LINK "Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture" LINK "Establishment of a Nondestructive Analysis Method for Lignan Content in Sesame using Near Infrared Reflectance Spectroscopy" LINK "Near Infrared Spectroscopy: A useful technique for inline monitoring of the enzyme catalyzed biosynthesis of third-generation biodiesel from waste cooking oil" LINK "A Study on Nitrogen Concentration Detection Model of Rubber Leaf Based on Spatial-Spectral Information with NIR Hyperspectral Data" LINK "Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement" LINK "Estimating Forest Soil Properties for Humus Assessment--Is Vis-NIR the Way to Go?" LINK "Association and solubility of chlorophenols in CCl4: MIR/NIR spectroscopic and DFT study" LINK "Prediction of rhodinol content in Java citronella oil using NIR spectroscopy in the initial stage ...

In 2018, Meta committed to minimizing its environmental footprint and is targeting net zero emissions for its value chain in 2030. However, it has plans to build eight data centers. To reduce the carbon emissions this one will generate, META's team, with the help of Lav Varshney and Nishant Garg from the University of Urbana-Champaign, designed a low-carbon concrete using generative machine learning algorithms that they tested at the Delkab, Illinois, facility. Concrete has been used for thousands of years to construct buildings and structures. Although it has evolved, cement is now one of its ingredients, but it is also the major source of its greenhouse gas emissions.

Drones and robots could form part of a new radiation-monitoring system at the Chernobyl power station in Ukraine, as scientists at the plant fear that existing sensor networks built after the nuclear accident in 1986 have been at least partially destroyed by Russian troops. When Russia seized the Chernobyl plant in February, the sensors monitoring gamma radiation levels quickly went offline and most remained that way.

Normalizing flows, a popular class of deep generative models, often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence among variables. In light of this shortcoming, we propose COMET (COpula Multivariate ExTreme) Flows, which decompose the process of modeling a joint distribution into two parts: (i) modeling its marginal distributions, and (ii) modeling its copula distribution. COMET Flows capture heavy-tailed marginal distributions by combining a parametric tail belief at extreme quantiles of the marginals with an empirical kernel density function at mid-quantiles. In addition, COMET Flows capture asymmetric tail dependence among multivariate extremes by viewing such dependence as inducing a low-dimensional manifold structure in feature space. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of COMET Flows in capturing both heavy-tailed marginals and asymmetric tail dependence compared to other state-of-the-art baseline architectures. All code is available on GitHub at https://github.com/andrewmcdonald27/COMETFlows.

How smart are electrical grids in India? The grids in India don't have enough resiliency. There are interruptions, and flickering, etc. There's quite a lot of work that must be done to improve the power quality of the grids, partly because it has not kept pace with growing electricity demand. We also do not have a lot of redundancy in the grid yet due to the infra.

The paper describes the experience of using a machine-learning model prepared by the ensemble method to prevent stuck-pipe events during well …

Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematical expressions and internal state variables (ISVs) governing path-dependent behaviors. Data-driven machine learning models, such as deep neural networks and recurrent neural networks (RNNs), have become viable alternatives. However, pure black-box data-driven models mapping inputs to outputs without considering the underlying physics suffer from unstable and inaccurate generalization performance. This study proposes a machine-learned physics-informed data-driven constitutive modeling approach for path-dependent materials based on the measurable material states. The proposed data-driven constitutive model is designed with the consideration of universal thermodynamics principles, where the ISVs essential to the material path-dependency are inferred automatically from the hidden state of RNNs. The RNN describing the evolution of the data-driven machine-learned ISVs follows the thermodynamics second law. To enhance the robustness and accuracy of RNN models, stochasticity is introduced to model training. The effects of the number of RNN history steps, the internal state dimension, the model complexity, and the strain increment on model performances have been investigated. The effectiveness of the proposed method is evaluated by modeling soil material behaviors under cyclic shear loading using experimental stress-strain data.

A team of researchers led by the University of Minnesota has significantly improved the performance of numerical predictions for agricultural nitrous oxide emissions. The first-of-its-kind knowledge-guided machine learning model is 1,000 times faster than current systems and could significantly reduce greenhouse gas emissions from agriculture. The research was recently published in Geoscientific Model Development, a not-for-profit international scientific journal focused on numerical models of the Earth. Researchers involved were from the University of Minnesota, the University of Illinois at Urbana-Champaign, Lawrence Berkeley National Laboratory, and the University of Pittsburgh. Compared to greenhouse gases such as carbon dioxide and methane, nitrous oxide is not as well-known.

The Department of Energy established the Artificial Intelligence Advancement Council earlier this month to coordinate funding and development of algorithms and hold agencies accountable for how they are used. A lean team consisting of five members, AIAC will quickly approve task forces, implementation plans and organizational changes for the AI & Technology Office, DOE Executive Secretariat, and AI Program Committee to execute. DOE stood up the Responsible and Trustworthy AI Task Force ahead of AIAC's first meeting, tentatively planned for June, to suggest departmental principles and practices, particularly around equity and ethics. "We are very mindful of the fact that there are activities and initiatives that are underway, as well as initiatives that should be considered, in AI space," AITO Director Pamela Isom told FedScoop. "And we're also needing the support at the level that the council brings."

Your smart phone can feel like a lifeline, helping you navigate a new town or delivering an urgent message to a friend. Many people have a funny or embarrassing anecdote about an autocorrected text message or a roundabout route to a destination. But these artificial intelligence (AI) flaws exist on a spectrum, from minor inconveniences to unfair treatment or even risk to human life. The people who create and use these AI technologies are also imperfect; we have our own biases, whether we are aware of them or not. Unconscious bias can influence our decisions and lead to unintended consequences; overt prejudice can result in our unethical and harmful exploitation of AI technologies.