Brain regions with significantly decreased activations for the Raven task are shown in blue. by Morteza Fattahi (15892028)

Linear covariate analysis of prognostically significant metabolites. Presenting the effect sizes of metabolites that showed significant differences among prognostic groups in ICU-t... by Emir Matpan (21655811)

Univariate linear regression analysis of scales. by Xiangyu Wang (341093)

Multivariate linear regression analysis of scales. by Xiangyu Wang (341093)

Vasopressor titration regimen in the balanced multimodal group. by Žiga Kalamar (22145810)

Subjects:

The schedule of enrollment, interventions and assessments. by Žiga Kalamar (22145810)

Subjects:

DDC for four anticoagulants from 2019 to 2023. by Wei Luo (80175)

“…The trends in the utilization and expenditure of anticoagulants were examined using linear regression analysis.</p><p>Results</p><p>From 2019 to 2023, the DDDs of rivaroxaban demonstrated a significant annual increase in most hospitals (<i>p</i> < 0.05). …”

Modeling the relationship between cancer level in hemolymph and MarBTN in tank eDNA. by Rachael M. Giersch (22331947)

Trends and projection of under-5 mortality in Bangladesh. by Shayla Naznin (13014015)

Subjects:

Visualization of under-5 mortality rate in Bangladesh. by Shayla Naznin (13014015)

Subjects:

Under-5 mortality rate and projections in Bangladesh. by Shayla Naznin (13014015)

Subjects:

Model performance comparison. by Shayla Naznin (13014015)

Subjects:

K-fold cross validation accuracy of different models. by Shayla Naznin (13014015)

Subjects:

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. …”