AN INDIVIDUAL‐BASED MODEL FOR FERAL HOGS IN GREAT SMOKY MOUNTAINS NATIONAL PARK

The expansion of feral hog (Sus scrofa) populations in the United States has resulted in increased efforts to develop and implement control strategies designed to minimize the impacts done by this invasive species. We describe an individual‐based model for feral hogs in Great Smoky Mountains National Park (GSMNP). The objectives of the model are to provide an understanding of the population dynamics of this feral hog population and to determine the efficacy of the annual harvest as a population control method. Results suggest that the dynamics of the population are driven by fall hard mast production and the GSMNP harvests currently limit growth of the population, but these control efforts have not reduced the population.     

Nonlinear Optimal Control Problems and the Method of Multiple Scales

The method of multiple scales is used to obtain an approximatesolution to the problem of the optimal control of a second-orderdifferential equation containing a small nonlinearity. Applicationsare made to the Duffing and van der Pol equations.     

The Numerical Solution of the Linear Regulator Problem with Positive Control

The standard linear regulator problem with positive controlis solved using a minimum principle, a maximum principle, andthe hypersphere inequality method. Comparisons are made betweenthe numerical results produced, to examine the theoretical superiorityof the hypersphere method.     

Calculation of accurate imaginary frequencies and tunnelling coefficients for hydrogen abstraction reactions using IRCmax

The effect of level of theory on the imaginary frequency and corresponding tunnelling coefficients has been studied for a test set of hydrogen abstraction reactions: ?CH₂X + CH₃Y → CH₃X + ?CH₂Y for (X,Y) = (H,H), (H,CN), (H,F), (H,Li) and (F,Li). It is found that the imaginary frequency is very sensitive to the level of theory used, with Hartree-Fock (HF) methods severely overestimating the imaginary frequency compared with high-level CCSD(T)/6-311G(d,p) calculations. The errors for the other methods are smaller but nonetheless significant, with MP2 methods overestimating the imaginary frequency and density functional theory (DFT) methods underestimating it. In the case of the HF methods, this leads to errors in the tunnelling coefficient of several orders of magnitude, while for the better DFT and MP2 methods errors of a factor of 2–3 are observed. To address this problem, an IRCmax procedure for estimating the imaginary frequency has been developed and it is found that IRCmax imaginary frequencies calculated with CCSD(T)/6-311G(d,p) single points along a low-level HF/6-31G(d) minimum energy path provide excellent approximations to the high-level values, at a fraction of the computational cost.     

UNDERSTANDING MULTIPLE SPECIES ECOSYSTEM DYNAMICS USING A CONSUMER RESOURCE MODEL

Most ecological systems comprise multiple species coexisting and the dynamics of these multiple species can be important for understanding, management, and conservation. One method to study such ecological system dynamics is the use of heterogeneous models. Here we formulate and analyze a multiple species (n patches or groups) consumer resource model. Initial insights are gained by analyzing the special cases and . A threshold consumption number C₀ is used to investigate system stability and hence the long‐term dynamics of the system. It is shown how this threshold consumption number can measure the effects and extent of multiple species coexistence in the system.     

OPTIMAL CONTROL INTERVENTION STRATEGIES USING AN N‐PATCH WATERBORNE DISEASE MODEL

Waterborne diseases are an important concern in public health, especially in communities with limited access to clean water. Different community subpopulations can require different copping strategies for the same diseases. Modeling is one method to assist understanding and the development of effective strategies. To this end, we investigated the use of meta‐population models with three types of control interventions: vaccination, treatment, and water purification. Important mathematical features of the model are determined and examined. Optimal control, applied to the model, is also formulated to determine the effective strategies to reduce the spread of the disease. For example, using optimal control, a four‐fold reduction in infected individuals is possible. The value of such an improvement to the communities involved would be significant.