بدائل البحث:
significantly we » significantly _ (توسيع البحث), significantly i (توسيع البحث), significantly lower (توسيع البحث)
linear decrease » linear increase (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
significantly we » significantly _ (توسيع البحث), significantly i (توسيع البحث), significantly lower (توسيع البحث)
linear decrease » linear increase (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
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2181
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2182
Data of soil response to nitrogen deposition.xlsx
منشور في 2024"…To fill this gap, we conducted an investigation into the effect of different N deposition levels on N-poor soil in tropical regions, aiming to ascertain the response of soil acidification to both increased and decreased N deposition.…"
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2183
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2184
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2185
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2186
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2187
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2188
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2189
Battery parameters.
منشور في 2025"…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …"
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2190
The aging parameters of each group of batteries.
منشور في 2025"…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …"
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2191
Minimal data set.
منشور في 2025"…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …"
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2192
Experimental lithium-ion batteries.
منشور في 2025"…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …"
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2193
Schematic diagram of two time intervals.
منشور في 2025"…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …"
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2194
Changes in alpha diversity in the weeks before anti-TNF-a treatment.
منشور في 2024"…The analysis found a significant association across all subjects for Shannon diversity with decreasing diversity in the lead up to the next treatment (lmer model, beta = -0.018, <i>p</i> = 0.036) and no association for observed diversity (<i>p</i>>0.1). …"
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2195
The overall framework of CARAFE.
منشور في 2025"…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …"
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2196
KPD-YOLOv7-GD network structure diagram.
منشور في 2025"…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …"
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2197
Comparison experiment of accuracy improvement.
منشور في 2025"…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …"
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2198
Comparison of different pruning rates.
منشور في 2025"…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …"
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2199
Comparison of experimental results at ablation.
منشور في 2025"…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …"
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2200
Result of comparison of different lightweight.
منشور في 2025"…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …"