Ophiostomatalean fungi may facilitate bark beetle colonization and reproduction.In the present study of the fungal community associated with bark beetle species belonging to Tomicus in Yunnan,China,six ophiostomatalea...Ophiostomatalean fungi may facilitate bark beetle colonization and reproduction.In the present study of the fungal community associated with bark beetle species belonging to Tomicus in Yunnan,China,six ophiostomatalean fungi(Ophiostoma canum,O.ips,O.tingens,Leptographium yunnanense,Leptographium sp.1 and Leptographium sp.2)were isolated from the beetles or their galleries;O.canum was the most common fungal species.The distribution of O.canum was associated with stands heavily damaged by Tomicus species and a higher percentage of valid galleries of Tomicus yunnanensis and T.minor in Yunnan pine(Pinus yunnanensis).After inoculation of Yunnan pine with the fungus,a phloem reaction zone formed and monoterpenes accumulated in the phloem.These results suggested that O.canum was pathogenic to Yunnan pine and that the wide distribution of the fungus might be benefi cial to reproduction of pine shoot beetles in Yunnan pine.However,because the reaction zone and monoterpene accumulation were mild,fungal damage of Yunnan pine might be limited.A more integrated study considering all the fungal species should be done to better understand the interactions among bark beetles,blue-stain fungi,and the tree hosts in the region.展开更多
The use of groups of unmanned aerial vehicles（UAVs） has greatly expanded UAV＇s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is impor...The use of groups of unmanned aerial vehicles（UAVs） has greatly expanded UAV＇s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar（dynamic） model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters.Simulation results have demonstrated the efficiency and flexibility of the proposed approach.展开更多
文摘Ophiostomatalean fungi may facilitate bark beetle colonization and reproduction.In the present study of the fungal community associated with bark beetle species belonging to Tomicus in Yunnan,China,six ophiostomatalean fungi(Ophiostoma canum,O.ips,O.tingens,Leptographium yunnanense,Leptographium sp.1 and Leptographium sp.2)were isolated from the beetles or their galleries;O.canum was the most common fungal species.The distribution of O.canum was associated with stands heavily damaged by Tomicus species and a higher percentage of valid galleries of Tomicus yunnanensis and T.minor in Yunnan pine(Pinus yunnanensis).After inoculation of Yunnan pine with the fungus,a phloem reaction zone formed and monoterpenes accumulated in the phloem.These results suggested that O.canum was pathogenic to Yunnan pine and that the wide distribution of the fungus might be benefi cial to reproduction of pine shoot beetles in Yunnan pine.However,because the reaction zone and monoterpene accumulation were mild,fungal damage of Yunnan pine might be limited.A more integrated study considering all the fungal species should be done to better understand the interactions among bark beetles,blue-stain fungi,and the tree hosts in the region.
基金supported by Fundamental Research Funds for Central Universities of China(No.NS2014090)
文摘The use of groups of unmanned aerial vehicles（UAVs） has greatly expanded UAV＇s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar（dynamic） model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters.Simulation results have demonstrated the efficiency and flexibility of the proposed approach.
