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. حسب Cristina Dolciotti (16317330)

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

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

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

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

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

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

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

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Flow diagram. حسب Cristina Dolciotti (16317330)

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Predicted confidence scores and 95% confidence intervals for the range of observed knowledge scores for barrier scores of 12, 23 and 36. حسب Lisa Thiele (6468056)

The top 20 candidate drugs demonstrating the most significant sensitivity differences between high- and low-GSRS subjects. حسب Yu Zhang (12946)

الموضوعات:

The drug sensitivity predicted by oncopredict in the TCGA-KIRC cohort. حسب Yu Zhang (12946)

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Network pharmacology analysis predicted the targets of NS on atherosclerosis. حسب Yihua Wang (491766)

Fig 6 - حسب Teagan McMahon (20108036)

Prediction formulas for fitting different indicators. حسب Yi Wang (32470)

الموضوعات:

ROC Results Predicted for Out of the Sample. حسب Fangyi Yang (18867853)

"…The model estimation results indicate that passenger patience significantly influences drop-off decisions. All models—static, dynamic, and Cox proportional hazards—achieved prediction accuracies exceeding 70%, with the dynamic model outperforming others when ample sample data is available. …"

SARIMA predicts season components. حسب Xiangjuan Liu (618000)

"…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. …"

Cohesion characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. حسب Muxi Chen (9294270)

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