One of the intriguing aspects of the popular 1960s television show "Mission Impossible" was the opening sequence of every episode, which featured a secret agent listening to a recorded message about an upcoming mission. At the end of the recording each week, the tape would sizzle, crackle, and disintegrate into a heap of smoke and debris, ensuring no one else could access the top-secret information it contained. Until recently, self-destructing electronic systems remained within the realm of science fiction, but advances in chemistry, engineering, and materials science are finally allowing researchers to construct circuits that break down on their own timetable. This includes systems that rely on conventional complementary oxide semiconductor (CMOS) technology. "The goal is to develop functional circuits that can operate for a period of time and then vaporize," says Amit Lal, Robert M. Scharf 1977 Professor of Engineering in the Electrical and Computer Engineering Department at Cornell University in Ithaca, NY, and director of the university's SonicMEMs lab.

For scale, consider the Statue of Liberty, standing 305 feet tall. At 466 feet, the average wind turbine in the U.S. dwarfs Lady Liberty by more than half. And when GE's next-generation monster wind turbine, the Haliade-X, hits the market in 2021, it will nearly double that size to 877 feet, just shy of the Eiffel Tower. A single Haliade-X rotor blade will stretch 315 feet, longer than a football field. As a general rule of thumb, when it comes to energy and energy exploration, bigger is better: the larger the machinery, the deeper the dig, the greater the production yield.

We employ a set of standard basis functions, e.g., polynomials, to approximate the governing equation with high accuracy. Upon recasting the problem into a function approximation problem, we discuss several important aspects for accurate approximation. Most notably, we discuss the importance of using a large number of short bursts of trajectory data, rather than using data from a single long trajectory. Several options for the numerical algorithms to perform accurate approximation are then presented, along with an error estimate of the final equation approximation. We then present an extensive set of numerical examples of both linear and nonlinear systems to demonstrate the properties and effectiveness of our equation recovery algorithms.

Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overload the user with too many patterns. Local Process Model (LPM) mining is an alternative approach coming from the field of process mining. While in traditional sequential pattern mining, a pattern describes one subsequence, an LPM captures a set of subsequences. Also, while traditional sequential patterns only match subsequences that are observed in the sequence database, an LPM may capture subsequences that are not explicitly observed, but that are related to observed subsequences. In other words, LPMs generalize the behavior observed in the sequence database. These properties make it possible for a set of LPMs to cover the behavior of a much larger set of sequential patterns. Yet, existing LPM mining techniques still suffer from the pattern explosion problem because they produce sets of redundant LPMs. In this paper, we propose several heuristics to mine a set of non-redundant LPMs either from a set of redundant LPMs or from a set of sequential patterns. We empirically compare the proposed heuristics between them and against existing (local) process mining techniques in terms of coverage, redundancy, and complexity of the produced sets of LPMs.

A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. Let's figure out how to end hunger forever.

Businesses across the world are rapidly leveraging the Internet-of-Things (IoT) to create new products and services that are opening up new business opportunities and creating new business models. The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story. For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable'smart machines' to simulate intelligent behavior and make well-informed decisions with little or no human intervention. Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017.

It's a simple question that any person with a watch can answer with minimal effort. But when you ask an Amazon Echo the same question, a vast system powered by natural resources and human labor is activated to drum up the answer. As many of us reckon with Silicon Valley's impact on the world and consider how it has upended life, work, and even democracy, we also must consider the infrastructure–and the tangible harm it can do–that usually remains hidden beneath these seemingly simple user experiences. It's an aspect of AI that is nearly impossible to comprehend, let alone visualize, but a new map created by the AI researcher Kate Crawford and data visualization specialist Vladan Joler attempts this dizzying task anyway. Called Anatomy of an AI, the map and the corresponding essay lay out the components of the Amazon Echo, from the human workers mining the rare earth materials that power its chips to the black box of Amazon Web Services to the submarine internet cables that pass information across oceans.

In an industry such as mining where improving efficiency and productivity is crucial to profitability, even small improvements in yields, speed and efficiency can make an extraordinary impact. Mining companies basically produce interchangeable commodities. The mining industry employs a modest amount of individuals--just 670,000 Americans are employed in the quarrying, mining and extraction sector--but it indirectly impacts nearly every other industry since it provides the raw materials for virtually every other aspect of the economy. It's already been 10 years since the British/Australian mining company Rio Tinto began to use fully autonomous haul trucks, but they haven't stopped there. Here are just a few ways Rio Tinto and other mining companies are preparing for the 4th industrial revolutions by creating intelligent mining operations.

Researchers are paving the way to total reliance on renewable energy as they study both large- and small-scale ways to replace fossil fuels. One promising avenue is converting simple chemicals into valuable ones using renewable electricity, including processes such as carbon dioxide reduction or water splitting. But to scale these processes up for widespread use, we need to discover new electrocatalysts--substances that increase the rate of an electrochemical reaction that occurs on an electrode surface. To do so, researchers at Carnegie Mellon University are looking to new methods to accelerate the discovery process: machine learning. Zack Ulissi, an assistant professor of chemical engineering (ChemE), and his group are using machine learning to guide electrocatalyst discovery.

Kespry announced the availability of the pulp and paper industry's first drone-based aerial intelligence solution. The new industry-specific solution improves the profitability of pulp and paper operations by delivering more accurate and timely supply chain material inventory data, while improving site operations and safety. "Measuring chip piles at a pulp mill has always been a challenge. In the past, a team of surveyors would climb onto the chip pile and arrive at a manual measurement," said Mitch Dunlop, Accounting Manager, Celgar, a leading North American pulp and paper organization. "This method is slow, poses safety concerns and is not very accurate.