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

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

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Consolidation of training-induced changes in unipedal stance: 95% Area, Y Sway Amplitude and LFS. 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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Participant demographics. * P-values were determined using Fisher-Freeman-Halton test when contingency tables are larger than 2 × 2. by Elie Mulhem (22537863)

The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…Each sample is a simulation of the entire assembly process for a scenario. Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …”

Theta (Θ) vs Phi (Φ) joint contact force (JCF) vector path of increased and decreased strength conditions in comparison to default condition (100%). by Jay R. Patel (11600758)

Scheme of g-λ model with larger values λ. by Zhanfeng Fan (20390992)

“…And if the value of λ assumes larger values, the distortion in the shape of the transmitted wave is associated with the plastic deformation in the uncoupled rock mass. …”

Mean ± SD of the different parameters relative to bipedal stance on soft support with the eyes open recorded in the Chewing and Hand Grip groups. by Cristina Dolciotti (16317330)

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Time course of Path Length, LFS and Velocity during postural training. 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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Relation between training-related changes in unipedal and bipedal stance. 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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Correlations between unipedal and bipedal stance parameters recorded at the beginning of the training and of the control session. by Cristina Dolciotti (16317330)

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Flow diagram. by Cristina Dolciotti (16317330)

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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. …”

A Locally Linear Dynamic Strategy for Manifold Learning. by Weifan Wang (4669081)

“…For 10-30% noise, where the Hebbian network employs a local linear transform, learning selectively increases signal direction alignment (blue) while simultaneously decreasing noise direction alignment (orange). …”

(A) Network map of the top ten anti-AD core targets and the top 30 KEGG pathways. The color of the pathway nodes changed from pink to light pink as the DC value decreased. (B) 19 core pathways with DC values (ranked by DC>  average value of (4.933)). by Shakeel Ahmad Khan (13202394)

“…The color of the pathway nodes changed from pink to light pink as the DC value decreased. (B) 19 core pathways with DC values (ranked by DC>  average value of (4.933)).…”

Table of values for <i>p.</i> by Long Di (9977453)

“…Furthermore, a probabilistic obstacle motion prediction framework is established through motion pattern analysis to actively optimize the robot’s motion strategy and reduce tracking errors. Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”