Balance test results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Mechanism analysis results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Endogenous test results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Heterogeneity analysis results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Robustness test results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Baseline results of the impact of RLM on HWSW. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

The influencing factors of RLM. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Variable definitions and basic statistics. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Average and marginal effects of RLM on HWSW. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. …”

Perturbational input-output matrix in feedback-driven condition. by Giulia Moreni (15063785)

“…Red signifies an increase in the mean firing rate exceeding 20% above the initial value (with only input to L5), blue denotes a decrease greater than 20%, and white represents a change less than 20%. …”

Figure 6 from Gut Microbiome Profiling in Eμ-TCL1 Mice Reveals Intestinal Changes and a Dysbiotic Signature Specific to Chronic Lymphocytic Leukemia by Sydney A. Skupa (18597728)

“…<b>B,</b> CLL disease burden was measured by the percentage of CD45⁺/CD19⁺/CD5⁺ cells in the peripheral blood via flow cytometric analysis beginning at 1 week after engraftment (<i>n</i> = 10 mice/cohort). …”

Data Sheet 1_Association between the systemic immune-inflammatory index and the immune response after hepatitis B vaccination: a cross-sectional analysis of NHANES data.docx by Jiayan Hu (18593615)

“…Furthermore, we utilized restricted cubic splines (RCSs) to analyze the linear relationship between the two variables.</p>Results<p>In our study, we included a total of 6,123 patients, of whom 2,770 tested positive for hepatitis B antibodies. …”

Image 1_Computed tomography and magnetic resonance imaging features of primary liver perivascular epithelioid cell tumor with renal angiomyolipoma: a case report and literature rev... by Ruoling Gao (12201178)

“…The patient presented with multiple fatty lesions in both kidneys, with the larger one located in the lower part of the left kidney, which was eventually confirmed as angiomyolipoma through surgical pathology. …”

Table 1_The cerebral metabolic mechanism of group computer magnanimous therapy based on magnetic resonance spectroscopy: effects on improving magnanimous-enterprising levels of lun... by Qianyu Liu (7543004)

“…Significant increases in NAA/Cr levels were observed in the right amygdala, and significant decreases in mI/Cr levels were observed in the right cingulate gyrus in the GCMTG. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Scree plot curve for five sets of models for the upper auricle. by Hitoshi Biwasaka (19923867)