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Computational tools for analyzing biochemical phenomena are becoming increasingly important. Recently, high-level formal languages for modeling and simulating biochemical reactions have been proposed. These languages make the formal modeling of complex reactions accessible to domain specialists outside of theoretical computer science. This research explores the use of genetic programming to automate the construction of models written in one such language. Given a description of desired timecourse data, the goal is for genetic programming to construct a model that might generate the data. The language investigated is Kahramano˘gullari’s and Cardelli’s PIM language. The PIM syntax is defined in a grammar-guided genetic programming system. All time series generated during simulations are described by statistical feature tests, and the fitness evaluation compares feature proximity between the target and candidate solutions. Target PIM models of varying complexity are used as target expressions for genetic programming. Results were very successful in all cases. One reason for this success is the compositional nature of PIM, which is amenable to genetic program search.

ii About the Author Martha Brogan is an independent library consultant with two decades of experience in academic libraries. Ms. Brogan has served as associate dean of libraries and director of collection development at Indiana University-Bloomington; as a social sciences librarian at Yale University; and as a western European library specialist and assistant to the provost and vice president of academic affairs at the University of Minnesota. In 2001, Ms. Brogan was a fellow in the Frye Leadership Institute sponsored by the Council on Library & Information Resources, EDUCAUSE and Emory University. 8 Based on a survey of Digital Library Aggregation Services conducted in summer 2003.

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Arterial traffic signal control is a very important aspect of traffic management system. Efficient arterial traffic signal control strategy can reduce delay, stops, congestion, and pollution and save travel time. Commonly used pre-timed or traffic actuated signal control do not have the capability to fully respond to real-time traffic demand and pattern changes. Although some of the well-known adaptive control systems have shown advantageous over the traditional per-timed and actuated control strategies, their centralized architecture makes the maintenance, expansion, and upgrade difficult and costly. Distributed artificial intelligence technologies such as multi-agent systems are well suited for arterial signal control and they have the ability to decompose and accomplish complicated control problems by cooperatively simple agents such that flexibility, efficiency, robustness, and cost effectiveness can be achieved. An in-depth investigation of applying the multi-agent technology in arterial signal control is conducted in this research, and two multi-agent arterial adaptive signal control systems based on neuro-fuzzy reinforcement learning are developed and evaluated using VISSIM simulation and real world traffic data collected in College Station, Texas. The two multi-agent arterial adaptive control systems are compared with

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Night vision devices mitigate the risks associated with operations in low levels of visibility. They help personnel to maximize visual resources through image intensification or infrared imaging. However, night vision devices also create new risks. They encourage personnel to conduct operations that would otherwise have been rejected. They also create a host of human factors problems that are implicated in a growing number of military accidents. In particular, the operation of night vision equipment has been associated with several different forms of spatial disorientation. The following pages describe how this disorientation exacerbates the hazards created by ‘brown-out ’ landings that occur when visibility is reduced by airborne particles, typically from helicopter downwash. The opening sections of this paper provide a high-level review of night vision operations and of previous accidents related to ‘brown out’ incidents. Subsequent paragraphs focus on a more detailed case study leading to the loss of a Royal Air Force Puma on operational duty in Iraq during November 2007. The closing sections of this paper argue that there is an urgent need to go beyond Boards of Inquiry. We must extend the scope of operational studies across the US and UK armed forced to ensure that we learn the lessons provided by the loss of this Puma and the growing number of similar accidents, which stem from complex interactions between new technology and a range of environmental hazards, including but not limited to ‘brown out ’ and ‘white out ’ conditions. 1.