Changes in the active H3K27ac and repressive H3K27me3 histone marks among Vasa2+/Piwi1+ and all cells in fed, starved, and refed juvenile polyps. حسب Eudald Pascual-Carreras (12115380)

"…Between fed, T_5ds and T_20ds timepoints, MFI levels of H3K27ac progressively and significantly decreased while levels H3K27me3 (M) did not change significantly (N). …"

The TOR inhibitors Rapamycin and AZD-8055 strongly reduce RPS6 phosphorylation and cell proliferation in Vasa2+/Piwi1+ cells. حسب Eudald Pascual-Carreras (12115380)

"…<i>n</i> = 2–4 biological replicates per condition, with 15 individuals per replicate. Significance levels for Student <i>t</i> test are indicated for adjusted <i>p</i> values: *<i>p</i> < 0.05, ***<i>p</i> < 0.001, ***<i>p</i> < 0.0001. d: day(s), n.s.: non-significant. …"

Numbers of nuclei and centrioles per cell. حسب Yang-In Yim (324355)

الموضوعات:

Testing set error. حسب Xiangjuan Liu (618000)

"…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. 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. …"

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

"…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. 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. …"

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

"…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. 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. …"

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

"…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. 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. …"

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

"…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. 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. …"

Flowchart of the STL. حسب Xiangjuan Liu (618000)

"…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. 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. …"

SARIMA predicts season components. حسب Xiangjuan Liu (618000)

"…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. 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. …"

BWO-BiLSTM model prediction results. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Bi-LSTM architecture diagram. حسب Xiangjuan Liu (618000)

"…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. 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. …"

LOSS curves for BWO-BiLSTM model training. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Analysis of STL-PCA prediction results. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Accumulated contribution rate of PCA. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Figure of ablation experiment. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Flowchart of the STL-PCA-BWO-BiLSTM model. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Parameter optimization results of BiLSTM. حسب Xiangjuan Liu (618000)

"…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. 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. …"

Descriptive statistical analysis of data. حسب Xiangjuan Liu (618000)

"…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. 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. …"

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

"…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. 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. …"