https://hal.archives-ouvertes.fr/hal-00382623/document

Functional fluoropolymers for fuel cell membranes

Suzuki–Miyaura Cross-Coupling Reactions of Alkylboronic Acid Derivatives or Alkyltrifluoroborates with Aryl, Alkenyl or Alkyl Halides and Triflates

Synthesis and (co)polymerization of monofluoro, difluoro, trifluorostyrene and ((trifluorovinyl)oxy)benzene

not available for EP1613595Abstract of corresponding document: US2005014753The invention provides a novel class of compounds, pharmaceutical compositions comprising such compounds and methods of using such compounds to treat or prevent diseases or disorders associated with abnormal or deregulated tyrosine kinase activity, particularly diseases associated with the activity of PDGF-R, c-Kit and Bcr-abl.

Novel compounds and compositions as protein kinase inhibitors

This article highlights the preparation of perfluorocyclobutyl (PFCB) aryl ether polymers for a multitude of commercial technologies that are of academic and commercial global interest. In this account, the synthesis of various aryl trifluorovinyl ether (TFVE) monomers tailored for specific applications is discussed. The preparation of PFCB aryl ether polymers and their properties is then presented. Topics of PFCB aryl ether polymers and their applications include photonics, polymer light emitting diodes (PLEDs), proton exchange membranes (PEMs) for fuel cells, atomic oxygen (AO) resistant coatings, and hybrid composites.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pola.22390

Science and technology of perfluorocyclobutyl aryl ether polymers

Synthesis of 4,6-disubstituted pyrimidines via Suzuki and Kumada coupling reaction of 4,6-dichloropyrimidine

Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting. It can be observed from recent research that a variety of transformer-based models have shown remarkable results in time-series forecasting. However, there are still some issues that limit the ability of transformer-based models on time-series forecasting tasks: (i) learning directly on raw data is susceptible to noise due to its complex and unstable feature representation; (ii) the self-attention mechanisms pay insufficient attention to changing features and temporal dependencies. In order to solve these two problems, we propose a transformer-based differentially reconstructed attention model DRAformer. Specifically, DRAformer has the following innovations: (i) learning against differenced sequences, which preserves clear and stable sequence features by differencing and highlights the changing properties of sequences; (ii) the reconstructed attention: integrated distance attention exhibits sequential distance through a learnable Gaussian kernel, distributed difference attention calculates distribution difference by mapping the difference sequence to the adaptive feature space, and the combination of the two effectively focuses on the sequences with prominent associations; (iii) the reconstructed decoder input, which extracts sequence features by integrating variation information and temporal correlations, thereby obtaining a more comprehensive sequence representation. Extensive experiments on four large-scale datasets demonstrate that DRAformer outperforms state-of-the-art baselines.

DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting

A series of 4,6-disubstituted pyrimidines were synthesized via Suzuki and Kumada coupling reaction of 4,6-dichloropyrimidine.

Benhan Li

Papers

Synthesis of 4,6-disubstituted pyrimidines via Suzuki and Kumada coupling reaction of 4,6-dichloropyrimidine

DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting

Synthesis of 4,6‐Disubstituted Pyrimidines via Suzuki and Kumada Coupling Reaction of 4,6‐Dichloropyrimidine.