Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.

A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.

To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households. With an emerging discussion of transferability in Non-Intrusive Load Monitoring (NILM), there is a need for domain-specific metrics to assess the performance of NILM algorithms on new test scenarios being unseen buildings. In this paper, we discuss several metrics to assess the generalisation ability of NILM algorithms. These metrics target different aspects of performance evaluation in NILM and are meant to complement the traditional performance evaluation approach. We demonstrate how our metrics can be utilised to evaluate NILM algorithms by means of two case studies. We conduct our studies on several energy consumption datasets and take into consideration five state-of-the-art as well as four baseline NILM solutions. Finally, we formulate research challenges for future work.

High-resolution radar and night vision cameras may help scientists protect bats from untimely deaths at wind farms, according to new research. Researchers are using these technologies to provide more specific details about the number of bats killed by wind turbines in Iowa. These details will improve scientists' understanding of bat activity and potentially save their lives, said Jing Teng, a graduate researcher at the University of Iowa who presented the work this week at the 2019 American Geophysical Union Fall Meeting in San Francisco. This work has broad impacts, according to Teng. "The more bats you kill, the more insects you have on farms; then, farmers will put more pesticides; and then, people will eat more pesticides," he said.

Imagine a world where plastic materials would never go to waste. Agilyx Corporation ("Agilyx"), the leader in chemical recycling of post-use plastics back into plastics chemicals and low carbon fuels, today announced a dynamic collaboration in artificial intelligence (AI) technology with the General Electric Company through its Licensing business unit that would bring the world closer than ever to making this green dream possible. Combining Agilyx's deep domain experience in chemical recycling with GE's vast experience in the application of Industrial AI, the two companies are aiming to increase the chemical recyclability of all post-use plastics from the current 10% to over 95%. This announcement is the result of a year-long, successful effort to assess GE's advanced modeling technology developed by GE Research, and its applicability to the database of chemical conversions of post-use plastics that Agilyx has amassed over the last 15 years. Together, the companies can greatly improve recycling rates by deploying an innovative set of artificial intelligence ("AI") technologies, including machine learning ("ML"), predictive modeling ("PM") and optimization tools, in combination with other supply chain innovations in partnership with a growing number of diverse leaders in the waste and recycling, petrochemical, consumer goods products and retail industries.

A savvy approach to artificial intelligence can radically enhance productivity and slash costs. You're not imagining it: the pace of technological change is indeed quickening and it's placing tremendous competitive pressures on every corner of the global economy Manufacturers are acutely feeling the squeeze. Eighty-five percent of industrial equipment execs surveyed by Accenture say they need to innovate ever faster just to keep up. That puts them in a perilous catch-22: it's prohibitively expensive to upgrade equipment to meet customer demands, yet they risk losing customers altogether if they don't. Enter artificial intelligence, the great equalizer for manufacturers.

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the $L_{1}$-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate improved performance over the regular tensor completion methods.

Research has found that many occupational accidents are foreseeable, being the result of people's unsafe behaviour from a retrospective point of view. The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates. In recent years, researchers have found that people do have stable predispositions to engage in certain safety behavioural patterns which vary among individuals as a function of personality features. In this study, an innovative forecasting model, which employs machine learning algorithms, is developed to estimate construction workers' behavioural tendency based on the Big Five personality taxonomy. The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour. The developed model is found to be sufficient to have satisfactory accuracy in explaining and predicting workers' safety behaviour. This finding provides the empirical evidence to support the usefulness of personality traits as effective predictors of people's safety behaviour at work. In addition, this study could have practical implications. The machine learning model developed can help identify vulnerable workers who are more prone to undertake unsafe behaviours, which is proven to have good prediction accuracy and is thereby potentially useful for decision making and safety management on construction sites.

This work follows the approach of multi - label classification for non - intrusive load monitoring (NILM) . We modify the popu lar sparse representation based classification (SRC) approach (developed for single label classification) to solve multi - label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state - of - the - art t echniques with small volume of training data . N non - intrusive load monitoring (NILM) the technical goal is to estimate the power consumption of different appliances given the aggregate smart - meter readings [1] . The broader social objective is to feedback this information to the household so that they can reduce power consumption and thereby save energy.

The objective of this work is to improve the accuracy of building demand forecasting . This is a more challenging t ask than grid level forecasting. For the said purpose, we develop a new technique called recurrent transform learning (RTL). The first one (RTL) is unsupervised; this is used as a feature extraction tool that is further fed into a regression model. Forecasting experiments have been carried out on three popular publicly available datasets. Both of our proposed techniques yield results superior to the state - of - the - art like long short term memory network, echo state network and sparse coding regression. Index Terms -- demand forecasting, dynamical model, load forecasting, transform learning . H E impor tance of electrical load forecasting is well known. The issue has gained even more significance with the advent of smartgrids, microgrids and smart buildings. An excellent review on this topic can be found in [1].