Preliminary investigations of Monte Carlo Simulations of neutron energy and LET spectra for fast neutron therapy facilities

No fast neutron therapy facility has been built with optimized beam quality based on a thorough understanding of the neutron spectrum and its resulting biological effectiveness. A study has been initiated to provide the information necessary for such an optimization. Monte Carlo studies will be used to simulate neutron energy spectra and LET spectra. These studies will be bench-marked with data taken at existing fast neutron therapy facilities. Results will also be compared with radiobiological studies to further support beam quality optimization. These simulations, anchored by this data, will then be used to determine what parameters might be optimized to take full advantage of the unique LET properties of fast neutron beams. This paper will present preliminary work in generating energy and LET spectra for the Fermilab fast neutron therapy facility.Comment: 9 pp. 11th Neutron and Ion Dosimetry Symposium (NEUDOS 11). 12-16 Oct 2009. Cape Town, South Afric

The Chicago Farmer\u27s Market Collective

Marketing 311 has been working with The Chicago Farmer\u27s Market Collective for the past four months. Our team has been utilizing thinking by design to help the Chicago Farmer\u27s Market Collective thrive as an organization. Our goal was to help CFMC digitally educate Chicago consumers in an engaging way

Monte Carlo Simulations Demonstrating Physics of Equivalency of Gamma, Electronbeam, and X-ray for Radiation Sterilization

The sterilization of medical devices using the gamma rays from the decay of cobalt-60 has accumulated decades of experience of the performance of the materials and devices that are irradiated. The use of radiation using electron beams and x-rays has much less experience and this leads to questions of equivalency between these three technologies. Computer simulations were conducted to model the relevant physical processes of the interactions of each of the three forms of radiation in order to compare the spectra of electron energies at energies below 500 keV. It is predominantly the electrons below this threshold that produce the sterilization dose. No difference in energy spectra was seen between the three types of initial radiation. It is concluded that there is no energy dependent difference between gamma, e-beam, and x-ray for radiation sterilization

Inference in particle tracking experiments by passing messages between images

Methods to extract information from the tracking of mobile objects/particles have broad interest in biological and physical sciences. Techniques based on simple criteria of proximity in time-consecutive snapshots are useful to identify the trajectories of the particles. However, they become problematic as the motility and/or the density of the particles increases due to uncertainties on the trajectories that particles followed during the images' acquisition time. Here, we report an efficient method for learning parameters of the dynamics of the particles from their positions in time-consecutive images. Our algorithm belongs to the class of message-passing algorithms, known in computer science, information theory and statistical physics as Belief Propagation (BP). The algorithm is distributed, thus allowing parallel implementation suitable for computations on multiple machines without significant inter-machine overhead. We test our method on the model example of particle tracking in turbulent flows, which is particularly challenging due to the strong transport that those flows produce. Our numerical experiments show that the BP algorithm compares in quality with exact Markov Chain Monte-Carlo algorithms, yet BP is far superior in speed. We also suggest and analyze a random-distance model that provides theoretical justification for BP accuracy. Methods developed here systematically formulate the problem of particle tracking and provide fast and reliable tools for its extensive range of applications.Comment: 18 pages, 9 figure

Calibration of Measurements

Traditional notions of measurement error typically rely on a strong mean-zero assumption on the expectation of the errors conditional on an unobservable “true score” (classical measurement error) or on the data themselves (Berkson measurement error). Weakly calibrated measurements for an unobservable true quantity are defined based on a weaker mean-zero assumption, giving rise to a measurement model of differential error. Applications show it retains many attractive features of estimation and inference when performing a naive data analysis (i.e. when performing an analysis on the error-prone measurements themselves), and other interesting properties not present in the classical or Berkson cases. Applied researchers concerned with measurement error should consider weakly calibrated errors and rely on the stronger formulations only when both a stronger model\u27s assumptions are justifiable and would result in appreciable inferential gains