Participant demographics. * P-values were determined using Fisher-Freeman-Halton test when contingency tables are larger than 2 × 2. by Elie Mulhem (22537863)

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Mean ± SD values of unipedal stance parameters evaluated at the different time points of the postural training session. by Cristina Dolciotti (16317330)

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Primary and secondary efficacy end points (intention-to-treat population). by Benjamin R. Lewis (22279166)

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Cephalometric measurement points. by Lijuan Liu (136685)

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Larger example of stimuli with cue relevant for Task 3. by Marcel F. Hinss (14675457)

Selection of soil particle sampling points. by Xueting Ma (390540)

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(A) Auxiliary marking points to ensure complete and accurate seating of the prosthesis. by Long Li (6555)

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Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Biases in larger populations. by Sander W. Keemink (21253563)

“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. Bias decreases with increasing neurons in the population. …”

The variance explained in sediment transport increased with sample size, but sampling beyond roughly 500 data points provided diminishing improvements. by Andrew Kulmatiski (833256)

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Sampling point (Source: by Yonggang Huang (223155)

Guidelines and policy changes for different alert levels in Gauteng. The time intervals are separated by points of inflection identified in Edholm <i>et al</i>. [10]; these points separate time periods where the rate of cumulative cases was increasing from periods when the rate of cumulative cases was decreasing [10], Fig 1].... by Folashade B. Agusto (3663010)

“…[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0325619#pone.0325619.ref010" target="_blank">10</a>]; these points separate time periods where the rate of cumulative cases was increasing from periods when the rate of cumulative cases was decreasing [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0325619#pone.0325619.ref010" target="_blank">10</a>], Fig 1]. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Time course of Path Length, LFS and Velocity during postural training. by Cristina Dolciotti (16317330)

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Time course of 95% Area, X and Y Amplitude Sway during postural training. by Cristina Dolciotti (16317330)

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Between-group differences in 95% Area, Y Sway Amplitude and LFS during postural training. by Cristina Dolciotti (16317330)

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Relation between training-related changes in unipedal and bipedal stance. by Cristina Dolciotti (16317330)

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Relation of the changes in Y Sway Amplitude and LFS induced by postural training with those in 95% Area. by Cristina Dolciotti (16317330)

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Consolidation of training-induced changes in unipedal stance: Path Length, X Sway Amplitude and Velocity. by Cristina Dolciotti (16317330)

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Training-induced changes in bipedal stance. by Cristina Dolciotti (16317330)

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