Microemulsion for stimulating wells with high gas and water cut by Baghir A. Suleimanov (20460195)

“…It has been found that the treatment of the bottomhole zone with the developed microemulsion leads to a significant reduction in the size of gas bubbles due to a decrease in interfacial tension, which facilitates their removal from the bottomhole zone and increases hydraulic diffusivity of the porous medium for oil approximately five times. …”

Table 1_Trends in cervical cancer incidence and mortality in the United States, 1975–2018: a population-based study.docx by Xianying Cheng (10677443)

Image 8_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 1_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 7_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Table 1_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.docx by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 4_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 3_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 2_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 5_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tiff by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

Image 6_Can posttreatment blood inflammatory markers predict poor survival in gynecologic cancer?: a systematic review and meta-analysis.tif by Minyong Choi (22465405)

“…Furthermore, among the inconsistent blood draw timing and analytical methods, we aimed to suggest the most suitable strategies in the clinical setting.…”

The overall framework of CARAFE. by Zhongjian Xie (4633099)

“…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. …”

KPD-YOLOv7-GD network structure diagram. by Zhongjian Xie (4633099)

“…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. …”

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

“…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. …”

Comparison of different pruning rates. by Zhongjian Xie (4633099)

“…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. …”

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

“…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. …”

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

“…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. …”

DyHead Structure. by Zhongjian Xie (4633099)

“…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. …”

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

“…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. …”

Structure of GSConv network. by Zhongjian Xie (4633099)

“…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. …”