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The occurrence of fog, mist, smog, or haze significantly reduces the visibility of the scenes and images, resulting in limited recognition of computer vision and computer graphics. So, removing haze from images is a must. In this paper, we regard image dehazing as a mathematical inversion process and image restoration based on atmospheric scattering models. The atmospheric light can be accurately estimated by combining the gray threshold segmentation and the skyline method. The improved least squares filtering method is used to optimize the transmittance map so that the edge details can be highlighted and the halo effect can be alleviated. A large number of test images show that our algorithm can achieve better experimental results than the other seven most advanced dehazing strategies. INDEX TERMS Grey-level threshold segmentation, skyline, least-square filter, halo artifact, dehazing.

The emerging and spreading of multi-drug resistant (MDR) bacteria have been becoming one of the most severe threats to human health. Enhancing oxidative stress as mimicking immune system was considered as a potential strategy to fight against infection of MDR bacteria. In this study, we investigated the antibacterial efficiency of such a strategy which combines silver nanoparticles (AgNPs) with ebselen. The results showed that AgNPs and ebselen combination had significant synergistic killing effects both on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) in vitro, including model strains of China Veterinary Culture Collection and MDR clinical isolates, which is similar as the combination of silver ion and ebselen. AgNPs exhibited to be a strong inhibitor of bacterial thioredoxin reductase, same as a free silver ion. Ebselen mitigated the cytotoxicity of AgNPs to HeLa cells. However, in a bacteria-cell coexistence condition, the synergistic bactericidal effect was only observed on S. aureus (p<.05), while the temporary synergistic inhibitory effect on E. coli within 4 hours treatment (p<.01). In mice infection model, a combination of AgNPs and ebselen did not increase protection against the challenge of clinical E. coli CQ10 strain. Our data demonstrated that AgNPs and ebselen combination may be a promising strategy to fight against the increasingly MDR bacteria targeting bacterial thiol redox system.

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Dominance relations exist extensively in decisionmaking problems. Dominance-based neighborhood rough sets (DNRS) using fuzzy preference relations (FPRs) are presented in this article to deal with attribute reduction in the large-scale decision-making problems. In this model, FPR is elicited to quantify the dominance-based rough set model, which can efficiently deal with the under-fitting problem of classical dominance-based rough sets. First, by formulating a quantified dominance-based neighborhood relation which satisfies reflexivity, the propositions of the quantified DNRSs are analyzed. Second, we propose approaches to attribute reduction based on upper-approximate and lowerapproximate discernibility matrices, respectively. Furthermore, we evaluate that the novel model performs efficiently and effectively in time consumption and space storage by experimental analysis. Finally, combining with parallel computing, we demonstrate that the new model can be used to deal with attribute reduction of large-scale datasets effectively.