Enhanced multi-component module. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

The architecture of the TCBiL. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the encoder. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the Fourier transform. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Detail of the decoder. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Encoder-decoder architecture. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Dataset description. by Yuanming Ding (12842858)

“…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”

Study design. by Moazzam Tanveer (20775814)

“…<div><p>Background</p><p>Childhood obesity poses a significant public health challenge, yet effective school-based physical activity (PA) interventions remain scarce, especially in Pakistan. …”

Prevaelnce of different pathogens by location. by Amete Mihret Teshale (12072758)

“…<div><p>Diarrheal illness remains a major global health challenge, causing millions of deaths annually. …”

Frontier Analysis Based on ASDR and SDI. by Guanghui Yu (423945)

“…<div><p>Background and Objectives</p><p>Hypertension is a major risk factor for aortic aneurysm (AA), but the global, regional, and national patterns of its related disease burden are not well studied. …”

Demographic characteristics of study subjects after PSM ^* . by Teng-Li Lin (5559284)

Model vs. WKY group KEGG pathways (Top 15). by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

Behavioral data. by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

OPLS-DA model parameter. by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

Model vs. SHR group KEGG pathways (Top 15). by Xiao Zhang (152326)

“…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”

Network pharmacology analysis predicted the targets of NS on atherosclerosis. by Yihua Wang (491766)

Damage characteristics of PU/WG layer. by Hao Yu (157186)

“…<div><p>Rock joints are widespread in nature and have become a major obstacle to the safe development of underground space. …”

Primers used for RT-qPCR. by Yihua Wang (491766)

“…The results indicated that NS combination promoted autophagy by inhibiting the PI3K/Akt/mTOR pathway. This significantly alleviated inflammation, reduced apoptosis, and decreasing lipid accumulation, thereby improving the pathological progression of atherosclerosis. …”

Study areas of sample collection in Ethiopia, eastern Africa by Amete Mihret Teshale (12072758)

Predicted values from a mixed model of speed of travel during a migratory leg (excluding sub-Saharan movements), with country and season as main effects, including individual bird... by Will Cresswell (567908)