Characteristics of included studies. by Rong Pi (21743379)

“…Studies have consistently linked occupational fatigue to decreased productivity, heightened error rates, and compromised decision-making abilities, posing significant risks to both individual nurses and healthcare organizations. …”

Sensitivity analysis for acute fatigue subscale. by Rong Pi (21743379)

“…Studies have consistently linked occupational fatigue to decreased productivity, heightened error rates, and compromised decision-making abilities, posing significant risks to both individual nurses and healthcare organizations. …”

Factors related to nurses’ occupational fatigue. by Rong Pi (21743379)

“…Studies have consistently linked occupational fatigue to decreased productivity, heightened error rates, and compromised decision-making abilities, posing significant risks to both individual nurses and healthcare organizations. …”

Including: S1 Data–S75 Data. by Eliana Nicolaisen-Sobesky (22662692)

“…In this perspective, future studies should further examine the effects of risk/protective factors on different brain regions in order to deepen our understanding of the clinical significance of such increased and decreased CT and GMV.…”

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. …”

Data Sheet 1_Interannual variability of the Barents Sea branch water in the northeastern part of the Barents Sea and the St. Anna Trough.pdf by Zinaida Zabudkina (14771266)

“…The observed warming of BSBW accompanied by its salinity decrease, which is observed during the last 15 years, could significantly affect thermohaline structure and circulation in the Arctic Ocean.…”

Rotarod performance. by Gerald Yu Liao (20490394)

“…</b> C57BL/6J mice at the same age groups also showed statistically significant age-related decrease in performance as seen in CB6F1J mice. …”

Data Sheet 1_An ictogenic marker in the mesial temporal epilepsy and its temporal evolutionary features.pdf by Hongjuan Lu (558920)

“…During preictal epoch, all of the seizures incorporated present evolutionary manner of type 1 characterized by smooth modification of HYP transients in morphology, including gradual shortening of the inter-transient interval, increase of amplitude and time duration of slow-wave proper and sharp wave, amplitude decrease of the post-slow component, as well as amplitude increases of ripple and fast ripple, and 2/3 seizures showed some more sophisticated transitional manners (type 2) following type 1, including reduction in amplitude with decrease of inter-transient intervals, superimposed or followed by the emergent low amplitude rhythmic activities, or both of them. …”

Data Sheet 1_Beta frequency binaural beats combined with preferred music enhance combat performance and recovery responses in amateur kickboxers: a randomized crossover trial.zip by Nidhal Jebabli (17631561)

“…Similarly, our results showed a significant improvement for heart rate, feeling scale and a significant decrease for RPE and lactate values post-round in the 15 Hz-BPM than in the other conditions.…”

<b>Spirulina platensis Supplementation in Obese Sedentary Women: Effects on Hematological Parameters, Metabolic Biomarkers, and Dietary Correlations</b> (Supp. Figures). by Luis Carlos Gonçalves (13153701)

“…While no significant inter-group differences were observed in anthropometric or primary metabolic markers due to the limited sample size of completers (n=11), intra-group analysis revealed significant alterations in uric acid (p=0.047) and HbA1c (p=0.043) within the spirulina group. …”