15 research outputs found
Use of unmanned drones to deliver an automated external defibrillator for out-of-hospital cardiac arrest
Characteristics of automated external defibrillator coverage in Philadelphia, PA, based on land use and estimated risk
Aim Approximately 424,000 out-of-hospital cardiac arrests (OHCA) occur in the US annually. As automated external defibrillators (AED) are an important part of the community response to OHCA, we investigated how well the spatial demand (likelihood of OHCA) was met by the spatial supply (AEDs) in a dense urban environment. Methods Using geographic information system (GIS) software, we applied kernel density and optimized hot spot procedures with two differently-sized radii to model OHCA incidence rates from existing studies, providing an estimate of OHCA likelihood at a given location. We compared these density maps to existing AED coverage in the study area. Descriptive statistics summarized coverage by land use. Results With a 420-ft buffer, we found that 56.0% (79.9%, 840-ft buffer) of the land area in the city center was covered by existing AEDs at, though 70.1 (91.5)% of the OHCA risk was covered using kernel density and 79.8% (98.1) was covered using hot spot analysis. Conclusions The difference in coverage by area and risk seems to indicate efficient placement of existing AEDs. Our findings also highlight the possible benefits to expanding the influence of AEDs by lowering search times, and identify opportunities to improve AED coverage in the study area. This article offers one method by which local officials can use spatial data to prioritize attention for AED placement and coverage
Numerical Irreducible Decomposition using PHCpack
Homotopy continuation methods have proven to be reliable and efficient to approximate all isolated solutions of polynomial systems. In this paper we show how we can use this capability as a blackbox device to solve systems which have positive dimensional components of solutions. We indicate how the software package PHCpack can be used in conjunction with Maple and programs written in C. We describe a numerically stable algorithm for decomposing positive dimensional solution sets of polynomial systems into irreducible components
