Data of soil response to nitrogen deposition.xlsx by Runcheng Zhu (20305665)

“…To fill this gap, we conducted an investigation into the effect of different N deposition levels on N-poor soil in tropical regions, aiming to ascertain the response of soil acidification to both increased and decreased N deposition.…”

Baseline characteristics of participants. by Mei Zhou (269746)

“…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …”

The framework diagram of this study. by Mei Zhou (269746)

“…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …”

<i>RR</i> of the temporal effect for human rabies in China from 2004–2020. by Weiwei Meng (304234)

Postnatal death of <i>aP2-Drp1</i>^f/f neonatal mice. by Yuta Ibayashi (20404811)

<i>RR</i> of the spatio-temporal interaction effect for Human Rabies in China from 2004–2020. by Weiwei Meng (304234)

Impaired development of the brain and iBAT of rarely alive adult <i>aP2-Drp1</i>^f/f mice. by Yuta Ibayashi (20404811)

Reduced expression of thermogenic and lipogenic genes of <i>aP2-Drp1</i>^f/f neonatal mice. by Yuta Ibayashi (20404811)

Battery parameters. by Peijian Jin (22265437)

“…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”

The aging parameters of each group of batteries. by Peijian Jin (22265437)

“…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”

Minimal data set. by Peijian Jin (22265437)

“…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”

Experimental lithium-ion batteries. by Peijian Jin (22265437)

“…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”

Schematic diagram of two time intervals. by Peijian Jin (22265437)

“…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”

The overall framework of CARAFE. 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. …”

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. …”