Showing 141 - 160 results of 287 for search '(((( elements ree algorithm ) OR ( complement low algorithm ))) OR ( level coding algorithm ))', query time: 0.53s Refine Results
  1. 141

    Integrating drought warning water level with analytical hedging for reservoir water supply operation by Wenhua Wan (8051543)

    Published 2025
    “…</p><p dir="ltr">2. R codes for the HR-based DP algorithm, the processes deriving seasonal DWWL, and the statistical performance of HR with DWWL during typical drought years.…”
  2. 142

    Linear mixed-effect model results. by Shirong Chen (22127046)

    Published 2025
    “…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”
  3. 143

    Visualizations of three clusters. by Shirong Chen (22127046)

    Published 2025
    “…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”
  4. 144

    Summary of three preparatory reading clusters. by Shirong Chen (22127046)

    Published 2025
    “…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”
  5. 145

    Data Sheet 1_Past and future trends in swiss snow cover: multi-decades analysis using the snow observation from space algorithm.docx by Charlotte Poussin (20718146)

    Published 2025
    “…This study addresses this gap by employing two products—the monthly NDSI (Normalized Difference Snow Index) and snow cover products—derived from the Snow Observation from Space (SOfS) algorithm to monitor snow cover dynamics across Switzerland over the past 37 years. …”
  6. 146

    Supplementary file 1_Past and future trends in swiss snow cover: multi-decades analysis using the snow observation from space algorithm.docx by Charlotte Poussin (20718146)

    Published 2025
    “…This study addresses this gap by employing two products—the monthly NDSI (Normalized Difference Snow Index) and snow cover products—derived from the Snow Observation from Space (SOfS) algorithm to monitor snow cover dynamics across Switzerland over the past 37 years. …”
  7. 147
  8. 148

    EvoFuzzy by Hasini Nakulugamuwa-Gamage (17344420)

    Published 2024
    “…The algorithm evolves a population of networks using fuzzy trigonometric differential evolution, with gene expression predictions based on confidence levels applied through a fuzzy logic-based predictor.…”
  9. 149

    TreeMap 2016: A tree-level model of the forests of the conterminous United States circa 2016 by Karin L. Riley (19657882)

    Published 2025
    “…The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB) or to the text and SQL files included in this data publication to produce tree-level maps or to map other plot attributes. The accompanying database files included in this publication also contain attributes regarding the FIA plot CN (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a code for cause of death where applicable. …”
  10. 150
  11. 151

    Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014 by Karin L. Riley (19657882)

    Published 2025
    “…Forest Service’s Forest and Inventory Analysis program (FIA) version 1.7.1 and 2) the landscape target data, which consisted of raster data at 30x30 meter (m) resolution provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE; https://landfire.gov/) FIA plots were imputed to the raster data by the random forests algorithm, providing a tree-level model of all forested areas in the conterminous U.S. …”
  12. 152
  13. 153

    TreeMap 2020 CONUS: A tree-level model of the forests of the conterminous United States circa 2020 by Scott N. Zimmer (20807459)

    Published 2025
    “…The raster of plot identifiers can be linked to the FIA databases available through the FIA DataMart to map hundreds of attributes available there, or to the comma-separated file included in this data publication to access a more limited set of tree-level attributes. The data files included in this publication also contain attributes for each tree in the plots that were assigned, including the FIA plot PLT_CN for the plot on which the tree was measured (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, the corresponding number of trees per acre it represents due to the study design, the status (live or dead), species, diameter, height, actual height (where broken), crown ratio and a code for cause of death where applicable. …”
  14. 154

    TreeMap 2022 CONUS: A tree-level model of the forests of the conterminous United States circa 2022 by Rachel M. Houtman (19658365)

    Published 2025
    “…The raster of plot identifiers can be linked to the FIA databases available through the FIA DataMart to map hundreds of attributes available there, or to the comma-separated file included in this data publication to access a more limited set of tree-level attributes. The data files included in this publication also contain attributes for each tree in the plots that were assigned, including the FIA plot PLT_CN for the plot on which the tree was measured (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, the corresponding number of trees per acre it represents due to the study design, the status (live or dead), species, diameter, height, actual height (where broken), crown ratio and a code for cause of death where applicable. …”
  15. 155
  16. 156
  17. 157
  18. 158
  19. 159
  20. 160