Department of Labor)-backed mine safety mission – achieved a historic unmanned underground mine inspection at one of the US' largest underground room and pillar limestone operations in this comprehensive IM report. Using ten ADR Explora XL unmanned robots, a Rajant wireless Kinetic Mesh below-ground communication network, and PBE hardware and technology, a horizontal mobile infrastructure distance of 1.7 km was achieved. This allowed the unmanned robots to record high-definition video and LiDAR to create a virtual 3D mine model to assess the condition of the mine, for the deepest remote underground mine inspection in history. The inspection made it possible for MSHA to conclude within a very short time that it was safe to re-enter the operation and begin remediation efforts, which included allowing mine personnel back into the mine to re-establish power and communications, after which mining was able to recommence quickly at the site. The project, in many ways, is the ultimate example of necessity breeding innovation.

According to the institute, its forecasting solution will help farmers deal with crop diseases in a timely manner and curb overuse of pesticides, which is rampant due to the lack of accurate information about the extent of crop infection. IIIT Naya Raipur's forecasting solution uses drones to monitor crops and capture live images if it detects any issues in them. The images are then sent from the drone in real time to the institute's servers, where an image classification model based on convolutional neural networks (CNN) is used to identify the disease and insects that are affecting it. CNNs are AI algorithms commonly used for image and video recognition. They can process an image, assign importance to its various attributes, and differentiate one image from another.

Inhomogeneities in the aortic wall can lead to localized stress accumulations, possibly initiating dissection. In many cases, a dissection results from pathological changes such as fragmentation or loss of elastic fibers. But it has been shown that even the healthy aortic wall has an inherent heterogeneous microstructure. Some parts of the aorta are particularly susceptible to the development of inhomogeneities due to pathological changes, however, the distribution in the aortic wall and the spatial extent, such as size, shape, and type, are difficult to predict. Motivated by this observation, we describe the heterogeneous distribution of elastic fiber degradation in the dissected aortic wall using a stochastic constitutive model. For this purpose, random field realizations, which model the stochastic distribution of degraded elastic fibers, are generated over a non-equidistant grid. The random field then serves as input for a uni-axial extension test of the pathological aortic wall, solved with the finite-element (FE) method. To include the microstructure of the dissected aortic wall, a constitutive model developed in a previous study is applied, which also includes an approach to model the degradation of inter-lamellar elastic fibers. Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis. The results show that the neural network is able to predict the stress distribution of the FE analysis while significantly reducing the computational time. In addition, it provides the probability for exceeding critical stresses within the aortic wall, which could allow for the prediction of delamination or fatal rupture.

We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is capable of accomplishing both at the same time. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We use a custom loss function for learning a PI range around optimized mean estimation with a desired coverage of a proportion of the target labels within the PI range. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We also examine the quality of our predictive uncertainty by evaluating on Active Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.

Investors looking to diversify their portfolios and coin collectors looking to add a new treasure to their collection are familiar with the benefits and value that precious metals can offer. To help make the purchasing process easier, IBM worked with TD Securities to launch an AI-based virtual assistant powered by IBM Watson Assistant that can help customers with inquiries on the TD Precious Metals digital store, including frequently asked questions. The TD Precious Metals digital store allows customers to buy physical gold, silver and platinum bullion and coins online from the comfort of their home. The new virtual assistant, now available as a feature on the TD Precious Metals digital store, provides customers with a convenient self-service option, available 24/7, for frequently asked questions about TD Precious Metals. Customers type their questions into the virtual assistant and receive an instant written response, along with links to help further assist them.

UBCO professor Abbas Milani and doctoral student Tina Olfatbakhsh use X-ray computed tomography to capture high-resolution 3D images of composite materials to study their internal structure. Researchers at UBC Okanagan have come up with an easier way to examine the complex structure of fibres and multiscale materials, helping to ensure newly developed composites won't fail under excessive loads. Using materials informatics and machine learning, the team has uncovered a new way to analyze the effectiveness of state-of-the-art fabric composites used in aerospace, construction, automotive and sports industries. The complex structures and configurations of these composites--while making them more durable and functional--are challenging to analyze, explains Dr. Abas Milani, a Professor in UBC Okanagan's School of Engineering and founding Director of the Materials and Manufacturing Research Institute. Fabric composites are interwoven materials that provide a lightweight, stronger and often more formable alternative to simpler one-dimensional composite materials, he explains.

Snow Lake Lithium is committed to creating and operating a fully renewable and sustainable lithium mine that can deliver a completely traceable, carbon-neutral, and zero harm product to the electric vehicle and battery markets. Snow Lake Lithium is a leading fully integrated, carbon-neutral lithium hydroxide provider operated by renewable hydroelectric power. Today, electric vehicles (EVs) run on Lithium-ion batteries. Lithium, therefore, is a critical, in-demand component of batteries needed for EVs, and securing domestic lithium hydroxide supply to the North American electric vehicle industry is a critical process in ensuring sustainability in the near future. Snow Lake Lithium has outlined plans to develop the world's first all-electric Lithium mine in Manitoba, Canada developing a domestic supply of this critical resource to the North American electric vehicle industry.

As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chromatography (TLC) represents a commonly used technique for polarity measurement. However, current TLC analysis presents several problems, including the need for a large number of attempts to obtain suitable conditions, as well as irreproducibility due to non-standardization. Herein, we describe an automated experiment system for TLC analysis. This system is designed to conduct TLC analysis automatically, facilitating high-throughput experimentation by collecting large experimental data under standardized conditions. Using these datasets, machine learning (ML) methods are employed to construct surrogate models correlating organic compounds' structures and their polarity using retardation factor (Rf). The trained ML models are able to predict the Rf value curve of organic compounds with high accuracy. Furthermore, the constitutive relationship between the compound and its polarity can also be discovered through these modeling methods, and the underlying mechanism is rationalized through adsorption theories. The trained ML models not only reduce the need for empirical optimization currently required for TLC analysis, but also provide general guidelines for the selection of conditions, making TLC an easily accessible tool for the broad scientific community.

In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to target domains. In previous works, it was assumed that the number of samples in the intermediate domains is sufficiently large; hence, self-training was possible without the need for labeled data. If access to an intermediate domain is restricted, self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with both artificial and real-world datasets. Codes are available at https://github.com/ssgw320/gdamf.

Mike Ananny walked his dog this morning. He did so with no expectation of privacy. "I know that I was subject to a wide variety of cameras, whether it's Ring doorbells, cars driving along, or even city traffic cameras," he said. "I didn't choose to participate in this whole variety of video surveillance systems. I just took my dog for a walk." Ananny understands that, wherever he goes, data about him is being collected, analyzed and monetized by artificial intelligence (AI). Kate Crawford drove a van deep into the arid Nevada landscape to get a good look at the evaporating brine ponds of the Silver Peak Lithium Mine.