بدائل البحث:
significant decrease » significant increase (توسيع البحث), significantly increased (توسيع البحث)
non decrease » nn decrease (توسيع البحث), point decrease (توسيع البحث), note decreased (توسيع البحث)
a decrease » _ decrease (توسيع البحث), _ decreased (توسيع البحث), _ decreases (توسيع البحث)
significant decrease » significant increase (توسيع البحث), significantly increased (توسيع البحث)
non decrease » nn decrease (توسيع البحث), point decrease (توسيع البحث), note decreased (توسيع البحث)
a decrease » _ decrease (توسيع البحث), _ decreased (توسيع البحث), _ decreases (توسيع البحث)
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3421
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3422
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3423
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3424
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3425
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3426
Stereotactic injection of AAV-miR-129-5p attenuated microglia activation in depressed mice.
منشور في 2025الموضوعات: -
3427
Stereotactic injection of AAV-miR-129-5p suppressed astrocyte activation in depressed mice.
منشور في 2025الموضوعات: -
3428
Overexpression of miR-129-5p alleviated depression-like behaviors by increasing ATP content.
منشور في 2025الموضوعات: -
3429
Overexpression of miR-129-5p alleviated the depressive-like phenotypes of CRS and LPS treated mice.
منشور في 2025الموضوعات: -
3430
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3431
Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry
منشور في 2025"…Simulations and experiments demonstrate that precise voltage adjustments effectively minimize ion leakage, enhancing resolving power by 50% (reaching a maximum of 106), while the corresponding decrease in signal intensity follows the <i>I</i><sub>p</sub>–<i>R</i><sub>p</sub> linear relationship. …"
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3432
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3433
Testing set error.
منشور في 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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3434
Internal structure of an LSTM cell.
منشور في 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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3435
Prediction effect of each model after STL.
منشور في 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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3436
The kernel density plot for data of each feature.
منشور في 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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3437
Analysis of raw data prediction results.
منشور في 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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3438
Flowchart of the STL.
منشور في 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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3439
SARIMA predicts season components.
منشور في 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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3440
BWO-BiLSTM model prediction results.
منشور في 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. …"