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Engineering - OSTI GOV

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Last Updated: 05 July 2022

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Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

Event times and types were also included in the event log, which also included event times and types. BIG Data Analysis of Synchrophasor Data U201d (U201d) 6 of the eleven study objectives identified in Funding Opportunity Announcement, DE-FOA-1861 u201d The investigation of this dataset focused on six of the eleven study objectives identified in Funding Opportunity Announcement. Additionally, we developed a PredictiveGrid precomputes and stores aggregate statistics for various temporal data resolutions, and thus our statistical event searches were able to refine the logs themselves through months of high resolution logs. The event epicenter and manifesting data type respectively, as the refined event time, the sensor and its stream with the highest deviation are returned, as the event epicenter and manifesting data type respectively, as the event epicenter and manifesting data type respectively, as the event time is established. Event detection and classification was also applied by machine learning algorithms to the tasks of event detection and classification. A window of data was ingested by a machine learning system from a single sensor by multiple stream types. determining the event class of the impending event out of a list of classes; determining if the data window contained an event; and determining if the event precursor for a data window that contains an event; and for a data window that contains an event; and determining the event class out of a set of classes.

Source link: https://www.osti.gov/biblio/1864556


User Guide to the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software

In the Research and Test Reactor Program at Argonne National Laboratory, the Advanced Dimensional Depletion for Engineering of Reactors software is being developed to satisfy the Conversion Program's reactor modeling and analysis requirements. The ADDER Python 3 application was created using modern software development methodologies subjected to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance requirements, which were subjected to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. The ADDER application is a Python 3 application developed using modern web design techniques. This is the user guide for the ADDER v1. 0. 1 software version that is referred to as ADDER v1. 0. 1. Fuel management analysis can be expensive, and it can be difficult to track an inventory that is multiple times greater than the base loading. Many reactors, both power and non-power reactors of various types, would find the capabilities of ADDER to perform key tasks that a fuel or core design engineer must complete with ease and documented functionality.

Source link: https://www.osti.gov/biblio/1866062


Engineered Cell Line Imaging Assay Differentiates Pathogenic from Non-Pathogenic Bacteria

Cell culture systems have greatly improved our understanding of how bacteria pathogens affect signaling pathways to manipulate the host and cause infection. In both wild-type and engineered cell lines, pathogenic P. aeruginosa caused significant cell death after 8 h, as opposed to non-pathogenic S. epidermidis. In engineered cells, we found that Fra1 signaling was interrupted in as little as 4 hrs after inoculation with bacterial pathogens, relative to delayed disruption of signaling by non-pathogenic S. epidermidis.

Source link: https://www.osti.gov/biblio/1846734


Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency is often measured as the ratio of yield to soil nutrient availability in the metric system, but neglects the contributions of root plant characteristics. Relevant plant characteristics can be grouped as root acquisition convenience, shoot radiation use effectiveness, and plant metabolic efficiency. To improve plant demand and soil supply of nutrients, Traits must be conceptualized in agricultural systems contexts, including consideration of crop mixtures.

Source link: https://www.osti.gov/biblio/1854477

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions