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437 records · Page 25

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DOE OSTI2020

Socioeconomic Effects of Pension Spending: Evidence from Spain

The purpose of this study is to provide broader understanding of the significant role that the pension system has in the Spanish economy by estimating the sectoral production, employment and income sustained by pensioners' consumption. Based on input-output tables by the World Input-Output Database and consumption data from the Household Budget Survey by the Spanish Statistical Office, a demoeconomic model is applied to quantify the direct impacts, indirect impacts from interindustry links and induced impacts from income-consumption connections over a nine-year period (2006–2014). Then, the factors driving the evolution of total output, employment and value added during such period have been examined by using structural decomposition analysis. The growing participation of consumption by pensioner households in final demand had proven crucial during the 2008 crisis to alleviate the negative trend in production and employment derived from the collapse in consumption suffered by the rest of households. Determining the underlying factors driving changes in both employment and income during the 2008 crisis can be of interest in political decision-making on the sustainability of the Spanish pension system. The results of estimating both the employment and income supported by pensioners' consumption reveal the significant stabilizing effect of the public spending on pensions, particularly during the 2008 crisis. The current Spanish approach of attaining the pension system sustainability by merely reducing social protection costs ignores the adverse consequences of a lower pensioners' demand. This paper addresses an alternative view in which pension spending is not considered a burden on economic growth but rather a means of improving the level of production and employment.

employment↗

DOE OSTI2021

Evolution of the SARS-CoV -2 proteome in three dimensions (3D) during the first 6 months of the COVID -19 pandemic

Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein–protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Here detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein–protein and protein–nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Development of an Unbiased Future Solar Dataset for Solar Resource Adequacy Research Over CONUS

A high-resolution, long-term solar dataset is essential for capturing the variability of solar energy resources and informing strategies to ensure grid reliability and resilience in systems with high levels of solar energy integration. This study focuses on generating unbiased, high-resolution projections of solar irradiance through a statistical downscaling framework, using Earth system model (ESM) simulations obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). The National Solar Radiation Database (NSRDB) is used to calibrate statistical downscaling models. The newly developed dataset provides solar irradiance, surface air temperature, and surface wind speed at 4-km and hourly resolutions across the contiguous United States (CONUS), based on two future scenarios (RCP4.5 and RCP8.5). This study outlines key steps in developing the high-resolution future solar dataset, including (1) regridding ESM data to a common 20-km resolution grid, (2) correcting ESM biases using the NSRDB, and (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar projections. Preliminary results indicate that downscaled projections (4-km) captured reasonable spatial patterns when compared to observations across CONUS for four variables. On average across all pixels, 4-km daily-total GHI and DNI projections showed normalized bias (nBias) less than 1% and 6% for GHI and DNI against NSRDB, respectively (nBias less than 1% and 5% for daily-average surface air temperature and surface wind speed). In terms of long-term trend for GHI and DNI, there was no strong increasing or decreasing trend (when compared to surface air temperature), but it showed a very weak decreasing trend.

14 SOLAR ENERGY↗

DOE OSTI2026

GridCoPilot for Thermal Events: An LLM-Based Platform for Power Grid Reliability Analysis

Large Language Models show promise for translating natural language into database queries, but deploying such systems in safety-critical domains requires high reliability. We present an application of GridCoPilot to thermal event analysis (heatwaves and coldwaves) that affect power grid reliability. Our approach uses a LangChain SQL Agent to translate natural language queries into auditable SQL statements, with deterministic visualization routines that parse the structured query results. We introduce structural framing as a design principle, we integrate a NERC-region-level event library with county-level meteorology and decompose the combined data into three relational tables (event metadata, county-level event details, and a county-to-NERC subregion mapping), using prompt-guided joins to direct the model toward correct multi-table queries. For two core analytical patterns (identifying worst events by region and by region-year), the system achieved 100% SQL accuracy across all 16 NERC subregions and both event types (64 queries total). These results validate the approach for target use cases, though performance on diverse natural language formulations requires further investigation. We discuss design trade-offs, failure modes including JSON output truncation, and pathways for extending this approach to other hazard domains.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Finding quadruply imaged quasars with machine learning – I. Methods

ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.

79 ASTRONOMY AND ASTROPHYSICS↗