Mathematical modeling of infectious diseases is increasingly used to explicate the mechanics of disease propagation, impact of controls, and sensitivity of countermeasures. The authors demonstrate use of a Rift Valley Fever (RVF) model to study efficacy of countermeasures to disease transmission parameters. RVF is a viral infectious disease that propagates through infected mosquitoes and primarily affects animals but also humans. Vaccines exist to protect against the disease but there is lack of data comparing efficacy of vaccination with alternative countermeasures such as managing mosquito population or destroying infected livestock. This paper presents a compartmentalized multispecies deterministic ordinary differential equation model of RVF propagation among livestock through infected Aedes and Culex mosquitoes and exercises the model to study the efficacy of vector adulticide, vector larvicide, livestock vaccination, and livestock culling on livestock population. Results suggest that livestock vaccination and culling offer the greatest benefit in terms of reducing livestock morbidity and mortality.

This article presents a practical approach to engineering a robot to effectively navigate in an urban environment. Inherent in this approach is the use of relatively simple sensors, actuators, and processors to generate robot vision, intelligence, and planning. Sensor data are fused from multiple low-cost, two-dimensional laser scanners with an innovative rotational mount to provide three-dimensional coverage with image processing using both range and intensity data. Information is combined with Doppler radar returns to yield a world view processed by a context-based reasoning control system to yield tactical mission commands forwarded to traditional proportional-integral-derivative (PID) control loops. As an example of simplicity and robustness, steering control successfully utilized a relatively simple follow-the-carrot guidance approach that has been successfully demonstrated at speeds of 60 mph (97 km/h). The approach yielded a robot that reached the finals of the Urban Challenge and completed approximately 2 h of the event before being forced to withdraw as a result of a global positioning system data failure. C 2008 Wiley Periodicals, Inc.

As a category A agent in the Center for Disease Control bioterrorism list, Rift Valley fever (RVF) is considered a major threat to the United States (USA). Should the pathogen be intentionally or unintentionally introduced to the continental USA, there is tremendous potential for economic damages due to loss of livestock, trade restrictions, and subsequent food supply chain disruptions. We have incorporated the effects of space into a mathematical model of RVF in order to study the dynamics of the pathogen spread as affected by the movement of humans, livestock, and mosquitoes. The model accounts for the horizontal transmission of Rift Valley fever virus (RVFV) between two mosquito and one livestock species, and mother-to-offspring transmission of virus in one of the mosquito species. Space effects are introduced by dividing geographic space into smaller patches and considering the patch-to-patch movement of species. For each patch, a system of ordinary differential equations models fractions of populations susceptible to, incubating, infectious with, or immune to RVFV. The main contribution of this work is a methodology for analyzing the likelihood of pathogen establishment should an introduction occur into an area devoid of RVF. Examples are provided for general and specific cases to illustrate the methodology.

This paper presents HCSM, a framework for behavior and scenario control based on communicating hierarchical, concurrent state machines. We specify the structure and an operational execution model of HCSM's state machines. Without providing formal semantics, we provide enough detail to implement the state machines and an execution engine to run them. HCSM explicitly marries the reactive (or logical) portion of system behavior with the control activities that produce the behavior. HCSM state machines contain activity functions that produce outputs each time a machine is executed. An activity function's output value is computed as a function of accessible external data and the outputs of lower-level state machines. We show how this enables HCSM to model behaviors that involve attending to multiple concurrent concerns and arbitrating between conflicting demands for limited resources. The execution algorithm is free of order dependencies that cause robustness and stability problems in behavior modeling. In addition, we examine the problems of populating virtual environments with autonomous agents exhibiting interesting behavior and of authoring scenarios involving such agents. We argue that HCSM is well suited for modeling the reactive behavior of autonomous agents and for directing such agents to produce desired situations. We demonstrate use of HCSM for modeling vehicle behavior and orchestrating scenarios in the Iowa Driving Simulator, an immersive real-time virtual driving environment.