Data Sheet 1_Intercropping grapevine with Solanum nigrum enhances their cadmium tolerance through changing rhizosphere soil microbial diversity.xlsx by Changbing Pu (14338416)

“…These groups were involved in the metal detoxification and nutrient cycling, indicating their potential role in enhancing Cd tolerance. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed the distinct metabolic adaptations in IntVVSN under Cd-contaminated soil, with significant upregulation of pathways related to the secondary metabolite synthesis, carbohydrate metabolism, glycan biosynthesis, nucleotide metabolism, and protein processing. …”

Table 1_Forest bathing enhances sleep, mood, and immunity: insights from low-latitude evergreen broad-leaved forests.xlsx by Xiangheng Dai (21684431)

Supplementary file 1_Metal ion-enhanced photosynthetic bacteria for bioplastic production from kitchen waste digestate: performance and mechanism.docx by Wei Zhao (14321)

“…</p>Conclusion<p>The concentration of 10 mg/L Fe³⁺ and 2 mg/L Mn²⁺ significantly enhanced PSB biomass and PHB accumulation (p < 0.05). …”

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. …”

Image 2_Bacillus Spizizenii FMH45-based biofertilizer enhances growth and halotolerance of cherry tomato plants under hydroponic cultivation systems.tif by Fatma Masmoudi (16819554)

“…These improvements included a notable increase in shoot elongation (>13%), enhanced SPAD index values (>8%), and significant rises in flower and fruit counts (≃ 11% and 22%, respectively). …”

Image 3_Bacillus Spizizenii FMH45-based biofertilizer enhances growth and halotolerance of cherry tomato plants under hydroponic cultivation systems.tif by Fatma Masmoudi (16819554)

“…These improvements included a notable increase in shoot elongation (>13%), enhanced SPAD index values (>8%), and significant rises in flower and fruit counts (≃ 11% and 22%, respectively). …”

Supplementary file 1_Wellness-enhancing effects of the canine growth hormone releasing hormone therapy mediated by plasmid and electroporation in healthy old dogs.docx by Min-Ok Ryu (5072594)

Differences in mean scores of upper extremity performance in all five accelerometer-derived variables across four time points- pre-HABIT, post-HABIT, 3-month, and 6-month post-HABI... by Shailesh S. Gardas (20490020)

Illinois agility test [22]. by Rıdvan Ergin (22265431)

“…At the same time, it has been noted that a decrease in age increases agility performance, while an increase in age enhances jump height. …”

Conceptual model created for research. by Rıdvan Ergin (22265431)

“…At the same time, it has been noted that a decrease in age increases agility performance, while an increase in age enhances jump height. …”