Hexagonal boron nitride (h-BN) catalyst has recently been reported to be highly selective in oxidative dehydrogenation of propane (ODHP) for olefin production. In addition to propene, ethylene also forms with much higher overall selectivities to C2-products than to C1-products. In this work, we report that the reaction pathways over the h-BN catalyst are different from the V-based catalysts in ODHP. Oxidative coupling reaction of methyl, an intermediate from the cleavage of C─C bond of propane, contributes to the high selectivities to C2-products, leading to more C2-products than C1-products over the h-BN catalyst. This work not only provides insight into the reaction mechanisms involved in ODHP over the boron-based catalysts but also sheds light on the selective oxidation of alkanes such as direct upgrading of methane via oxidative upgrading to ethylene or CHxOy on boron-based catalysts.

Today, new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF) based algorithm has been the base of real-time tracking algorithms because of the high tracking efficiency. However, CF based algorithm are usually failed to track objects under complex environments. Therefore, this paper proposes a fuzzy detection strategy to pre-judge the tracking result. If the pre-judge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. Testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.

In the era of rapid development of artificial intelligence, the integration of multimedia and human-artificial intelligence (H-AI) has become an important research hotspot. Especially in the multimedia environment, effective remote visual monitoring has become the exploration direction of many scholars. The use of traditional filtering algorithm (CF) for real-time monitoring in the context of multimedia is a practical strategy. However, most existing filtering-based visual monitoring algorithms still have the problems of insufficient robustness and effectiveness. Therefore, by considering the strategy of updating human memory, this paper proposes a multi-layer template update mechanism to achieve effective monitoring in a multimedia environment. In this strategy, the weighted template of the high-confidence matching memory is used as the confidence memory, and the unweighted template of the low-confidence matching memory is used as the cognitive memory. Through the alternate use of confidence memory, matching memory, and cognitive memory, it is ensured that the target will not be lost during the monitoring process. Experimental result s show that this strategy does not affect the speed (still real-time) and improves the robustness in the multimedia background.

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