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. …”

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. …”

Numbers of nuclei and centrioles per cell. by Yang-In Yim (324355)

“…Note that the values in (D) for HSET KD and combined KD of Myo10 KD and HSET, while significantly different from the control, represent decreases in centriole number, not increases (these decreases may be due to the anti-proliferative effects of HSET KD). …”

Functional and strength parameters. by Susanne S. Rauh (21192252)

“…An overall tendency to an increase in FF and a decrease in functional measures were observed over 2 years. …”

Prediction performance of each model. by Ahmed S. Alkhalifah (20721713)

“…Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. …”

Patient inclusion flow chart. by Ahmed S. Alkhalifah (20721713)

“…Nonsurvivors had a significantly higher time-weighted average MP (TWA-MP) than survivors. …”

Changes in relative abundance of genera in both therapeutic groups. by Flavia Furlaneto (20161022)

“…<p>Box plots represent the 3 genera that differed significantly between groups in terms of changes in abundance. …”

Sample collection methods and preliminary analysis of sequencing data. by Zhen Wu (358187)

Inverse Simpson index (A) and richness (B) shown for the post-operative and perioperative groups. by Allison J. Collier (13977858)

“…<p>Significant decreases in the Inverse Simpson index (A) were noticed between Baseline and Recheck 1 (p = 0.01 perioperative group, p = 0.04 post-operative group), as well as between Baseline and Recheck 2 (p = 0.01 perioperative, p = 0.02 post-operative) in both the perioperative and post-operative groups. …”

Original images for explaining Fig 4. by Reiri Takeuchi (20460788)

“…Treatment with cyclosporine A also increased <i>MYC</i> and <i>ATM</i> mRNA expression levels and decreased <i>CDK2</i>, <i>ATR</i>, <i>P27</i>, <i>P53</i> and <i>RB1</i> mRNA expression levels but not significantly. …”

Flowchart of the study. by Flavia Furlaneto (20161022)

“…At species level, <i>Schaalia</i> spp., <i>Streptococcus gordonii</i>, and <i>Leptotrichia wadei</i> increased in Placebo and decreased in the Probiotic group after treatment. <i>Granulicatella adiacens</i> decreased significantly after the probiotic therapy, while <i>Saccharibacteria</i> (TM7) spp., <i>Solobacterium moorei</i>, and <i>Catonella morbi</i> increased significantly. …”

Systemic Inflammation associated with a 24-hour septic insult [cecal slurry (CS; 1.6mg/g) + hyperoxia (HO; 95% O₂)] in female MMP7KO mice and their wild-type littermates... by Brandon Baer (21276471)

“…Although female septic MMP7KO mice showed similar numerical increases across all pro-inflammatory cytokines, they were not significantly different compared to MMP7KO controls. …”