Comparison of experimental results at ablation. by Zhongjian Xie (4633099)

“…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. …”

Result of comparison of different lightweight. by Zhongjian Xie (4633099)

“…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. …”

DyHead Structure. by Zhongjian Xie (4633099)

“…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. …”

The parameters of the training phase. by Zhongjian Xie (4633099)

“…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. …”

Structure of GSConv network. by Zhongjian Xie (4633099)

“…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. …”

Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

“…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. …”

Improved model distillation structure. by Zhongjian Xie (4633099)

“…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. …”

Elevated liver enzyme levels suggest hepatic damage. by J. Jose Corbalan (22439198)

Metabolic and molecular effects of high-fat diet (HFD). by Ping-yu Cai (12389929)

IL-33 supplementation fails to prevent trTreg reduction in established infection. by Santiago Boccardo (5927954)

Obese <i>mMgat2</i> and <i>HuMgat2</i> mice exhibit increased blood and liver lipid levels. by J. Jose Corbalan (22439198)

Obese <i>mMgat2</i> and <i>HuMgat2</i> mice have elevated Krebs cycle intermediate levels. by J. Jose Corbalan (22439198)

Obese <i>mMgat2</i> and <i>HuMgat2</i> mice show defects in solute transporter protein levels. by J. Jose Corbalan (22439198)

miR-150-5p inhibition modulates pyroptosis and FoxO3a/ARC signaling in ApoE^-/- mice. by Ping-yu Cai (12389929)

Therapeutic effects of miR-150-5p inhibition on ox-LDL-induced pyroptosis in RAW264.7 macrophages. by Ping-yu Cai (12389929)

Rut inhibited the degradation of TJPs to reduce BBB breakdown and also suppressed apoptosis of brain tissue cells. by Zhixuan Huang (6750410)

Therapeutic effects of miR-150-5p inhibition on atherosclerotic lesion development. by Ping-yu Cai (12389929)

Tregs frequency is reduced in lymphoid and non-lymphoid target tissues during acute <i>T</i>. <i>cruzi</i> infection. by Santiago Boccardo (5927954)

<i>mMgat2</i> and <i>HuMgat2</i> mice become obese when fed a HFD. by J. Jose Corbalan (22439198)

Rut alleviated BBB damage in MCAO rats and exerted neuroprotective effects. by Zhixuan Huang (6750410)