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Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the feature‐matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization‐based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network‐flow model to approach the feature‐matching problem, as well as the important factors for designing optimization‐based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization‐based conflation models. We conclude with a summary of findings and point out directions of future research.

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Beacon messages and emergency messages in vehicular ad hoc networks (VANETs) require a lower delay and higher reliability. The optimal MAC protocol can effectively reduce data collision in VANETs communication, thus minimizing delay and improving reliability. In this paper, we propose a Q-learning MAC protocol based on detecting the number of two-hop neighbors. The number of two-hop neighbors in highway scenarios is calculated with very little overhead using the beacon messages and neighbor locations to reduce the impact of hidden nodes. Vehicle nodes are regarded as agents, using Q-learning and beacon messages to train the near-optimal contention window value of the MAC layer under different vehicle densities to reduce the collision probability of beacon messages. Furthermore, based on the contention window value after training, a multi-hop broadcast protocol combined with contention window adjustment for emergency messages in highway scenarios is proposed to reduce forwarding delay and improve forwarding reliability. We use the trained contention window value and the state information of neighboring vehicles to assign an appropriate forwarding waiting time to the forwarding node. Simulation experiments are conducted to evaluate the proposed MAC protocol and multi-hop broadcast protocol and compare them with other related protocols. The results show that our proposed protocols outperform the other related protocols on several different evaluation metrics.

Muskmelon pedicel is the fruit stalk of muskmelon and one of the traditional Chinese medicines, which can be used to treat jaundice, diabetes and neuropathy. However, in recent years, agricultural soil heavy metal cadmium (Cd) pollution has become serious, coupled with the imperfect sales management of herbal medicine, increasing the potential health risk of contaminated herbal medicine in the human body. In this paper, the comprehensive quality of contaminated muskmelon was tested. The results showed that Cd stress significantly inhibited the growth of muskmelon plants, reduced the anthocyanin and chlorophyll contents, and increased the fruit size and sweetness of muskmelon. In addition, heavy metal Cd can also cause oxidative stress in plants, resulting in a series of changes in antioxidant enzyme activities. In the experimental group, the content of polyphenols and saponins increased by 27.02% and 23.92%, respectively, after high-concentration Cd treatment, which may be a mechanism of plant resistance to stress. This paper reveals that the content of bioactive substances in Chinese herbal medicine is high, but the harm in heavy metals cannot be underestimated, which should be paid attention to by relevant departments.