EMI of 0.25% Roadyes-treated red clay after failure. by Quan Shen (37564)

Subjects:

Table 1_The impact of decreased prognostic nutritional index on the prognosis of patients with pneumonia treated with glucocorticoids: a multicenter retrospective cohort study.docx by Fengwang Xue (22245625)

“…Further validation using RCS analysis revealed a robust inverse relationship between PNI scores and ACM, and subgroup analyses revealed no significant interactions.</p>Conclusion<p>Among pneumonia patients receiving glucocorticoid therapy, a decreased PNI was associated with an increased risk of 30-day and 90-day mortality, particularly in those with a PNI < 43.…”

Table 2_The impact of decreased prognostic nutritional index on the prognosis of patients with pneumonia treated with glucocorticoids: a multicenter retrospective cohort study.docx by Fengwang Xue (22245625)

“…Further validation using RCS analysis revealed a robust inverse relationship between PNI scores and ACM, and subgroup analyses revealed no significant interactions.</p>Conclusion<p>Among pneumonia patients receiving glucocorticoid therapy, a decreased PNI was associated with an increased risk of 30-day and 90-day mortality, particularly in those with a PNI < 43.…”

Table 3_The impact of decreased prognostic nutritional index on the prognosis of patients with pneumonia treated with glucocorticoids: a multicenter retrospective cohort study.docx by Fengwang Xue (22245625)

“…Further validation using RCS analysis revealed a robust inverse relationship between PNI scores and ACM, and subgroup analyses revealed no significant interactions.</p>Conclusion<p>Among pneumonia patients receiving glucocorticoid therapy, a decreased PNI was associated with an increased risk of 30-day and 90-day mortality, particularly in those with a PNI < 43.…”

Table 5_The impact of decreased prognostic nutritional index on the prognosis of patients with pneumonia treated with glucocorticoids: a multicenter retrospective cohort study.docx by Fengwang Xue (22245625)

“…Further validation using RCS analysis revealed a robust inverse relationship between PNI scores and ACM, and subgroup analyses revealed no significant interactions.</p>Conclusion<p>Among pneumonia patients receiving glucocorticoid therapy, a decreased PNI was associated with an increased risk of 30-day and 90-day mortality, particularly in those with a PNI < 43.…”

Table 4_The impact of decreased prognostic nutritional index on the prognosis of patients with pneumonia treated with glucocorticoids: a multicenter retrospective cohort study.docx by Fengwang Xue (22245625)

“…Further validation using RCS analysis revealed a robust inverse relationship between PNI scores and ACM, and subgroup analyses revealed no significant interactions.</p>Conclusion<p>Among pneumonia patients receiving glucocorticoid therapy, a decreased PNI was associated with an increased risk of 30-day and 90-day mortality, particularly in those with a PNI < 43.…”

Image 1_The impact of decreased prognostic nutritional index on the prognosis of patients with pneumonia treated with glucocorticoids: a multicenter retrospective cohort study.tif by Fengwang Xue (22245625)

“…Further validation using RCS analysis revealed a robust inverse relationship between PNI scores and ACM, and subgroup analyses revealed no significant interactions.</p>Conclusion<p>Among pneumonia patients receiving glucocorticoid therapy, a decreased PNI was associated with an increased risk of 30-day and 90-day mortality, particularly in those with a PNI < 43.…”

Image 1_Chromosome 1 variants associated with decreased HIV set-point viral load correlate with PRKAB2 expression changes.png by Riley H. Tough (14432838)

Supplementary file 1_Biodegradable plastic film mulch increased nitrous oxide emissions in organic leek but decreased emissions in organic cabbages.docx by Martin Samphire (22525445)

The nsP4-Q192L and C483Y mutations decrease the 4′-FlU sensitivity of both replication and transcription. by Peiqi Yin (6533747)

Testing set error. 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. …”

Internal structure of an LSTM cell. 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. …”

Prediction effect of each model after STL. 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 kernel density plot for data of each feature. 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. …”

Analysis of raw data prediction results. 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. …”

Flowchart of the STL. 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. …”

SARIMA predicts season components. 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. …”

BWO-BiLSTM model prediction results. 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. …”

Bi-LSTM architecture diagram. 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. …”

STL Linear Combination Forecast Graph. 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. …”