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

Subjects:

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

Subjects:

Consolidation of training-induced changes in unipedal stance: 95% Area, Y Sway Amplitude and LFS. by Cristina Dolciotti (16317330)

Subjects:

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

Subjects:

Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreas... by Eric Wang (30587)

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

“…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. …”

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

Subjects:

Percentage of exhale flux penetrating through the mask fabric for different values of <i>c</i>_<i>k</i>; a–c and d–f decreasing porosity for outward and inward protection model respectively; <i>k</i>_<i>L</i> is head loss coefficient for inward protection model. by Akshay Anand (118579)

Class-wise performance on the test set. by Muhammad Zubair (728141)

Subjects:

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

“…The stress-deformation model of the single uncoupled joint (g-λ model with λ ≥ 1) is employed to depict the nonlinearity of uncoupled joints, with a greater value of the parameter λ signifying a lower degree of non-linearity in the joint model curve. …”

Confusion Metrics: Presenting classification results on the test set. by Muhammad Zubair (728141)

Subjects:

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. …”

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

Comparison of the Classification performance of the proposed model calculated on the test set with state-of-the-art Approaches. by Muhammad Zubair (728141)

Subjects:

Results of Analysis of Co-variance (ANCOVA) for change in parameter predicted by treatment group with pre-treatment value as a covariate. * p < 0.05; ** p < 0.01. by Lijuan Liu (136685)

Subjects:

The MAE value of the model under raw data. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …”

Three error values under raw data. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …”