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The linear mixed models analysis revealed statistically significant differences in both active and passive ROM between the 8 times of measurement (p < 0.001).
The linear mixed models analysis revealed that there were statistically significant differences in KOOS scoring between the 7 times of measurement.
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Our model analysis revealed the same basic trends as found in the experiments.
Structural equation modelling analysis revealed that control orientation of sport motivation regulates desired benefits of socialization and diversion.
Mixed model analysis revealed that respiration rate and minute ventilation are significantly associated with workload levels and evaluator scores controlling for "vanilla baseline" condition.
A generalized linear mixed model analysis revealed that in addition to forest type, proximity to seed sources also explained seedling densities.
The results of the path model analysis revealed acceptable overall fit indices: a Chi square of 24.60 with degrees of freedom of 1 (p < .001), GFI of.99, CFI of.99, NFI of.99, IFI of.99, and SRMR of.025.
General linear model analysis revealed that the radiation treatments significantly affected both quercetin content and epidermal flavonol levels in broccoli flower buds during storage, with the highest levels observed after a combination of visible light and UV-B irradiation treatment.
Model analysis revealed that a two-step molecular elimination pathway exclusively led to the fuel decomposition under pyrolysis conditions, while in the premixed flame the fuel was consumed mainly by hydrogen abstractions from the secondary carbon atoms.
The model analysis revealed that PQI increases the total frequency of sphere generation by five times, decreases the rate constant of coalescence by an order of magnitude, and decreases the linear growth rate to less than half.
General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com