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. …”

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. …”

Dynamics of Solid–Liquid Compound Droplets on Cylindrically Concave Superhydrophobic Surfaces by Niju K. Mohammed (22631779)

“…The generation of compound droplets through droplet–particle interactions has garnered significant attention due to its relevance in diagnostics, biomolecule encapsulation, functional coating, and targeted drug delivery. …”

Dynamics of Solid–Liquid Compound Droplets on Cylindrically Concave Superhydrophobic Surfaces by Niju K. Mohammed (22631779)

“…The generation of compound droplets through droplet–particle interactions has garnered significant attention due to its relevance in diagnostics, biomolecule encapsulation, functional coating, and targeted drug delivery. …”

Dynamics of Solid–Liquid Compound Droplets on Cylindrically Concave Superhydrophobic Surfaces by Niju K. Mohammed (22631779)

“…The generation of compound droplets through droplet–particle interactions has garnered significant attention due to its relevance in diagnostics, biomolecule encapsulation, functional coating, and targeted drug delivery. …”

Dynamics of Solid–Liquid Compound Droplets on Cylindrically Concave Superhydrophobic Surfaces by Niju K. Mohammed (22631779)

“…The generation of compound droplets through droplet–particle interactions has garnered significant attention due to its relevance in diagnostics, biomolecule encapsulation, functional coating, and targeted drug delivery. …”

Dynamics of Solid–Liquid Compound Droplets on Cylindrically Concave Superhydrophobic Surfaces by Niju K. Mohammed (22631779)

“…The generation of compound droplets through droplet–particle interactions has garnered significant attention due to its relevance in diagnostics, biomolecule encapsulation, functional coating, and targeted drug delivery. …”

Two magnifications SEM images for the produced SiO₂ nanoparticles. by Nasser A. M. Barakat (11705885)

Subjects:

XRD pattern for the prepared titanium oxide nanofibers. by Nasser A. M. Barakat (11705885)

Subjects: