We characterized the paramagnetic effects of nine metal ions on NMR signals of isotropic bicelles with headgroup-modified lipids. We found that Mn(2+), Gd(3+) and Dy(3+) show evidence for influencing NMR signals on the surface more than inside and on the disc edge, providing distance information in the bilayers.
Aim
Uptake of COVID‐19 vaccines for children aged 5–11 years old in Australia has plateaued. Persuasive messaging is an efficient and adaptable potential intervention to promote vaccine uptake, but evidence for its effectiveness is varied and dependent on context and cultural values. This study aimed to test persuasive messages to promote COVID‐19 vaccines for children in Australia.
Methods
A parallel, online, randomised control experiment was conducted between 14 and 21 January 2022. Participants were Australian parents of a child aged 5–11 years who had not vaccinated their child with a COVID‐19 vaccine. After providing demographic details and level of vaccine hesitancy, parents viewed either the control message or one of four intervention texts emphasising (i) personal health benefits; (ii) community health benefits; (iii) non‐health benefits; or (iv) personal agency. The primary outcome was parents' intention to vaccinate their child.
Results
The analysis included 463 participants, of whom 58.7% (272/463) were hesitant about COVID‐19 vaccines for children. Intention to vaccinate was higher in the community health (7.8%, 95% confidence interval (CI) −5.3% to 21.0%) and non‐health (6.9%, 95% CI −6.4% to 20.3%) groups, and lower in the personal agency group (−3.9, 95% CI −17.7 to 9.9) compared to control, but these differences did not reach statistical significance. The effects of the messages among hesitant parents were similar to the overall study population.
Conclusion
Short, text‐based messages alone are unlikely to influence parental intention to vaccinate their child with the COVID‐19 vaccine. Multiple strategies tailored for the target audience should also be utilised.
Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emerge in many applications, such as face recognition and mobile medical. One fundamental question arises: what will be the fairest neural architecture for edge devices? By examining the existing neural networks, we observe that larger networks typically are fairer. But, edge devices call for smaller neural architectures to meet hardware specifications. To address this challenge, this work proposes a novel Fairness-and Hardware-aware Neural architecture search framework, namely FaHaNa. Coupled with a model freezing approach, FaHaNa can efficiently search for neural networks with balanced fairness and accuracy, while guaranteed to meet hardware specifications. Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset. Target edge devices, FaHaNa finds a neural architecture with slightly higher accuracy, 5.28× smaller size, 15.14% higher fairness score, compared with MobileNetV2; meanwhile, on Raspberry PI and Odroid XU-4, it achieves 5.75× and 5.79× speedup.
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