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significantly better » significantly greater (Expand Search), significantly higher (Expand Search), significantly lower (Expand Search)
better decrease » greater decrease (Expand Search), teer decrease (Expand Search), between decreased (Expand Search)
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a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
significantly better » significantly greater (Expand Search), significantly higher (Expand Search), significantly lower (Expand Search)
better decrease » greater decrease (Expand Search), teer decrease (Expand Search), between decreased (Expand Search)
point decrease » point increase (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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1
Treatment with vitamin D3 reduced the viability of cancer cell lines: <i>1A & 1B.</i>
Published 2025“…Mouse EAC cells showed a decrease in cell viability starting from 250 µM at 24h of treatment. …”
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2
Melt Memory Effect on Biaxially Oriented Random Copolyamides: Direct Evidence for Stretch-Induced Crystal Retention above the Equilibrium Melting Point
Published 2025“…In the processing of semicrystalline polymers, the oriented crystal through flow-induced crystallization (FIC) represents a significant phenomenon that can profoundly influence the final properties of materials. …”
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3
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4
Regression situation of each cross-section.
Published 2025“…This study’s calculation method more accurately reflects the surface deformation behavior caused by shield tunnel construction in strongly weathered rock layers, particularly when coarse particle content is taken into account. It also provides a better understanding of the combined impact of various construction parameters as the coarse particle content changes during the excavation process.…”
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5
Statistical Table of Formation Loss Rate <i>V1.</i>
Published 2025“…This study’s calculation method more accurately reflects the surface deformation behavior caused by shield tunnel construction in strongly weathered rock layers, particularly when coarse particle content is taken into account. It also provides a better understanding of the combined impact of various construction parameters as the coarse particle content changes during the excavation process.…”
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6
Fitting Results for Each Operating Condition.
Published 2025“…This study’s calculation method more accurately reflects the surface deformation behavior caused by shield tunnel construction in strongly weathered rock layers, particularly when coarse particle content is taken into account. It also provides a better understanding of the combined impact of various construction parameters as the coarse particle content changes during the excavation process.…”
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7
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Passive sensing data.
Published 2025“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”
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9
Surveys.
Published 2025“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”
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10
Accuracy test results.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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11
Experiment environment and parameter.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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12
Test results for NME and FR.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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13
DARTS algorithm process.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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14
Comparison result of memory usage.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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15
LKA model structure.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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Test results on different datasets.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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17
Comparison result of memory usage.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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18
Residual configuration.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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19
Test results for P, R, F1, and OA.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”
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20
Schematic diagram of DARTS-VAN model structure.
Published 2025“…In the testing on the ImageNet dataset, the classification accuracy of the research model is 94.01, the search parameter required is only 4.8MB, the search time is shortened to 0.5d, and the minimum number of floating-point operations is 3.7G, significantly better than other mainstream algorithms. …”