Data for faba bean. by Beamlaku Alemayehu (20648920)

“…The soil analysis result indicated that vermicompost and lime significantly increased soil pH and decreased exchangeable acidity. …”

Selected soil chemical properties after harvest. by Beamlaku Alemayehu (20648920)

“…The soil analysis result indicated that vermicompost and lime significantly increased soil pH and decreased exchangeable acidity. …”

Partial budget analysis. by Beamlaku Alemayehu (20648920)

“…The soil analysis result indicated that vermicompost and lime significantly increased soil pH and decreased exchangeable acidity. …”

ERI Thresholds Between Adjacent Zones. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Location of the study area. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Random Forest structure with cross-validation. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Land use change from 1990 to 2020. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Geographic data. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Classification accuracy. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Fig 3 - by Razieh Bidhendi-Yarandi (7139387)

Subjects:

The results of the subgroup and meta-regression analyses, stratified by follow-up duration, age, gender, study design, and study setting. by Razieh Bidhendi-Yarandi (7139387)

Subjects:

Characteristics of studied included. by Razieh Bidhendi-Yarandi (7139387)

Subjects:

Flowchart of the search process. by Razieh Bidhendi-Yarandi (7139387)

Subjects:

Funnel plot for testing asymmetry hypothesis using Freeman-Tukey double arcsine transformation for proportions. by Razieh Bidhendi-Yarandi (7139387)

Subjects:

Results of random effect meta-analysis using Freeman-Tukey double arcsine transformation for proportions, regardless of the duration of their symptoms. by Razieh Bidhendi-Yarandi (7139387)

Subjects:

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. …”