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Objective: The purpose of this study was to investigate the prognostic value of the family history of cancer (FHC) in predicting survival and clinicopathological features in oral squamous cell carcinoma (OSCC) patients. Materials and Methods: This single‐institution study utilized data from 610 patients undergoing surgery from 2014 to 2020 that was prospectively collected and cataloged for research purposes. All patients underwent standard surgery with/without radiotherapy or chemoradiotherapy. We statistically evaluated whether FHC was associated with changes in disease‐free survival (DFS) and disease‐specific survival (DSS). Results: Among 610 patients, 141 (23.1%) reported a family history of cancer. The distribution of clinicopathological characteristics was balanced between FHC‐positive and FHC‐negative OSCC patients. FHC‐positive patients had decreased DFS (p = 0.005) and DSS (p = 0.018) compared to FHC‐negative patients. Conclusions: FHC‐positive OSCC patients have a poorer prognosis. FHC positivity is an independent predictor of negative outcomes based on DFS and DSS. FHC should be a consideration in screening, evaluating, counseling, and treating OSCC patients. [ABSTRACT FROM AUTHOR]

Multi-view clustering (MVC) often provides superior effectiveness to single-view clustering due to the integration of information from diverse views. Nonetheless, existing MVC methods are limited to large-scale real-world data by the drawbacks of low efficiency and poor robustness. To address these issues, we propose a novel robust anchor-based MVC model via spectral embedded concept factorization (RAMCSF). RAMCSF builds anchor graphs to approximate full-sample graphs and decomposes these anchor graphs by concept factorization (CF). To improve the clustering effectiveness, factor matrices of CF are constrained as orthogonal matrices to reduce the freedom of decomposition, and a novel small-scale anchor-based spectral embedding is designed to explore the high-order neighbor relationships. To restrain complex noises distributed in real-world data, we employ correntropy to measure the error between the original data and the learned representation. Moreover, RAMCSF can get a clustering indicator matrix directly, avoiding additional post-processing and ensuring that changes in data dimensions have a limited impact on efficiency. The model is then optimized by a novel fast half-quadratic-based optimization strategy that combines the orthogonal properties and the traces of matrices. Extensive experiments indicate that RAMCSF can achieve higher efficiency and robustness while maintaining comparable effectiveness to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multiview clustering (MVC) has attracted considerable attention owing to its remarkable capacity to reconcile diverse information from multiple perspectives. However, traditional MVC generally has a narrow scope of application owing to its limited efficiency. Consequently, various efficient MVC (EMVC) methods have emerged recently. Despite their promising performance, these EMVC methods still have several unresolved issues: (1) They suffer from reduced effectiveness caused by representation non-alignment across views and information mismatch between stages, and (2) they fail to efficiently resist complex noises and outliers. To address these issues, we propose an efficient correntropy-based multiview clustering method with alignment discretization (ECMCAD). Specifically, a correntropy-based multipartition learning model was developed to efficiently learn view-specific robust partition-level representations. Additionally, a novel alignment discretization strategy was designed to align the learned cross-view representations into a consensus discrete indicator to integrate representation learning, representation alignment, and discrete label acquisition into a unified framework. Furthermore, an efficient alternating optimization method was developed to solve the model. Numerous experiments illustrated the superiority of the proposed method over state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

High-dimensional data has attracted much attention because it contains more comprehensive information about samples. How to cluster these high-dimensional data has become a crucial topic in unsupervised learning. Existing clustering methods often show limited applicability due to their high computational complexity and low anti-noise ability. To address this issue, we propose a novel robust landmark graph-based clustering algorithm for high-dimensional data (RLGCH), which inherits the advantages of both k-means++ and graph-based clustering by using the results of k-means++ as pseudo labels for landmark graph-based clustering. In particular, RLGCH can achieve more reasonable clustering effectiveness than methods that just operate in the low-dimensional space or the original space since it performs k-means++ in the low-dimensional space and landmark graph-based spectral clustering in the original feature space. To avoid post-processing after optimization, the embedded factor matrix is constrained as an indicator matrix rather than a simple nonnegative matrix. To enhance the clustering robustness, the L 2 , 1 -norm is adopted to minimize the error of results between k-means++ and landmark graph-based clustering. To solve the model of RLGCH, we established a novel efficient optimization strategy to obtain all sample categories directly. Combining our clustering model and optimization strategy, the computational complexity is reduced to linear and insensitive to data dimensions. Extensive experiments on seven real-world datasets and sixteen noisy datasets show that compared with other state-of-the-art methods, RLGCH can improve the clustering efficiency and robustness greatly while guaranteeing comparable or even better clustering effectiveness. [ABSTRACT FROM AUTHOR]

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]

Graph-based clustering commonly provides promising clustering effectiveness as it can preserve samples' local geometric information. Inspired by it, multi-view graph clustering was developed to integrate complementary information among graphs of diverse views and it has received intensive attention recently. Nevertheless, on the one hand, most existing methods require extra k-means after obtaining embedding representations to generate a discrete cluster indicator, which reduces effectiveness due to the two-stage mismatch. On the other hand, numerous complex noises in real-world multi-view data challenge the robustness of existing clustering methods. In this paper, we established a discrete correntropy-based multi-view anchor-graph clustering (DCMAC) model that not only emphasizes the aforementioned issues but also makes use of anchor graphs to improve the efficiency of the graph construction stage. To optimize this non-convex model, we propose a fast half-quadratic-based coordinate descent strategy to acquire the discrete cluster indicator directly without extra k-means. Furthermore, we extend the DCMAC model to a single-view form and provide optimization strategies for it. Extensive experiments illustrate that the proposed method is effective and robust compared to those advanced baselines. • A new discrete correntropy-based multi-view anchor-graph clustering (DCMAC) model is proposed. • A novel half-quadratic-based coordinate descent algorithm is proposed to solve the DCMAC model. • To handle single-view clustering instances, DCMAC is extended to a single-view form named DCAC. • Numerous experiments demonstrate the outstanding effectiveness and robustness of DCMAC. [ABSTRACT FROM AUTHOR]

Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the meteorological data of the source power station, thus generating the target power station scenario in which the cluster information serves as the label of the robust auxiliary classifier generative adversarial network (RAC-GAN) model and the output data of the source power station is considered as the basis. Finally, the actual wind farm output and meteorological data of a region in northeast China are employed for arithmetic analysis to verify the effectiveness of the proposed method. It is proved that the proposed method can effectively reflect the characteristics of wind power output and solve the problem of insufficient historical data of new wind farm output. [ABSTRACT FROM AUTHOR]

Concept factorization (CF) has attracted widespread attention for its promising performance in document clustering. Among various CF variants, graph-regularized CF is the most impressive type, which can improve clustering effectiveness by exploring structural information. Nevertheless, their clustering efficiency is restricted by the following considerations: (1) the introduction of the full-sample graph is accompanied by an increase in computational complexity; (2) most of them require intensive multiplications in optimization, which impair the optimization efficiency. To address these issues, in this work, we propose an anchor-graph regularized orthogonal concept factorization (AROCF) method to enhance the clustering efficiency and effectiveness in document clustering tasks. Firstly, AROCF approximates the full-sample graph with a small-scale anchor graph to reduce the complexity of graph construction from quadratic to linear. Then, one of the factor matrices is constrained as the cluster indicator matrix in our method, which can avoid extra efficiency loss in K-means after optimization. Finally, an orthogonal constraint is employed to restrict the freedom of factorization to increase the clustering effectiveness. To optimize the AROCF model, we develop a fast optimization strategy by combining the trace and orthogonality of matrices. Extensive experiments on various document datasets demonstrate the effectiveness and efficiency of AROCF. [ABSTRACT FROM AUTHOR]