Data Sheet 1_Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.pdf by Elena Pastorelli (7024235)

“…A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. …”

SPSS Data File for Sample 1. by Mariusz Zięba (21369920)

“…The response functions switch attention and processing to what is likely to be helpful in alleviating suffering, distress and need, called action. …”

Indices of fit for two three-factor models. by Mariusz Zięba (21369920)

“…The response functions switch attention and processing to what is likely to be helpful in alleviating suffering, distress and need, called action. …”

SPSS Data File for Sample 3. by Mariusz Zięba (21369920)

“…The response functions switch attention and processing to what is likely to be helpful in alleviating suffering, distress and need, called action. …”

SPSS Data File for Sample 2. by Mariusz Zięba (21369920)

“…The response functions switch attention and processing to what is likely to be helpful in alleviating suffering, distress and need, called action. …”

Gene subtyping analysis based on DEGs among PRG molecular subtypes. by Xiangxin Wu (22320058)

Identification of PRG molecular subtypes and prognostic outcome analysis. by Xiangxin Wu (22320058)

Nguyen Dupuis Network. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Relative error iteration curve. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Schematic diagram of spiral update. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

The architecture of the BLPM. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Specific parameters of Nguyen-Dupuis network. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Road saturation and total travel cost. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Schematic diagram of shrink wrap. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Road density and connected vehicle road sections. by Anqi Jiang (4583380)

“…Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. …”

Data Sheet 3_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Data Sheet 1_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Data Sheet 2_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Data Sheet 4_Continuous wavelet based transfer function analysis of cerebral autoregulation dynamics for neuromonitoring using near-infrared spectroscopy.pdf by Marcus Thudium (6441863)

“…Wavelet transfer function analysis can be employed to estimate coherence, gain and phase relationship between two signals without these restrictions.…”

Integration of datasets. by Meili Liu (327309)