Relative abundance of the phyla of dominant bacteria based on 16S rRNA gene amplicon sequencing. by Melissa S. Shinfuku (19942206)

Subjects:

Soil organic carbon, soil organic nitrogen, z-score transformed ecosystem functions, and ecosystem multifunctionality indices dataset. by Melissa S. Shinfuku (19942206)

Subjects:

Counts and age-standardized rate of gastric adenocarcinoma incidence per 100,000 and average annual percent change from 2000 to 2019 in the United States, by age, sex, and race. by Armin Aslani (13009188)

Subjects:

S1 Data - by Jan Willem Koten (17743224)

Subjects:

Connectivity statistics. by Jan Willem Koten (17743224)

Subjects:

The relationship between ecosystem multifunctionality and bacterial Chao1 estimated richness. by Melissa S. Shinfuku (19942206)

Subjects:

Counts and age-standardized rate of signet ring carcinoma incidence per 100,000 and average annual percent change from 2000 to 2019 in the United States, by age, sex, and race. by Armin Aslani (13009188)

Subjects:

Hypothetical versus random connectomes. by Jan Willem Koten (17743224)

Subjects:

Thresholds and group reproducibility. by Jan Willem Koten (17743224)

Subjects:

Soil organic carbon, soil organic nitrogen, z-score transformed ecosystem functions, and ecosystem multifunctionality indices metadata. by Melissa S. Shinfuku (19942206)

Subjects:

Counts and age-standardized rate of gastrointestinal stromal tumor incidence per 100,000 and average annual percent change from 2000 to 2019 in the United States, by age, sex, and... by Armin Aslani (13009188)

Subjects:

Reproducibility of response time and brain measures. by Jan Willem Koten (17743224)

Subjects:

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. …”

Yearly crude rates for alcohol-induced deaths among females by state in 2019, 2021, and 2024. by Tony Wong (779773)

Subjects:

Fitting law of maximum offset under different solutions. by Wen Wang (6570)

Subjects:

Microbial four-stage chain reaction mechanism. by Wen Wang (6570)

Subjects: