Growth of bacteria in various media and temperatures with or without Sf14 infection. by Nykki D. Ross (20933968)

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Growth of bacteria in various media and temperatures with or without Sf14 infection. by Nykki D. Ross (20933968)

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Expression of the major capsid protein <i>gp34</i> gene in nutrient-poor (M9) or -rich (LB) conditions at various temperatures. by Nykki D. Ross (20933968)

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Flow Chart of Study Participant Selection. by Zhi Jin (3742471)

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Lipidomic data from infected and uninfected adults and their embryos. by Hala Tamim El Jarkass (16625309)

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Participants data. by Mohamed Achraf Harrabi (22446782)

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Flow chart of study participants. by Mohamed Achraf Harrabi (22446782)

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

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

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Data of hemoglobin (Hb) and serum Ferritin (sF) concentrations, muscle endurance, maximal voluntary isometric contractions (MVIC), aerobic fitness (VO2max) and fatigue (MFI) in wom... by Mohamed Achraf Harrabi (22446782)

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

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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. …”

Estimated results of the mediation effect. by Getachew Magnar Kitila (19935139)

“…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …”

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. …”

Panel unit root test result. by Getachew Magnar Kitila (19935139)

“…The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. …”

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. …”