Patient characteristics. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Comparison with baseline methods. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

The details of the channel attention module. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Ablation experiments. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Pathological diagnosis. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

The details of ECRNet. by Ruijie Wang (5989841)

“…On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. …”

Meth induces a biphasic and depressive effect on cardiac electrophysiology. by Jimmy Zhang (159595)

Spiking neuron network model with spike frequency adaptation (SFA) and synaptic depression (STD). by Brian Nils Lundstrom (14659973)

Raw data for figures and tables in the paper. by Runhai Jiang (19871068)

“…<i>rwandensis</i> could promote the transformation of Pb fractions and a functional change in the soil bacterial community, ultimately decreasing Pb accumulation in the biomass and promoting the development of maize.…”

Power spectra changes related to the seizure onset zone and increased adaptation. by Brian Nils Lundstrom (14659973)

Improving Stability of CO₂ Electroreduction by Incorporating Ag NPs in N‑Doped Ordered Mesoporous Carbon Structures by Järi Van den Hoek (17692678)

“…The optimized Ag/soft-NOMC catalysts showed both a promising selectivity (∼80%) and stability compared with commercial Ag NPs while decreasing the loading of the transition metal by more than 50%. …”

Improving Stability of CO₂ Electroreduction by Incorporating Ag NPs in N‑Doped Ordered Mesoporous Carbon Structures by Järi Van den Hoek (17692678)

“…The optimized Ag/soft-NOMC catalysts showed both a promising selectivity (∼80%) and stability compared with commercial Ag NPs while decreasing the loading of the transition metal by more than 50%. …”

Flow chart of the study participants. by Milton W. Musaba (8431944)

“…Neonatal morbidity was lower: APGAR score <7 at 5 minutes (aPR: 0.36; 95%CI: 0.26–0.50), transfer to postnatal ward with mother (aPR: 9.27; 95%CI: 2.23–38.48), transfer to neonatal unit (aPR: 0.66; 95%CI: 0.56–0.78). …”

Suggested modifications for the BabySaver. by Milton W. Musaba (8431944)

“…Neonatal morbidity was lower: APGAR score <7 at 5 minutes (aPR: 0.36; 95%CI: 0.26–0.50), transfer to postnatal ward with mother (aPR: 9.27; 95%CI: 2.23–38.48), transfer to neonatal unit (aPR: 0.66; 95%CI: 0.56–0.78). …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…The investigation focuses on two types of microtextured surfaces with totally different surface peak–valley features: a negatively skewed surface with micropit arrays (<i>Ssk</i> < 0) and a positively skewed surface with micropillar arrays (<i>Ssk</i> > 0). …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…The investigation focuses on two types of microtextured surfaces with totally different surface peak–valley features: a negatively skewed surface with micropit arrays (<i>Ssk</i> < 0) and a positively skewed surface with micropillar arrays (<i>Ssk</i> > 0). …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…The investigation focuses on two types of microtextured surfaces with totally different surface peak–valley features: a negatively skewed surface with micropit arrays (<i>Ssk</i> < 0) and a positively skewed surface with micropillar arrays (<i>Ssk</i> > 0). …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…The investigation focuses on two types of microtextured surfaces with totally different surface peak–valley features: a negatively skewed surface with micropit arrays (<i>Ssk</i> < 0) and a positively skewed surface with micropillar arrays (<i>Ssk</i> > 0). …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…The investigation focuses on two types of microtextured surfaces with totally different surface peak–valley features: a negatively skewed surface with micropit arrays (<i>Ssk</i> < 0) and a positively skewed surface with micropillar arrays (<i>Ssk</i> > 0). …”

Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

“…The investigation focuses on two types of microtextured surfaces with totally different surface peak–valley features: a negatively skewed surface with micropit arrays (<i>Ssk</i> < 0) and a positively skewed surface with micropillar arrays (<i>Ssk</i> > 0). …”