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. …”

Data Sheet 1_Increasing donor kidney age significantly aggravates the negative effect of pretransplant donor-specific anti-HLA antibodies on kidney graft survival.pdf by Michiel G. H. Betjes (8407413)

“…However, increasing donor kidney age significantly aggravated the negative effect of preDSA on graft survival. …”

Table 2_Integration analysis of microRNAs as potential biomarkers in early-stage lung adenocarcinoma: the diagnostic and therapeutic significance of miR-183-3p.docx by Guodong Huang (1595956)

“…Introduction<p>Lung adenocarcinoma (LUAD) poses a significant therapeutic challenge, primarily due to delayed diagnosis and the limited efficacy of existing treatments.…”

Table 1_Prognostic significance of early alpha fetoprotein and des-gamma carboxy prothrombin responses in unresectable hepatocellular carcinoma patients undergoing triple combinati... by Teng Zhang (457128)

“…We aimed to investigate the prognostic significance of early alpha fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP) responses in these patients.…”

Table 1_Integration analysis of microRNAs as potential biomarkers in early-stage lung adenocarcinoma: the diagnostic and therapeutic significance of miR-183-3p.docx by Guodong Huang (1595956)

“…Introduction<p>Lung adenocarcinoma (LUAD) poses a significant therapeutic challenge, primarily due to delayed diagnosis and the limited efficacy of existing treatments.…”

Table 3_Integration analysis of microRNAs as potential biomarkers in early-stage lung adenocarcinoma: the diagnostic and therapeutic significance of miR-183-3p.docx by Guodong Huang (1595956)

“…Introduction<p>Lung adenocarcinoma (LUAD) poses a significant therapeutic challenge, primarily due to delayed diagnosis and the limited efficacy of existing treatments.…”

Basic features of microtopography. by Qingping Gou (22641045)

“…The results showed that: the difference of shallow soil moisture in different microtopographies on a slope surface was not significant, the difference of deep soil moisture was significant, and the soil moisture overuse was the largest in the gully (GU), amounting to 386.36 mm, and the smallest in the ephemeral gully (EG) (131.02 mm); the GU, the sink hole (SH), and the scarp (SC) showed a trend of decreasing and then increasing with the increase of soil depth, and the platform (PL) has little overall trend of change in soil moisture with the increase of soil depth, and the soil moisture of US and EG shows the trend of “decreasing-then increasing-then decreasing” with the increase of soil depth. …”

Soil bulk density of different microtopographies. by Qingping Gou (22641045)

“…The results showed that: the difference of shallow soil moisture in different microtopographies on a slope surface was not significant, the difference of deep soil moisture was significant, and the soil moisture overuse was the largest in the gully (GU), amounting to 386.36 mm, and the smallest in the ephemeral gully (EG) (131.02 mm); the GU, the sink hole (SH), and the scarp (SC) showed a trend of decreasing and then increasing with the increase of soil depth, and the platform (PL) has little overall trend of change in soil moisture with the increase of soil depth, and the soil moisture of US and EG shows the trend of “decreasing-then increasing-then decreasing” with the increase of soil depth. …”

Categorisation of districts based on type of correlation observed and results of Moran’s local test for spatial correlation of coefficients and lags. by Gabriel Carrasco-Escobar (794055)

Spearman rank correlations of each subject between SPL and RAP_GAW. by Jonas Kirsch (20642910)

Spearman rank correlations of each subject between SPL and RAP_audio. by Jonas Kirsch (20642910)

Spearman rank correlations of each subject between SPL and OQ_EGG. by Jonas Kirsch (20642910)