Matching estimation results of propensity score. by Yanling Li (269030)

“…<div><p>In the context of increasing population aging and decreasing birth rate, it is of great practical significance to explore the impact of social support on the health of elderly men, which is of great practical significance to smoothly promote the strategy of healthy China and actively implement the strategy of population aging. …”

Sample balance test. by Yanling Li (269030)

“…<div><p>In the context of increasing population aging and decreasing birth rate, it is of great practical significance to explore the impact of social support on the health of elderly men, which is of great practical significance to smoothly promote the strategy of healthy China and actively implement the strategy of population aging. …”

Robustness test results. by Yanling Li (269030)

“…<div><p>In the context of increasing population aging and decreasing birth rate, it is of great practical significance to explore the impact of social support on the health of elderly men, which is of great practical significance to smoothly promote the strategy of healthy China and actively implement the strategy of population aging. …”

Results of baseline regression. by Yanling Li (269030)

“…<div><p>In the context of increasing population aging and decreasing birth rate, it is of great practical significance to explore the impact of social support on the health of elderly men, which is of great practical significance to smoothly promote the strategy of healthy China and actively implement the strategy of population aging. …”

Unmatched kernel density function diagram. by Yanling Li (269030)

“…<div><p>In the context of increasing population aging and decreasing birth rate, it is of great practical significance to explore the impact of social support on the health of elderly men, which is of great practical significance to smoothly promote the strategy of healthy China and actively implement the strategy of population aging. …”

Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

“…On average, meliponiculturists lost 43.4 % of their stingless bee colonies annually, 33.3 % during the rainy season, and 22.0 % during the dry season. We found that colony losses during the rainy season decreased with higher abundance of forested areas and increased with higher abundance of agricultural area around meliponaries. …”

Impact of p53 T253I on DNA damage responsive mRNA expression. by Nathaniel C. Holcomb (22793595)

Stable incorporation of p53 lentiplasmids is detectible and does not impact cellular growth rates. by Nathaniel C. Holcomb (22793595)

S1 File - Climate change and its impact on wheat distribution in semi-arid ecosystems: A case study from the Sultanate of Oman by Khalifa M. Al-Kindi (3719854)

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. …”

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. …”