Reusable Amino Acid/<i>N</i>‑Isopropylacrylamide-Based Organogels for Efficient Oil and Solvent Removal from Water by Sandeep K. Sahoo (2891726)

“…Oil spills, waste disposal, synthetic organic compounds (SOCs), volatile organic compounds (VOCs), and other organic pollutants significantly contaminate the food chain and water supply. …”

Reusable Amino Acid/<i>N</i>‑Isopropylacrylamide-Based Organogels for Efficient Oil and Solvent Removal from Water by Sandeep K. Sahoo (2891726)

“…Oil spills, waste disposal, synthetic organic compounds (SOCs), volatile organic compounds (VOCs), and other organic pollutants significantly contaminate the food chain and water supply. …”

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. …”

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. …”

Baseline characteristics of population. by Youngmin Yoon (1472608)

“…The subgroup analysis also showed that higher urine Na/K were significantly related to the risk of uncontrolled HTN in the presence of proteinuria or CKD.…”

Flow diagram of study population. by Youngmin Yoon (1472608)

“…The subgroup analysis also showed that higher urine Na/K were significantly related to the risk of uncontrolled HTN in the presence of proteinuria or CKD.…”

Original images for explaining Fig 4. by Reiri Takeuchi (20460788)

“…Treatment with cyclosporine A also increased <i>MYC</i> and <i>ATM</i> mRNA expression levels and decreased <i>CDK2</i>, <i>ATR</i>, <i>P27</i>, <i>P53</i> and <i>RB1</i> mRNA expression levels but not significantly. …”

Characteristics of the study population. by Alexander Ritter (21982738)

“…Psychological assessments showed increased anxiety in participants requiring follow-up, particularly those with low resilience. …”

Follow up costs for incidental findings. by Alexander Ritter (21982738)

“…Psychological assessments showed increased anxiety in participants requiring follow-up, particularly those with low resilience. …”

Mutation of 1G05 epitope glycan sites impacts avidity. by Zachary T. Berndsen (9230176)

The <i>ENT2</i> knockout effects on CRC proliferation and survival. by Safaa M. Naes (22075434)

“…Plating Efficiency (PE) was calculated as the ratio of the number of colonies to the number of cells seeded. PE decreased significantly in both HKO1 and HKO2. No significant difference occurred between NTC and DKO cells. …”