The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork. However, a reliable estimate of the admittance matrix may either be missing or quicklybecome obsolete for temporally varying grids. In this work, we propose a data-driven identificationmethod utilising voltage and current measurements collected from micro-PMUs. More precisely,we first present a maximum likelihood approach and then move towards a Bayesian framework,leveraging the principles of maximum a posteriori estimation. In contrast with most existing con-tributions, our approach not only factors in measurement noise on both voltage and current data,but is also capable of exploiting available a priori information such as sparsity patterns and knownline parameters. Simulations conducted on benchmark cases demonstrate that, compared to otheralgorithms, our method can achieve significantly greater accuracy.

Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, the maps of BR damages are typically lacking in forest information systems. We predicted timber volume damaged by BR at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). We utilized random forest (RF) models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). We found that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation achieved an RMSE of 11.4 $m^3ha^{-1}$ (pseudo $R^2$: 0.66) whereas the mapping case resulted in a pseudo $R^2$ of 0.60. When the spatially distinct k-means clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo $R^2$ associated with the mapping case were 15.6 $m^3ha^{-1}$ and 0.37, respectively. This indicates that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages.

Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model) and exploit this reasoning to navigate through dense traffic safely. This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. We explore the connection between human driving behavior and their velocity changes when interacting. Hence, we propose to learn, via deep Reinforcement Learning (RL), an interaction-aware policy providing global guidance about the cooperativeness of other vehicles to an optimization-based planner ensuring safety and kinematic feasibility through constraint satisfaction. The learned policy can reason and guide the local optimization-based planner with interactive behavior to pro-actively merge in dense traffic while remaining safe in case the other vehicles do not yield. We present qualitative and quantitative results in highly interactive simulation environments (highway merging and unprotected left turns) against two baseline approaches, a learning-based and an optimization-based method. The presented results demonstrate that our method significantly reduces the number of collisions and increases the success rate with respect to both learning-based and optimization-based baselines.

MONTREAL and VANCOUVER, British Columbia, July 08, 2021 (GLOBE NEWSWIRE) -- Rithmik Solutions, whose mission is building the world's most advanced and reliable analytics for mobile mining equipment, today announced the closing of a US$1.2M investment led by Chrysalix Venture Capital and joined by Fonds Ecofuel. The funding will accelerate the commercialization of the company's flagship product, Rithmik Asset Health Analyzer (AHA), which has been in development for the past three years and is currently undergoing real-time onsite trials in Alberta, Quebec and Zambia. Rithmik AHA applies a multi-tiered machine learning approach to increase mobile equipment uptime while reducing maintenance costs and lowering greenhouse gas emissions. Mining companies typically spend anywhere from 20%-50% of their annual operating budgets on equipment maintenance, and lost production from unplanned downtime has an even bigger financial impact. "We were impressed by the Rithmik team's deep technical experience in the space of mobile mining equipment data, across equipment types and OEM brands, and that experience has strongly resonated with their early customers," said Alicia Lenis, Vice President at Chrysalix Venture Capital, an industrial innovation fund.

Boiling is not just for heating up dinner. Turning liquid into gas removes energy from hot surfaces, and keeps everything from nuclear power plants to powerful computer chips from overheating. But when surfaces grow too hot, they might experience what's called a boiling crisis. In a boiling crisis, bubbles form quickly, and before they detach from the heated surface, they cling together, establishing a vapor layer that insulates the surface from the cooling fluid above. Temperatures rise even faster and can cause catastrophe.

Photographs of boiling surface taken using a scanning electron microscope: indium tin oxide (upper left), copper oxide nanoleaf (upper right), zinc oxide nanowires (lower left), and porosity of silicon dioxide nanoparticles obtained layer by layer. Quality coating deposit (bottom right).Credit: Massachusetts Institute of Technology Boiling is not just for warming a supper. It’s also to …

Buildings and motor vehicles are fundamental parts of the modern metropolis – and both are among the largest contributors to global carbon emissions, given the former's need for heat and power, and the latter's continuing dependence on the internal combustion engine. The good news is that sustainability concerns are at the forefront of several initiatives to shape the cities of the future. Planet Mark is an organisation that's committed to transforming society through the measurement of carbon and social data. "To keep the certification, an organisation must reduce its carbon footprint every year," explains Planet Mark's founder and CEO, Steve Malkin. "On average, certified businesses make a 16% carbon saving per employee through efficiencies in energy, waste, water, travel and procurement."

Boiling is not just for heating up dinner. Turning liquid into gas removes energy from hot surfaces, and keeps everything from nuclear power plants to powerful computer chips from overheating. But when surfaces grow too hot, they might experience what's called a boiling crisis. In a boiling crisis, bubbles form quickly, and before they detach from the heated surface, they cling together, establishing a vapor layer that insulates the surface from the cooling fluid above. Temperatures rise even faster and can cause catastrophe. Operators would like to predict such failures, and new research offers insight into the phenomenon using high-speed infrared cameras and machine learning.

The recent ransomware attack on Colonial Pipeline is another painful reminder of how vulnerable we are to such attacks and difficult it is to defend our infrastructures against them. RaaS (Ransomware as a service) is a thriving industry in many dark corners of the world, and protecting against it at the intelligent edge is particularly difficult. Challenges include day zero detection with no previous example or known signature, low latency response time, and high detection throughput rate needed to handle the ever-increasing online transactions at the intelligent edge. Additional challenges included limited compute and power resources and hardware architecture flexible enough to change when threat conditions change. Center for Advanced Electronics through Machine Learning (CAEML) researchers have been investigating machine learning hardware solutions that can accelerate ransomware detection at the intelligent edge.

Learning algorithms tend to struggle [4]. Hierarchical Reinforcement Learning, or HRL, takes advantage of the hierarchical Pipelines networks are the fulcrum of the oil and gas policy decomposition to exploit underlying problem industries and of gas and water mains. These pipes must structures and simplify the learning of complex tasks. The hierarchical be periodically inspected to guarantee the safety and proper decomposition can be either defined by using prior functioning of the plants. However, inspection is usually knowledge [5], [6], [7], [8], or can be automatically learned a long, expensive and tedious procedure that requires the during training [4], [9], [10]. While the latter category of shut-down of the whole plant and, in the specific case of algorithm does not require expert knowledge for defining industrial pipelines, the removal of the insulation around the the hierarchy, the autonomous discovery of the options often pipes. With metal pipes, the inspection is currently performed leads to sub-optimal policies if additional regularizers are not from the outside using ultrasonic or magnetic probes that used during the learning phase [7], [10].