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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”

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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”

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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”

Three error values under raw data. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”

Decomposition of time scries plot. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. 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. …”

Comparison of ecological indices of potential prey communities for <i><i>E. erythropterus</i></i> in the NRE before and after barrage opening. RA: Relative Abundance; ind.: number... by Jeong-Soo Gim (18058588)

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The results of SOM clustering. by Jeong-Soo Gim (18058588)

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NGS datasets recovered from stomach contents of <i>E. erythropterus</i> in the NRE. by Jeong-Soo Gim (18058588)

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The results of self-organising map (SOM) clustering. by Jeong-Soo Gim (18058588)

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Distribution of zooplankton abundance by depth (A) before and (B) after and phytoplankton abundance by depth (C) before and (D) after the opening of the NRE. by Jeong-Soo Gim (18058588)

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The patterning results for stomach of <i>Erythroculter erythropterus</i> on the SOM plane. by Jeong-Soo Gim (18058588)

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Descriptive statistics and estimated parameters of length-weight relationships (w = <i><i>a</i></i>L^<i>b</i>) for <i><i>E. erythropterus</i></i> before and after opening... by Jeong-Soo Gim (18058588)

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Description of the study sites in the NRE. by Jeong-Soo Gim (18058588)

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Comparison of water quality parameters in the surface water before and after Nakdong River Estuary (NRE) opening. DO: dissolved oxygen; TN: total nitrogen; TP: total phosphorus. by Jeong-Soo Gim (18058588)

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S1 File - Changes in prey selection and fitness of non-native <i>Erythroculter erythropterus</i> following estuarine restoration in the Nakdong River, South Korea by Jeong-Soo Gim (18058588)

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Diverse regulatory gate forms. by Jeong-Soo Gim (18058588)

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Rarefaction curves of in <i>Erythroculter erythropterus</i>’s stomach contents in NGS results. by Jeong-Soo Gim (18058588)

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Increased Mcd1 levels significantly suppress sister chromatid cohesion defects in <i>smc1-259</i> cells. by Gurvir Singh (22822181)

Types of interaction between two factors. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Image 3_Significance of LRFN4 in prognosis and tumor microenvironment of lung adenocarcinoma.tif by Nana Wang (533812)