Simulation parameters [13]. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Displacement of soil particles in x direction. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Soil particle simulation model. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Multi-factor experimental results. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Frequency-displacement curve. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Bonding bonds between soil particles. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Test factor coding. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Displacement of soil particles in y direction. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

Frame structure and key dimensions. by Xueting Ma (390540)

“…The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. …”

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. …”

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. …”