3results about How to "Improve polymerization effect" patented technology

The application discloses an organic silicon emulsion, a preparation method and a method for dehydrating and shaping water-saturated wooden cultural relics, and relates to the field of water-saturated wooden cultural relic protection research.The preparation method of the organic silicon emulsion comprises the following steps: taking 2-5 parts of organic silicon monomer, 1-3 parts of a surfactant, 0.3-1 parts of a co-surfactant and 3-6 parts of pure water for standby; the surfactant and the co-surfactant are added into the organic silicon monomer, and uniform stirring or oscillation is continuously carried out to form a transparent organic silicon dispersion liquid; and the organic silicon dispersion liquid is poured into the pure water, and sufficient stirring or oscillation is carried out to form a stable organic silicon emulsion.The organic silicon emulsion material prepared by the application has good dehydrating and shaping performance, and can significantly enhance the dimensional stability, mechanical properties, microbial resistance and environmental stability of the cultural relics, and is environment-friendly and efficient.

One embodiment according to the present disclosure relates to a monomer for an optical component, including: a compound represented by the formula (1), in which a pH obtained through a pH measurement method below is 4.0 to 7.5. (pH Measurement method) 80 g of a sample, 80 mL of toluene, and 250 mL of water are mixed together in a liquid separation funnel, an aqueous phase is separated off, and the pH of the aqueous phase is measured.

The present application belongs to the technical field of distributed machine learning and edge intelligence, and discloses a self-adaptive quantization decentralized learning method and system for heterogeneous edge devices. The present application is directed to a plurality of edge devices with heterogeneous computing resources, heterogeneous storage capabilities and time-varying communication links. Under the condition of no central server participation, through training parameter and communication relationship initialization, training quantization scale self-adaptive determination, low-precision local training, communication quantization scale self-adaptive determination, quantization model information exchange and neighbor aggregation update, decentralized collaborative learning is completed. The present application can simultaneously alleviate the resource limitation problem of heterogeneous edge devices in the training stage and the communication stage, ensure the implementability of decentralized collaborative learning, improve resource utilization efficiency and improve model convergence stability.