Third. by Cuicui Liu (1496260)

Validation of child labor measures using third-party data, households with all children in school. by Guilherme Lichand (11410286)

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

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …”

Table 1_Higher oxidative balance score is associated with a decreased risk of infertility: an analysis of NHANES 2013–2020.docx by Mingjun Ma (11518765)

DataSheet1_Decreasing viscosity and increasing accessible load by replacing classical diluents with a hydrotrope in liquid–liquid extraction.docx by Asmae El Maangar (19690522)

“…We show that using hydrotropes as a diluent decreases the viscosity of solutions by more than a factor of ten, even under high load by extracted cations. …”

Maternal group B <i>Streptococcus</i> decreases infant length and alters the early-life microbiome: a prospective cohort study by Shanshan Li (114847)

“…This study aimed to explore the effects of maternal vaginal GBS during pregnancy on early infant growth, microbiome, and metabolomics.</p> <p>We recruited and classified 453 pregnant women from southern China into GBS or healthy groups based on GBS vaginal colonization. …”

Intraoperative facial and intraoral photographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

Posttreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

Pretreatment facial, intraoral photographs, and radiographs of the case with mandibular third molar extraction. by Lijuan Liu (136685)

Superimposition of the cephalometric tracings obtained before and after treatment of case with mandibular third molar extraction. by Lijuan Liu (136685)

Comparison of pretreatment cephalometric variables between premolar extraction and mandibular third molar extraction groups. by Lijuan Liu (136685)

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

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

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

Comparison of upper and lower arch crowding between premolar extraction group and the mandibular third molar extraction groups. by Lijuan Liu (136685)

Testing set error. by Xiangjuan Liu (618000)

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …”

Internal structure of an LSTM cell. by Xiangjuan Liu (618000)

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …”

Prediction effect of each model after STL. by Xiangjuan Liu (618000)

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …”

The kernel density plot for data of each feature. by Xiangjuan Liu (618000)

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …”

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. 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. …”