Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

Selection frequencies plotted in decreasing order. by Hannah P. Rowe (7830599)

Wetland classification using the ensemble simulations. by Nic Gedney (19762301)

Innovation, nightlights and a continuous measure of city size. by Saul Estrin (8629173)

SHAP waterfall plot. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP decision plot. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP dependence plots. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

Water Withdrawal and Consumption Trends for Thermoelectric-Power Plants in the Conterminous United States, 2008–2020 by Kenneth D. Skinner (22280459)

Trust levels before and after the COVID-19 pandemic (n = 10,000). by Alain Abi-Rizk (8549358)

Trust change by gender. by Alain Abi-Rizk (8549358)

“…Results indicate that respondnatds from North America and Europe Experienced an increase in trust where as Africa and South America Witnessed a decline in trust. Males exhibited a greater decline in trust than females (p = 0.038), and undergraduate degree holders showed the largest decrease (p = 0.001). …”

Trust change by age group. by Alain Abi-Rizk (8549358)

“…Results indicate that respondnatds from North America and Europe Experienced an increase in trust where as Africa and South America Witnessed a decline in trust. Males exhibited a greater decline in trust than females (p = 0.038), and undergraduate degree holders showed the largest decrease (p = 0.001). …”

Trust change by education level. by Alain Abi-Rizk (8549358)

“…Results indicate that respondnatds from North America and Europe Experienced an increase in trust where as Africa and South America Witnessed a decline in trust. Males exhibited a greater decline in trust than females (p = 0.038), and undergraduate degree holders showed the largest decrease (p = 0.001). …”

Trust change by source of science information. by Alain Abi-Rizk (8549358)

“…Results indicate that respondnatds from North America and Europe Experienced an increase in trust where as Africa and South America Witnessed a decline in trust. Males exhibited a greater decline in trust than females (p = 0.038), and undergraduate degree holders showed the largest decrease (p = 0.001). …”

The raw data used in this study. by Yu Fu (16582)

“…<div><p>Objectives</p><p>Hematological malignancies (HMs) pose a severe threat to human health and contribute substantially to the disease burden in mainland China and Taiwan. …”

The distribution of water footprint levels in China. a, 2005. b, 2010. c, 2015. d, 2022. by Jianqin Ma (20328414)

Intent to decrease use of OTC NSAIDs and NSAID exposure. by Matthew G. Vinson (22593374)

“…<p>(A) Scatter plot of the group effect on intent to decrease use of OTC NSAIDs at pre-intervention and post-intervention. …”

SHAP Tree explainer for (a) Model 1: Presentation and (b) Model 6: Closures. by Nathan C. Hurley (10510304)

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things by Arash GhorbanniaDelavar (22563696)

Primers used in this study. by Hadi Alizadeh (20854757)