Corrosion Behavior of AM60 Magnesium Alloy Based Composites with and without Plasma Electrolytic Oxidation Coating

7 vol.% Fibre/ AM60, and (7 vol.% Fibre + 3 vol.% nano-Particle)/AM60 composites, named 7FC and MHNC-7F3NP, respectively, and the unreinforced matrix alloy AM60 were prepared by using the preform-squeeze casting technique. The corrosion behaviors of the 7FC and MHNC-7F3NP composites and the matrix alloy were investigated by using the potential-dynamic polarization test in 3.5 wt.% NaCl aqueous solution. Compared with the matrix alloy, the introduction of micron-sized alumina fibres decreased the corrosion resistance of the matrix alloy AM60, however the addition of the nano-sized particles led to almost no further reduction in the corrosion resistance of the MHNC-7F3NP. To enhance the corrosion resistance of as-cast magnesium alloy AM60-based composite, the plasma electrolytic oxidation (PEO) coatings with NaAlO2 and KOH electrolytes were applied on the surfaces of as-cast AM60 (PEO-AM60), 7 vol.% Al2O3 fibres/AM60 (PEO-7FC) and 7 vol.% Al2O3 fibres +3 vol.% Al2O3 nano-sized particles/AM60 (PEO-MHNC-7F3NP). By comparing the corrosion test results of the coated AM60 and the composites with those of the uncoated counterparts, it was found that the PEO coating significantly improved the corrosion resistances by up to 154 times. The corrosion rates of the coated composites were higher than the coated AM60, although the PEO coating reduced the corrosion rates of the composites by two orders of magnitudes. The addition of nano-sized particles barely increased the corrosion rate of the composite. The microstructure of each uncoated and coated material was observed and analysed by scanning electron microscopy (SEM) and X-ray energy dispersive spectroscopy (EDS)

Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens

Contrastive learning-based vision-language pre-training approaches, such as CLIP, have demonstrated great success in many vision-language tasks. These methods achieve cross-modal alignment by encoding a matched image-text pair with similar feature embeddings, which are generated by aggregating information from visual patches and language tokens. However, direct aligning cross-modal information using such representations is challenging, as visual patches and text tokens differ in semantic levels and granularities. To alleviate this issue, we propose a Finite Discrete Tokens (FDT) based multimodal representation. FDT is a set of learnable tokens representing certain visual-semantic concepts. Both images and texts are embedded using shared FDT by first grounding multimodal inputs to FDT space and then aggregating the activated FDT representations. The matched visual and semantic concepts are enforced to be represented by the same set of discrete tokens by a sparse activation constraint. As a result, the granularity gap between the two modalities is reduced. Through both quantitative and qualitative analyses, we demonstrate that using FDT representations in CLIP-style models improves cross-modal alignment and performance in visual recognition and vision-language downstream tasks. Furthermore, we show that our method can learn more comprehensive representations, and the learned FDT capture meaningful cross-modal correspondence, ranging from objects to actions and attributes.Comment: Accepted to CVPR 202

Comparison of pollution characteristics and magnetic response of heavy metals in dustfall before and after COVID-19 outbreak in Shanghai

In this study, dustfall samples were systematically collected in various regions of Shanghai before and after the occurrence of COVID-19 in December 2019 and December 2020. The magnetic response, content and pollution status of relevant heavy metal elements in the samples were analyzed using environmental magnetism, geochemistry, scanning electron microscopy (SEM) and the enrichment factor (EF) method. The results show that the magnetic particles in the dustfall samples are mainly pseudo-single-domain (PSD) and multi-domain (MD) ferrimagnetic minerals, and Fe, Zn, Cr, and Cu are mainly concentrated in the districts with intensive human activities. Due to restrictions on human activities following the COVID-19 epidemic, both the values of magnetic parameters and the heavy metal pollution level in 2019 are more significant than those in 2020, which is consistent with the Air Quality Index (AQI) results. In addition, magnetic susceptibility (χlf), non-hysteresis remanence (χARM) and saturation isothermal remanence (SIRM) have different degrees of correlation with heavy metal elements, and the correlations with Fe, Pb, Cr and Zn are extremely prominent. The magnetic parameters can effectively and quickly reflect the level of particulate matter pollution, making them a useful tool for monitoring urban air quality

Diversity Transfer Network for Few-Shot Learning

Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in the lack of intra-class diversity within insufficient training samples. To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. The learning problem of the sample generation (i.e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works. Besides, an organized auxiliary task co-training over known categories is proposed to stabilize the meta-training process of DTN. We perform extensive experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet, CIFAR100 and CUB. The results show that DTN, with single-stage training and faster convergence speed, obtains the state-of-the-art results among the feature generation based few-shot learning methods. Code and supplementary material are available at: \texttt{https://github.com/Yuxin-CV/DTN}Comment: 9 pages, 3 figures, AAAI 202

Impacts of Duck-Origin Parvovirus Infection on Cherry Valley Ducklings From the Perspective of Gut Microbiota

Duck-origin goose parvovirus (D-GPV) is the causative agent of beak atrophy and dwarfism syndrome (BADS), characterized by growth retardation, skeletal dysplasia, and persistent diarrhea. However, the pathogenic mechanism of D-GPV remains undefined. Here, we first reported the gut microbiome diversity of D-GPV infected Cherry Valley ducks. In the investigation for the influence of D-GPV infection on gut microbiota through a period of infection, we found that D-GPV infection caused gut microbiota dysbiosis by reducing the prevalence of the dominant genera and decreasing microbial diversity. Furthermore, exfoliation of the intestinal epithelium, proliferation of lymphocytes, up-regulated mRNA expression of pro-inflammatory TNF-α, IL-1β, IL-6, IL-17A, and IL-22 and down-regulated mRNA expression of anti-inflammatory IL-10 and IL-4 occurred when D-GPV targeted in cecal epithelium. In addition, the content of short chain fatty acids (SCFAs) in cecal contents was significantly reduced after D-GPV infection. Importantly, the disorder of pro-inflammatory and anti-inflammatory cytokines was associated with the decrease of SCFAs-producing bacteria and the enrichment of opportunistic pathogens. Collectively, the decrease of SCFAs and the enrichment of pathogen-containing gut communities promoted intestinal inflammatory injury. These results may provide a new insight that target the gut microbiota to understand the progression of BADS disease and to research the pathogenic mechanism of D-GPV

Quercetin Alleviates Pulmonary Fibrosis in Mice Exposed to Silica by Inhibiting Macrophage Senescence

Quercetin exerts anti-inflammatory, anti-oxidant and other protective effects. Previous studies have shown that senescent cells, such as fibroblasts and type II airway epithelial cells, are strongly implicated in the development of pulmonary fibrosis pathology. However, the role of senescent macrophages during silicosis remains unclear. We investigated the effects of quercetin on macrophage senescence and pulmonary fibrosis, and explored underlying mechanisms. Mice were randomized to six model groups. Vitro model was also established by culturing RAW264.7 macrophages with silica (SiO2). We examined the effects of quercetin on fibrosis, senescence-associated β-galactosidase (SA-β-Gal) activity, and senescence-specific genes (p16, p21, and p53). We showed that quercetin reduced pulmonary fibrosis and inhibited extracellular matrix (ECM) formation. Quercetin also attenuated macrophage senescence induced by SiO2 both in vitro and in vivo. In addition, quercetin significantly decreased the expressions of the senescence-associated secretory phenotype (SASP), including proinflammatory factors (interleukin-1α (Il-1α), Il-6, tumor necrosis factor-α (TNF-α), and transforming growth factor-β1 (TGF-β1)) and matrix metalloproteinases (MMP2, MMP9, and MMP12). In conclusion, quercetin mediated its anti-fibrotic effects by inhibiting macrophage senescence, possibly via SASP