creating a substantial risk of serious bodily injury or death."

It's no small achievement - the evolution of the power grid from its mechanical beginnings to the where it is today – a modernized, digitally connected entity that is pulsating with not just electricity but with information. The Last article4 discussed the modernization of the power grid and its steady advancement towards an intelligent "Self-Learning Power Grid". Where does it go from here? If it follows trends in other industries, that data becomes an increasingly valuable asset along with the physical assets of a company. Data, is the new oil2.

Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

ExxonMobil Research and Engineering Company's Corporate Strategic Research (CSR) laboratory has an immediate opening for a full-time staff position in the area of machine learning in our Data Analytics and Optimization Section. Our facilities are centrally located in scenic Annandale, New Jersey, approximately one hour from both New York City and Philadelphia. The successful candidate will join a dynamic group of scientists performing breakthrough research for the Corporation, developing new scientific approaches and innovative solutions using state-of-the-art clusters and GPU machines. We are seeking a talented and creative individual to conduct fundamental research in machine learning, statistics, signal processing and pattern recognition. This researcher will seek solutions for a multitude of challenges involving large-scale physics, chemistry, geophysics and engineering data sets and models.

As a result, in recent years many people have found it more appealing to lease new cars. Why commit to owning a car that will run for 11 or 12 years when you can make a less onerous short-term commitment and then upgrade substantially in a few years without paying significantly more. This dynamic is particularly evident when it comes to the most technologically advanced cars, plug-in electrics and battery-powered vehicles. Thanks to rising competition and volumes, and to significant advances in battery design and production, the state of the art is improving rapidly. Thanks to Moore's Law, the minute you bought a PC it was effectively out of date--slower than the one you could buy for less in 18 months' time.

The Middle East and North Africa's youthful, fast-urbanizing population are perfectly placed to embrace technology and reap the rewards of the Fourth Industrial Revolution. Much has been written already about the arrival of the Fourth Industrial Revolution (4IR) and the opportunity that the convergence of its new technologies offers in terms of building value into production systems and economies around the world. In one sense, the playing field could be levelled out. Localized production is being made more feasible for many small producers, setting developing communities on a path towards self-sufficiency, while falling costs could enable factories of all sizes to boost their productivity levels. However, on the opposite side of the equation, news headlines have been dominated by predictions that human workers will be substituted by robots, leading to widespread job losses and heightened societal challenges. Additionally, doubt has been shed on the ability of regions that are less industrialized, or those with fractured economies and infrastructure, to be able to respond to these disruptions and compete effectively in the future.

In her critical role overseeing AccuWeather's dedicated global weather data science and analytics team, Radich identifies marketplace advantages for clients, applying the most accurate, robust and detailed big data sources, AccuWeather's proprietary IP, and comprehensive, advanced analyses to inform predictive models. She enables clients to maximize ROI in areas such as sales, marketing, supply chain, logistics, and operations through custom weather-based predictive modeling in addition to analyzing internal data within AccuWeather to help increase efficiency and accuracy. Rosemary received her M.A. from Wichita State University It's hard to find an article on the future of technology and business without reading phrases like "big data," "machine learning" and "artificial intelligence." When we hear these phrases, we get excited about leveraging the power of this new technology, but despite the great availability of data many business leaders don't know where to start or how to utilize it in a meaningful, practical way. Business leaders are looking for data to provide insight to make informed decisions that will impact their bottom line, and consumers are looking for products and services that are contextually relevant and seamlessly integrate into their day to help make their lives easier.

Computational Fluid Dynamics (CFD) is a hugely important subject with applications in almost every engineering field, however, fluid simulations are extremely computationally and memory demanding. Towards this end, we present Lat-Net, a method for compressing both the computation time and memory usage of Lattice Boltzmann flow simulations using deep neural networks. Lat-Net employs convolutional autoencoders and residual connections in a fully differentiable scheme to compress the state size of a simulation and learn the dynamics on this compressed form. The result is a computationally and memory efficient neural network that can be iterated and queried to reproduce a fluid simulation. We show that once Lat-Net is trained, it can generalize to large grid sizes and complex geometries while maintaining accuracy. We also show that Lat-Net is a general method for compressing other Lattice Boltzmann based simulations such as Electromagnetism.

Abstract--The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices requires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on probabilistic graphical models, capable of combining heterogeneous data sources with classical state estimation nodes and other customised computational nodes, is proposed. The framework allows flexible extension of the notion of grid state beyond the view of flows and injection in bus-branch models, and an efficient, naturally distributed inference algorithm can be derived. An application of the data fusion model to the quantification of distributed solar energy is proposed through numerical examples based on semisynthetic simulations of the standard IEEE 14-bus test case. The electrical grid is going through a significant transformation towards a more distributed architecture for demand-supply balancing, due to a higher penetration of distributed sources of renewable generation, storage and demand flexibility. Internet-of-Things (IOT) technologies are an integral part of the transformation, with energy utilities availing of more and more highly-distributed intelligent devices which produce an ever-increasing amount of heterogeneous data significantly different in terms of format, resolution and quality [1], [2].