Gene profiles of leaf and root endophytic bacterial communities of <i>Synsepalum dulcificum</i> predicted using Tax4Fun 2. by Rabiath F. R. Adigoun (14189090)

a. Positive ion mode significant difference metabolite expression difference factor analysis (A) mild PTOA group VS severe PTOA group (B) mild PTOA group VS normal group (C) Severe... by Peng-fei Han (694841)

GSRS was positively associated with T cell exhaustion (TEX) and predicted a reduced likelihood of response to immunotherapy. by Yu Zhang (12946)

Data processing and predictive model construction. by Yajia Chen (20685780)

Dynamic characteristics of (A) FIB and (B) PCT between the survival and deceased groups of SFTS patients with HLH. by Bo Zhang (6559)

Baseline characteristics of the participants. by Junichi Kushioka (12236447)

“…This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. …”

Internal validation by cross-validation. by Junichi Kushioka (12236447)

“…This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. …”

Analysis of independent prognostic factors. by Yajia Chen (20685780)

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. …”

Schematic diagram of numerical simulation model. by Mengyun Li (7490870)

“…<div><p>After prolonged waterflooding development, the main integrated oil reservoirs in the X Oilfield have largely entered the late stage of waterflooding, with an average water cut reaching approximately 98%. However, a significant amount of remaining oil still accumulates in the weak water-flushed zones of low-permeability layers, which presents substantial development potential. …”

ENP C-term phosphomimic variants and <i>enp</i> rescue. by Michaela S. Matthes (21587677)

Variable evaluation and construction of a prognostic nomogram. by Kai Yang (73590)

Group results for Experiment 1 and 2. by Adam M. Roth (19845864)

Image 2_A nomogram model to predict the high risk of lower live birth probability in young women undergoing the first IVF-ET cycle: a retrospective study.tif by Chang Liu (35901)

“…Objective<p>To build a prediction nomogram for early prediction of live birth probabilities according to number of oocytes retrieved in women ≤ 35 years of age.…”

Image 1_A nomogram model to predict the high risk of lower live birth probability in young women undergoing the first IVF-ET cycle: a retrospective study.jpeg by Chang Liu (35901)

“…Objective<p>To build a prediction nomogram for early prediction of live birth probabilities according to number of oocytes retrieved in women ≤ 35 years of age.…”

Image 3_A nomogram model to predict the high risk of lower live birth probability in young women undergoing the first IVF-ET cycle: a retrospective study.tif by Chang Liu (35901)

“…Objective<p>To build a prediction nomogram for early prediction of live birth probabilities according to number of oocytes retrieved in women ≤ 35 years of age.…”