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Comments on An Improvement to the Brent’s Method
Authored by: Steven A. Stage, Ph.D., IEM Atmospheric Physicist/Dispersion Modeler
Say that for any given input value, x, we know how to compute an output value, y. There are many situations in which we know the output value, y, and would like to be able to determine the value of x that produced it. Sometimes this can easily be done using algebra or other math, but other times it is not possible to get an exact mathematical answer. Fortunately, several methods are available that enable computers to find good approximate answers. Dr. Stage’s paper, published in the International Journal of Experimental Algorithms (IJEA), examines several of these methods, including a method recently proposed in IJEA by Zhang, to see how quickly each method is able to find a good answer and provides guidance to help programmers select the method best suited to their specific needs.
The demand for mission-critical, real-time information is exploding in the Intelligence Community. With sensors becoming increasingly sophisticated, inexpensive, and interconnected, the response to this demand is coming in the form of massive multi-sensor networks. While such systems tantalize us with the ever-increasing potential for analytical omniscience, the reality is that analysts and decision-makers are overwhelmed by the large data sets being generated. This paper, published in the April 2012 issue of IQT Quarterly, presents a path toward effectively transforming the exponential growth in data streams into an asset for analysts through automated suggestions of future outcomes and behavioral intent.
Mass Prophylaxis Dispensing Concerns: Traffic and Public Access to PODs
Presented by: Dr. Sid Baccam, IEM Computational Epidemiologist
Policymakers have become increasingly concerned about the possibility of a terrorist attack using a biological agent on a civilian population. In response to this threat, a federally funded effort has been developed to prepare major U.S. cities and metropolitan areas to respond quickly and effectively to a large-scale bioterrorism event.
Points of Dispensing, or PODs, are used by emergency responders to dispense post-exposure prophylaxis (PEP) to the public following a bioterrorism event. Any failure in PEP dispensing could have serious public health consequences, which is why IEM has focused study efforts on issues related to POD access.
The project described in the paper was partially funded by the U.S. Department of Health and Human Services (HHS) as part of a larger study on PEP dispensing logistics and medical consequences.
The Mixing Layer Terrain Wind Adjustment Model (MILTWAM) for Airflow over Complex Terrain
Presented by: Steven A. Stage, Innovative Emergency Management, Inc., Baton Rouge, LA; and Z. Wu, N. Mainkar, J. Weltman, and M. Myirski
This paper presents the Mixing Layer Terrain Wind Adjustment Model (MILTWAM) for airflow over complex terrain. MILTWAM is a diagnostic, mass-consistent, wind-field model based on NUATMOS (Ross, 1988). It is specifically designed for use in the D2-Puff dispersion model developed by Innovative Emergency Management Inc. and it produces realistic estimates of winds, even when only a few wind observations are available. This model is also fast enough for use in an emergency response dispersion model that runs on a personal computer (PC). Key features of the MILTWAM model are:
- The height of the top of the mixing layer is explicitly included in the model and imposes a non-porous upper lid on the flow. This upper lid is a major influence in determining the flow over the terrain.
- A three-dimensional model with terrain-following coordinates is used when the top of the mixing layer is above the highest terrain; a vertically-averaged two-dimensional model is used when the mixing layer is below the highest terrain.
- The winds output by the model are designed to agree with the observed winds at the observation points.
Model results are shown for simple geometric terrain and for real terrain.
Quantitative Assessment of Emergency Preparedness and Response Using QEM-World™