Perovskites are a family of materials that are currently the leading contender to potentially replace today's silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle. Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.

Processor power prediction Researchers from Duke University, Arm Research, and Texas A&M University developed an AI method for predicting the power consumption of a processor, returning results more than a trillion times per second while consuming very little power itself. "This is an intensively studied problem that has traditionally relied on extra circuitry to address," said Zhiyao Xie, a PhD candidate at Duke. "But our approach runs directly on the microprocessor in the background, which opens many new opportunities. I think that's why people are excited about it." The approach, called APOLLO, uses an AI algorithm to identify and select just 100 of a processor's millions of signals that correlate most closely with its power consumption. It then builds a power consumption model off of those 100 signals and monitors them to predict the entire chip's performance in real-time.

Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re-computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments.

Since frequent severe droughts are lengthening the dry season in the Amazon Rainforest, it is important to detect wildfires promptly and forecast possible spread for effective suppression response. Current wildfire detection models are not versatile enough for the low-technology conditions of South American hot spots. This deep learning study first trains a Fully Convolutional Neural Network on Landsat 8 images of Ecuador and the Galapagos, using Green and Short-wave Infrared bands to predict pixel-level binary fire masks. This model achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus band using K-Means Clustering to simplify continuous pixel values into three discrete classes representing differing degrees of cirrus cloud contamination. Three additional Convolutional Neural Networks are trained to conduct a sensitivity analysis measuring the effect of simplified features on model accuracy and train time. The Experimental model trained on the segmented cirrus images provides a statistically significant decrease in train time compared to the Control model trained on raw cirrus images, without compromising binary accuracy. This proof of concept reveals that feature engineering can improve the performance of wildfire detection models by lowering computational expense.

When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned. As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time. "The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan," said Shriram Ramanathan, a professor in Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

The flexibility of Gas-fired thermal #power plants refers to the speed and extent to which they may be ramped up or dispatched, according to the power demand needed to balance the load and variable supply. This power demand is called residual or netload – the total power demand minus the output of variable #renewableenergy (VRE). From a technical perspective, the flexibility of gas-fired thermal power plants comprises three different dimensions: 1. The #minimum_load represents the lowest load level at which the power plant can operate under stable conditions and without other supporting fuels. The ability to operate at a low minimum load reduces losses at times of low (or even negative) prices and avoids shutdown and subsequent start-up. Shutdown and start-up cycles cause #thermal_stress to equipment (due to rapidly changing temperatures), adding costs for maintenance and repair as well as shortening plant or equipment lifetime.

The Machine Learning in Utilities Market report studies the sales and consumption of the industry products/goods in the major geographic markets …

Gardeners want to make their grass even greener. As petrol prices rocket and people become ever more conscious of their environmental impact, many are turning to the latest generation of lawnmowers to keep their gardens looking good. While the fronts of our houses are gradually seeing the replacement of petrol cars with electric vehicles, advances in lithium-ion batteries have meant that the trusted back garden mower has also been given a modern overhaul – but at a price. So is it time to replace your current mower with a battery-powered or "robot" version, stick with petrol despite the spiralling costs, or stay plugged in? The length and breadth of your garden will heavily influence what type of machine you need.

The Artificial Intelligence (AI) in Construction Market Research Report is a professional asset that provides dynamic and statistical insights into regional and global markets. It includes a comprehensive study of the current scenario to safeguard the trends and prospects of the market. Artificial Intelligence (AI) in Construction Research reports also track future technologies and developments. Thorough information on new products, regional and market investments is provided in the report. This Artificial Intelligence (AI) in Construction research report also scrutinizes all the elements businesses need to get unbiased data to help them understand the threats and challenges ahead of their business.

For all the modern conveniences technology brings to the home – Wi-Fi-enabled washing machines, powerful gaming systems and enormous smart televisions – one of the downsides is paying to power it all. In fact, home utility costs are continuing to spike for many parts of the country, with 2021 electricity prices rising at the fastest rate since 2008, says the U.S. Energy Information Administration (EIA) – already hitting Americans facing skyrocketing inflation, resulting in higher costs for many goods and services. Not only does the average household have dozens of consumer electronics products plugged into power outlets at any given time, most consume electricity when not in use. "Vampire power" – also referred to as "phantom power" or "standby power" – can account for as much as 10% of a household's electricity bill, says the Environmental Protection Agency (EPA). This can really add up.