بدائل البحث:
greater decrease » greatest decrease (توسيع البحث), greater increase (توسيع البحث), greater disease (توسيع البحث)
step decrease » sizes decrease (توسيع البحث), teer decrease (توسيع البحث), we decrease (توسيع البحث)
gap decrease » gain decreased (توسيع البحث), mean decrease (توسيع البحث), _ decrease (توسيع البحث)
a decrease » _ decrease (توسيع البحث), _ decreased (توسيع البحث), _ decreases (توسيع البحث)
greater decrease » greatest decrease (توسيع البحث), greater increase (توسيع البحث), greater disease (توسيع البحث)
step decrease » sizes decrease (توسيع البحث), teer decrease (توسيع البحث), we decrease (توسيع البحث)
gap decrease » gain decreased (توسيع البحث), mean decrease (توسيع البحث), _ decrease (توسيع البحث)
a decrease » _ decrease (توسيع البحث), _ decreased (توسيع البحث), _ decreases (توسيع البحث)
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1621
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1622
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1623
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1624
Transcriptome and Metabolome Based Mechanisms Revealing the Accumulation and Transformation of Sugars and Fats in Pinus armandii Seed Kernels during the Harvesting Period
منشور في 2024"…The results revealed that during the maturation of P. armandii seed kernels, there was a significant increase in the width, thickness, and weight of the seed kernels, as well as a significant accumulation of sucrose, soluble sugars, proteins, starch, flavonoids, and polyphenols and a significant decrease in lipid content. …"
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1625
Flowchart of the study protocol.
منشور في 2024"…Moreover, contrasting with a decrease in the control group, TC group demonstrated significance increased theta oscillatory power in C3, C4, F4, P3, T7, and T8, and a significant negative correlations were observed between state anxiety and F4-θ (r = -0.31, p = 0.04), T7-θ (r = -0.43, p = 0.01), and T8-θ (r = -0.30, p = 0.05).…"
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1626
Subject characteristics (n = 45).
منشور في 2024"…Moreover, contrasting with a decrease in the control group, TC group demonstrated significance increased theta oscillatory power in C3, C4, F4, P3, T7, and T8, and a significant negative correlations were observed between state anxiety and F4-θ (r = -0.31, p = 0.04), T7-θ (r = -0.43, p = 0.01), and T8-θ (r = -0.30, p = 0.05).…"
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1627
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1628
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1629
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1630
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1631
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1632
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1633
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1634
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1635
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1636
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1637
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1638
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1639
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1640
Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model
منشور في 2025"…This model predicts the microstructure evolution leading to the formation of the electrode as a time-series along the drying process. The hybrid approach consists in performing a certain amount of DEM simulation steps, <i>n</i><sub>DEM</sub>, after every DL prediction, mitigating the risk of unphysical predictions, like overlapping particles. …"