Half-titanocene 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate chlorides: Synthesis, characterization and ethylene (co-) polymerization behavior

A series of half-titanocene chloride complexes bearing 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate ligands (L), CpTiLCl₂, has been synthesized in acceptable yields by the stoichiometric reaction of CpTiCl₃ with the respective potassium 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate. All half-titanocene complexes were fully characterized by elemental analysis and NMR spectroscopy, and the molecular structures of the representative complexes C1 and C2 were confirmed as pseudo octahedral at titanium by single-crystal X-ray diffraction. When activated with methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), all titanium complexes exhibited good activities (up to 4.8 × 10⁵ g mol⁻¹(Ti) h⁻¹) towards ethylene polymerization. The obtained polyethylene exhibited ultra-high molecular weight (up to 11.82 × 10⁵ g mol⁻¹) with narrow polydispersity. Furthermore, effective co-polymerization of ethylene with 1-hexene or 1-octene was achieved with several percentages of co-monomer incorporation in the resultant polyethylenes

Biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridyl-cobalt complexes: synthesis, characterization and ethylene polymerization behavior

A series of biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridine derivatives reacted with cobalt dichloride in dichloromethane/ethanol to afford the corresponding binuclear cobalt complexes. The cobalt complexes were characterized by FT-IR spectroscopy and elemental analysis, and the structure of a representative complex was confirmed by single-crystal X-ray diffraction. Upon activation with either MAO or MMAO, these cobalt complexes performed with high activities of up to 1.2 × 10⁷ g (mol of Co)⁻¹ h⁻¹ in ethylene polymerization, which represents one of the most active cobalt-based catalytic systems in ethylene reactivity. These biphenyl-bridged bis(imino)pyridylcobalt precatalysts exhibited higher activities than did their mononuclear bis(imino)pyridylcobalt precatalyst counterparts, and more importantly, the binuclear precatalysts revealed a better thermal stability and longer lifetimes. The polyethylenes obtained were characterized by GPC, DSC, and high-temperature NMR spectroscopy and mostly possessed unimodal and highly linear features

Network representation learning: From traditional feature learning to deep learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field

Robust graph neural networks via ensemble learning

Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive scenarios. Some researchers concentrate on constructing an ensemble model to mitigate the nonrobustness issues. Nevertheless, these methods ignore the interaction among base models, leading to similar graph representations. Moreover, due to the deterministic propagation applied in most existing GNNs, each node highly relies on its neighbors, leaving the nodes to be sensitive to perturbations. Therefore, in this paper, we propose a novel framework of graph ensemble learning based on knowledge passing (called GEL) to address the above issues. In order to achieve interaction, we consider the predictions of prior models as knowledge to obtain more reliable predictions. Moreover, we design a multilayer DropNode propagation strategy to reduce each node’s dependence on particular neighbors. This strategy also empowers each node to aggregate information from diverse neighbors, alleviating oversmoothing issues. We conduct experiments on three benchmark datasets, including Cora, Citeseer, and Pubmed. GEL outperforms GCN by more than 5% in terms of accuracy across all three datasets and also performs better than other state-of-the-art baselines. Extensive experimental results also show that the GEL alleviates the nonrobustness and oversmoothing issues. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

1,2,3-Triazole-Containing Compounds as Anti–Lung Cancer Agents: Current Developments, Mechanisms of Action, and Structure–Activity Relationship

Lung cancer is the most common malignancy and leads to around one-quarter of all cancer deaths. Great advances have been achieved in the treatment of lung cancer with novel anticancer agents and improved technology. However, morbidity and mortality rates remain extremely high, calling for an urgent need to develop novel anti–lung cancer agents. 1,2,3-Triazole could be readily interact with diverse enzymes and receptors in organisms through weak interaction. 1,2,3-Triazole can not only be acted as a linker to tether different pharmacophores but also serve as a pharmacophore. This review aims to summarize the recent advances in 1,2,3-triazole–containing compounds with anti–lung cancer potential, and their structure–activity relationship (SAR) together with mechanisms of action is also discussed to pave the way for the further rational development of novel anti–lung cancer candidates