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

“…</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.…”

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

“…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. …”

Visualizations of three clusters. by Shirong Chen (22127046)

“…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. …”

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

“…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. …”

S1 File - by Xiaojie Feng (6873953)

EvoFuzzy by Hasini Nakulugamuwa-Gamage (17344420)

“…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.…”

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

“…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. …”

TSProm: Deciphering the Genomic Context of Tissue Specificity by Pallavi Surana (8921885)

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

“…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. …”

WGCNA analysis of GSE21374. by Xihao Shen (20347942)

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

“…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. …”

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

“…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. …”

Experimental setup. by Hui Yang (91136)

Depth division situation. by Hui Yang (91136)

The division of CTU in polar regions. by Hui Yang (91136)

Minimal data set definition. by Hui Yang (91136)

CU segmentation process. by Hui Yang (91136)

Division accuracy. by Hui Yang (91136)

Selection strategy process. by Hui Yang (91136)

Division accuracy. by Hui Yang (91136)