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. …”

S1 Graphical abstract - by Huimin Xian (20390006)

“…</p><p>Conclusions</p><p>In patients with MVD-STEMI, the incidence of MACEs was lower in FCR than in FIR, and the decrease was particularly significant in the DM cohort. …”

Procedural characteristics. by Huimin Xian (20390006)

“…</p><p>Conclusions</p><p>In patients with MVD-STEMI, the incidence of MACEs was lower in FCR than in FIR, and the decrease was particularly significant in the DM cohort. …”

Clinical characteristics. by Huimin Xian (20390006)

“…</p><p>Conclusions</p><p>In patients with MVD-STEMI, the incidence of MACEs was lower in FCR than in FIR, and the decrease was particularly significant in the DM cohort. …”

Study flowchart. by Huimin Xian (20390006)

“…</p><p>Conclusions</p><p>In patients with MVD-STEMI, the incidence of MACEs was lower in FCR than in FIR, and the decrease was particularly significant in the DM cohort. …”

Data. by Huimin Xian (20390006)

“…</p><p>Conclusions</p><p>In patients with MVD-STEMI, the incidence of MACEs was lower in FCR than in FIR, and the decrease was particularly significant in the DM cohort. …”

Excel data extraction. by Berihun Agegn Mengistie (18781020)

“…Early detection and treatment of precancerous cervical lesions and human papillomavirus (HPV) infection are strongly advised to decrease the incidence of cervical cancer and death. …”

The upper plots show the changes in ZMK for summer and autumn. by Peyman Karami (5909264)

“…<p>Red indicates an increasing trend and green indicates a decreasing trend in fire density. The lower plots show significant increasing and decreasing trends for different biomes in Iran.…”

Fig 1 - by Taehyeong Kim (8991383)

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”