IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods
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Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during the conceptual phase of an experiment when hundreds of materials are screened for radiopurity. A tool is needed to manage and make use of the radioassay screening data to quantitatively assess detector design options. We have developed a Materials Database Application for the nEXO experiment to serve this purpose. Furthermore, this paper describes this database application, explains how it functions, and discusses how it streamlines the design of the experiment.
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Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.
This report analyzes the power consumption of large dynamic digital loads using the open-source MIT supercloud and SURF datasets. With an emphasis on the MIT data, we calculate important power consumption characteristics to help system operators improve generation planning and resource allocation. We also introduce a rudimentary model for generating synthetic load profiles.
Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.
Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the US Department of Energy (DOE) Office of Science supported an instrumented Gulfstream 1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final 6 years of G-1 operations (2013 to 2018, https://doi.org/10.5439/1999133; Mei and Gaustad, 2024). The integrated dataset includes data collected from 236 flights (766.4 h), which covered the Arctic, the US Southern Great Plains (SGP), the US West Coast, the eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability in the thermodynamic quantities and aerosol and cloud properties for addressing essential science questions in Earth system process studies. This paper describes the DOE ARM merged G-1 datasets, including information on the acquisition, data collection challenges and future potentials, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package.
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The test program conducted to characterize the steady state stability, thermal, and performance characteristics of the integrated thrust chamber assembly, as well as limited tests to investigate transient characteristics are described.
Volume 3 of this three-volume set provides a detailed analysis of the data in Volumes 1 and 2, most of which was generated for the Galileo Orbiter Program in support of NASA space programs. Volume 1 includes total ionizing dose radiation test data on diodes, bipolar transistors, field effect transistors, and miscellaneous discrete solid-state devices. Volume 2 includes similar data on integrated circuits and a few large-scale integrated circuits. The data of Volumes 1 and 2 are combined in graphic format in Volume 3 to provide a comparison of radiation sensitivities of devices of a given type and different manufacturer, a comparison of multiple tests for a single data code, a comparison of multiple tests for a single lot, and a comparison of radiation sensitivities vs time (date codes). All data were generated using a steady-state 2.5-MeV electron source (Dynamitron) or a Cobalt-60 gamma ray source. The data that compose Volume 3 represent 26 different device types, 224 tests, and a total of 1040 devices. A comparison of the effects of steady-state electrons and Cobat-60 gamma rays is also presented.
Current trends in the use of remotely sensed data include integration of multiple data sources of various formats and use of complex models. These trends have placed a strain on information processing systems because an enormous number of capabilities are needed to perform a single application. A solution to this problem is to create a general set of capabilities which can perform a wide variety of applications. General capabilities for the Image-Based Information System (IBIS) are outlined in this report. They are then cross-referenced for a set of applications performed at JPL.