A type of novel retention aid, cross-linked gelatin, was prepared using lowgrade industrial gelatin as the raw material and glutaraldehyde as the crosslinking agent. The structure of cross-linked gelatin was characterized according to the crosslinking degree, isoelectric point, Fourier transform infrared spectroscopy, and ultraviolet-visible spectroscopy. The results indicated that the crosslinking reaction was successfully performed between the primary amine group of gelatin and the aldehyde group of glutaraldehyde, resulting in the formation of a Schiff base structure. The retention test showed that the addition of cross-linked gelatin remarkably improved the retention of filler. This effect was mainly attributed to the fact that cross-linked gelatin, with a high molecular weight and highly branched structure, exhibited favorable bridging flocculation and induced filler aggregation into the flocs, which were retained in the paper sheet. The drainage test showed that the cross-linked gelatin exhibited a poor drainage effect, which was attributed to the synergic effects of excellent hydrophilicity, film forming property, and sealing property.

In order to obtain the information of the vehicle tags in adverse traffic conditions, we proposed a novel reservation framework named reservation to cancel idle-dynamic frame slotted ALOHA (RTCI-DFSA) algorithm. Firstly, the framework employed reservation mechanism to remove idle slot, and thus improve the system identification efficiency. Secondly, the vehicle information was identified by the tag serialization polling identification method. The experimental results showed that the proposed RTCI-DFSA algorithm performed better than the traditional frame slotted ALOHA (FSA) and dynamic frame slotted ALOHA (DFSA) algorithms. More specifically, the tag loss rate of the proposed framework is significantly lower than the frame length fixed and conventional dynamic vehicle identification algorithms. In addition, the experimental results demonstrated that the throughput rate of the proposed algorithm increased from 0.368 to 0.6. Besides, the identification efficiency and applicability of the proposed framework were both higher than other tag identification algorithms.

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