The influence of concurrent disturbances on plant community dynamics in northern hemlock-hardwood forests

Throughout the Upper Great Lakes region, alterations to historic disturbance regimes have influenced plant community dynamics in hemlock-hardwood forests. Several important mesic forest species, eastern hemlock (Tsuga canadensis), yellow birch (Betula alleghaniensis), eastern white pine (Pinus strobus), and Canada yew (Taxus canadensis), are in decline due to exploitive logging practices used at the turn of the 20th century and the wave of intense fires that followed. Continued regeneration and recruitment failure is attributed to contemporary forest management practices and overbrowsing by white-tailed deer (Odocoileus virginianus). Therefore, I examined the influence of two concurrent disturbances, overstory removal and herbivory, on plant community dynamics in two hemlock-hardwood forests. I measured the post-disturbance regeneration response (herbaceous and woody species) inside and outside of deer exclosures in 20 artificial canopy gaps (50 – 450 m2) and monitored survival and growth for hundreds of planted seedlings. The results of this research show that interacting disturbances can play a large role in shaping plant community composition and structure in hemlock-hardwood forests. White-tailed deer herbivory homogenized the post-disturbance plant communities across the experimental gradient of gap areas, essentially making species compositions in small gaps “look like” those in large gaps. Deer browsing also influenced probability of survival for planted Canada yew cuttings; all else being equal an individual was nearly seven times more likely to survive if protected from herbivory (P < 0.001). In contrast, the ability of sugar maple (Acer saccharum) to persist under high levels of herbivory and respond rapidly to overstory release appears to be related to the presence of stem layering(i.e., portions of below-ground prostrate stem). Layering occurred in 52% of excavated saplings (n = 100) and was significantly associated with increased post-disturbance height growth. Understory light was also important to planted seedling establishment and height growth. Higher levels of direct under-canopy light negatively impacted survival for shade-tolerant hemlock and Canada yew, while an increase in diffuse light was linked to a higher probability of survival for yellow birch and height growth for hemlock and Canada yew. Increases in white pine height growth were also significantly associated with a decrease in canopy cover.

DESIGN AND IMPLEMENT DYNAMIC PROGRAMMING BASED DISCRETE POWER LEVEL SMART HOME SCHEDULING USING FPGA

With the development and capabilities of the Smart Home system, people today are entering an era in which household appliances are no longer just controlled by people, but also operated by a Smart System. This results in a more efficient, convenient, comfortable, and environmentally friendly living environment. A critical part of the Smart Home system is Home Automation, which means that there is a Micro-Controller Unit (MCU) to control all the household appliances and schedule their operating times. This reduces electricity bills by shifting amounts of power consumption from the on-peak hour consumption to the off-peak hour consumption, in terms of different “hour price”. In this paper, we propose an algorithm for scheduling multi-user power consumption and implement it on an FPGA board, using it as the MCU. This algorithm for discrete power level tasks scheduling is based on dynamic programming, which could find a scheduling solution close to the optimal one. We chose FPGA as our system’s controller because FPGA has low complexity, parallel processing capability, a large amount of I/O interface for further development and is programmable on both software and hardware. In conclusion, it costs little time running on FPGA board and the solution obtained is good enough for the consumers.

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency.