Search alternatives:
significantly linear » significant linear (Expand Search), significantly lower (Expand Search), significantly longer (Expand Search)
linear decrease » linear increase (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
significantly linear » significant linear (Expand Search), significantly lower (Expand Search), significantly longer (Expand Search)
linear decrease » linear increase (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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5144
SEM morphology of power: (a)17−4PH; (b)TiC.
Published 2025“…Compared to the commercial blade, the wear of the laser-cladded blade was decreased by 67%. This study successfully applied wear-resistant laser cladding coatings on the surface of harvester blades with small substrate thickness, significantly extending their service life.…”
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5145
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SlABCG9 Functioning as a Jasmonic Acid Transporter Influences Tomato Resistance to Botrytis cinerea
Published 2025“…Assays using Xenopus oocytes, yeast cell sensitivity, and JA-inhibited primary root growth confirmed that SlABCG9 functions as a JA influx transporter. The knockout mutant lines of <i>SlABCG9</i> showed decreased JA contents, suppressed defense gene <i>PDF1.2</i>’s expression, reduced antioxidant enzyme activity, and severe disease symptoms compared to wild-type controls. …”
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5147
The overall framework of CARAFE.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5148
KPD-YOLOv7-GD network structure diagram.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5149
Comparison experiment of accuracy improvement.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5150
Comparison of different pruning rates.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5151
Comparison of experimental results at ablation.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5152
Result of comparison of different lightweight.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5153
DyHead Structure.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5154
The parameters of the training phase.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5155
Structure of GSConv network.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5156
Comparison experiment of accuracy improvement.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5157
Improved model distillation structure.
Published 2025“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”
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5158
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