Search alternatives:
significant contribution » significant correlation (Expand Search), significant correlations (Expand Search), significant interaction (Expand Search)
contribution rates » contribution rate (Expand Search)
ns decrease » nn decrease (Expand Search), _ decrease (Expand Search), we decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
significant contribution » significant correlation (Expand Search), significant correlations (Expand Search), significant interaction (Expand Search)
contribution rates » contribution rate (Expand Search)
ns decrease » nn decrease (Expand Search), _ decrease (Expand Search), we decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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Downregulation of DOM decreases the abundance of PER and TIM.
Published 2019“…Error bars represent ± SEM; n.s., non significant,*<i>P</i> < 0.05,**p < 0.01, ***p < 0.001, one-way ANOVA. …”
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Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)).
Published 2025“…<p>Summary of the effect of MPDD on SDLP across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease)).…”
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Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease).
Published 2025“…<p>Summary of the effect of MPDD on ART and TIBL across all participants, and also participants categorized by driving styles (“NS” (no significant), “+” (significant increase), and “-” (significant decrease).…”
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Accumulated contribution rate of PCA.
Published 2025“…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. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”
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