Long COVID prevalence decreases with vaccine uptake in the U.S. by Manlio De Domenico (626037)

“…<p>(A) Prevalence in U.S. states and the U.S. exhibits a decreasing trend with respect to vaccine uptake, both in the population vaccinated with at least one dose (top) and two doses (bottom), with the largest gap between 100% vaccinated and 100% unvaccinated scenarios observed in the reference population of adults who had COVID-19. …”

Distribution of responses to vital signs. by Lisa Thiele (6468056)

“…A strong negative correlation was present between barriers and confidence. …”

Survey tool development process. by Lisa Thiele (6468056)

“…A strong negative correlation was present between barriers and confidence. …”

Respondent characteristics. by Lisa Thiele (6468056)

“…A strong negative correlation was present between barriers and confidence. …”

Frequency of tobacco smoking among smokers. by Prasanna Herath (20714794)

“…There was a significantly negative correlation of FVC, FEV1, FEV1/ FVC, and PEF, FEF 25–75% with the duration of smoking, and the Brinkman Index. …”

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

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

Screening process diagram. by Wentao Yang (205781)

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

SHAP waterfall plot. by Wentao Yang (205781)

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

SHAP decision plot. by Wentao Yang (205781)

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

LASSO regression visualization plot. by Wentao Yang (205781)

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

SHAP dependence plots. by Wentao Yang (205781)

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

Tertile stratified subgroup analysis. by Wentao Yang (205781)

“…</p><p>Conclusion</p><p>This first NHANES-based study demonstrates a significant negative correlation between eGDR and frailty, confirming DM’s partial mediating role. …”

Data_GDP/ Ndvi. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Flow chart of the study. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Example of manual identification. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Data_soil. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Data_road. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Excel_ESs and transfer matrix. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Data sources and descriptions. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”

Coupling coordination types. by Qianhong Mao (22305184)

“…The urban, urban fringe, and rural areas were firstly identified in 2010 and 2022 using Deep Neural Network (DNN) based on multi-source geographical data. Then, seven typical ESs were assessed using multiple models, and their interactions were examined through correlation analysis, coupling coordination degree model, and a self-organizing feature mapping network approach. …”