EVALUATION AND MONITORING OF ASH (FRAXINUS SPP.) TOLERANT TO LONG-TERM EMERALD ASH BORER (AGRILUS PLANIPENNIS [COLEOPTERA: BUPRESTIDAE]) EXPOSURE

Invasive insects that successfully establish in introduced areas can significantly alter natural communities. These pests require specific establishment criteria (e.g. host suitability) that, when known, can help quantify potential damage to infested areas. Emerald ash borer (Agrilus planipennis [Coleoptera: Buprestidae]) is an invasive phloem-feeding pest which is responsible for the death of millions of ash trees (Fraxinus spp. L.). Over 200 surviving ash trees were previously identified in the Huron-Clinton Metroparks located in southeast Michigan. Trees were assessed over a four year period and a hierarchical cluster analysis was performed on dieback, vigor, and presence of signs and symptoms, in order to place trees into one of three tolerance groups. The clustering of trees with different responses to emerald ash borer attack suggests that there are different tolerance levels in North American ash trees in southeastern Michigan, and these groups were designated as apparently tolerant, not tolerant and intermediate tolerance. Adult landing rates and evidence of adult emergence were significantly lower in the apparently tolerant group compared with the not tolerant group, but larval survival from eggs placed on trees did not differ between tolerance groups. Therefore, it appears that apparently tolerant trees survive because they are less attractive to adult beetles which results in fewer eggs being laid on them. Trees in the apparently tolerant group remained of higher vigor over the four years of the study. North American ash may survive the emerald ash borer epidemic due to natural variation and inherent resistance regardless of the lack of co-evolutionary history with emerald ash borer.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.