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2,6-di-tert-butyl-p-cresol (T501) is the most commonly used antioxidant for insulating oils. It has been found that the amount of T501 added to the insulating oil affects the physical and electrical properties of the insulating oil. In this paper, the effects of antioxidants on insulating oils at different concentrations were studied. The research focused on acid value, water-soluble acid or alkali, flash point, kinematic viscosity, volume resistivity and interfacial tension. An aging test was conducted at 115°C to test changes in acid value, sludge, and volume resistivity. Add the same quality insulating paper to the oil sample with the blank oil and T501 added in the amount of 1% (mass fraction), and isolate the oxygen to the effect of furfural before and after the addition of the antioxidant after aging at 130 °C. The results show that with the increase of the amount of antioxidant T501, the interfacial tension, acid value and electrical resistivity will decrease its performance index, but the effect on flash point, viscosity and pour point is not obvious. The aging test by heating the open cup shows that the antioxidant has an obstructive effect on the acid value and the sludge, delays the aging of the insulating oil, but accelerates the aging hydrolysis of the insulating paper. Therefore, it is recommended that the antioxidant content in the insulating oil should not be excessively added. In the insulating oil, the proportion of the antioxidant in the insulating oil should be reduced on the premise that the oxidation resistance can be achieved, and the T501 part consumes the logistics.

Exploring and predicting the hot scientific researching issues have been a focus among recent sci-tech information research. In recent years, scholars and academic literature are increasing steadily in number. It is hard to artificially track and deal with developing trends of scientific researching hotspot. The most commonly used methods in the past are simple statistical ways for keywords and high term frequency. Most of these methods spend a lot of time and manpower, and ignore the relevance between different words. In the paper we use RNN method to design our prediction and push system, which could perceive the potential researching hotspot for some time to come. The hot issues are generated based on the relevance among sci-tech words. It finds the relevant technical application to hotspots and recommends them to academic researchers. The extensive experiments demonstrate that our proposed approach has higher accuracy rate and lower false positive ratio. It can do better forward looking forecasts than word frequency statistics as well.