Regioselective Intramolecular Oxidation of Phenols and Anisoles by Dioxiranes Generated in Situ

A novel method for regioselective oxidation of phenols and anisoles has been developed in which dioxiranes, generated in situ from ketones and Oxone, oxidize phenol derivatives in an intramolecular fashion. A series of ketones with electron-withdrawing groups, such as CF3, COOMe, and CH2Cl, were attached to phenols, anisoles, or aryl rings via a C2 or C3 methylene linker. In a homogeneous solvent system of CH3CN and H2O, oxidation of phenol derivatives 1−10 afforded spiro 2-hydroxydienones in 24−55% yields regardless of the presence of other substituents (ortho Me, meta Me or Br) on the aryl ring and the length of the linker. Experimental evidences were provided to support the mechanism that involves a regioselective π bond epoxidation of aryl rings followed by epoxide rearrangement and hemiketal formation

Data for: Extreme Natural and Man-Made Events and Human Adaptive Responses Mediated by Information and Communication Technologies’ Use: A Systematic Literature Review

File #1: Data coding scheme and categories File #2: Includes a more extensive analysis of the 60 articles (also available online on OSF at https://osf.io/n7r9e/?view_only=e3f515294a3e449a9792060c484d24cc ). This analysis focused on: study characteristics (number of participants and observations/messages; geographical coverage; period of observation/data collection; studied events); extreme event(s) studied; ICT devices and platforms from which data was collected; coding in the three adaptive functions categories; and source of coping response (individuals or organizations). In addition, full bibliographic references of these 60 articles were included in the file, rather than in the article’s reference list, due to its extension

Regioselective Intramolecular Oxidation of Phenols and Anisoles by Dioxiranes Generated in Situ

A novel method for regioselective oxidation of phenols and anisoles has been developed in which dioxiranes, generated in situ from ketones and Oxone, oxidize phenol derivatives in an intramolecular fashion. A series of ketones with electron-withdrawing groups, such as CF3, COOMe, and CH2Cl, were attached to phenols, anisoles, or aryl rings via a C2 or C3 methylene linker. In a homogeneous solvent system of CH3CN and H2O, oxidation of phenol derivatives 1−10 afforded spiro 2-hydroxydienones in 24−55% yields regardless of the presence of other substituents (ortho Me, meta Me or Br) on the aryl ring and the length of the linker. Experimental evidences were provided to support the mechanism that involves a regioselective π bond epoxidation of aryl rings followed by epoxide rearrangement and hemiketal formation

Adaptive multi-view subspace learning based on distributed optimization

As the rapid development of Internet of Things (IoT), the data is collected from different sensors and stored in distributed devices, these data can be regarded as the multi-view data. There are currently numerous clustering algorithms designed to handle multi-view data. However, most of these algorithms still suffer from the following problems: They are designed to operate directly on raw data, which preserves excessive redundant information and increases the computational burden for subsequent tasks. They primarily focus on pairwise relationships between views, neglecting the intricate high-order connections among multiple views. The prior information of singular values is not taken into account in multi-view. Different views are considered to have equal contributions for clustering. To efficiently address the above problems, adaptive multi-view subspace learning based on distributed optimization (AMSLDO) is proposed in this paper. Specifically, the original multi-view data is projected to a low-dimensional space for subspace representation, and multiple representation matrices are stacked in a tensor with weighted tensor nuclear norm to obtain high-order correlations and discover the prior information of singular values. Furthermore, adaptive graph learning automatically assigns weights to obtain a consensus graph. Meanwhile, the samples are partitioned into the ideal number of clusters through Laplacian rank constraint. An efficient distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) framework is designed to solve the proposed model. Extensive experiments are conducted on six datasets, demonstrating the superiority of the proposed model compared with eleven state-of-art methods.</p

sj-pdf-1-iji-10.1177_20587384221076472 – Supplemental Material for ETS variant transcription factor 6 enhances oxidized low-density lipoprotein-induced inflammatory response in atherosclerotic macrophages via activating NF-κB signaling

Supplemental Material, sj-pdf-1-iji-10.1177_20587384221076472 for ETS variant transcription factor 6 enhances oxidized low-density lipoprotein-induced inflammatory response in atherosclerotic macrophages via activating NF-κB signaling by Xiaofang Xiong, Zheng Yan, Wei Jiang and Xuejun Jiang in International Journal of Immunopathology and Pharmacology</p

Adaptive multi-view subspace learning based on distributed optimization

As the rapid development of Internet of Things (IoT), the data is collected from different sensors and stored in distributed devices, these data can be regarded as the multi-view data. There are currently numerous clustering algorithms designed to handle multi-view data. However, most of these algorithms still suffer from the following problems: They are designed to operate directly on raw data, which preserves excessive redundant information and increases the computational burden for subsequent tasks. They primarily focus on pairwise relationships between views, neglecting the intricate high-order connections among multiple views. The prior information of singular values is not taken into account in multi-view. Different views are considered to have equal contributions for clustering. To efficiently address the above problems, adaptive multi-view subspace learning based on distributed optimization (AMSLDO) is proposed in this paper. Specifically, the original multi-view data is projected to a low-dimensional space for subspace representation, and multiple representation matrices are stacked in a tensor with weighted tensor nuclear norm to obtain high-order correlations and discover the prior information of singular values. Furthermore, adaptive graph learning automatically assigns weights to obtain a consensus graph. Meanwhile, the samples are partitioned into the ideal number of clusters through Laplacian rank constraint. An efficient distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) framework is designed to solve the proposed model. Extensive experiments are conducted on six datasets, demonstrating the superiority of the proposed model compared with eleven state-of-art methods.</p

Direct Growth of Bilayer Graphene on SiO₂ Substrates by Carbon Diffusion through Nickel

Here we report a transfer-free method of synthesizing bilayer graphene directly on SiO2 substrates by carbon diffusion through a layer of nickel. The 400 nm nickel layer was deposited on the top of SiO2 substrates and used as the catalyst. Spin-coated polymer films such as poly(methyl methacrylate), high-impact polystyrene or acrylonitrile–butadiene–styrene, or gas-phase methane were used as carbon sources. During the annealing process at 1000 °C, the carbon sources on the top of the nickel decomposed and diffused into the nickel layer. When cooled to room temperature, bilayer graphene was formed between the nickel layer and the SiO2 substrates. The nickel films were removed by etchants, and bilayer graphene was then directly obtained on SiO2, eliminating any transfer process. The bilayer nature of the obtained graphene films on SiO2 substrates was verified by Raman spectroscopy and transmission electron microscopy. The Raman spectroscopy mapping over a 100 × 100 μm2 area indicated that the obtained graphene is high-quality and bilayer coverage is approximately 70%

Rational Design of Hybrid Graphene Films for High-Performance Transparent Electrodes

Transparent, flexible conducting films were fabricated by using a metallic grid and graphene hybrid film. Transparent electrodes using the hybrid film and transparent substrate such as glass or polyethylene terephthalate (PET) films were assembled. The sheet resistance of the fabricated transparent electrodes was as low as 3 Ω/◻ with the transmittance at ∼80%. At 90% transmittance, the sheet resistance was ∼20 Ω/◻. Both values are among the highest for transparent electrode materials to date. The materials used for the new hybrid electrode are earth-abundant stable elements, which increase their potential usefulness for replacement of indium tin oxide (ITO) in many applications

Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of high-dimensional data. Most of the existing multi-view clustering methods are based on non-negative matrix factorization (NMF), which can achieve dimensionality reduction and interpretable representation. However, there are following issues in the existing researches: (1) The existing methods based on NMF using Frobenius norm are sensitive to noises and outliers. (2) Many methods only use the information shared by multi-view data, while ignoring the diverse information between views. (3) The data graph constructed by the conventional K Nearest Neighbors (KNN) method may misclassify neighbors and degrade the clustering performance. To address the above problems, we propose a novel robust multi-view clustering method. Specifically, -norm is introduced to measure the factorization error to improve the robustness of NMF. Additionally, a diversity constraint is utilized to learn the diverse relationship of multi-view data, and an adaptive graph method via information entropy is designed to overcome the shortcomings of misclassifying neighbors. Finally, an iterative updating algorithm is developed to solve the optimization model, which can make the objective function monotonically non-increasing. The effectiveness of the proposed method is substantiated by comparing with eleven state-of-the-art methods on five real-world and four synthetic multi-view datasets for clustering tasks

Tensor Factorization with Sparse and Graph Regularization for Fake News Detection on Social Networks

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.</p