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Ultrahigh-molecular-weight polyethylene (UHMWPE), with its excellent wear resistance and biocompatibility, has important application as artificial joints. The most commonly used method to improve its wear resistance is to introduce crosslinking through irradiation. However, free radicals are introduced after radiation, which are easy to cause inflammation in the human body. Some antioxidants, such as vitamin E (V E), need to be added. However, the addition of V E could reduce the crosslinking density of UHMWPE and worsen the wear resistance of the material. Therefore, obtaining new antioxidants that could eliminate free radicals without affecting or even improving the crosslinking density of materials during irradiation is an important subject worth exploring. In this paper, a kind of natural polyphenol substance, resveratrol (RSV), was introduced as a new antioxidant and crosslinking promoter during irradiation crosslinking of UHMWPE. RSV can better eliminate free radicals than V E and further improve the crosslinking density to obtain UHMWPE materials with excellent oxidation resistance and high wear resistance. The multi-active phenolic hydroxyl structure of RSV molecule provides its free-radical trapping ability, and the appropriate molecular size of RSV may promote the H -type crosslinking of UHMWPE, thereby increasing the crosslinking density. This work provides a new pathway for developing high-performance artificial joint prosthesis materials. [Display omitted] • A kind of natural polyphenol substance, resveratrol (RSV), is introduced as a new antioxidant and crosslinking promoter for the irradiation-crosslinked UHMWPE. • RSV can effectively eliminate free radicals due to its free radical trapping ability provided by the multi-active phenolic hydroxyl structure. • The appropriate molecular size of RSV may promote the H -type crosslinking of UHMWPE, thereby increasing the crosslinking density. [ABSTRACT FROM AUTHOR]

Concept factorization (CF), unlike nonnegative matrix factorization (NMF), can handle data with negative values by approximating the original data with two low-dimensional nonnegative matrices and itself. Nevertheless, existing CF-based methods continue to suffer from the two issues specified as follows: (1) Their effectiveness is reduced by the high degree of factorization freedom and the two-stage mismatch between factorization and category acquisition, and (2) their robustness drops significantly when dealing with complex noise. In response to the aforementioned issues, we propose a robust spectral-embedded bilateral orthogonal concept factorization (RSOCF) model for clustering. It constrains the factor matrices as orthogonal matrices to decrease the freedom and obtain samples' categories directly after factorization, which can significantly improve clustering effectiveness. Moreover, correntropy is introduced into RSOCF to improve its robustness to complex noise. To optimize the non-convex RSOCF model, a half-quadratic-based algorithm is devised. Numerous experiments demonstrate that RSOCF surpasses other state-of-the-art methods in terms of clustering effectiveness and robustness. • A novel robust concept factorization-based clustering model is proposed. • A fast algorithm is proposed to optimize the non-convex model. • Numerous experiments validate its clustering effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Polyvinyl alcohol (PVA) films have significant rigidity due to the large number of hydrogen bonds within and between molecular chains. This feature hinders their industrial applications. Small molecule plasticizers can effectively improve the toughness of PVA films. However, this improvement decreases their modulus and strength. In this study, the effect of individual plasticizer, namely, diethanolamine (DEA), glycerol (GLY), and H 2 O, on the mechanical properties was first studied. It was found that adding an individual plasticizer increase the toughness but decrease the modulus for all the three plasticized PVA films, with DEA decreased least. Then, the property changes caused by of H 2 O and plasticizer content in two synergic plasticizing systems of DEA/H 2 O and GLY/H 2 O were systematically investigated and compared to demonstrate further the different effects of DEA and GLY on the toughness and modulus of the PVA film. Results indicated that the modulus and toughness of PVA casted film with a 10% DEA content improved after the addition of 3% H 2 O for the DEA/H 2 O plasticizing system. Despite a large improvement in toughness, the strength was always largely reduced with the increase in H 2 O or GLY content for the GLY/H 2 O system. The infrared spectrum results showed that H 2 O could be physically combined with both PVA and DEA to construct a cross-linked hydrogen bond network, because of the relatively strong electronegativity of the N atom in DEA. However, forming such a cross-linked hydrogen bond network in the GLY/H 2 O system is difficult. Thus, only a plasticizing effect was observed in the PVA/GLY/H 2 O films. Our work provides a better understanding of the effects of plasticizer and H 2 O on the hydrogen bonding and properties of PVA films to achieve toughness-strength balance via synergic plasticization. [Display omitted] • Investigate the synergistic plasticization effect between water and diethanolamine (DEA) or glycerol (GLY) on PVA films. • PVA/DEA films exhibit stronger strength and modulus after adding a small amount of water, while PVA/GLY films are different. • H 2 O can form hydrogen bonds with both PVA and DEA simultaneously, forming a complex hydrogen bonding cross-linking network. • Hydrogen bonding cross-linking network enables PVA/DEA films to have higher melting points. [ABSTRACT FROM AUTHOR]

Owing to its ability to handle negative data and promising clustering performance, concept factorization (CF), an improved version of non-negative matrix factorization, has been incorporated into multi-view clustering recently. Nevertheless, existing CF-based multi-view clustering methods still have the following issues: (1) they directly conduct factorization in the original data space, which means its efficiency is sensitive to the feature dimension; (2) they ignore the high degree of factorization freedom of standard CF, which may lead to non-uniqueness factorization thereby causing reduced effectiveness; (3) traditional robust norms they used are unable to handle complex noises, significantly challenging their robustness. To address these issues, we establish a fast multi-view clustering via correntropy-based orthogonal concept factorization (FMVCCF). Specifically, FMVCCF executes factorization on a learned consensus anchor graph rather than directly decomposing the original data, lessening the dimensionality sensitivity. Then, a lightweight graph regularization term is incorporated to refine the factorization process with a low computational burden. Moreover, an improved multi-view correntropy-based orthogonal CF model is developed, which can enhance the effectiveness and robustness under the orthogonal constraint and correntropy criterion, respectively. Extensive experiments demonstrate that FMVCCF can achieve promising effectiveness and robustness on various real-world datasets with high efficiency. [ABSTRACT FROM AUTHOR]