Analysis of STL-PCA 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. …”

Accumulated contribution rate of PCA. 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. …”

Figure of ablation experiment. 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-PCA-BWO-BiLSTM model. 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. …”

Parameter optimization results of BiLSTM. 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. …”

Descriptive statistical analysis of data. 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 MAE value of the model under raw data. 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. …”

Three error values under raw data. 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. …”

Decomposition of time scries plot. 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)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

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

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Kernel density estimation for CO2. by Getachew Magnar Kitila (19935139)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Change in panel quantile regression coefficients. by Getachew Magnar Kitila (19935139)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Definitions of variables and measurements. by Getachew Magnar Kitila (19935139)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Regression estimates: Double threshold model. by Getachew Magnar Kitila (19935139)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Results from cross sectional dependence test. by Getachew Magnar Kitila (19935139)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Panel quantile regression results. by Getachew Magnar Kitila (19935139)

“…Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. …”

Table1_Neutrophil extracellular traps as immunofibrotic mediators in RA-ILD; pilot evaluation of the nintedanib therapy.docx by Aliki I. Venetsanopoulou (12197432)

“…Objective<p>Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a significant pulmonary complication of RA. This study tried to elucidate the mechanisms enhancing inflammation and causing lung injury in RA-ILD, focusing on the role of neutrophil extracellular traps (NETs). …”

Image1_Neutrophil extracellular traps as immunofibrotic mediators in RA-ILD; pilot evaluation of the nintedanib therapy.tif by Aliki I. Venetsanopoulou (12197432)

“…Objective<p>Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a significant pulmonary complication of RA. This study tried to elucidate the mechanisms enhancing inflammation and causing lung injury in RA-ILD, focusing on the role of neutrophil extracellular traps (NETs). …”

A working model for the RRSV Pns10-promoted RRSV infection in <i>N. lugens</i> through inducing NlNDUFS1-mediated mitochondrial apoptosis. by Lianshun Zheng (22086585)

“…This impairment of mitochondrial complex I activity results in a significant decrease of ATP production and an increase of mitochondrial ROS production, which causes mitochondrial apoptosis to facilitate RRSV infection in <i><i>N. lugens</i></i>.…”