Finding sources of pollution across vast supply chains may be one of the largest barriers to eliminating carbon pollution. But for others like agriculture or consumer electronics, tracing and quantifying greenhouse gas emissions can be a time-consuming, laborious process. It generally takes an expert around three to six months--sometimes more--to come up with an estimate for a single product. Typically, researchers have to probe vast supply chains, comb the scientific literature, digest reports, and even interview suppliers. They may have to dive into granular details, estimating the footprint of everything from gypsum in drywall to tin solder on circuit boards.

Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

A new center at Bigelow Laboratory is using cutting-edge artificial intelligence algorithms to forecast ocean activity, from toxic algal blooms to right whale migration, with the hopes of benefitting both coastal industries and the environment. People are expecting forecasts of all different kinds now, from COVID forecasts to political forecasts," said Nick Record, a senior research scientist at Bigelow Laboratory for Ocean Sciences in East Boothbay. "We're trying to tap into this societal need and demand for forecasts and apply it to ocean systems that we live in and rely on." The ability to accurately forecast complex ocean dynamics alone, such as temperature and salinity, is useful for the industries that use the coastline and the scientists that study it. With artificial intelligence, though, these forecasts will be constantly improving in accuracy even as the climate changes -- and, with it, Maine's ability to adapt to the changing coastline will improve as well.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. IBM today launched the Environmental Intelligence Suite, a set of AI-powered software that customers can use to prepare for climate risks that could disrupt operations. By combining AI, weather data, climate risk analytics, and carbon accounting capabilities, the Environmental Intelligence Suite can be used to help organizations assess their impact on the planet while reducing the complexity of regulatory compliance, IBM says. Companies are facing climate-related damage to their assets, as well as increasing expectations from consumers to perform as environmental leaders. McKinsey predicts that climate change could mean more disruptions in global supply chains, interrupting production and raising costs and prices.

The catastrophic Fukushima nuclear disaster in 2011 caused an estimated 250,000 people to evacuate their homes, but scientists have determined certain wildlife species in the area are thriving, suggesting people could eventually return to the region, according to a new study. Researchers at Colorado State University, the University of Georgia and Fukushima University's Institute of Environmental Radioactivity have found that multiple generations of wild boar and rat snakes have not suffered from any significant adverse health effects. Multiple generations of animals have been exposed to radiation levels above the threshold for human occupancy, but have suffered no ill effects. That may be due to the fact that cesium-134, one of the major radioactive materials released during the accident, saw its levels decrease by almost 90 percent. The researchers looked at biomarkers of DNA damage and stress to determine that the boar and snakes were thriving in the area. The researchers looked at the wild boars and snakes between 2016 and 2018, or five to seven years after the earthquake and resulting tsunami destroyed the Fukushima Dai-ichi Nuclear Power Plant, releasing massive amounts of radioactive material in the environment.

An exoskeleton that lets amputees feel like they are'walking with two normal legs' has been developed by scientists using battery-powered electric motors. The powerful exoskeleton, which wraps around the wearer's waist and leg, was developed by a team of engineers at the University of Utah in Salt Lake City. It has been designed for above-the-knee amputees and uses battery-powered electric motors and embedded microprocessors to reduce walking effort. The 5.4lb frame is made of carbon-fibre material, plastic composites and aluminium and can walk for miles between charges, according to its creators. Those wearing it saw a 15.6 per cent reduction in their metabolic rate, equivalent to taking off a 26-pound backpack while out on a long walk, the team said.

Day by day you can find more companies that adopt AI in their work processes. AI also can be leveraged to improve the stakeholder experience as well. So today we will take a look at the top AI trends expected in 2022. Artificial intelligence will play a significant role in the widespread adoption of cloud solutions in 2022. Artificial intelligence will make it possible to track and manage cloud resources as well as the massive amounts of data available.

The theory-guided convolutional neural network (TgCNN) framework, which can incorporate discretized governing equation residuals into the training of convolutional neural networks (CNNs), is extended to two-phase porous media flow problems in this work. The two principal variables of the considered problem, pressure and saturation, are approximated simultaneously with two CNNs, respectively. Pressure and saturation are coupled with each other in the governing equations, and thus the two networks are also mutually conditioned in the training process by the discretized governing equations, which also increases the difficulty of model training. The coupled and discretized equations can provide valuable information in the training process. With the assistance of theory-guidance, the TgCNN surrogates can achieve better accuracy than ordinary CNN surrogates in two-phase flow problems. Moreover, a piecewise training strategy is proposed for the scenario with varying well controls, in which the TgCNN surrogates are constructed for different segments on the time dimension and stacked together to predict solutions for the whole time-span. For scenarios with larger variance of the formation property field, the TgCNN surrogates can also achieve satisfactory performance. The constructed TgCNN surrogates are further used for inversion of permeability fields by combining them with the iterative ensemble smoother (IES) algorithm, and sufficient inversion accuracy is obtained with improved efficiency.

Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network. Intelligent control of smart inverters and other smart building energy management systems can be leveraged to alleviate these issues. GridLearn is a multiagent reinforcement learning platform that incorporates both building energy models and power flow models to achieve grid level goals, by controlling behind-the-meter resources. This study demonstrates how multi-agent reinforcement learning can preserve building owner privacy and comfort while pursuing grid-level objectives. Building upon the CityLearn framework which considers RL for building-level goals, this work expands the framework to a network setting where grid-level goals are additionally considered. As a case study, we consider voltage regulation on the IEEE-33 bus network using controllable building loads, energy storage, and smart inverters. The results show that the RL agents nominally reduce instances of undervoltages and reduce instances of overvoltages by 34%.

The U.S. Department of Energy (DoE) recently announced the names of 83 scientists who have been selected for their 2021 Early Career Research Program. The list includes four faculty members from MIT: Riccardo Comin of the Department of Physics; Netta Engelhardt of the Department of Physics and Center for Theoretical Physics; Philip Harris of the Department of Physics and Laboratory for Nuclear Science; and Mingda Li of the Department of Nuclear Science and Engineering. Each year, the DoE selects researchers for significant funding the "nation's scientific workforce by providing support to exceptional researchers during crucial early career years, when many scientists do their most formative work." The quantum technologies of tomorrow –– more powerful computing, better navigation systems, and more precise imaging and magnetic sensing devices –– rely on understanding the properties of quantum materials. Quantum materials contain unique physical characteristics, and can lead to phenomena like superconductivity.