Baseline characteristics of the patients. by Denis Witham (12517162)

“…PaCO2 reduced after 20 minutes with both techniques (IAPV: from 65 to 52 mmHg, p < 0.01, relative effect (CI) 0.15 (0.01–0.28); ERCC: from 61 to 51 mmHg, p= < 0.01, relative effect (CI) 0.22 (0.07–0.37)). A transient decrease in oxygenation was fully and rapidly reversible. …”

Increased ventilation and decarboxylation by IAPV and ERCC. by Denis Witham (12517162)

Training set data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane recognition effect. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane classification. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Mixup data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Marking example. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Quadrant chart of energy consumption efficiency. by YanFei Lei (19532983)

“…<div><p>The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China’s transformation from a manufacturing to an innovation cluster. …”

Drill image dataset for training part II. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

U²-Net network structure diagram [8]. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Environmental regulation intensity of each city. by YanFei Lei (19532983)

“…<div><p>The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China’s transformation from a manufacturing to an innovation cluster. …”

RSU-7 structure diagram [8]. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Drill image dataset for training part I. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Thermotactic simulation for building a neuroanatomical model. by Muneki Ikeda (5166362)

Hyperconjugation in methyl triphosphate, a model for ATP. by Jean C. Summerton (631765)

Calculation of braiding angle of internal cell A. by Weiliang Zhang (11231646)

Fig 2 - by Marc S. Penn (16624541)

A dynamin-based inhibitory peptide (DYN) blocks LTP and the reduction of A-type K⁺ currents. by Sung-Cherl Jung (261358)

“…DYN completely blocked the decrease of I_A peak after paired stimulation while a significant reduction of I_A was still observed 30 min post-LTP in the “Paired+sDYN” group. …”

The <i>TgCRT</i>-deficient parasites displayed a swollen vacuole and a disrupted endolysosomal system. by L. Brock Thornton (6808385)

“…<p>(A) Extracellular Δ<i>crt</i> parasites showed an enlarged concave subcellular structure, indicated by the arrow, under the differential interference contrast (DIC) microscopy. …”

A map of (a) fokontany in Fianarantsoa and (b) a differentiation of included clusters within fokontany. by Eva Lorenz (6382616)