The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.

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Abstract: The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. Fortunately, previous works such as Deep Fluids (DF) (Kim et al., 2019) show that large simulation speedups are achievable by replacing CFD simulators with neural network (NN) surrogates that faithfully mimic the CFD simulation process. This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization. Thus, here, we build on the DF approach to develop surrogates that can successfully be applied to our complex carbon-capture CFD simulations.

Predicting how climate and the environment will change over time or how air flows over an aircraft are problems too complex even for the most powerful supercomputers to solve. Scientists rely on models to fill in the gap between what they can simulate and what they need to predict. But, as every meteorologist knows, models often rely on partial or even faulty information which may lead to bad predictions. Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) are forming what they call "intelligent alloys", combining the power of computational science with artificial intelligence to develop models that complement simulations to predict the evolution of science's most complex systems. In a paper published in Nature Communications, Petros Koumoutsakos, the Herbert S. Winokur, Jr. Professor of Engineering and Applied Sciences and co-author Jane Bae, a former postdoctoral fellow at the Institute of Applied Computational Science at SEAS, combined reinforcement learning with numerical methods to compute turbulent flows, one of the most complex processes in engineering.

Verdict lists the top five terms tweeted on artificial intelligence (AI) in Q4 2021, based on data from GlobalData's Technology Influencer Platform. The top trends are the most mentioned terms or concepts among Twitter discussions of more than 150 AI experts tracked by GlobalData's Technology platform in Q4 2021. Common functions of RPA, and AI and machine learning (ML) allowing organisations to streamline their operations, were some popularly discussed topics on RPA in Q4 2021. Giuliano Liguori, CEO of Kenovy, an IT services and consulting company, further shared an article on understanding the use cases and applications of RPA across industries. The article highlighted how RPA is used by businesses to streamline customer or employee boarding, updating customer relationship management (CRM), and other processes and to feed data into systems to automatically extract relevant data.

Soil consolidation is closely related to seepage, stability, and settlement of geotechnical buildings and foundations, and directly affects the use and safety of superstructures. Nowadays, the unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multi-directional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications, but it is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed, (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training, and (3) the PDE of two-dimensional soil consolidation and the model of the neural network is connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Using this method, the excess pore water pressure could be predicted simply and efficiently. In addition, the method was applied to predict the soil excess pore water pressure in the foundation in a real case at Tianjin port, China. The proposed deep learning approach can be used to investigate the large and complex multi-directional soil consolidation.

The foundations of ESG reporting are built on data, yet simply learning the'lay of the land' is no longer enough – organisations must be able to identify and assemble enterprise data across their entire supply chain, in all operations and jurisdictions. Compounding the issue are complex corporate structures. Legally relevant data is often siloed; whether that be across various cloud storage environments, different computers due to Bring Your Own Device and remote working, or even in the minds of employees following personnel changes. The scale of this challenge is obvious, but with next-generation technology like AI at organisations' disposal, difficulty is no longer an excuse. With ESG set to remain a major compliance responsibility in the coming years, organisations must turn to AI technology as a solution.

Nissan Motor Co. is betting that its experience pioneering lithium-ion batteries for electric vehicles over a decade ago will give it an upper hand in producing a new battery type that, despite being new and still relatively unproven, is considered by some as key to unlocking the future potential of EVs. Nissan is producing prototype solid-state battery cells -- which replace the electrical current-conducting liquid found in conventional batteries with a solid substance -- at a facility resembling a pop-up lab inside its research grounds near its Yokohama headquarters. The Japanese automaker plans to bring the new type of batteries to market by fiscal year 2028, readying a pilot plant for them ahead of that around 2024. If they can be manufactured, solid-state batteries would unlock cheaper, safer and faster-charging EVs, according to automotive executives and battery experts. Using different material combinations, Nissan predicts it will eventually be able to produce a solid-state battery pack that costs $65 (¥8,063) per kilowatt-hour -- a level at which analysts say EVs could reach price parity with gasoline-engine cars.

However, the high cost and low portability/wearability of haptic devices remain unresolved issues, severely limiting the adoption of this otherwise promising technology. Electrotactile interfaces have the advantage of being more portable and wearable due to their reduced actuators' size, as well as their lower power consumption and manufacturing cost. The applications of electrotactile feedback have been explored in human-computer interaction and human-machine-interaction for facilitating hand-based interactions in applications such as prosthetics, virtual reality, robotic teleoperation, surface haptics, portable devices, and rehabilitation. This paper presents a technological overview of electrotactile feedback, as well a systematic review and meta-analysis of its applications for hand-based interactions. We discuss the different electrotactile systems according to the type of application. We also discuss over a quantitative congregation of the findings, to offer a high-level overview into the state-of-art and suggest future directions. Electrotactile feedback systems showed increased portability/wearability, and they were successful in rendering and/or augmenting most tactile sensations, eliciting perceptual processes, and improving performance in many scenarios. However, knowledge gaps (e.g., embodiment), technical (e.g., recurrent calibration, electrodes' durability) and methodological (e.g., sample size) drawbacks were detected, which should be addressed in future studies.