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Actinidia arguta pollen owns abundant nutrients, such as vitamins, polyphenols, etc., however, little research on its antioxidant ability and biological function was conducted. In this study, we observed A. arguta pollen spore structure by SEM (Scanning electron microscope), analyzed the phenolic composition of A. arguta pollen extract (AAPE) obtained by four extraction methods (A: ultrasound-assisted extraction with water, B: heat reflux extraction with water, C: ultrasound-assisted extraction with ethanol, and D: heat reflux extraction with ethanol). Total phenolic content, total flavonoid content, antioxidant activities (ferric reducing/antioxidant power [FRAP], chelating activity, and DPPH·scavenging activity) were also determined. Finally, we investigated its protective effect on DNA and lymphocytes damage response to oxidative stress. The results showed that the morphology of A. arguta pollen was similar to other pollen of the genus A. Lindl., but differs from them slightly in the specific morphology indicators. What is more, AAPE obtained by different extraction methods exhibited a protective effect against DNA oxidative damage, they also possessed a strong cytoprotection effect on mouse lymphocytes, especially the extraction obtained by method C, which had the highest total phenolic content (15.05 ± 0.34 mg GAE/g), strong ferrous ion-chelating ability (0.37 ± 0.023 mg Na2EDTA/g), DPPH· scavenging activity (IC50 = 0.14 ± 0.04 mg/ml), and FRAP (7.13 ± 0.33 mg Trolox/g). This paper provided a new edible natural antioxidant for human which will protect us from oxidative stress. Practical applications This study was the first to report the morphology of Actinidia arguta pollen by SEM analysis, and studied the effects of different extraction methods on antioxidant activities and cytoprotective effect of A. arguta pollen extract. We believed that our research makes a significant contribution to the literature, because the results of this research provide a reference for the development of a new edible natural antioxidant. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect. [ABSTRACT FROM AUTHOR]

Consistent normal orientation is challenging in the presence of noise, non-uniformities and thin sharp features. None of any existing local or global methods is capable of orienting all point cloud models consistently, and none of them offers a mechanism to rectify the inconsistent normals. In this paper, we present a new normal orientation method based on the multi-source propagation technique with two insights: faithful normals respecting sharp features tend to cause incorrect orientation propagation, and propagation orientation just using one source is problematic. It includes a novel orientation-benefit normal estimation algorithm for reducing wrong normal propagation near sharp features, and a multi-source orientation propagation algorithm for orientation improvement. The results of any orientation methods can be corrected by adding more credible sources, interactively or automatically, then propagating. To alleviate the manual work of interactive orientation, we devise an automatic propagation source extraction method by visibility voting. It can be applied directly to find multiple credible sources, combining with our orientation-benefit normals and multi-source propagation technique, to generate a consistent orientation, or to rectify an inconsistent orientation. The experimental results show that our methods generate consistent orientation more or as faithful as those global methods with far less computational cost. Hence it is more pragmatic and suitable to handle large point cloud models. [ABSTRACT FROM AUTHOR]