A typical cross signalized intersection. by Bin Zhao (276445)

“…Numerical experiments using actual survey data from Kunshan City yield several noteworthy findings: (1) An optimal moderate-sized time step exists for rolling optimization to minimize either the average delay time or total costs; specifically, an excessively small time step may increase vehicle average delay time or total costs; (2) The percentage of delay reduction achieved by our method, compared to Synchro software, reaches a maximum of approximately 70% when traffic demand is moderate and the initial state is low; and (3) The percentage reduction in average delay or total costs compared to Synchro initially increases and then decreases with rising traffic intensity.…”

Adverse event list. by Stefan Halbherr (21353882)

“…No statistically significant improvements were seen in the Montreal Cognitive Assessment (MoCA) (mean decrease −0.73; 95% CI; −2.1, 0.62; <i>p</i> = 0.255), Epworth Sleepiness Scale (mean increase 0.09; 95% CI; −2.6, 2.8; <i>p</i> > 0.999), Beck Depression Inventory (BDI) (mean decrease −1.27; 95% CI; −3.8, 1.3; <i>p</i> = 0.257), and the Starkstein Apathy Scale (mean increase 0.36; 95% CI; −1.6, 2.4; <i>p</i> = 0.822). …”

Patient characteristics. by Stefan Halbherr (21353882)

“…No statistically significant improvements were seen in the Montreal Cognitive Assessment (MoCA) (mean decrease −0.73; 95% CI; −2.1, 0.62; <i>p</i> = 0.255), Epworth Sleepiness Scale (mean increase 0.09; 95% CI; −2.6, 2.8; <i>p</i> > 0.999), Beck Depression Inventory (BDI) (mean decrease −1.27; 95% CI; −3.8, 1.3; <i>p</i> = 0.257), and the Starkstein Apathy Scale (mean increase 0.36; 95% CI; −1.6, 2.4; <i>p</i> = 0.822). …”

Inhibition of AurA or HDAC6 by inhibitors preserves ciliary structure during porcine CoVs infection. by Tenghan Zhuang (22238809)

Various parameters characterizing Lb related to lung volume or alveolar surface before, during and after bulk alveolarization (adulthood). by Julia Hüttmann (22656283)

“…During alveolarization values remain constant. At pnd 21 a significant decrease compared to 3 days old pups is visible. …”

Summary of correlations in previous studies. by Dan Kuang (136225)

“…During this period, the number of weekly new cases exhibited a similar trend, and the results indicated a significant correlation between the viral concentration and the number of weekly new cases (spearman’s r = 0.93, P < 0.001). …”

The characteristics of nine WWTPs. by Dan Kuang (136225)

“…During this period, the number of weekly new cases exhibited a similar trend, and the results indicated a significant correlation between the viral concentration and the number of weekly new cases (spearman’s r = 0.93, P < 0.001). …”

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. …”

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. …”

Improved model distillation 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. …”

Serum metabolomic response to aging. by Gwangho Yoon (5771678)

“…<b>(B)</b> Metabolites that decreased with aging. Statistical significance is indicated in the heatmap with asterisks. …”

Table 1_Imeglimin may affect hemoglobin A1c accuracy via prolongation of erythrocyte lifespan in patients with type 2 diabetes mellitus: insights from the INFINITY clinical trial.d... by Takeshi Osonoi (1951555)

“…While HbA1c and GA decreased and 1,5-AG increased one month after imeglimin initiation, GA and 1,5-AG showed rapid changes compared to the gradual decrease in HbA1c. …”