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Kripke is a simple 3D deterministic discrete ordinates (Sn) particle transport code that maintains the computational load and communications pattern of a real transport code. It is intended to be a research tool to explore different data layouts, new programming paradigms and computer architectures.
DoZen (pronounced “do zen”) is for processing, visualizing, and exploring electromagnetic data that is stored in Zonge’s .z3d format. It was created specifically for processing time-lapse controlled source electromagnetic data.
Radioactive contaminants pose risks to human health and the environment. These risks are influenced by factors such as disposal/release and transport mechanisms, location in the environment, potential exposure receptors, and the presence of comingled contaminants. Understanding the behavior, transport, and distribution of radionuclides in the environment is essential to facilitating risk-informed decision-making related to remedial design, action, and monitoring. These decisions are based on significant amounts of complex data that must be accessed, integrated, and analyzed to effectively evaluate data and formulate appropriate conclusions and recommendations. Often, data volume, complexity, and legacy access issues pose severe challenges for analysts, regulators, and stakeholders interested in evaluating scientific data to support decision-making. To address these challenges at the U.S. Department of Energy Hanford Site, Pacific Northwest National Laboratory (PNNL) has developed PHOENIX (PNNL-Hanford Online Environmental Information exchange) to facilitate risk-informed decision-making by streamlining investigative and analytical processes through the integration and visualization of previously isolated datasets created under distinct regulatory or operational regimes, each serving a specific purpose. With the large volume of data dispersed across many Hanford environmental databases, finding the correct sources of relevant data, accessing them, and relating them in a meaningful way can prove challenging and time consuming. By analyzing Hanford databases, performing substantial optimization using modern data processing methods and developing extensive queries on the refactored data, PHOENIX enables users to access, integrate, display, visualize, and retrieve data that would have been historically unfeasible to examine. The data and information accessible through PHOENIX help inform remediation-related assessments and evaluations, predict and quantify the risks that radioactive and chemical contaminants pose to human health and the environment, and communicate this information to decision makers responsible for risk-based remediation actions. (authors)
SAND2022-14692 O Virtual Volumes is a CAD2VR plugin that gives users a powerful and intuitive way of exploring volumetric data in a three-dimensional (3D) virtual reality (VR) environment. It enables viewing of and interaction with volumetric and voxel data derived from sources such computed tomography (CT) scans. Current 2D software solutions for viewing CT scans and other volumetric data forces the user to look through "slices" of their data across anatomical planes. In Virtual Volumes, the user can intuitively interact, scale, crop, colorize, and threshold their data in a 3D VR environment. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
The grant entitled 'Dynamics Explorer 2 - continued FPI and NACS instrument data analysis and associated scientific activity at the University of Michigan' is a continuation of a grant that began with instrument development for the Dynamics Explorer 2 (DE 2) satellite. Over the years, many publications and presentations at scientific meetings have occurred under the aegis of this grant. This present report details the progress that has been made in the final three years of the grant. In these last 4 years of the grant 26 papers have been published or are in press and about 10 more are in preparation or have been submitted. A large number of presentations have been made in the same time span: 36 are listed in Appendix 2. Evidence of the high educational utility of this research is indicated by the list of Ph. D. and M. S. theses that have been completed in the last 3 years that have involved work connected with NAG5-465. The structure of this report is as follows: a brief synopsis of the aims of the grant NAG5-465 is given in the next section; then there is a summary of the scientific accomplishments that have occurred over the grant period; last, we make some brief concluding remarks. Reprints of articles that have recently appeared in refereed journals are appended to the end of this document.
This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.
Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.
Computer printouts of numerous scientific experiments conducted in mission support for extended lunar exploration
Using newly released data from the Wide-field Infrared Survey Explorer, we report the discovery of rapid infrared variability in three radio-loud narrow-line Seyfert 1 galaxies (NLS1s) selected from the 23 sources in the sample of Yuan et al. J0849+5108 and J0948+0022 clearly show intraday variability, while J1505+0326 has a longer measurable timescale within 180 days. Their variability amplitudes, corrected for measurement errors, are {approx}0.1-0.2 mag. The detection of intraday variability restricts the size of the infrared-emitting region to {approx}10{sup -3} pc, significantly smaller than the scale of the torus but consistent with the base of a jet. The three variable sources are exceptionally radio-loud, have the highest radio brightness temperature among the whole sample, and all show detected {gamma}-ray emission in Fermi/LAT observations. Their spectral energy distributions resemble those of low-energy-peaked blazars, with a synchrotron peak around infrared wavelengths. This result strongly confirms the view that at least some radio-loud NLS1s are blazars with a relativistic jet close to our line of sight. The beamed synchrotron emission from the jet contributes significantly to and probably dominates the spectra in the infrared and even optical bands.
In order to further improve the accuracy of rovibrational IR line lists generated from the “Best Theory +Reliable High-resolution Experiment” (BTRHE) strategy from 0.01-0.05 cm-1, or 300-1500 MHz, to ~10 MHz, we explore the current limits of the Data-Model-Theory synergy by examining the accuracy and consistency of existing data, then propose that “hot” bands in microwave (MW) spectra is the solution we need for future enhancements. The Ames SO2 J=0-20 rovibrational energy levels computed on the semi-empirically refined Ames-2 potential energy surface (PES) are fit to the Effective Hamiltonian (EH) model regularly used in the experimental infrared (IR) analysis for SO2 isotopologues. In the fitted EH(Ames) model, the rotational constants A/B/C and all 5 quartic centrifugal distortion constants display clear, systematic, and consistent patterns along the vibrational state energy or quanta. Such consistent patterns may facilitate the vibrational assignments for MW hot bands and extract more information from high temperature MW spectra. Some EH(Expt) analyses were carried out with the lowest order Coriolis Coupling term, C1. Their constants should not be directly compared with other EH(Expt) and EH(Ames) results. After excluding them, our = EH(Ames)- EH(Expt) analyses for 5 isotopologues (626, 636, 646, 628 and 828) indicates some loss of accuracy and consistency starting from vibrational states as low as 22 or 1000 cm-1. Some EH parameters, e.g. K, may have relative deviations as large as 50-100% and totally lose any recognizable patterns. This simply means that current EH(Expt) models do not have the system-wide consistency we need to further refine the EH(Ames) and Ames rovibrational IR line lists. A large part of such defects are probably inherited from the limited precision of experimental line positions, i.e. 1E-3 ~ 1E-4 cm-1, or 3-30 MHz. This is confirmed in a series of truncation tests using the Ames data. Although the EH(Ames) consistency may help identify unreliable rovibrational EH(Expt) parameters, and make reliable predictions for minor isotopologues and unobserved vibrational bands, we believe only the highly accurate “hot” MW transitions can provide real enhancements for EH(Expt) accuracy and consistency. “Hot” MW spectra should play a more significant role in the future synergy of Data, Model, and Theory in the field of rovibrational IR studies.
Geothermal resource exploration is generally limited to areas with surface expressions of thermal activity (fumaroles and hot springs), or relies on expensive geophysical exploration techniques. The hidden subsurface distribution of geothermal fluids has been identified using a free and publicly available water quality dataset from agricultural and domestic water wells in Surprise Valley, northeastern California. Thermally evolved waters in Surprise Valley have element ratios that vary in response to Ca carbonate and Mg silicate mineral precipitation, and have elevated total dissolved solids (TDS). The arid climate in Surprise Valley leads to surface water evaporation in a closed basin, producing high TDS Na-Cl-CO 3 -SO 4 brines in three ephemeral alkali lakes and in shallow groundwater under elevated soil CO 2 conditions. Evaporated fluids in Surprise Valley follow a chemical divide that leads to Ca carbonate and Mg silicate mineral precipitation. Plots of dissolved element ratios can be used to distinguish groundwater affected by evaporation from water affected by thermal water-rock interaction, however it is challenging to select components for plotting that best illustrate different fluid evolution mechanisms. Here, we use a principal component analysis of centered log-ratio transformed data, coupled with geochemical models of fluid evaporation and thermal mixing pathways, to identify components to plot that distinguish between groundwater samples influenced by evaporation from those influenced by thermal processes. We find that groundwater samples with a thermal signature come from wells that define a coherent, linear geographical distribution that closely matches the location of known and inferred faults. Finally, modification of the general approach employed here provides promise for identifying blind geothermal resources in other locations, by applying low-cost geochemical modeling and statistical techniques to areas where large groundwater quality geochemical datasets are available.
Fleets exploring the possibility of adding electric vehicles (EVs), battery electric vehicles (BEVs), and plug-in hybrid electric vehicles (PHEVs) seek an efficient, data-driven means to justify both expenditures for vehicle and charging infrastructure and the appropriate placement of them to help ensure the cost-effective adoption of these technologies. Exploring data collection and analytic methodologies across different telematics providers offers the opportunity to better understand the strengths, weaknesses, and possibilities for employing different methods of data collection, including smartphone-based telematics and more traditional telematics with hardware installed on a vehicle's onboard diagnostics port. This report presents results of five pilot programs that collected data from the operation of conventional light-duty fleet vehicles to generate recommendations for transitioning these fleet vehicles to EVs, implementing adequate charging infrastructure, and establishing management practices to maximize the benefits of these new fleet technologies.
Here, we explore the physics of an atomic force microscopy (AFM) cantilever tip interacting with a generalized viscoelastic sample containing an arbitrary number of characteristic times, when the cantilever's base is driven with constant velocity toward the sample. This mode of operation, often called static force spectroscopy (SFS), can be harnessed to thoroughly analyze time-dependent viscoelastic information frequently overlooked in experiments. We generalize the solution of previous authors who have studied the standard linear solid model, and offer a solution applicable to any linear viscoelastic model. This generalization is crucial for the prediction of the model's response over wide ranges of time-scale. As a demonstration, successful predictions of harmonic functions (e.g., loss tangent) over a wide frequency range are obtained through analysis of simulated SFS results. In addition, we show that analysis through the generalized solution and previous expressions is no longer valid when the force does not grow linearly in time, so we also deliver an alternate route for extracting the viscoelastic information, which does not rely on the force linearity assumption. Despite the large amount of theoretical content (included for theoretical rigor's sake), the practical user can also benefit from the new procedures offered and the corresponding explanations.
Abstract We present the launch of the Hyper Suprime-Cam Legacy Archive (HSCLA), a public archive of processed, science-ready data from the Hyper Suprime-Cam (HSC). HSC is an optical wide-field imager installed at the prime focus of the Subaru Telescope and has been in operation since 2014. While ∼1/3 of the total observing time of HSC has been used for the Subaru Strategic Program (SSP), the remainder of the time is used for Principal Investigator (PI)-based programs. We have processed the data from these PI-based programs and make the processed, high-quality data available to the community through HSCLA. The current version of HSCLA includes data taken in the first year of science operation, 2014. We provide both individual and coadd images as well as photometric catalogs. The photometric catalog from the coadd is loaded to the database, which offers a fast access to the large catalog. There are other online tools such as an image browser and an image cutout tool and they will be useful for science analyses. The coadd images reach 24–27th magnitudes at 5σ for point sources and cover approximately 580 square degrees in at least one filter with 150 million objects in total. We perform extensive quality assurance tests and verify that the photometric and astrometric quality of the data is good enough for most scientific explorations. However, the data are not without problems and users are referred to the list of known issues before exploiting the data for science. All the data and documentations can be found at the data release site, 〈https://hscla.mtk.nao.ac.jp/〉.
The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on data for roughly 1.9 million residential rooftop solar systems installed through 2019, representing 82% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: Solar adopters generally skew towards higher incomes, though that trend continues to diminish over time. Solar adopter incomes vary considerably and encompass many low-to-moderate income (LMI) households. Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes. Solar adopters differ from the broader U.S. population in terms of a variety of other demographic and socioeconomic measures. State-level comparisons indicate that solar-adopters tend to live in neighborhoods with relatively high non-Hispanic White and Asian populations, and with relatively low Hispanic and Black populations. In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households.
The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 2.8 million residential rooftop solar systems installed through 2021, representing 86% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: -Median solar adopter income was about $\$110$k/year in 2021, compared to a U.S. median of about $\$63$k/year for all households and $\$79$k/year for all owner-occupied households -The degree of income skew varies significantly across all states, but all states exhibit some positive income skew, with median solar-adopter incomes ranging from 131-168% of the respective county-median income for all households -Notwithstanding the fact that solar adopter incomes skew high, a substantial share of adopters could be considered low-to-moderate income (LMI), with 22% of all 2021 adopters earning less than 80% of area median income, and an additional 21% between 80% and 120% of area median income. -Solar-adopter incomes are declining over time, with median incomes dropping from $\$129$k in 2010 to $\$110$k in 2021, as adoption becomes more proportionately distributed across the population and has started to broaden into low- and middle-income states since 2016. -Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes; higher income adopters also consistently install larger systems. -Solar adopters tend to live in Census Tracts not identified as “disadvantaged communities” (using the U.S. Department of Energy’s interim definitions developed March 2022), making up 11% of adopters compared to 18% of U.S. households. -Compared to the broader population, solar adopters tend to: identify as Non-Hispanic White, be primarily English-speaking, have higher education levels, be middle-aged, work in business and finance-related occupations, and live in higher-value homes In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households; requests for analytical support may be submitted through this online form.
The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 3.4 million residential rooftop solar systems installed through 2022, representing 86% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: (1) Median solar adopter income was about $\$117$k/year in 2022, compared to a U.S. median of about $\$69$k/year for all households and $\$86$k/year for all owner-occupied households; (2) The degree of income skew varies significantly across all states, but all exhibit some positive income skew relative to all households in the state, with median solar-adopter incomes ranging from 108-180% of the respective state-median income for all households; (3) Roughly 45% of solar adopters in 2022 had incomes below 120% of their area median income (AMI), a threshold sometimes used to define “low-and-moderate income” (or LMI), while 23% were below 80% of AMI, often used to define “low-income”; (4) Solar adoption continues to shift toward less affluent households, with the median current income of solar adopters dropping from $\$140$k for households that installed systems in 2010 to $\$117$k in 2022; (5) PV systems installed in 2022 by households earning less than $50k had a median size of 6.1 kW, 34% were third-party owned, and 5% included battery storage, compared to corresponding values of 7.6%, 17%, and 15% for households earning more than 200 dollars k; and (6) Compared to all households in their respective state, solar adopters tend to be negligibly more rural; have higher home values; and are more likely to be college educated, identify as non-Hispanic white, live outside a disadvantaged community (DAC), be middle-aged, work in a business or financial occupation, and own a single-family home In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households.
The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on address-level data for roughly 4.1 million residential rooftop solar systems installed through 2023, representing 87% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: -The median income of households that installed solar in 2023 was about $\$$115k/year, compared to a U.S. median of $\$$75k/year for all households and $\$$94k/year for all U.S. owner-occupied households. -Compared to owner-occupied households in the same state, 2023 solar-adopter incomes were 7% higher in the median case, and in 10 states, median solar-adopter incomes were below the corresponding median income for all owner-occupied households. -Roughly 49% of solar adopters in 2023 had incomes below 120% of their area median income (AMI), a threshold sometimes used to define “low-and-moderate income” (or LMI), while 26% were below 80% of AMI, often used to define “low-income”. -Solar adoption continues to shift toward less affluent households over time, with the median present-day income of solar adopters dropping from $\$$141k for households that installed systems in 2010 to $\$$115k in 2023. -PV systems installed in 2023 by households earning less than $\$$50k had a median size of 6.4 kW, 33% were third-party owned, and 6% included battery storage, compared to corresponding values of 8.0 kW, 18%, and 14% for households earning more than $\$$200k. -Compared to all households in their respective state, solar adopters in 2023 were slightly more likely to be college educated and to live in rural areas; had higher home values; and were more likely to live outside a disadvantaged community (DAC), be middle-aged, identify as non-Hispanic white, work in a business or financial occupation, and own a single-family home. In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households; requests for analytical support may be submitted through this online form.