Table of step height. by Jianbo Jia (717814)

“…Finally, by making corresponding test blocks, it was found that the peak value of forward resistance when the 10.5°test block crossed the steps of 1mm, 2mm and 3mm height decreased by 19.8%, 25.0% and 13.9% respectively, and the mean value decreased by 30.8%, 27.2% and 24.1% respectively, which was close to the simulation results, and verified the accuracy of the finite element analysis results.…”

The well wall step. by Jianbo Jia (717814)

“…Finally, by making corresponding test blocks, it was found that the peak value of forward resistance when the 10.5°test block crossed the steps of 1mm, 2mm and 3mm height decreased by 19.8%, 25.0% and 13.9% respectively, and the mean value decreased by 30.8%, 27.2% and 24.1% respectively, which was close to the simulation results, and verified the accuracy of the finite element analysis results.…”

Results of multiple logistic regression analysis to identify potential factors associated with the successful reduction of HPC and F+ coliphage in the POU UVC LED effluent. Single... by Hania Zanib (21864477)

State transition dynamics of reputation considering third-order information and multiple reputation values: by Hitoshi Yamamoto (738861)

Different steps of the fabrication process. by Md. Abu Daud (21485629)

Decrease in loss during the training process. by Muhammad Idrees (92773)

Setting of experimental environment and model parameters. by Guozhu Sui (21672798)

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.…”

WT data of set A398E/V and set M1425V/I pooled for better comparison. by Yousra El Ghaleb (22075892)

Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

Basis spread regression results – Soybeans. by Theodoros Skevas (6728168)

“…It uses basis spread data from nearly 5,000 U.S. midwestern corn and soybean elevators spanning from 2012 to 2020, along with natural disaster declarations to represent weather extremes affecting crop transportation. Using a three-step process, it calculates least cost transportation routes to a port, adjusts for weather disruptions, and integrates disaster, transportation cost, and control variables into a fixed effects, panel data model that explains variation in basis spread. …”

Basis spread regression results – Corn. by Theodoros Skevas (6728168)

“…It uses basis spread data from nearly 5,000 U.S. midwestern corn and soybean elevators spanning from 2012 to 2020, along with natural disaster declarations to represent weather extremes affecting crop transportation. Using a three-step process, it calculates least cost transportation routes to a port, adjusts for weather disruptions, and integrates disaster, transportation cost, and control variables into a fixed effects, panel data model that explains variation in basis spread. …”

Minimal data set. by Wei Huang (36889)

Descriptive statistics of the mean %MVC values for each stage. by Brienna Eteson (21416834)

Comparison of WEI values for two scenarios. by Danuta Roman-Liu (8532645)

Distribution of <i>L</i>(<i>p</i>) with an absolute error when a single observation is randomly sampled at each time point, with the smallest observation (blue) and the largest obs... by Hyeontae Jo (20469463)

Class-wise performance on the test set. by Muhammad Zubair (728141)

Impact of changes in competitive strength of direct marketing channels on pharmaceutical pricing and profits. by Yan Wen (416285)

Validation results for the different scenario parameter sets and the resulting KGE_mean value. by Fabian Mitze (21084251)

Prediction (a-d) and infusion deviation (e-f) results under different training sets and test sets. by Leijie Wang (21485157)

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