Sphingolipid biosynthetic pathway. حسب Hala Tamim El Jarkass (16625309)

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<i>N. parisii</i> induces global changes to <i>C. elegans</i> lipidome. حسب Hala Tamim El Jarkass (16625309)

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All infection data from the study. حسب Hala Tamim El Jarkass (16625309)

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Prime sequences. حسب Qiaozhen Qin (13159201)

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Relationship between miR-129-5p and Mysm1 in depressed mice. حسب Qiaozhen Qin (13159201)

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Stereotactic injection of AAV-miR-129-5p attenuated microglia activation in depressed mice. حسب Qiaozhen Qin (13159201)

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Stereotactic injection of AAV-miR-129-5p suppressed astrocyte activation in depressed mice. حسب Qiaozhen Qin (13159201)

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Overexpression of miR-129-5p alleviated depression-like behaviors by increasing ATP content. حسب Qiaozhen Qin (13159201)

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Overexpression of miR-129-5p alleviated the depressive-like phenotypes of CRS and LPS treated mice. حسب Qiaozhen Qin (13159201)

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Gating strategy of experiment using 30 min EdU pulses experiments in mOr2-Piwi1 juvenile polyps. حسب Eudald Pascual-Carreras (12115380)

Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry حسب Jiyao Wang (2121157)

"…Simulations and experiments demonstrate that precise voltage adjustments effectively minimize ion leakage, enhancing resolving power by 50% (reaching a maximum of 106), while the corresponding decrease in signal intensity follows the <i>I</i>_p–<i>R</i>_p linear relationship. …"

Dynamics and functional role of TOR signaling during cell cycle re-entry in Vasa2+/Piwi1+ cells after refeeding. حسب Eudald Pascual-Carreras (12115380)

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. …"