Model generalisation validation 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. …”

Empirical 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. …”

Fitting curve parameters. 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. …”

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. …”

Schematic overview of the study workflow. by Yu Zhang (12946)

Mass spectrometric analyses for crystallins. by Akosua K. Boateng (21672931)

“…We also determined the changes in crystallin proteomic profiles in water-soluble, water-insoluble-urea-soluble, and water-insoluble-urea-insoluble fractions. …”

RNA-seq data showing top 15 downregulated genes. by Akosua K. Boateng (21672931)

“…We also determined the changes in crystallin proteomic profiles in water-soluble, water-insoluble-urea-soluble, and water-insoluble-urea-insoluble fractions. …”

RNA-seq data showing top 15 upregulated genes. by Akosua K. Boateng (21672931)

“…We also determined the changes in crystallin proteomic profiles in water-soluble, water-insoluble-urea-soluble, and water-insoluble-urea-insoluble fractions. …”

Overview of selected datasets. by Tejaswini Baral (14320736)

“…</p><p>Results</p><p>Our analysis revealed statistically significant alpha diversity differences in West Africa with decreased microbial diversity in pulmonary tuberculosis patients after two months of antitubercular therapy. …”

The sequences of si-RNAs used in this study. by Changji Li (4806930)

“…Our study observed a significant increase in CRNN expression in cSCC samples compared to healthy skin. …”

Survey sample distribution. by Yin Liu (50073)

“…The overall efficiency effect on ‘low→high ‘initial endowment farmers shows a decreasing trend. Therefore, in order to ensure the effectiveness of financial precision assistance, we should promote the microcredit policy of the poverty-alleviated population from the aspects of policy stability and implementation precision.…”

Primary antibodies used for immunoblot analysis. by Iratxe Zuazo-Gaztelu (21071939)

“…Known cancer dependency on IRE1 entails its enzymatic activation of the transcription factor XBP1s and of regulated RNA decay. We discovered surprisingly that some cancer cell lines require IRE1 but not its enzymatic activity. …”