Interfacial thermal conductance in graphene/black phosphorus heterogeneous structures

Graphene, as a passivation layer, can be used to protect the black phosphorus from the chemical reaction with surrounding oxygen and water. However, black phosphorus and graphene heterostructures have low efficiency of heat dissipation due to its intrinsic high thermal resistance at the interfaces. The accumulated energy from Joule heat has to be removed efficiently to avoid the malfunction of the devices. Therefore, it is of significance to investigate the interfacial thermal dissipation properties and manipulate the properties by interfacial engineering on demand. In this work, the interfacial thermal conductance between few-layer black phosphorus and graphene is studied extensively using molecular dynamics simulations. Two critical parameters, the critical power Pcr to maintain thermal stability and the maximum heat power density Pmax with which the system can be loaded, are identified. Our results show that interfacial thermal conductance can be effectively tuned in a wide range with external strains and interracial defects. The compressive strain can enhance the interfacial thermal conductance by one order of magnitude, while interface defects give a two-fold increase. These findings could provide guidelines in heat dissipation and interfacial engineering for thermal conductance manipulation of black phosphorus-graphene heterostructure-based devices.Comment: 33 pages, 22 figure

CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics

Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance, but rely on plenty of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially under-activated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method with Continuous Gradient CAM and its nonlinear multi-scale fusion (CG-fusion CAM). The method redesigns the way of back-propagating gradients and non-linearly activates the multi-scale fused heatmaps to generate more fine-grained class activation maps with appropriate activation degree for different sizes of damage sites. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms

MADS-Box Genes and Gibberellins Regulate Bolting in Lettuce (Lactuca sativa L.)

Bolting in lettuce is promoted by high temperature and bolting resistance is of great economic importance for lettuce production. But how bolting is regulated at the molecular level remains elusive. Here, a bolting resistant line S24 and a bolting sensitive line S39 were selected for morphological, physiological, transcriptomic and proteomic comparisons. A total of 12204 genes were differentially expressed in S39 vs S24. Line S39 was featured with larger leaves, higher levels of chlorophyll, soluble sugar, anthocyanin and auxin, consistent with its up-regulation of genes implicated in photosynthesis, oxidation-reduction and auxin actions. Proteomic analysis identified 30 differentially accumulated proteins in lines S39 and S24 upon heat treatment, and 19 out of the 30 genes showed differential expression in the RNA-Seq data. Exogenous gibberellins (GA) treatment promoted bolting in both S39 and S24, while 12 flowering promoting MADS-box genes were specifically induced in line S39, suggesting that although GA regulates bolting in lettuce, it may be the MADS-box genes, not GA, that plays a major role in differing the bolting resistance between these two lettuce lines

Barriers and facilitators to COVID-19 vaccine uptake among Australian health professional students during the pandemic: a nationwide study

Using a cross-sectional online survey we investigated knowledge, attitudes, and risk perception about COVID-19 vaccination and identified factors influencing vaccine uptake among Australian health professional students from October 2021 to January 2022. We analysed data from 1114 health professional students from 17 Australian universities. Most participants were enrolled in nursing programs (n = 958, 86.8%), and 91.6% (n = 858) of the participants received COVID-19 vaccination. Approximately 27% believed COVID-19 was no more serious than seasonal influenza and that they had a low risk of acquiring COVID-19. Nearly 20% disagreed that COVID-19 vaccines in Australia were safe and perceived they were at higher-risk of acquiring COVID infection than the general population. Higher-risk perception viewing vaccination as their professional responsibility, and vaccine mandate strongly predicted vaccination behaviour. Participants consider COVID-19 information from health professionals, government websites, and World Health Organization as the most trusted information sources. The findings highlight that healthcare decision-makers and university administrators need to monitor students’ hesitancy with vaccination to improve students’ promotion of the vaccination to the general population