Scheme of the SiTFarm tool–farm to sector level. by Jure Brečko (20314959)

“…However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. …”

S1 File - by Jure Brečko (20314959)

“…However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. …”

GHG emissions in TAHs. by Jure Brečko (20314959)

“…However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. …”

Variation law of UCS. by Wenyu Lv (20139458)

“…Notably, the attenuation constant λ follows a monotonically decreasing pattern with increasing loading rate. …”

Detail of the personalized-enhanced GCN. 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. …”

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). …”