Test instrument. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model establishment process. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

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

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting curve parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

<i>SLC2A2</i> is associated with IGF1R pathways differentiation. by Yejin Kim (740789)

<i>SLC2A2</i> is essential for liver differentiation in developing vertebrates by Yejin Kim (740789)

Baseline analysis, nominal factors. by Hiroaki Yoshikawa (4615801)

Baseline analysis, continuous factors. by Hiroaki Yoshikawa (4615801)

Results of redundancy analysis. by Bianhua Zhang (22430652)

“…<div><p>In order to clarify the effects of long-term coal gangue(CG) dump on the surrounding soil bacterial community structure, we selected the CG dump formed during the mining of Tunlan coal mine in Gujiao city, Shanxi province in China as the study area to conduct a comprehensive study, the experimental design included six distinct zones: control soil area with no impaction (NC), undisturbed control sediment area (NL), atmospheric dry and wet deposition area (MC), upstream (MLS), midstream (MLZ) and downstream (MLX) in the leachate flow area (LFA), Using high-throughput sequencing technology and related software analysis, we obtained the following key findings: The heavy metal contents of Cr and Cd were different significantly in MC and NC (p < 0.05),Cr (90.18 mg·kg-1) in MC was higher than that in NC (65.29 mg·kg-1) (p < 0.05), while Cd (0.09 mg·kg-1) was lower than that in NC (0.14 mg·kg-1) (p < 0.05), and there was no significant differences in Cu, Zn, As and Pb between MC and NC (p > 0.05). …”

Flow chart of animal experiment. by Shudan Liu (7049483)

“…The changes of SCFAs in intestinal microbial metabolites of rats after PMSC intervention were analyzed: caproic acid level was markedly increased, butyric acid showed a decreased trend. Notably, we found a closed and complicated potential correlation among differential microbiota, inflammatory factors and hormones after PMSCs intervention. …”

Chance of infection with <i>Schistosoma haematobium.</i> by Lydia Trippler (10136316)

Characteristics of households participating in the SchistoBreak study in Pemba, Tanzania. by Lydia Trippler (10136316)

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

The hourly temperatures in the phytotron. by Lifeng Guo (17320116)

“…After 10 days of exposure to LTS, the pollen viability decreased most significantly at the heading stage by 44.67%, followed by the booting and the tillering stages. …”

Original data2. by Lan Zeng (638254)

“…We investigated the role of <i>RP11-502I4.3</i> in DR by examining its regulation of vascular endothelial growth factor (VEGF). …”