Renderings of different models generated based on the same prompt. by Tao Geng (32392)

The forest plots of pooled estimates of standardized mean differences of circulating apelin (A), apelin/HDL-c (B), apelin/LDL-c (C), apelin/triglycerides (D), and apelin/total chol... by Hamed Akbari (8119361)

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Summary statistics from linear models with biomarkers. by Margaret Sunitha Selvaraj (13994016)

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Summary statistics from LADA interaction models. by Margaret Sunitha Selvaraj (13994016)

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Summary statistics from interaction models with metabolites. by Margaret Sunitha Selvaraj (13994016)

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Summary statistics from linear models with metabolites. by Margaret Sunitha Selvaraj (13994016)

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Summary statistics from interaction models with biomarkers. by Margaret Sunitha Selvaraj (13994016)

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Forest plot of sex-based differences for distress/stress. by Liang-Tseng Kuo (4861600)

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Forest plot of sex-based differences in sleep problems. by Liang-Tseng Kuo (4861600)

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Variable sources of agar formulations have significant differences in stiffness. by Maria Oorloff (19658113)

Success rate of depth estimation for different signal-to-noise ratios. by Du Siqi (20721422)

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Synaptopathy at IHC-SGN synapses decreases the peak of the CAP significantly, without changes to peak latency and width. by Maral Budak (6680351)

“…Synaptopathy had smaller effects on CAP peak amplitude and latency when it affected only HT fiber synapses compared to affecting all fiber types randomly. …”

Detection results of different models for different defects on NEU-DET. by Zishuo Wang (6790781)

Differences in the motifs inferred using hypothesis testing when using different null models. by Alexis Bénichou (19832361)

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Parallel bonding model. by Qingwen Li (1510687)

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Linear contact model. by Qingwen Li (1510687)

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The comparison of different optimization models. by Jiefei Zhang (8787494)

Comparison of different classification models. by Shuang Hong (17558808)

The MDAE does not vary significantly for different sizes of subsampled graphs. by Niklas Smedemark-Margulies (3539957)

Model test results. by Xiaoyu Wan (644814)

“…It is found that: (1) the scale and vitality of China’s digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China’s digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.…”