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Abstract:Cargo ships arriving at US ports are inspected for unauthorized materials. Because opening and manually inspecting every container is costly and time-consuming, tests are applied to decide whether a container should be opened. By utilizing a polyhedral description of decision trees, we develop a large-scale linear programming model for sequential container inspection that determines an optimal inspection strategy under various limitations, improving on earlier approaches in several ways: (a) we consider mixed strategies and multiple thresholds for each sensor, which provide more effective inspection strategies; (b) our model can accommodate realistic limitations (budget, sensor capacity, time limits, etc.), as well as multiple container types; (c) our model is computationally more tractable allowing us to solve cases that were prohibitive in preceding models, and making it possible to analyze the potential impact of new sensor technologies.

A strain-specific vaccine is unlikely to be available in the early phases of a potential H5N1 avian influenza pandemic. It could be months and at the current production rate may not provide timely protection to the population. Intervention strategies that control the spread of infection will be necessary in this situation, such as the use of the US stockpile of antiviral medication coupled with a 6-month school closure. The agent-based simulation model, EpiSimS, was used to assess the impact of this intervention strategy followed by three different vaccine approaches: (1) 2-dose, 80% effective, (2) 1-dose, 30% effective, and (3) 1 dose, 80% effective. Simulations show that the combination of antivirals, school closures, and a strain-specific vaccine can reduce morbidity and mortality while in effect. A significant second infection wave can occur with current vaccine technology once school closures are relaxed, though an ideal vaccine is able to contain it. In our simulations, worker absenteeism increases in all cases mostly attributed to household adults staying home with children due to the school closures.

In basic genetic algorithm (GA) applications, the fitness of a solution takes a value that is certain and unchanging. There are two classes of problem for which this formulation is insufficient. The first consists of ongoing searches for better solutions in a nonstationary environment, where the expected fitness of a solution changes with time in unpredictable ways. The second class consists of applications in which fitness evaluations are corrupted by noise. For problems belonging to either or both of these classes, the estimated fitness of a solution will have an associated uncertainty. Both the uncertainty due to environmental changes (process noise) and the uncertainty due to noisy evaluations (observation noise) can be reduced, at least temporarily, by re-evaluating existing solutions. The Kalman formulation provides a well-developed formal mechanism for treating uncertainty within the GA framework. It provides the mechanics for determining the estimated fitness and uncertainty when a new solution is generated and evaluated for the first time. It also provides the mechanics for updating the estimated fitness and uncertainty after an existing solution is re-evaluated, and for increasing the uncertainty with the passage of time. A Kalman-extended genetic algorithm (KGA) is developed to determine when to generate a new individual, when to re-evaluate an existing individual, and which one to re-evaluate. This KGA is applied to the problem of maintaining a network configuration with minimized message loss, in which the nodes are mobile, and the transmission over a link is stochastic. As the nodes move, the optimal network changes, but information contained within the population of solutions allows efficient discovery of better-adapted solutions. The ability of the KGA to continually find near-optimal solutions is demonstrated at several levels of process and observation noise. The sensitivity of the KGA performance to several control parameters is explored. Index terms-Genetic algorithm, Kalman filter, adaptive control, network optimization.

The effects of Nuclear Elastic Scattering (NES) on the properties of Cat-D and D-3 He plasmas are investigated, with special attention given to the discrete nature of NES. A space-independent, linear multi-group method is used for the study. It is found that NES can significantly reduce the nr requirements for plasma ignition; for plasmas characterized by relatively large cyclotron radiation losses, this reduction can exceed 50%. In addition, the treatment of the discrete NES behaviour is shown to be essential for an accurate prediction of the superthermal ion distribution functions.