Patellar 3D-MRI before and after OWHTO in all cases. by Yusuke Fuchioka (22806178)

Comparison of simulation results. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Technical parameters of the shearer. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Simulation-related parameters. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Chain drive specification parameters. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Femoral tensile test data. by Bo Xie (669374)

“…These values are significantly lower than the cable clamp’s breaking tensile strength of 70 kN, with peak values of 57.4 N and 94.1 N, respectively. …”

Group differences in perceptual bias scores. by Daniel E. Lidstone (21567523)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

LEfSe Analysis. by Tejaswini Baral (14320736)

Analysis of cellular proliferation in βA3ΔG91 vs. WT mice lenses using BrdU assay. by Akosua K. Boateng (21672931)

GSRS was positively associated with T cell exhaustion (TEX) and predicted a reduced likelihood of response to immunotherapy. by Yu Zhang (12946)

Fig 1B raw image. by Rachel K. Meade (22216529)

“…From a Ugandan household contact study, we identify significant associations between <i>CTSZ</i> variants and TB disease severity. …”

S1A Fig raw image. by Rachel K. Meade (22216529)

“…From a Ugandan household contact study, we identify significant associations between <i>CTSZ</i> variants and TB disease severity. …”

Structure diagram of ensemble 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. …”

Fitting formula 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. …”

Test plan. 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 surface 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. …”

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. …”