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

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Prime sequences. by Qiaozhen Qin (13159201)

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

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Cultural heritage dataset. by Jie Li (15030)

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

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

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

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

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Loss of C1-FDX results in a reduction of the CI and CV activity. by Baoyin Chen (2635399)

Illumination distributions (a-c) and variations in illuminance values along the x-axis (d-f) of the CL at different angles. by Fei Li (143236)

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Illumination distributions (a-c) and variations in illuminance values along the x-axis (d-f) of the RHL at different angles. by Fei Li (143236)

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A funnel plot shows the distribution of HIV-related perceived stigma among people living with HIV/AIDS in Africa. by Gebresilassie Tadesse (18069820)

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Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry by 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. …”

Testing set error. by 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. by 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. by 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. by 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. by 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. by 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. by 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. …”