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. …”

Bluetooth beacons with colour coded lanyards. by J Mark Ansermino (13958512)

“…During the baseline period, the time to antimicrobials decreased significantly in Kenya (132 and 58 minutes) at control and intervention sites. …”

Table 1_United States military working dogs from 2019 to 2021: analysis of causes of service discharge and decreased service life.docx by Dakota Discepolo (16804191)

“…ANOVA analysis comparing mean service life resulted in significant differences of mean overall service with main effects of breed (p = 0.0252), outcome (p = 0.0004), service discharge category (p < 0.0001), and subpopulation (p < 0.0001).…”

data & R code_20250504.zip<b>Plant invasion decreases the likelihood of community persistence </b><b>through asymmetric competition</b> by Chengjin Chu (19476862)

The mean scores of epidermis and dermis healing processes in burned rats after 10 and 21 days treatment with silver sulfadiazine 1% (silver), opium 0.05% (OP 0.05%), opium 0.5% (OP... by Omid Mehrpour (9385929)

Causes and consequences of long-term defaunation in multi-species aggregations: Species traits predict freshwater mussel declines and decreases in biogeochemical storage and recycl... by Jonathan Lopez (16993383)

Tandem Imaging of Breath Ethanol and Acetaldehyde Based on Multiwavelength Enzymatic Biofluorometry by Kenta Iitani (4175995)

“…While multi-VOC imaging methods have the potential to facilitate step-by-step metabolic tracking and improve disease screening accuracy, no such system currently exists. …”

Tandem Imaging of Breath Ethanol and Acetaldehyde Based on Multiwavelength Enzymatic Biofluorometry by Kenta Iitani (4175995)

“…While multi-VOC imaging methods have the potential to facilitate step-by-step metabolic tracking and improve disease screening accuracy, no such system currently exists. …”

Tandem Imaging of Breath Ethanol and Acetaldehyde Based on Multiwavelength Enzymatic Biofluorometry by Kenta Iitani (4175995)

“…While multi-VOC imaging methods have the potential to facilitate step-by-step metabolic tracking and improve disease screening accuracy, no such system currently exists. …”

Tandem Imaging of Breath Ethanol and Acetaldehyde Based on Multiwavelength Enzymatic Biofluorometry by Kenta Iitani (4175995)

“…While multi-VOC imaging methods have the potential to facilitate step-by-step metabolic tracking and improve disease screening accuracy, no such system currently exists. …”