Empirical model establishment process. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Model prediction error trend chart. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Basic physical parameters of red clay. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

BP neural network structure diagram. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Structure diagram of GBDT model. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Model prediction error analysis index. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Fitting curve parameter table. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Model prediction error analysis. by Hongqi Wang (2208238)

“…We systematically investigate the impact of water content, dry density, and freeze-thaw cycles (with a freezing temperature set at -10°C) on the thermal conductivity of stabilized soil, a crucial parameter for analyzing soil temperature fields that is influenced by numerous factors. …”

Supplementary file 1_Water stress reduces cellulose deposition in the cell wall and increases wax content, resulting in decreased fiber quality.docx by Yongchao Han (10797698)

“…Compared with WW irrigation, the rate decreased by 23.62% and 30.82% respectively. WD treatment significantly inhibited the expression of the genes encoding sucrose synthase GhSusy and cellulose synthase GhCesA in cotton fibers. …”

Supplementary file 2_Water stress reduces cellulose deposition in the cell wall and increases wax content, resulting in decreased fiber quality.xlsx by Yongchao Han (10797698)

“…Compared with WW irrigation, the rate decreased by 23.62% and 30.82% respectively. WD treatment significantly inhibited the expression of the genes encoding sucrose synthase GhSusy and cellulose synthase GhCesA in cotton fibers. …”

The normal force between two particles with De = 6.30 mm for different aspect ratios and sphericities: (a) hertz normal contact force; (b) normal viscous contact force. by Yongfeng Zhu (7361045)

The tangential force between two particles with different aspect ratios and sphericities for De = 6.30 mm: (a) elastic tangential contact force; (b) viscous tangential contact forc... by Yongfeng Zhu (7361045)

Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model by Diego E. Galvez-Aranda (9436672)

“…In this study, we present a hybrid Physics-Assisted Machine Learning (PAML) model that integrates Deep Learning (DL) techniques with the classical Discrete Element Method (DEM) to simulate slurry drying during a lithium-ion battery electrode manufacturing process. …”

Covid-19 timeline in the UK: A comprehensive Covid-19 timeline for the United Kingdom from March 2020 to March 2021 detailing national and local restrictions and national lockdowns... by Katy Andrews (22311170)

Overview of selected datasets. by Tejaswini Baral (14320736)

“…In this study, we analyzed publicly available 16S amplicon sequencing datasets from four geographical locations using a single workflow.…”

Effect of sample size on reproducibility. by Jan Willem Koten (17743224)

S1 Data - by Jan Willem Koten (17743224)

“…Moreover, it is unclear how within-subject time course reliability limits the robust detection of connectivity on the group level. We estimated connectivity from a working memory task. …”

Connectivity statistics. by Jan Willem Koten (17743224)

“…Moreover, it is unclear how within-subject time course reliability limits the robust detection of connectivity on the group level. We estimated connectivity from a working memory task. …”

Transcriptome and Metabolome Based Mechanisms Revealing the Accumulation and Transformation of Sugars and Fats in Pinus armandii Seed Kernels during the Harvesting Period by Nan Li (155066)

“…The results revealed that during the maturation of P. armandii seed kernels, there was a significant increase in the width, thickness, and weight of the seed kernels, as well as a significant accumulation of sucrose, soluble sugars, proteins, starch, flavonoids, and polyphenols and a significant decrease in lipid content. …”

Flowchart of the study protocol. by Min Wang (21070)

“…Moreover, contrasting with a decrease in the control group, TC group demonstrated significance increased theta oscillatory power in C3, C4, F4, P3, T7, and T8, and a significant negative correlations were observed between state anxiety and F4-θ (r = -0.31, p = 0.04), T7-θ (r = -0.43, p = 0.01), and T8-θ (r = -0.30, p = 0.05).…”