Two-tailed Spearman correlation analysis results between different phenotypic data tested above and lectin microarray results. by Yiying Zhang (198443)

Design of the D-trial. by Torsten Schober (20485754)

“…Empirical models for the relationships between the investigated plant traits and PD/DVP were created using linear regression analysis preceded by a lack-of-fit test. An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Estimated mean values for light interception. by Torsten Schober (20485754)

“…Empirical models for the relationships between the investigated plant traits and PD/DVP were created using linear regression analysis preceded by a lack-of-fit test. An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Raw data V-trial. by Torsten Schober (20485754)

“…Empirical models for the relationships between the investigated plant traits and PD/DVP were created using linear regression analysis preceded by a lack-of-fit test. An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Raw data D-trial. by Torsten Schober (20485754)

“…Empirical models for the relationships between the investigated plant traits and PD/DVP were created using linear regression analysis preceded by a lack-of-fit test. An increase in PD led to a linear decrease in inflorescence yield per plant (<i>p</i> = 0.02), whereas a positive linear relationship was found for inflorescence yield (<i>p</i> = 0.0001) and CBD yield (<i>p</i> = 0.0002) per m². …”

Detailed table of two-tailed Pearson correlation analysis results between different phenotypic data tested above and lectin microarray results. by Yiying Zhang (198443)

Detailed information of the observation datasets. by Weidong Ji (129916)

“…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”

General technical specification for GW154/6700. by Weidong Ji (129916)

“…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”

Statistical summaries. by Magdalena Kunat-Budzyńska (20565222)

“…In this study, it was shown for the first time that honeybees kept in apiaries have higher levels and activity of the studied parameters than honeybees kept in the laboratory. The greatest difference was noted in the case of PO activity in 28-day-old infected honeybees in May, when the activity was 32.3 times higher in honeybees kept in the apiary than in the laboratory, suggesting that environmental conditions have a significant influence on the immune response of honeybees. …”

Image 3_Significance of LRFN4 in prognosis and tumor microenvironment of lung adenocarcinoma.tif by Nana Wang (533812)

Image 2_Significance of LRFN4 in prognosis and tumor microenvironment of lung adenocarcinoma.tif by Nana Wang (533812)

Image 1_Significance of LRFN4 in prognosis and tumor microenvironment of lung adenocarcinoma.jpeg by Nana Wang (533812)

Average Vehicle Arrival Time on Weekends for a Month. by Zhendong Sun (4723221)

Average Vehicle Arrival Time on Weekdays for a Month. by Zhendong Sun (4723221)

Functional principle of a fiber Bragg grating. by Guoli Li (291427)

“…<div><p>To solve the problems of low detection efficiency and inability to adapt to distributed measurement in traditional detection methods, a water temperature field detection system based on a fiber Bragg grating string was designed. …”

Split structural models for belief. by Abigail A. Lee (19935335)

“…More conservative political views and higher income correlated with decreased hesitancy. No other latent variables or covariates had a significant effect on vaccine hesitancy. …”

Loss of RBM-26 function causes a reduction in the density of mitochondria in the PLM axon. by Tamjid A. Chowdhury (14149846)

Risk Illustration. by Iftikhar Ali Ch (20783753)

“…However, patients prescribed P2Y12 antagonists (HR = 0.285, 95% CI: 0.135–0.603, p < 0.001), those with a GFR >  60 ml/min (HR = 0.314, 95% CI: 0.158–0.624, p < 0.001), and those with a higher ejection fraction (HR = 0.962, 95% CI: 0.939–0.986, p = 0.002) were less likely to experience SCD following CABG. …”

Predictors of SCD. by Iftikhar Ali Ch (20783753)

“…However, patients prescribed P2Y12 antagonists (HR = 0.285, 95% CI: 0.135–0.603, p < 0.001), those with a GFR >  60 ml/min (HR = 0.314, 95% CI: 0.158–0.624, p < 0.001), and those with a higher ejection fraction (HR = 0.962, 95% CI: 0.939–0.986, p = 0.002) were less likely to experience SCD following CABG. …”

Clinical outcomes. by Iftikhar Ali Ch (20783753)

“…However, patients prescribed P2Y12 antagonists (HR = 0.285, 95% CI: 0.135–0.603, p < 0.001), those with a GFR >  60 ml/min (HR = 0.314, 95% CI: 0.158–0.624, p < 0.001), and those with a higher ejection fraction (HR = 0.962, 95% CI: 0.939–0.986, p = 0.002) were less likely to experience SCD following CABG. …”