TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

TITAN decreaser macroinvertebrate heatmap Heatmap of sensitive (i.e., <i>z-</i>) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of numbe... by Brent J. Bellinger (21156150)

Displacement cloud maps. by Maogang Tian (21485116)

Explanation of condition nodes and decision nodes in the SCM, ordered by decreasing centrality. by Stella D. Juventia (19961247)

TITAN increaser macroinvertebrate heatmap Heatmap of tolerant (i.e., <i>z</i>+) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of number... by Brent J. Bellinger (21156150)

Displacement cloud map (geology). by Hui Cheng (195456)

TITAN thresholds and percentile estimates for benthic macroinvertebrate and diatom communities deemed to be sensitive decreasers or tolerant increasers. The thresholds represent the largest fsum <i>z</i> value in the main data analysis run (i.e., the median), whereas the 5^th and 95^th percentile change points are determined from 500 bootstrap replicate runs. Sensitive taxa change points derived from decreaser sum <i>z</i>- relative to increasing impervious cover (I.C.; %), specific conductivity (SpCond; μS cm^-1), total nitrogen (TN; μg L^-1), and total phosphorus (TP; μg L^-1).... by Brent J. Bellinger (21156150)

“…Sensitive taxa change points derived from decreaser sum <i>z</i>- relative to increasing impervious cover (I.C.; %), specific conductivity (SpCond; μS cm^-1), total nitrogen (TN; μg L^-1), and total phosphorus (TP; μg L^-1). …”

Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using Eq 11, shown in decreasing order of influence. Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown. by Harry Saxton (19165441)

“…Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown.…”

Calculation of peak force and impulse values from trial with overlap. by Leena Inkilä (20614460)

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Changes in blood pressure, HbA1c, blood lipid and liver function-related indicator values compared with baseline values in all patients over time. by Jia Song (495396)

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Calculation of peak force and impulse values from force plate data. by Leena Inkilä (20614460)

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HDECO: A method for Decreasing energy and cost by using virtual machine migration by considering hybrid parameters by Arash GhorbanniaDelavar (22563696)

“…<h2>Summary</h2><p dir="ltr">This research introduces <b>HDECO</b> (Hybrid Decreasing Energy and Cost Optimization) — a method designed to reduce both energy consumption and execution cost in cloud datacenters through intelligent virtual machine migration. …”

Changes in inflammatory cytokine levels compared with baseline values in all patients over time. by Jia Song (495396)

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Point cloud fusion instance effect. by Hongliang Zou (20707270)

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Changes in the UACR and urinary β2-MG levels compared with baseline values over time. by Jia Song (495396)

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Changes in the eGFRs and serum potassium levels compared with values at the end of 1 month of treatment in all patients over time. by Jia Song (495396)

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Changes in the eGFRs and serum potassium levels compared with baseline values in all patients over time. by Jia Song (495396)

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Results obtained from different regression models along with their respective AIC values. by Farzana Afroz (20700794)

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Box plots showing baseline prevalence and mean egg count at pre-MDA1 in districts that were responders compared to those that were persistent hotspots of decreasing praziquantel ef... by Takafira Mduluza (39754)

Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 1. by Hongliang Zou (20707270)

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