In 2003, Mother Nature turned off the lights on the East Coast. The reason: a short circuit a hot summer day caused on by a chance encounter between an overgrown tree branch and a sagging power line. The problem quickly cascaded through the system, triggering the biggest blackout in North American history. The outage left 50 million people in the U.S. and Canada without power and by some estimates cost more than $6 billion. But the truth is, most people don't give much thought to our electrical grid until something goes wrong.

President Trump's threat of "severe" repercussions if the Saudis are found to have killed journalist Jamal Khashoggi were met with more threats from the Saudi side, including from a media figure close to the royal family who warned of high oil prices and a shift of alliances.

Thermal Imaging sensors are commonly referred to terminology such as thermal camera, temperature camera, heat vision camera, infrared camera, thermal imaging sensor, heat signature camera, and even thermal heat vision sensor. In this post we will refer to this type of imaging as infrared or thermal imaging. Infrared energy is generated by the vibration of atoms and molecules. The higher the temperature of an object, the faster its molecules and atoms move. This movement is emitted as infrared radiation which our eyes cannot see but our skin can feel. Thermal imaging is the use of a special infrared camera sensors to illuminate a spectrum of light invisible to the naked eye.

Imagine for a second that you're a crab, and a fellow crustacean called a mantis shrimp has decided to make you its lunch. The truth is, it's not worth struggling. The mantis shrimp uses muscles to cock back two hammer-like appendages under its face, storing energy in a saddle-like divot in the limbs. When it releases the latch, the hammers accelerate so quickly, and strike your shell with such brutality, that they produce cavitation bubbles in the water, which collapse and release a secondary shockwave that knocks you out cold. That's a lot to unpack, and no one knows the struggle better than scientists. For years, they've been using high-speed photography to figure out how a little crustacean can manage what is perhaps the most powerful pound-for-pound punch in the animal kingdom--and in the significant extra drag of water, no less.

For Ferrero, OpenAg created what it calls a hazelnut computer--an indoor farm, made from structural steel and Styrofoam panels, that resembles a giant walk-in freezer. Inside, 16 hazelnut trees are maturing. LED lights simulate the sun, and every variable--air temperature, humidity, pH and carbon dioxide levels, water circulation--is controlled and optimized by artificial intelligence. Once OpenAg's algorithm determines the ideal hazelnut-growing recipe, Ferrero will compare it with climate and soil data from around the world as the company searches for a new place to farm. "We call it climate prospecting," says Caleb Harper, age 36, the founder and director of OpenAg.

Three major opportunities for improvement in early-stage in vitro and animal model studies are to improve the predictive capability of in vitro models themselves, the extraction of more complete data from cell cultures and animal models, and to shift from qualitative histological evaluation to a quantitative digital pathology approach. Through this webinar, Visikol will focus its discussion on the use of 3D cell culture models, the application of 3D tissue imaging for studying complex phenomena such as angiogenesis and how digital pathology and machine learning can be used to extract quantitative data from tissues. Several areas of ongoing research being pursued at Visikol will be discussed. Michael Johnson, PhD is a 2017 Forbes 30 Under 30 honoree and the CEO and Co-Founder of Visikol Inc., which is a bio-imaging company that spun out of Rutgers University in 2016 and that Michael founded along with his fellow PhD candidate Thomas Villani and colleague Nick Crider. Michael's research background has focused on a wide range of projects from remote sensing research with NASA to building light sheet microscopes and producing biofuels.

New research has revealed the areas where real-life'waterworld' riots are most likely to happen. Researchers mapped the areas where future global conflict is most likely to break out as a result of climate change-fueled water shortages. Researchers believe vulnerable areas could face'hydro-political issues' due to water shortages within the next 50 to 100 years. Researchers used machine learning to identify'pre-conditions and factors' that might lead to depleting water resources, particularly areas that contain water shared by bordering nations Researchers said the areas most likely to be hit by'hydro-political' issues are those with already stressed water basins. This includes the Nile, Ganges-Brahmaputra, Indus, Tigris-Euphrates and Colorado rivers. They believe water-related conflict or cooperation is likely to develop in the next 50 to 100 years as a result of climate change and population growth.

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.

Condition monitoring is one of the routine tasks in all major process industries. The mechanical parts such as a motor, gear, bearings are the major components of a process industry and any fault in them may cause a total shutdown of the whole process, which may result in serious losses. Therefore, it is very crucial to predict any approaching defects before its occurrence. Several methods exist for this purpose and many research are being carried out for better and efficient models. However, most of them are based on the processing of raw sensor signals, which is tedious and expensive. Recently, there has been an increase in the feature based condition monitoring, where only the useful features are extracted from the raw signals and interpreted for the prediction of the fault. Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data. This of course requires the prior knowledge of the nature of data and related processes. This limits the feature extraction process. However, recent development in the autoencoder based feature extraction method provides an alternative to the traditional handcrafted approaches; however, they have mostly been confined in the area of image and audio processing. In this work, we have developed an automated feature extraction method for on-line condition monitoring based on the stack of the traditional autoencoder and an on-line sequential extreme learning machine(OSELM) network. The performance of this method is comparable to that of the traditional feature extraction approaches. The method can achieve 100% detection accuracy for determining the bearing health states of NASA bearing dataset. The simple design of this method is promising for the easy hardware implementation of Internet of Things(IoT) based prognostics solutions.