Mann-Kendall test for the mean temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Variation curve of the extreme temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Fluctuation trend of the mean temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Variation curve of the mean temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

FFMQ scores of two groups at three time points. by Xiaoli Liu (165371)

“…</p><p>Results</p><p><i><i>Linear mixed model</i></i> analysis showed significant group, time, and group-time interaction effects on SPS-6 scores (<i><i>P</i></i> < 0.05). …”

SPS-6 scores of two groups at three time points. by Xiaoli Liu (165371)

“…</p><p>Results</p><p><i><i>Linear mixed model</i></i> analysis showed significant group, time, and group-time interaction effects on SPS-6 scores (<i><i>P</i></i> < 0.05). …”

Mindfulness-based stress reduction interventions. by Xiaoli Liu (165371)

“…</p><p>Results</p><p><i><i>Linear mixed model</i></i> analysis showed significant group, time, and group-time interaction effects on SPS-6 scores (<i><i>P</i></i> < 0.05). …”

Inclusion and exclusion criteria. by Xiaoli Liu (165371)

“…</p><p>Results</p><p><i><i>Linear mixed model</i></i> analysis showed significant group, time, and group-time interaction effects on SPS-6 scores (<i><i>P</i></i> < 0.05). …”

Dataset. by James M. Smoliga (9074225)

“…Similarly, Swift’s attendance did not result in a significant increase in the Chiefs’ likelihood of winning. …”

Statistical Analysis Code. by James M. Smoliga (9074225)

“…Similarly, Swift’s attendance did not result in a significant increase in the Chiefs’ likelihood of winning. …”

Descriptive statistics of variables. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Moran’s I Index from 2009 to 2020. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Direct, indirect and total effects. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Double threshold effect. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Research frame diagram. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Threshold characteristics estimation results. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Regression results based on SDM. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Threshold regression parameter results. by Li Yan (107001)

“…Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. …”

Odds of meeting muscle dysmorphia clinical cut-off (≥ 40 on MDDI) by use of muscle-building supplements. by Kyle T. Ganson (9773007)

Input-output table forecasting. by Jian Jin (351142)

“…<div><p>With the continuous growth of China’s economy, marine economy plays an increasingly important role in the national economy. …”