Search alternatives:
point decrease » point increase (Expand Search)
nn decrease » _ decrease (Expand Search), gy decreased (Expand Search), b1 decreased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
point decrease » point increase (Expand Search)
nn decrease » _ decrease (Expand Search), gy decreased (Expand Search), b1 decreased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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67541
Table1_Case Report: Immune Microenvironment and Mutation Features in a Patient With Epstein–Barr Virus Positive Large B-Cell Lymphoma Secondary to Angioimmunoblastic T-Cell Lymphom...
Published 2022“…Analysis of 22 kinds of immune cells showed that the numbers of activated NK cells and activated memory T cells increased, while the T-follicular helper population decreased in the transformed sample. In addition, compared with the primary sample, RHOA (G17V) mutation was not detected, while JAK2 and TRIP12 gene mutations were detected in the transformed sample. …”
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67542
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67543
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67544
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67545
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67546
Table 3_Association between platelet-to-red cell distribution width ratio and all-cause mortality in critically ill patients with non-traumatic cerebral hemorrhage: a retrospective...
Published 2024“…As PRR increased, restrictive cubic splines showed a progressive decrease in the probability of all-cause mortality. …”
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67547
Table 1_Association between platelet-to-red cell distribution width ratio and all-cause mortality in critically ill patients with non-traumatic cerebral hemorrhage: a retrospective...
Published 2024“…As PRR increased, restrictive cubic splines showed a progressive decrease in the probability of all-cause mortality. …”
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67548
Table 2_Association between platelet-to-red cell distribution width ratio and all-cause mortality in critically ill patients with non-traumatic cerebral hemorrhage: a retrospective...
Published 2024“…As PRR increased, restrictive cubic splines showed a progressive decrease in the probability of all-cause mortality. …”
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67549
Data_Sheet_1_A pooled analysis of temporal trends in the prevalence of anxiety-induced sleep loss among adolescents aged 12–15 years across 29 countries.docx
Published 2023“…</p>Conclusion<p>Trends in the prevalence of anxiety-induced sleep loss in adolescents varied significantly across different countries. Generally, a stable trend was observed in 21 of the 29 countries surveyed. …”
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67550
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67551
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67553
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67557
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67558
Major hyperparameters of RF-SVR.
Published 2024“…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 R<sup>2</sup> 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. …”
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67559
Pseudo code for coupling model execution process.
Published 2024“…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 R<sup>2</sup> 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. …”
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67560
Major hyperparameters of RF-MLPR.
Published 2024“…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 R<sup>2</sup> 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. …”