Signed random walk diffusion for effective representation learning in signed graphs.

How can we model node representations to accurately infer the signs of missing edges in a signed social graph? Signed social graphs have attracted considerable attention to model trust relationships between people. Various representation learning methods such as network embedding and graph convolutional network (GCN) have been proposed to analyze signed graphs. However, existing network embedding models are not end-to-end for a specific task, and GCN-based models exhibit a performance degradation issue when their depth increases. In this paper, we propose Signed Diffusion Network (SidNet), a novel graph neural network that achieves end-to-end node representation learning for link sign prediction in signed social graphs. We propose a new random walk based feature aggregation, which is specially designed for signed graphs, so that SidNet effectively diffuses hidden node features and uses more information from neighboring nodes. Through extensive experiments, we show that SidNet significantly outperforms state-of-the-art models in terms of link sign prediction accuracy.

Catalytic effect of laser-combined atmospheric pressure plasma in lowering the reduction temperature of hematite.

Atmospheric pressure plasma (APP) generates highly reactive species that are useful for surface activation. We demonstrate a fast regeneration of iron oxides, that are popular catalysts in various industrial processes, using microwave-driven argon APP under ambient conditions. The surface treatment of hematite powder by the APP with a small portion of hydrogen (0.5 vol%) lowers the oxide's reduction temperature. A near-infrared laser is used for localized heating to control the surface temperature. Controlled experiments without plasma confirm the catalytic effect of the plasma. Raman, XRD, SEM, and XPS analyses show that the plasma treatment changed the chemical state of the hematite to that of magnetite without sintering.

Thymic Stromal Lymphopoietin Induction Is Mediated by the Major Whey Proteins α-Lactalbumin and ß-Lactoglobulin through the NF-κB Pathway in Immune Cells.

α-Lactalbumin and ß-lactoglobulin are two major whey proteins that specifically bind immunoglobulin E and are suspected as major allergens causing cow's milk allergy (CMA). Recent studies have shown that thymic stromal lymphopoietin is a critical factor linking at the interface of the body and environment to the T-helper 2 response. However, it is not known whether thymic stromal lymphopoietin expression is changed by α-lactalbumin and ß-lactoglobulin in immune cells. Using RT-PCR and ELISA, the present study was conducted to examine if intravenous injection of α-lactalbumin and ß-lactoglobulin increased pro-inflammatory cytokines, T-helper 2 cytokines, and thymic stromal lymphopoietin expression in several immune cells, including macrophages, mast cells, and keratinocytes. Results showed that α-lactalbumin and ß-lactoglobulin induced thymic stromal lymphopoietin, interleukin-6, and tumor necrosis factor-α expression. It was concluded that the allergenicity of α-lactalbumin and ß-lactoglobulin may be attributed to thymic stromal lymphopoietin induction, T-helper 2 cytokines, and pro-inflammatory cytokines.

Adsorption of copper from the sulphate solution of low copper contents using the cationic resin Amberlite IR 120.

In view of the increasing importance of the waste processing and recycling to meet the strict environmental regulations, the present investigation reports an adsorption process using the cationic exchanger Amberlite IR 120 for the recovery/removal of copper from the synthetic sulphate solution containing copper

Enrichment of the metallic components from waste printed circuit boards by a mechanical separation process using a stamp mill.

Printed circuit boards incorporated in most electrical and electronic equipment contain valuable metals such as Cu, Ni, Au, Ag, Pd, Fe, Sn, and Pb. In order to employ a hydrometallurgical route for the recycling of valuable metals from printed circuit boards, a mechanical pre-treatment step is needed. In this study, the metallic components from waste printed circuit boards have been enriched using a mechanical separation process. Waste printed circuit boards shredded to <10mm were milled using a stamp mill to liberate the various metallic components, and then the milled printed circuit boards were classified into fractions of <0.6, 0.6-1.2, 1.2-2.5, 2.5-5.0, and >5.0mm. The fractions of milled printed circuit boards of size <5.0mm were separated into a light fraction of mostly non-metallic components and a heavy fraction of the metallic components by gravity separation using a zig-zag classifier. The >5.0mm fraction and the heavy fraction were subjected to two-step magnetic separation. Through the first magnetic separation at 700 Gauss, 83% of the nickel and iron, based on the whole printed circuit boards, was recovered in the magnetic fraction, and 92% of the copper was recovered in the non-magnetic fraction. The cumulative recovery of nickel-iron concentrate was increased by a second magnetic separation at 3000 Gauss, but the grade of the concentrate decreased remarkably from 76% to 56%. The cumulative recovery of copper concentrate decreased, but the grade increased slightly from 71.6% to 75.4%. This study has demonstrated the feasibility of the mechanical separation process consisting of milling/size classification/gravity separation/two-step magnetic separation for enriching metallic components such as Cu, Ni, Al, and Fe from waste printed circuit boards.