Entomopathogenic fungi-derived copper nanoparticles (CuNPs) exhibited adulticidal activity against adult of <i>A</i>. <i>albopictus</i> at 24 and 48h post treatment. by Perumal Vivekanandhan (4863484)

The response of the <i>A</i>. <i>albopictus</i> larval Glutathione S-transferase (GST) enzyme increased 7.8-fold in response to <i>M</i>. <i>robertsii</i>-derived copper nanopartic... by Perumal Vivekanandhan (4863484)

Entomopathogenic fungi-derived copper nanoparticles (CuNPs) exhibited larvicidal activity against larvae of <i>A</i>. <i>albopictu</i> at 24 and 48h post treatment. by Perumal Vivekanandhan (4863484)

Entomopathogenic fungi-derived copper nanoparticles (CuNPs) exhibited minimal toxicity effects against <i>E</i>. <i>eugeniae</i> at 24 and 48h post treatment. by Perumal Vivekanandhan (4863484)

Entomopathogenic fungi-derived copper nanoparticles (CuNPs) exhibited minimal toxicity effects against <i>A</i>. <i>salina</i> at 24 and 48h post treatment. by Perumal Vivekanandhan (4863484)

Channel pruning process. by Zhongjian Xie (4633099)

KEGG enrichment pathway chart (bubble chart) (A) mild PTOA group VS severe PTOA group (B) mild PTOA group VS normal group (C) Severe PTOA group VS normal group. by Peng-fei Han (694841)

ox-LDL stimulates cytotoxicity and pyroptosis among RAW264.7. by Ping-yu Cai (12389929)

Characterization of muscle and systemic tissue damage during acute <i>T</i>. <i>cruzi</i> infection. by Santiago Boccardo (5927954)

The loss of bone in the femoral distal epiphysis is affected by housing type and weightlessness conditions in microgravity. by Rukmani Cahill (20939813)

“…(F) Conn.D is decreased in FL. Data shown are the mean ±  standard deviation with a scatter plot (ns: non-significant, * : p <  0.033, **: p <  0.002, ***: p <  0.0002). …”

Peripheral target tissues display inflammatory responses during acute <i>T</i>. <i>cruzi</i> infection. by Santiago Boccardo (5927954)

The overall framework of CARAFE. 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. …”

KPD-YOLOv7-GD network structure diagram. 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. …”

Comparison of different pruning rates. 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 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. …”