With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestria...With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58%of energy consumption compared with the baseline methods.展开更多
The development of heterogeneous acid catalysts with higher activity than homogeneous acid catalysts is critical and still challenging.In this study,acidic poly(ionic liquid)s with swelling ability(SAPILs)were designe...The development of heterogeneous acid catalysts with higher activity than homogeneous acid catalysts is critical and still challenging.In this study,acidic poly(ionic liquid)s with swelling ability(SAPILs)were designed and synthesized via the free radical copolymerization of ionic liquid monomers,sodium p-styrenesulfonate,and crosslinkers,followed by acidification.The 31P nuclear magnetic resonance chemical shifts of adsorbed trimethylphosphine oxide indicated that the synthesized SAPILs presented moderate and single acid strength.The thermogravimetric analysis results in the temperature range of 300–345°C revealed that the synthesized SAPILs were more stable than the commercial resin Amberlite IR-120(H)(245°C).Cryogenic scanning electron microscopy testing demonstrated that SAPILs presented unique three-dimensional(3D)honeycomb structure in water,which was ascribed to the swelling-induced self-assembly of the molecules.Moreover,we used SAPILs with micron-sized honeycomb structure in water as catalysts for the hydrolysis of cyclohexyl acetate to cyclohexanol,and determined that their catalytic activity was much higher than that of homogeneous acid catalysts.The equilibrium concentrations of all reaction components inside and outside the synthesized SAPILs were quantitatively analyzed using a series of simulated reaction mixtures.Depending on the reaction mixture,the concentration of cyclohexyl acetate inside SAPIL-1 was 7.5–23.3 times higher than that outside of it,which suggested the high enrichment ability of SAPILs for cyclohexyl acetate.The excellent catalytic performance of SAPILs was attributed to their 3D honeycomb structure in water and high enrichment ability for cyclohexyl acetate,which opened up new avenues for designing highly efficient heterogeneous acid catalysts that could eventually replace conventional homogeneous acid catalysts.展开更多
基金the National Natural Science Foundation of China under Grant No.61972230the Natural Science Foundation of Shandong Province of China under Grant No.ZR2021LZH006.
文摘With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58%of energy consumption compared with the baseline methods.
文摘The development of heterogeneous acid catalysts with higher activity than homogeneous acid catalysts is critical and still challenging.In this study,acidic poly(ionic liquid)s with swelling ability(SAPILs)were designed and synthesized via the free radical copolymerization of ionic liquid monomers,sodium p-styrenesulfonate,and crosslinkers,followed by acidification.The 31P nuclear magnetic resonance chemical shifts of adsorbed trimethylphosphine oxide indicated that the synthesized SAPILs presented moderate and single acid strength.The thermogravimetric analysis results in the temperature range of 300–345°C revealed that the synthesized SAPILs were more stable than the commercial resin Amberlite IR-120(H)(245°C).Cryogenic scanning electron microscopy testing demonstrated that SAPILs presented unique three-dimensional(3D)honeycomb structure in water,which was ascribed to the swelling-induced self-assembly of the molecules.Moreover,we used SAPILs with micron-sized honeycomb structure in water as catalysts for the hydrolysis of cyclohexyl acetate to cyclohexanol,and determined that their catalytic activity was much higher than that of homogeneous acid catalysts.The equilibrium concentrations of all reaction components inside and outside the synthesized SAPILs were quantitatively analyzed using a series of simulated reaction mixtures.Depending on the reaction mixture,the concentration of cyclohexyl acetate inside SAPIL-1 was 7.5–23.3 times higher than that outside of it,which suggested the high enrichment ability of SAPILs for cyclohexyl acetate.The excellent catalytic performance of SAPILs was attributed to their 3D honeycomb structure in water and high enrichment ability for cyclohexyl acetate,which opened up new avenues for designing highly efficient heterogeneous acid catalysts that could eventually replace conventional homogeneous acid catalysts.
