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a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
largest decrease » marked decrease (Expand Search)
larger decrease » marked decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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6801
Income satisfaction measure.
Published 2025“…In this paper, we apply a novel approach to try and address this issue. …”
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6802
Marginal effect of HTD on income and SWB.
Published 2025“…In this paper, we apply a novel approach to try and address this issue. …”
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6803
Estimation procedure flow-chart.
Published 2025“…In this paper, we apply a novel approach to try and address this issue. …”
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6804
Structural model outline.
Published 2025“…In this paper, we apply a novel approach to try and address this issue. …”
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6805
Amino acid metabolic pathways are influenced by the NC1 POM cycle over expression.
Published 2025“…Box and whisker plots of selected metabolites that displayed significant increase or decrease in the NC1 POM cycle producing strain (NC), red boxes, or empty vector producing strain (YC) green boxes. …”
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6806
Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s...
Published 2025“…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …”
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6807
Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s...
Published 2025“…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …”
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6808
Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s...
Published 2025“…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …”
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6809
Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s...
Published 2025“…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …”
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6810
Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s...
Published 2025“…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …”
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6811
Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s...
Published 2025“…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …”
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6812
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6813
Prediction of transition readiness.
Published 2025“…In most transition domains, help needed did not decrease with age and was not affected by function. …”
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6814
Dataset visualization diagram.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
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6815
Dataset sample images.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
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6816
Performance comparison of different models.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
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6817
C2f and BC2f module structure diagrams.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
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6818
YOLOv8n detection results diagram.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
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6819
YOLOv8n-BWG model structure diagram.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
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6820
BiFormer structure diagram.
Published 2025“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”