The operator of the tsunami-wrecked Fukushima nuclear plant began removing fuel Monday from a cooling pool at one of three reactors that melted down in the 2011 disaster, a milestone in what will be a decades-long process to decommission the facility. Tokyo Electric Power Co. said workers started removing the first of 566 used and unused fuel units stored in the pool at Unit 3. The fuel units in the pool located high up in reactor buildings are intact despite the disaster, but the pools are not enclosed, so removing the units to safer ground is crucial to avoid disaster in case of another major earthquake similar to the one that caused the 2011 tsunami. TEPCO says the removal at Unit 3 will take two years, followed by the two other reactors, where about 1,000 fuel units remain in the storage pools. Removing fuel units from the cooling pools comes ahead of the real challenge of removing melted fuel from inside the reactors, but details of how that might be done are still largely unknown. Removing the fuel in the cooling pools was delayed more than four years by mishaps, high radiation and radioactive debris from an explosion that occurred at the time of the reactor meltdowns, underscoring the difficulties that remain.
The operator of the tsunami-wrecked Fukushima nuclear plant has begun removing fuel from a cooling pool at one of three reactors that melted down in the 2011 disaster, a milestone in the decades-long process to decommission the plant. Tokyo Electric Power Co (Tepco) said on Monday that workers started removing the first of 566 used and unused fuel units stored in the pool at Unit 3. The fuel units in the pool located high up in reactor buildings are intact despite the disaster, but the pools are not enclosed so removing the units to safer ground is crucial to avoid disaster in case of another major quake. Tepco said the removal at Unit 3 would take two years, followed by the two other reactors. The step comes ahead of the real challenge of removing melted fuel from inside the reactors, but details of how that might be done are still largely unknown. Removing the fuel in the cooling pools was delayed five years by mishaps, high radiation and radioactive debris from an explosion that occurred at the time of the reactor meltdown, underscoring the difficulties that remain.
Last week's breaking news story on The Robot Report was unfortunately the demise of Helen Greiner's company, CyPhy Works (d/b/a Aria Insights). The high-flying startup raised close to $40 million since its creation in 2008, making it the second business founded by an iRobot alum that has shuttered within five months. While it is not immediately clear why the tethered-drone company went bust, it does raise important questions about the long-term market opportunities for leashed robots. The tether concept is not exclusive to Greiner's company, there are a handful of drone companies that vie for marketshare, including: FotoKite, Elistair, and HoverFly. The primary driver towards chaining an Unmanned Ariel Vehicle (UAV) is bypassing the Federal Aviation Administration's (FAA) ban on beyond line of sight operations.
Juniper Networks said on Monday that it plans to acquire Mist Systems, makers of a wireless LAN network powered by artificial intelligence, for $405 million. Juniper plans to use the purchase to bolster its software-defined enterprise portfolio and multicloud offerings and expand its presence in the cloud-managed segment of the wireless networking market. The cleanup will take decades in places humans can't go. More specifically, Juniper will combine Mist's wireless LAN platform with Juniper's wired LAN, SD-WAN and security services. Juniper also plans to extend Mist's AI capabilities across the Juniper networking portfolio for software-defined architectures.
The cleanup will take decades in places humans can't go. It can be challenging trying to find the correct influencers for your marketing campaign. Brands need to assess whether corporate influencers are more important than celebrities, whether Instagram fraud will ruin their campaign, or whether we trust AI at all. Also: What do influencers look for when working with brands? Now a new AI solution may deliver the intelligence that brands need to run successful campaigns.
The cleanup will take decades in places humans can't go. The complaints are often heard, coming from those who claim to know. Apple's products aren't as good as Samsung's, they say. Also: Homepod long term review: What I like -- and don't -- about Apple's first smart speaker Worse, some say -- those some includes Samsung -- Apple just waits for others to innovate and then copies them with a slightly different look. The complainers can't believe how much emotional commitment Apple enjoys from customers.
The power grid of the United States is one of the most complex and technical systems in operation around the world. In order to deliver consistent electricity to the entire country, a number of regional transmission organizations (RTOs) must interact and manage resources. Like with any wide-scale network-dependent system, the electric grid is vulnerable to cyberattacks from outsiders. Hackers may be looking to cause disruptions in service or may have a larger goal of affecting the supply chain of energy resources. The U.S. government and the electric and gas companies are now moving into a more technology-focused future where new sciences like artificial intelligence and machine learning can be leveraged to help secure the power grid, its infrastructure, and customers nationwide.
I was honored when MIT Technology Review invited me to be the first guest curator of its 10 Breakthrough Technologies. Narrowing down the list was difficult. I wanted to choose things that not only will create headlines in 2019 but captured this moment in technological history--which got me thinking about how innovation has evolved over time. My mind went to--of all things--the plow. Plows are an excellent embodiment of the history of innovation. Humans have been using them since 4000 BCE, when Mesopotamian farmers aerated soil with sharpened sticks. We've been slowly tinkering with and improving them ever since, and today's plows are technological marvels.
Digital transformation continues to be important as companies of all sizes modernize their operations. For large organizations, "becoming digital" is a complex effort that involves technology and business process changes together with adopting new mindsets, business models, and corporate cultures. Given all these components, we can hardly overstate the complexity of genuine digital transformation. Infrastructure and energy giant, ABB, is currently undertaking a large-scale program of digital transformation. With $36 billion in revenue and 135,000 employees, the company is driving change across its large portfolio of operations. The firm traces its founding back to 1883. ABB describes its offering within two value propositions: "bringing electricity from power plant to plug" and "automating industries from natural resources to finished products." These broad points cover the extensive scope of the company's operations.
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.